Category: Data Insights

Data Mesh: Revolutionizing Data Management for Modern Enterprises

Data mesh was once a mystical concept, but now, thanks to modern technology, it’s a more viable and accessible data management approach for enterprises. The framework offers a decentralized, domain-driven data platform architecture that empowers organizations to leverage their data assets more efficiently and effectively. 

In this article, we’ll dive deeper into data mesh by exploring how it works, understanding its use cases, and differentiating it from traditional data management approaches, such as data lakes.

 

What is Data Mesh?

Data mesh is an innovative data platform architecture that capitalizes on the abundance of data within the enterprise through a domain-oriented, self-serve design. It’s an emerging approach to data management. Traditionally, organizations have leveraged a centralized data architecture, like a data lake, but data mesh advocates for a decentralized approach where data is organized into domain-oriented data products managed by domain teams. This new model breaks down silos, empowering domain teams to take ownership of their data, collaborate efficiently, and ultimately drive innovation.

There are four core principles of data mesh architecture: 

  1. Domain Ownership: Domain teams own their data and enable business units to build their data products.
  2. Self-Service Architecture: Data mesh provides tools and capabilities that empower teams to abstract complexity away from building data products.
  3. Data Products: Data mesh facilitates interoperability, trust, and discovery of data products.
  4. Federated Governance: Data mesh allows users to deploy policy at global and local levels for data products.

These principles make data mesh a very intriguing prospect for industries like financial services, retail, and legal. Organizations in these particular industries contend with huge data challenges, such as massive amounts of data, highly siloed data, and strict compliance requirements. Therefore, any company that faces these data challenges needs an approach that can create flexibility, coherence, and cohesiveness across its entire ecosystem.

 

The Benefits of Data Mesh

Data mesh supports a domain-specific distributed data architecture that leverages “data-as-a-product,” with each domain handling its own data pipelines. These domain-driven data and pipelines federate data ownership among data teams who are held accountable for providing their data as products while facilitating communication among data distributed across different locations.

Within this domain-driven process, the infrastructure provides necessary solutions for domains to effectively process data. Domains are tasked with managing, ingesting, cleaning, and aggregating data to generate assets that are to be leveraged by business intelligence applications. Each domain is responsible for owning its own ETL pipelines – which help move data from source to database – and, once completed, enable domain owners to leverage said data for analytics or operational needs of the enterprise.

The self-serve functionality of data mesh simplifies technical complexity while focusing more on individual use cases with the data they collect. Data mesh extracts data infrastructure capabilities into a central platform that handles data pipeline engines and other infrastructure. At the same time, domains remain responsible for leveraging those components to run custom ETL pipelines providing necessary support to efficiently serve data and autonomy to own every step of the process.

Additionally, a universal set of standards under each domain helps facilitate collaboration between domains when necessary. Data mesh standardizes formatting, governance, discoverability, and metadata fields, creating cross-domain collaboration. With this interoperability and standardization of communication, data mesh overcomes the ungovernability of data lakes and the bottlenecks that monolithic data warehouses can present. 

Another benefit of data mesh architecture is that it allows end-users to easily access and query data without moving or transforming it beforehand. In doing so, as data teams take ownership of domain-specific data products, they are aligned with business needs. By treating data as a product, organizations can unleash its true value, driving innovation and agility across the enterprise. 

 

Functions of Data Mesh

In a data mesh ecosystem, data products become the building blocks of data consumption. These tailored data solutions cater to the unique requirements of data consumers, allowing them to access domain-specific datasets seamlessly. With self-serve capabilities, data consumers can make data-driven decisions independently, freeing the IT team from repetitive tasks and fostering a culture of data-driven autonomy.

Compared to some benefits of a data mesh, modern data lake architecture falls short because it provides less control over increasing volumes of data and places a heavy load on the central platform as more data continues to come in, requiring different transformations for different use cases. Data mesh addresses the shortcomings of data lake architecture through greater autonomy and flexibility for data owners, which encourages greater experimentation and innovation while lessening the burden on data teams looking to field the needs of all data consumers through a single pipeline.

Organizations can create a more efficient and scalable data ecosystem with data mesh architecture. Its method of distributing data ownership and responsibilities to domain-oriented teams fosters data collaboration and empowers data consumers to access and utilize data directly for specific use cases. Adopting an event-driven approach makes real-time data collaboration possible across the enterprise, notifying relevant stakeholders as events occur. The event-driven nature supports seamless integration and synchronization of data between different domains.

DataOps plays a significant role within the data mesh environment, streamlining data pipelines, automating data processing, and ensuring smooth data flow from source to destination. By adopting the principles of this fusion between data engineering and DevOps practices, organizations can accelerate data delivery more effectively, minimize data errors, and optimize the overall data management process. Federated governance becomes a large factor as it unites data teams, business units, and IT departments to manage data assets collaboratively. This further ensures data quality, security, and compliance while empowering domain experts to take ownership of their data. Federated governance ultimately bridges data management and consumption, encouraging data collaboration across the enterprise.

 

The Difference Between Data Mesh and Data Lakes

The architecture and data management approach is the primary differentiator between data mesh and central data lakes. Data lakes are a centralized repository that stores raw and unprocessed data from various sources. Data mesh supports a domain-driven approach in which data is partitioned into domain-specific data products that are owned and managed by individual domain teams. Data mesh emphasizes decentralization, data observability, and federal governance, allowing greater flexibility, scalability, and collaboration in managing data throughout organizations.

Data Ownership: Unlike traditional data lake approaches that rely on centralized data storage, data mesh promotes distribution. Data mesh creates domain-specific data lakes where teams manage their data products independently. This distribution enhances data autonomy while reducing the risk of data bottlenecks and scalability challenges. 

Data Observability: Data observability is an essential component of data mesh and provides visibility into the performance and behavior of data products. Data teams can monitor, troubleshoot, and optimize their data pipelines effectively. By ensuring transparency, data observability empowers data teams to deliver high-quality data products and enables continuous improvement.

Data mesh is an architecture for analytical data management that enables end users to easily access and query data where it lives without first transporting it to a data lake or data warehouse. Using data mesh, data consumers and scientists can revolutionize how data is consumed and empower data consumers with self-serve capabilities. With access to domain-specific data products, data scientists can extract insights from rich, decentralized data sources that enable innovation. Data analytics present in data mesh environments takes center stage in value creation. With domain-specific data readily available, organizations can perform detailed data analysis, identify growth opportunities, and optimize operational processes, maximizing the potential of data products and driving improved decision-making and innovation.

 

Mesh Without the Mess

Data is the pinnacle of innovation, and building a data mesh architecture could be crucial to leveling up your enterprise’s data strategy. At 2nd Watch, we can help you every step of the way: from assessing the ROI of implementing data mesh to planning and executing implementation. 2nd Watch’s data strategy services will help you drive data-driven insights for the long haul.

Schedule a whiteboard session with the 2nd Watch team, and we can help you weigh all options and make the most fitting decision for you, your business, and your data usage. Start defining your organization’s data strategy today!


5 Key Security Questions for Data Solution Implementations

In a data-driven world, implementing robust data solutions is essential for organizations to thrive and stay competitive. However, as data becomes increasingly valuable and interconnected, ensuring its security and protection is of the utmost importance. Data breaches and cyber threats can have far-reaching consequences, ranging from financial losses to irreparable damage to an organization’s reputation. Therefore, before embarking on any data solution implementation journey, it’s vital for organizations to ask themselves critical security questions that will lay the groundwork for a secure and trusted data environment.

In this blog post, we’ll explore five fundamental security questions that every organization should address prior to implementing data solutions. By proactively addressing these questions, organizations can fortify their data security measures, protect sensitive information, and establish a robust foundation for the successful implementation of data-driven initiatives.

1. What sensitive data do you possess, and why is it important?

Identify the sensitive data you possess and understand its significance to your organization and objectives. This may require classifying data into categories such as customer information, financial records, intellectual property, or other relevant subject areas. Sensitive data may also include protected health information (PHI), research and development data, or account holder data, depending on the nature of your organization’s operations.

The loss or exposure of such data can lead to severe financial losses, damage to research efforts, and potential legal disputes. By recognizing the importance of your organization’s sensitive data, you can prioritize its protection and allocate appropriate security measures.

2. Who should have access to data, and how will you control it?

Determine who should have access to your sensitive data and consider implementing role-based access control (RBAC) or column-level security so data access is granted based on personnel roles and responsibilities. By carefully managing data access, you can mitigate the risk of internal data breaches and prevent unauthorized exposure of sensitive information. With column-level security on Snowflake, Google BigQuery, or Amazon Redshift, dynamic data masking can be applied to protect sensitive data from unauthorized access as data is queried.

In addition, implementing the principle of least privilege assures that individuals are only granted the minimum level of access required to perform their specific job functions. By adhering to this principle, you further limit the potential damage caused by any compromised accounts or insider threats, as employees will only have access to the data necessary for their tasks, reducing the overall attack surface and enhancing data protection.

3. How will you encrypt data to ensure its confidentiality?

Encrypt your data to safeguard from unauthorized access and theft. Implementing encryption at rest ensures that data stored on servers or devices remains unreadable without the proper decryption keys. Likewise, encryption in transit secures data as it travels over networks, preventing interception by malicious actors. Proper key management and protection are essential to maintain the confidentiality of encrypted data.

Snowflake’s Data Cloud platform employs a comprehensive approach to encryption, ensuring that data remains encrypted throughout its entire lifecycle, from the moment it enters the system to the moment it leaves. Snowflake’s end-to-end encryption approach provides organizations with a high level of confidence in the confidentiality and security of their sensitive data every step of the way.

4. Where and how will you securely store the data?

Choose a secure data storage solution to maintain data integrity and ensure your data is well-protected from vulnerabilities. Additionally, establish proper backup and disaster recovery plans to ensure data availability and resilience in the face of unforeseen events. Consider utilizing reputable cloud storage options that adhere to rigorous security standards, including the International Organization for Standardization (ISO), the National Institute of Standards and Technology (NIST), and the Payment Card Industry Data Security Standard (PCI DSS).

Leading cloud service providers like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP), all offer advanced security features like data encryption, multi-factor authentication, and robust access controls. These cloud providers employ industry-leading security practices, including compliance certifications, regular security audits, and continuous monitoring to safeguard data from various threats.

5. How will you establish security governance and ensure compliance?

Build a robust security governance framework that will support data security at your organization. Organization leaders and/or data governance boards should define roles and responsibilities, establish security policies, and work to foster a culture of security awareness and data literacy across the organization.

Regular security assessments and audits are essential to identify areas for improvement and address potential weaknesses. Data managers must also stay up to date with industry best practices, maintain comprehensive documentation, and ensure compliance with relevant data protection regulations to preserve a secure and resilient data environment. Furthermore, data retention policies, multi-factor authentication (MFA), and regularly tested incident response plans contribute to the organization’s data security resilience.

Data governance is not a one-time management decision, but rather an ongoing and evolving process that will support an organization’s long-term data strategy. As a result, it’s crucial for leaders to be on board with data initiatives to balance the overhead required for data governance with the size and scope of the organization.

By asking yourself these five crucial security questions, you can develop a comprehensive data security strategy that protects sensitive information and effectively mitigates potential risks. Prioritizing data security in the early stages of implementing data solutions will help you build a solid foundation for a safe and trusted data environment that you can build upon for more advanced data, analytics, and AI ventures.

Still not quite sure where to begin? Schedule a complimentary 60-minute whiteboarding session with 2nd Watch.


Are You Ready for Real Time Analytics?

Real-time analytics is a discipline that revolves around swiftly processing and interpreting data as it’s generated, providing instant insights and actionable information useful for improving business decisions. In contrast, traditional analytics relies on batch processing, leading to delayed results. Real-time analytics empowers businesses, industries, and even sports venues to gain a competitive edge, optimize operations, and elevate customer experiences.

To demonstrate its practical application, let’s transport ourselves to Wrigley Field on a Sunday afternoon, where the Chicago crosstown rivals are about to compete. As fans eagerly enter the ballpark, an advanced fan occupancy dashboard diligently tracks each entry into the venue. This real-time data collection and analysis play a pivotal role in ensuring a seamless and enjoyable experience for both fans and event organizers.

Assess Your Infrastructure for Scalability

To successfully implement real-time analytics, organizations – including professional baseball teams – must establish a scalable data infrastructure. Creating a scalable data infrastructure involves building a skilled team of data engineers and selecting the appropriate technology stack. Before delving into real-time analytics, it’s crucial for organizations to conduct a thorough assessment of your current infrastructure.

This assessment entails evaluating the scalability of existing data systems to ascertain their ability to handle the growing volumes of data. Moreover, the data processing and storage systems, including cloud data warehouses, must demonstrate resilience to manage the continuous influx of data without compromising performance. By ensuring a robust and scalable data infrastructure, organizations can lay the groundwork for effective real-time analytics and gain valuable insights from high-velocity data streams. This also applies to incoming data. An organization’s ability to make decisions will be impacted by how quickly they can factor in new information as it arises; thus, being able to ingest large amounts of data as soon as it becomes available is a vital capability.

Ensure Data Quality and Governance

As an organization begins to ingest and process data in real-time, a standardized approach to data governance becomes essential. Data governance is the process of creating an accountability framework and designating decision rights around an organization’s data with the intention of ensuring the appropriate creation, consumption, and management thereof. Users need access to relevant, high-quality data in a timely manner so they can take action. By implementing data governance policies, organizations can define metrics around data accuracy and work to improve those.

Starting a data governance process requires first identifying essential data. A retail company, for instance, may consider customer purchase patterns as key user behavior intel. Maintaining data integrity, using strategies like automated validation rules for data accuracy, is vital to protect this historical data and ensure its usefulness going forward. Setting measurable metrics and monitoring adherence helps in maintaining quality. If data errors exceed a set limit, it triggers a data cleaning process.

Identifying authority for final decisions on data, like a chief data officer or a data governance board, is essential. Their authority should reflect in data access permissions, limiting who can change or view sensitive data. When implementing data governance policies, the organization must consider the type of stored information, its intended use, and the user type. These factors impact data security, privacy, and regulatory compliance.

Confirm Resource Availability

Skilled personnel are equally as important, if not more so, than the foundation of infrastructure and data governance practices. An organization needs to assess if their IT team has the capacity to maintain the tools and processes surrounding real-time analytics. IT personnel must be able to ingest and process this data for instant consumption in a sustainable manner to gain maximum value.

Additionally, “skilled” is a keyword in “skilled personnel.” Does your IT team have the knowledge and experience to handle real-time data analytics, or do you need to look into hiring? Is there someone on the team who can help with upskilling other staff? Make sure you have this people-focused infrastructure in place in conjunction with your data infrastructure.

Identify Business Use Cases

In situations that demand swift decision-making based on extensive data, an organization can realize considerable advantages through the use of real-time analytics. Instantaneous insights derived from data equip businesses to adjust to rapid market changes and strategically place themselves for prosperity.

Pivoting back to Wrigley Field, tracking fan turnout is simply one among potentially 100 business circumstances where real-time analytics can demonstrate its value. The home team’s concession management can promptly assess sales of merchandise and concessions, and they can begin amending their forecast for the next day’s game right away. In tandem, their chief marketing officer could fine-tune marketing strategies based on ticket sale trends, consequently improving stadium fill rates. Beyond that, there are opportunities to delve into game-generated data and player statistics to understand their potential effects on audience behavior.

Furthermore, keep in mind the impact of data lag when you’re exploring your industry or business for typical or standard operations that suffer due to a delay in data access. How about fraud detection? Or even using the power of streaming data to enable enhanced business intelligence, predictive analytics, and machine learning? Identifying these situations will be key in assisting you to unearth the most effective applications of real-time analytics within your enterprise.

Consider Security and Compliance

Whenever changes are made to your digital framework, it’s crucial to tackle possible security threats. Your organization needs to understand the nature of the sensitive data it holds and who has the right to access it. For example, think about a healthcare company managing patient data. There is a necessity for strict controls over access to such sensitive data. The company must ensure that only individuals with the right authorization can access this information. Moreover, they should be thorough in overseeing their cloud service provider and any other related entities that might handle or use this data. This approach safeguards individual privacy and adheres to regulatory standards like HIPAA in the United States.

Depending on the specifics of the data, infrastructure adjustments may also be required to keep in line with data protection rules. Using our Wrigley Field example, there may be collection of personal financial information through ticket and concession sales. In these circumstances, it’s critical to ensure that this data is handled securely and in compliance with all appropriate regulations.

Evaluate Financial Implications and ROI

A crucial aspect of this evaluation involves analyzing the expenses and the ROI associated with the adoption of real-time analytics. There could be monetary considerations related to storage and computational costs, as well as the potential need for more personnel. These factors can fluctuate based on an organization’s existing infrastructure, the skill level of its employees, and the complexity and amount of data to be processed. All these elements need to be balanced against the anticipated ROI and enduring advantages, both quantifiable and qualitative.

Does faster response time decrease operational expenses, enhance customer interactions, or even mitigate security threats? By optimizing operations and reacting swiftly to market fluctuations, organizations can reap significant financial rewards.

Embrace and Implement Real-Time Analytics

Once an organization recognizes an opportunity to apply real-time analytics, the next phase involves identifying and evaluating the data sources that can facilitate this implementation. Subsequently, the organization needs to manage data ingestion, processing, and storage, before defining and constructing any final products. During each of these phases, the choice of suitable tools and technologies is crucial. Your organization should take into account your current infrastructure, maintenance requirements, team skill sets, and any fresh data you wish to integrate into your solution.

Consequently, real-time analytics can give your organization a distinct advantage by allowing data processing as soon as it’s generated, leading to swift and well-informed decision-making. A well-executed implementation has the potential to help anticipate significant issues, boost predictability, optimize operations, and enhance customer relations. Given our society’s data-rich environment, organizations can harness this asset to produce improved solutions and customer experiences. Ready to take action but unsure of the initial steps? Contact 2nd Watch for a complimentary real-time analytics roadmap whiteboarding session.


Private Equity Data Strategy: Fueling Growth and Success

Private equity (PE) data management and the ability to leverage PE big data effectively and efficiently are of growing importance for firms seeking long-term growth and sustainability. As the number of firms grows, the opportunity cost of not utilizing data-driven decision-making in private equity could be the difference between success and failure.

Acquisitions and ongoing tracking of portfolio companies are data-dense and can be labor-intensive if firms aren’t properly leveraging the tools at their disposal, but data integration doesn’t happen overnight. Well-thought-out data strategy in private equity can position firms to use data most effectively to drive growth and maximize returns within their portfolio of companies. 

In building out a data strategy framework, the goal of any firm is to create on-demand analytics that can provide real-time insights into the performance of their portfolio companies. Data integration in private equity is an ongoing priority as each new acquisition comes with new data sources, metrics, definitions, etc., that can create complications in proper reporting, especially in the case of roll-ups. 

Private equity big data and analysis provide an opportunity for firms to more effectively and efficiently measure success, track financial performance, improve operations, and much more. Generating a data-focused strategy is the first step for firms looking to generate on-demand analytics and reporting; and it requires them to define the technology, processes, people, and private equity data governance and data security required to manage all assets effectively and safely.

Key Components of Private Equity Data Strategy

Data optimization for private equity is key as firms continue to expand acquisitions, but this can only be achieved through the development of a sound data strategy. It requires front-end work to get off the ground; however, the benefits of PE digital transformation much outweigh what can sometimes be seen as a daunting task.

These benefits include a clarified vision across the organization and the ability to plan and budget effectively in alignment with this vision – accounting for anticipated challenges and risks. Additionally, private equity portfolio data analysis allows for smarter and more educated buying decisions by leveraging real-time data and market insights. The broad usability of data and analytics tools drives adoption and encourages change across the organization which translates to increased success for firms. Best of all, a clear and well-executed data strategy allows everyone to focus on a single source of truth with the transparency that effective data integration offers.

Each private equity firm will require a unique strategy that is catered to its needs and the needs of its portfolio of companies. Regardless of a specific firm’s needs, there are individual tools and tools that can combine to perform the necessary functions to improve the processes of any firm in question.

Financial Reporting

In the case of firms conducting a roll-up, the financial reporting process that comes with the consolidation can be a bear. With many firms still relying on manual reporting processes, slow and inconsistent reporting can translate to slow and inconsistent decision-making. Firms in this position need the ability to execute quick and easy portfolio data analysis to understand their financial performance, and many have realized that without harnessing tools to ease this process, their current approach is neither practical nor scalable as they continue to seek out additional acquisitions.

With clearly defined goals and the knowledge of how to leverage applicable PE data platforms, firms can improve their processes and optimize them in a scalable way. There are four steps for a firm in this position to streamline its financial reporting during the roll-up process.

The first is some level of restructuring of the data into a format that supports accurate reporting of pre- and post-acquisition date ranges. From there, a model is implemented to effectively manipulate data among necessary databases. Next comes data consolidation: data mining and machine learning in private equity assist in financial consolidation and reporting by connecting multiple functions, accelerating the speed and accuracy with which data can be processed.

Finally, through custom dashboard creation, firms can leverage more effective data visualization in private equity to provide an in-depth and interactive view for any members of the organization – in this case, financial advisors. With the front-end work in place, ongoing management and development are made much easier with streamlined processes for adding additional acquisitions to the data management platform.

Portfolio Data Analysis and Consolidation

Similarly, PE roll-ups naturally lead to an influx of data from each target company. Firms need a way to effectively and efficiently consolidate data, standardize KPIs across the organization, and analyze data in a user-friendly way. This provides an opportunity to take advantage of private equity business intelligence to improve operations, financial performance, and cash-flow predictability.

Clear strategy and design aligned with the firm and catering of the solution to their long-term growth are essential in any successful digital transformation. The next step involves the centralization of data from multiple source systems through the implementation of custom data pipelines to a centralized data warehouse. Once there, it’s time to leverage tools to organize, standardize, and structure the data to ensure consistency and preparedness for analytics.

At this point, it’s up to firms to create interactive, user-friendly dashboards to easily visualize KPIs and track performance across individual companies within their portfolio and organizations as a whole. By leveraging AI in private equity, firms can create smart reports that fit the needs of any queries they may be interested in improving or learning more about. When PE firms become more effective at analyzing specific data, they position themselves to make more well-educated and efficient decisions as they continue to build their portfolio.

Predictive Analytics

In certain cases, data analytics for private equity can be leveraged to improve portfolio companies. By using predictive analytics in private equity, firms can forecast future trends and performance to make more accurate predictions about opportunities and risks.

In seasons of fast-paced growth, the ability to automatically aggregate and evaluate real-time incoming data, leveraging AI and machine learning, can allow for smarter and faster decision-making that translates to increased growth. Some firms and target companies that are still utilizing manual processes can exponentially increase their bandwidth by leveraging these tools. By combining inputs into a centralized data hub, many processes can be automated and run simultaneously to optimize efficiency and scalability. By connecting these different tools and processes, data is more accurate and more quickly available. This allows for significantly increased output, translating to smarter and faster decision-making that makes scaling effortless when compared to the processes prior.

What This Means for Private Equity Firms

Now more than ever, it’s crucial for PE firms to understand the opportunities presented by developing an optimized data strategy. In an increasingly competitive environment, a data strategy could be the difference between continued growth or failure as other firms adopt these practices.

Leveraging data science in private equity can be daunting and confusing, especially if you are trying to tackle it yourself. At 2nd Watch, we have a team who is ready to help you understand how your firm can benefit from these tools and help you implement them to continue to accelerate your growth trajectory. Many of these examples were pulled directly from our work with other clients, so whether you find yourself facing similar challenges or something unique to your specific situation, we are confident we can help find and create a solution that’s right for you. To begin building a solution aligned with your firm’s vision and continued growth, contact us today for a Private Equity Data Strategy Whiteboarding Session.


Fundamentals of Private Equity Digital Transformation

Private equity firms are increasingly looking to digital transformation to create value and remain competitive in an ever-changing market. The digital transformation process revolutionizes companies with a range of innovative tech: big data analytics, artificial intelligence, machine learning, the Internet of Things, blockchain, cloud solutions, Software as a Service (Saas), cloud computing, and more. Relative to other industries moving toward digitalization, private equity firms have been considered late movers with the exceptions of large firms, like Blackstone and The Carlyle Group, who have given themselves an edge with the early interest they’ve taken.

Traditionally, PE firms have made investment decisions based on cyclical data sources such as quarterly earnings, financial reports, tax filings, etc., that could be limited in terms of scope and frequency. With increasing competition in the private equity sector, digital transformation provides an opportunity for firms to make real-time assessments and avoid falling behind the competition.

Specifically within private equity, firms seek to leverage these technologies to improve operational efficiency, streamline processes, and enhance the overall value of portfolio companies. Along with the improvement of portfolio companies, firms applying these technologies internally position themselves as well as possible to adapt to the quickly changing, increasingly competitive standards that are being called upon for survival in the private equity industry.

This blog post will highlight best practices that PE firms can utilize during the digital transformation process and analyze the value-creation opportunities that forward-thinking firms can take advantage of by leaning into implementation.

Best Practices for Digital Transformation in Private Equity

Before taking on any digital transformation initiatives, PE firms should have a clear transformation strategy outlining their objectives, priorities, and timelines while taking into account the unique characteristics of their portfolio companies and the industry landscape. While the digital transformation process doesn’t need to be complicated, it is critical that firms are strategic in how they carry out implementation. As a firm, the most valuable reason for transformation is the ability to convert acquisitions into data-driven smart companies.

Operating partners are playing an increasingly important role in this process as their close work with portfolio companies allows them to help identify opportunities, assess risk, and execute initiatives aligned with the digital transformation process. Getting them involved early in the process can allow firms to retain valuable input and buy-in while also helping to build necessary capabilities within the portfolio companies.

Due Diligence

Within the due diligence process, firms must be able to identify areas where potential acquisitions can benefit most from digital transformation.

From a production side, firms have the ability to investigate supplier evaluations and see the production process, in real-time, to identify bottlenecks and opportunities to optimize workflow. Additionally, firms can evaluate inventory tracking and the potential to optimize working capital, track customer satisfaction to facilitate omnichannel sales and personalized marketing, and perform automated analysis for fund managers to judge the feasibility and profitability of new products and business models the target company aims to promote.

Both private equity and venture capital funds can leverage the technological innovations of digitalization to improve the efficiency of the due diligence process while also implementing them into their acquisitions as a means of value creation. As the number of PE firms continues to rise, the ability to understand these areas quickly is of growing importance to ensure firms aren’t losing out on acquisition opportunities to competitors.

Emerging Technology and Innovation

It is critical for private equity firms to leverage the correct digital and industry 4.0 technologies to maximize value creation within their portfolio of companies.

Digital investment is a key part of firms’ digital transformation strategies positioning them to disrupt traditional industries, improve efficiency, and enhance the value of their portfolio companies. The specific modalities and use cases for digital technology depend largely on the target company in question:

Artificial Intelligence & Machine Learning: These technologies are becoming increasingly important and can allow firms to better understand and analyze data, identify new investment opportunities, and improve operational efficiency within portfolio companies.

Big Data Analytics: With access to vast amounts of data, firms can acquire insights into market trends, customer behavior, and other key metrics to drive growth and innovation.

E-Commerce & Fintech: With the rise of online shopping and digital payments, these industries are experiencing significant growth and disruption, making them attractive targets for investment and tools for streamlining processes.

Blockchain: Firms are still beginning to explore this technology that offers the potential to revolutionize the way transactions are conducted, making them faster, more secure, and more transparent.

SaaS: This technology offers the ability to deliver software and other digital products over the internet, making it easier and more cost-effective for private equity firms to adopt new technologies and stay competitive.

Industry 4.0: Technologies like the Internet of Things (IoT), 5G, and edge computing are transforming how businesses operate. This provides private equity firms with an opportunity to improve efficiency, reduce cost, and enhance the customer experience within portfolio companies.

Value Creation

Digital transformation in private equity firms provides an exceptional opportunity to leverage technology and create value in portfolio companies. By strategically leveraging the innovations at their disposal, firms are able to improve target companies in a variety of ways:

Operational Efficiency:

Private equity firms can use digital technology to streamline their portfolio companies’ operations and improve efficiency. For example, by implementing automation and machine learning solutions, firms can automate repetitive tasks and improve decision-making based on data analysis. This can reduce costs, increase productivity, and even improve profitability.

Customer Experience:

Digital transformation can enable firms to leverage big data and AI to gain insights into customer preferences and behavior. By using this information, private equity firms can create offerings within their portfolio companies that are personalized to their customer base and improve customer engagement and overall customer experience.

Accelerated Growth:

Through digitalization, private equity firms can accelerate the growth of their portfolio companies by allowing them to quickly scale operations and enter new markets. Implementing cloud computing and SaaS solutions can assist companies in rapidly deploying new products and services, expanding their customer base. 

 

These are just a few examples of how private equity firms can create value through digital transformation – internally and within their portfolio companies. Building a solid understanding of the opportunities that technological innovations have presented for private equity firms could be the difference between sinking or swimming in an increasingly competitive market.

2nd Watch partners with private equity firms to help them understand and execute their digital transformation strategy to ensure they are equipped to continue growing and separating themselves from the competition. With expertise in cloud migration and management services, we’re well-versed in the most effective ways PE firms can leverage digital transformation to create value. If you’re aware of the opportunity that digital transformation presents but feel like you could benefit from expert guidance, a whiteboard session with the 2nd Watch team is a great place to start. To learn more and begin the digital transformation process for your firm, contact us today and we can get started.

 


Snowflake Summit 2023: Harnessing LLMs & Doc AI for Industry Change

Large language models are making waves across all industries and Document AI is becoming common as organizations look to unlock even greater business potential. But where do you begin? (Hint: Strategy is essential, and 2nd Watch has been working through the implications of LLMs and Document AI for more than a year to help you navigate through the hype.)

Beyond the continued splash of LLM and Document AI discussions, this year’s Snowflake Summit focused on a couple of practical but still substantial announcements: an embrace of open source (both in applications and in AI/LLM models) and – maybe most impactful in the long run – the native first-party Microsoft Azure integration and expanded partnership. I’ll start there and work backwards to fully set the stage before digging into what some of the transformative LLM and Document AI use cases actually are across industries and sharing which use cases are trending to have the greatest and most immediate impact according to participants in 2nd Watch’s LLM industry use case battle, which ran through Snowflake Summit.

Snowflake + Microsoft Azure: Simplifying Integration and Enabling Native Snowflake Apps

The Snowflake and Microsoft Azure integration and expanded partnership is a big deal. Snowflake and Azure have paved the path for their customers, freeing them up from making difficult integration decisions.

For 2nd Watch, as a leader working with both Microsoft and Snowflake since as early as 2015, seeing a roadmap that integrates Snowflake with Azure’s core data services immediately brought to mind a customer value prop that will drive real and immediate decisions throughout enterprises. With a stronger partnership, Azure customers will reap benefits from both a technology standpoint and an overall go-to-market effort between the two organizations, from data governance via Azure Purview to AI via Cognitive Services.

Running your workloads where you want, how you want, has always been a key vision of Snowflake’s long-term roadmap, especially since the introduction of Snowpark. While the Microsoft announcement expanded on that roadmap, Snowflake continued to push even further with performance upgrades and new features for both Snowpark and Apache Iceberg (allowing for data to be stored as parquet files in your storage buckets). Customers will be able to build and run applications and AI models in containers, natively on Snowflake, whether that’s using Streamlit, built using Snowflake’s Native App Framework, or all the above. With all your data in a centralized place and Apache Iceberg allowing for portability, there’s a compelling reason to consider building and deploying more apps directly in Snowflake, thereby avoiding the need to sync data, buy middleware, or build custom integrations between apps.

Snowflake + NVIDIA: Embracing Open Source for AI and LLM Modeling

Another major theme throughout Summit was an embrace of openness and open source. One of the first major cornerstones of the event was the announcement of NVIDIA and Snowflake’s partnership, an integration that unlocks the ability for customers to leverage open-source models.

What does this mean for you? This integration opens up the ability to both run and train your own AI and LLM models directly where your data lives – ensuring both privacy and security as the data no longer needs to be pushed to an external, third-party API. From custom Document AI models to open-source, fine-tuned LLMs, the ability to take advantage of NVIDIA’s GPU cloud reduces the latency both in training/feedback loops and use in document and embedding-based retrieval (such as document question answering across vast amounts of data).

Document AI: Introducing Snowflake’s Native Features

The 2nd Watch team was excited to see how spot on our 2023 data and AI predictions were, as we even went so far as to feature Document AI in our exhibit booth design and hosted an LLM industry use case battle during expo hours. Document AI will be key to transformative industry use cases in insurance, private equity, legal, manufacturing – you name it. From contract analysis and risk modeling to competitive intelligence and marketing personalization, Document AI can have far-reaching impacts; and Snowflake is primed to be a major player in the Document AI space. 

Many organizations are just beginning to identify use cases for their AI and LLM workloads, but we’ve already spent the past year combining our existing offerings of Document AI with LLM capabilities. (This was the starting point of our previously mentioned industry use case battle, which we’ll discuss in more detail below.) With Snowflake’s announcement of native Document AI features, organizations now have the ability to tap into valuable unstructured data that’s been sitting across content management systems, largely unused, due to the incredibly costly and time-consuming efforts it takes to manually parse or extract data from documents – particularly when the formats or templates differ across documents.

Snowflake’s Document AI capabilities allow organizations to extract structured data from PDFs via natural language and, by combining what is likely a Vision transformer with an LLM, build automations to do this at scale. The data labeling process is by far the most crucial step in every AI workload. If your model doesn’t have enough high-quality examples, it will produce the same result in automated workloads. Third-party software products, such as SnorkelAI, allow for automated data labeling by using your existing data, but one of the key findings in nearly every AI-related research paper is the same: high-quality data is what matters, and the efforts you put in to building that source of truth will result in exponential benefits downstream via Document AI, LLMs, and other data-centric applications.

Leveraging Snowflake’s Data Cloud, the end-to-end process can be managed entirely within Snowflake, streamlining governance and privacy capabilities for mitigating the risk of both current and future regulations across the globe, particularly when it comes to assessing what’s in the training data you feed into your AI models.

Retrieval Augmented Generation: Exploring Those Transformative Industry Use Cases

It’s likely become clear how widely applicable Document AI and retrieval augmented generation are. (Retrieval augmented generation, or RAG: retrieving data from various sources, including image processors, auto-generated SQL, documents, etc., to augment your prompts.) But to show how great of an impact they can have on your organization’s ability to harness the full bulk and depth of your data, let’s talk through specific use cases across a selection of industries.

AI for Insurance

According to 2nd Watch’s LLM industry use case battle, contract analytics (particularly in reinsurance) reigned supreme as the most impactful use case. Unsurprisingly, policy and quote insights also stayed toward the top, followed by personalized carrier and product recommendations.

Insurance organizations can utilize both Document AI and LLMs to capture key details from different carriers and products, generating personalized insurance policies while understanding pricing trends. LLMs can also alert policy admins or automate administration tasks, such as renewals, changes, and cancellations. These alerts can allow for human-in-the-loop feedback and review, and feed into workflow and process improvement initiatives.

AI in Private Equity Firms

In the private equity sector, firms can leverage Document AI and question-answering features to securely analyze their financial and research documents. This “research analyst co-pilot” can answer queries across all documents and structured data in one place, enabling analysts to make informed decisions rapidly. Plus, private equity firms can use LLMs to analyze company reports, financial and operational data, and market trends for M&A due diligence and portfolio company benchmarking.

However, according to the opinions shared by Snowflake Summit attendees who stopped by our exhibit booth, benchmarking is the least interesting application of AI in private equity, with its ranking dropping throughout the event. Instead, Document AI question answering was the top-ranked use case, with AI-assisted opportunity and deal sourcing coming in second.

Legal Industry LLM Insights

Like both insurance and private equity, the legal industry can benefit from LLM document review and analysis; and this was the highest-ranked LLM use case within legal. Insights from complex legal documents, contracts, and court filings can be stored as embeddings in a vector database for retrieval and comparison, helping to speed up the review process and reduce the workload on legal professionals.

Case law research made a big comeback in our LLM battle, coming from sixth position to briefly rest in second and finally land in third place, behind talent acquisition and HR analytics. Of course, those LLM applications are not unique to law firms and legal departments, so it comes as no surprise that they rank highly.

Manufacturing AI Use Cases

Manufacturers proved to have widely ranging opinions on the most impactful LLM use cases, with rankings swinging wildly throughout Snowflake Summit. Predictive maintenance did hold on to the number one spot, as LLMs can analyze machine logs and maintenance records, identify similar past instances, and incorporate historical machine performance metrics to enable a predictive maintenance system. 

Otherwise, use cases like brand perception insights, quality control checks, and advanced customer segmentation repeatedly swapped positions. Ultimately, competitive intelligence landed in a tie with supply chain optimization and demand forecasting. Gleaning insights from unstructured data within sources like news articles, social media, and company reports, and coupled with structured data like factual market statistics and company performance data, LLMs can produce well-rounded competitive intelligence outputs. It’s no wonder this use case tied with supply chain and demand forecasting – in which LLMs analyze supply chain data and imaging at ports and other supply chain hubs for potential risks, then combining that data with traditional time-series demand forecasting for optimization opportunities. Both use cases focus on how manufacturers can optimally position themselves for an advantage within the market.

Even More LLM Use Cases

Not to belabor the point, but Document AI and LLM have such broad applications across industries that we had to call out several more:

  • Regulatory and Risk Compliance: LLMs can help monitor and ensure compliance with financial regulations. These compliance checks can be stored as embeddings in a vector database for auditing and internal insights.
  • Copyright Violation Detection: LLMs can analyze media content for potential copyright violations, allowing for automated retrieval of similar instances or known copyrighted material and flagging.
  • Personalized Healthcare: LLMs can analyze patient symptoms and medical histories from unstructured data and EHRs, the latest medical research and findings, and patient health records, enabling more effective treatment plans.
  • Medical Imaging Analysis: Use LLMs to help interpret medical imaging, alongside diagnoses, treatment plans, and medical history, allowing for patient imaging to suggest potential diagnoses and drug therapies based on the latest research and historical data.
  • Automated Content Tagging: Multimodal models and LLMs can analyze media content across video, audio, and text to generate relevant tags and keywords for automated content classification, search, and discovery.
  • Brand Perception Insights: LLMs can analyze social media and online reviews to assess brand perception.
  • Customer Support Copilots: LLMs can function as chatbots and copilots for customer service representatives, enabling customers to ask questions, upload photos of products, and allow the CSR to quickly retrieve relevant information, such as product manuals, warranty information, or other internal knowledge base data that is typically retrieved manually. By storing past customer interactions in a vector database, the system can retrieve relevant solutions based on similarity and improve over time, making the CSR more effective and creating a better customer experience.

More broadly, LLMs can be utilized to analyze company reports, research documents, news articles, financial data, and market trends, storing these relationships natively in Snowflake, side-by-side with structured data warehouse data and unstructured documents, images, or audio. 

Snowflake Summit 2023 ended with the same clear focus that I’ve always found most compelling within their platform – giving customers simplicity, flexibility, and choice for running their data-centric workloads. That’s now been expanded to Microsoft, to the open-source community, to unstructured data and documents, and to AI and LLMs. Across every single industry, there’s a practical workload that can be applied today to solve high-value, complex business problems.

I was struck by not only the major (and pleasantly unexpected) announcements and partnerships, but also the magnitude of the event itself. Some of the most innovative minds in the data ecosystem came together to engage in curiosity-driven conversation, sharing what they’re working on, what’s worked, and what hasn’t worked. And that last part – especially as we continue to push forward on the frontier of LLMs – is what made the week so compelling and memorable.

With 2nd Watch’s experience, research, and findings in these new workloads, combined with our history working with Snowflake, we look forward to having more discussions like those we held throughout Summit to help identify and solve long-standing business problems in new, innovative ways. If you’d like to talk through Document AI and LLM use cases specific to your organization, please get in touch.


Snowpark: Streamlining Workflow in Big Data Processing and Analysis

The Snowflake Data Cloud’s utility expanded further with the introduction of its Snowpark API in June of 2021. Snowflake has staked its claim as a significant player in cloud data storage and accessibility, enabling workloads including data engineering, data science, data sharing, and everything in between.

Snowflake provides a unique single engine with instant elasticity that is interoperable across different clouds and regions so users can focus on getting value out of their data, rather than trying to manage it. In today’s data-driven world, businesses must be able to quickly analyze, process, and derive insights from large volumes of data. This is where Snowpark comes in.

Snowpark expands Snowflake’s functionality, enabling users to leverage the full power of programming languages and libraries within the Snowflake environment. The Snowpark API provides a new framework for developers to bring DataFrame-style programming to common programming languages like Python, Java, and Scala. By integrating Snowpark into Snowflake, users can perform advanced data transformations, build complex data pipelines, and execute machine learning algorithms seamlessly.

The interoperability empowers organizations to extract greater value from their data, accelerating their speed of innovation.

What is Snowpark?

Snowpark’s API enables data scientists, data engineers, and software developers to perform complex data processing tasks efficiently and seamlessly. It has eliminated the need for data transfer through its high-level programming interface that allows users to write and execute code in their preferred programming language, all within the Snowflake platform. Snowpark comprises a client-side library and a server-side sandbox that enables users to work with their preferred tools and languages while leveraging the benefits of Snowflake virtual warehouses.

When developing applications, users can leverage the capabilities of Snowpark’s DataFrame API to process and analyze complex data structures and support various data processing operations such as filtering, aggregations, and sorting. In addition, users can create User Defined Functions (UDFs) whose code is uploaded to an internal stage in the Snowpark library that, when called on, is executed on the server side.

This enables the creation of custom functions to process and transform data according to their specific needs, along with greater flexibility and customization in data processing and analysis. These DataFrames are executed lazily, meaning they only run when an action to retrieve, store, or view the data they represent is run. Users write code within the client-side API in Snowpark, which is executed in Snowflake, so no data leaves unless the app asks.

Moreover, users can build queries within the DataFrame API, providing an easy way to work with data within the Structured Query Language (SQL) framework while integrating common languages like Python, Java, and Scala. Those queries are then converted to SQL within Snowpark before they distribute computation through Snowflake’s Elastic Performance Engine which enables collaboration across multiple clouds and regions.

From its support of the DataFrame API, UDFs, and seamless integration with data in Snowflake, Snowpark is an ideal tool for data scientists, data engineers, and software developers who need to work with big data in a fast and efficient manner.

Snowpark for Python

With the growth in data science and machine learning (ML) in past years, Python is closing the gap on SQL as a popular choice for data processing. Both are powerful in their own right, but they’re most valuable when they’re able to work together. Knowing this, Snowflake built Snowpark for Python “to help modern analytics, data engineering, data developers, and data science teams generate insights without complex infrastructure management for separate languages” (Snowflake, 2022). Snowpark for Python enables users to build scalable data pipelines and machine-learning workflows while utilizing the performance, elasticity, and security benefits of Snowflake.

Furthermore, with Snowflake virtual warehouses optimized for Snowpark, machine learning training is now possible due to its ability to process larger data sets by providing resources such as CPU, memory, and temporary storage. This enables Snowpark functions, including the execution of SQL statements that require compute sources (e.g., retrieving rows from tables) and performing Data Manipulation Language (DML) operations such as updating rows in tables, loading data into tables, and unloading data from tables.

With the compute infrastructure to execute memory-intensive operations, data scientists and teams can further streamline ML pipelines at scale with the interoperability of Snowpark and Snowflake.

Snowpark and Apache Spark 

If you’re familiar with the world of big data, you may know a thing or two about Apache Spark. In short, Spark is a distributed system used for big data processing and analysis.

While Apache Spark and Snowpark share similar utilities, there are some distinct differences and advantages to leveraging Snowpark over Apache Spark. Within Snowpark, users can manage all data within Snowflake as opposed to the need to transfer data to Spark. This not only streamlines workflows but also eliminates the potential adverse effects of sensitive data being taken out of the databases you’re working within and into a new ecosystem.

Additionally, the ability to remain in the Snowflake ecosystem simplifies processing by reducing the complexity of setup and management. While Spark requires significant hands-on time due to its more complicated setup, the ease of data transfer that is present between Snowflake and Snowpark requires no setup. You simply choose a warehouse and are ready to run commands within the database of your choosing.

Another major advantage Snowpark offers against its more complex counterpart is the simplified security measures. Leveraging the same security architecture that is in place within Snowflake eliminates the need to build out a specific complex security protocol like what is necessary within Spark.

The interoperability of Snowpark within the Snowflake ecosystem provides an assortment of advantages when compared with Apache Spark. Being a stand-alone processing engine, Spark comes with a significant amount of complexity from setup, ongoing management, transference of data, and creating specific security protocols. By choosing Snowpark, you opt out of the unnecessary complexity and into a streamlined functional process that can improve the efficiency and accuracy of any actions surrounding the big data you are handling – two things that are front of mind for any business in any industry whose decisions are derived from their ability to process and analyze complex data.

Why It Matters

Regardless of the industry, there is a growing need to process big data and understand how to leverage it for maximum value. When looking specifically at Snowpark’s API, leveraging a simplified programming interface with support for UDFs simplifies processing large data volumes in the users programming languages of choice. In uniting the simplified process with all the benefits of the Snowflake Data Cloud platform, there is a unique opportunity for businesses to take advantage of.

As a proud strategic Snowflake consulting partner, 2nd Watch recognizes the unique value that Snowflake provides. We have a team of certified SnowPros to help businesses implement and utilize their powerful cloud-based data warehouse and all the possibilities that their Snowpark API has to offer.

In a data-rich world, the ability to democratize data across your organization and make data-driven decisions can accelerate your continued growth. To learn more about implementing the power of Snowflake with the help of the 2nd Watch team, contact us and start extracting all the value your data has to offer.


Value Focused Due Diligence with Data Analytics

Private equity funds are shifting away from asset due diligence toward value-focused due diligence. Historically, the due diligence (DD) process centered around an audit of a portfolio company’s assets. Now, private equity (PE) firms are adopting value-focused DD strategies that are more comprehensive in scope and focus on revealing the potential of an asset.

Data analytics are key in support of private equity groups conducting value-focused due diligence. Investors realize the power of data analytics technologies to accelerate deal throughput, reduce portfolio risk, and streamline the whole process. Data and analytics are essential enablers for any kind of value creation, and with them, PE firms can precisely quantify the opportunities and risks of an asset.

The Importance of Taking a Value-Focused Approach to Due Diligence

Due diligence is an integral phase in the merger and acquisition (M&A) lifecycle. It is the critical stage that grants prospective investors a view of everything happening under the hood of the target business. What is discovered during DD will ultimately impact the deal negotiation phase and inform how the sale and purchase agreement is drafted.

The traditional due diligence approach inspects the state of assets, and it is comparable to a home inspection before the house is sold. There is a checklist to tick off: someone evaluates the plumbing, another looks at the foundation, and another person checks out the electrical. In this analogy, the portfolio company is the house, and the inspectors are the DD team.

Asset-focused due diligence has long been the preferred method because it simply has worked. However, we are now contending with an ever-changing, unpredictable economic climate. As a result, investors and funds are forced to embrace a DD strategy that adapts to the changing macroeconomic environment.

With value-focused DD, partners at PE firms are not only using the time to discover cracks in the foundation, but they are also using it as an opportunity to identify and quantify huge opportunities that can be realized during the ownership period. Returning to the house analogy: during DD, partners can find the leaky plumbing and also scope out the investment opportunities (and costs) of converting the property into a short-term rental.

The shift from traditional asset due diligence to value-focused due diligence largely comes from external pressures, like an uncertain macroeconomic environment and stiffening competition. These challenges place PE firms in a race to find ways to maximize their upside to execute their ideal investment thesis. The more opportunities a PE firm can identify, the more competitive it can be for assets and the more aggressive it can be in its bids.

Value-Focused Due Diligence Requires Data and Analytics

As private equity firms increasingly adopt value-focused due diligence, they are crafting a more complete picture using data they are collecting from technology partners, financial and operational teams, and more. Data is the only way partners and investors can quantify and back their value-creation plans.

During the DD process, there will be mountains of data to sift through. Partners at PE firms must analyze it, discover insights, and draw conclusions from it. From there, they can execute specific value-creation strategies that are tracked with real operating metrics, rooted in technological realities, and modeled accurately to the profit and loss statements.

This makes data analytics an important and powerful tool during the due diligence process. Data analytics can come in different forms:

  • Data Scientists: PE firms can hire data science specialists to work with the DD team. Data specialists can process and present data in a digestible format for the DD team to extract key insights while remaining focused on key deal responsibilities.
  • Data Models: PE firms can use a robustly built data model to create a single source of truth. The data model can combine a variety of key data sources into one central hub. This enables the DD team to easily access the information they need for analysis directly from the data model.
  • Data Visuals: Data visualization can aid DD members in creating more succinct and powerful reports that highlight key deal issues.
  • Document AI: Harnessing the power of document AI, DD teams can glean insights from a portfolio company’s unstructured data to create an ever more well-rounded picture of a potential acquisition.

Data Analytics Technology Powers Value

Value-focused due diligence requires digital transformation. Digital technology is the primary differentiating factor that can streamline operations and power performance during the due diligence stage. Moreover, the right technology can increase or decrease the value of a company.

Data analytics ultimately allows PE partners to find operationally relevant data and KPIs needed to determine the value of a portfolio company. There will be enormous amounts of data for teams to wade through as they embark on the DD process. However, savvy investors only need the right pieces of information to accomplish their investment thesis and achieve value creation. Investing in robust data infrastructure and technologies is necessary to implement the automated analytics needed to more easily discover value, risk, and opportunities. Data and analytics solutions include:

  • Financial Analytics: Financial dashboards can provide a holistic view of portfolio companies. DD members can access on-demand insights into key areas, like operating expenses, cash flow, sales pipeline, and more.
  • Operational Metrics: Operational data analytics can highlight opportunities and issues across all departments.
  • Executive Dashboards: Leaders can access the data they need in one place. This dashboard is highly tailored to present hyper-relevant information to executives involved with the deal.

Conducting value-focused due diligence requires timely and accurate financial and operating information available on demand. 2nd Watch partners with private equity firms to develop and execute the data, analytics, and data science solutions PE firms need to drive these results in their portfolio companies. Schedule a no-cost, no-obligation private equity whiteboarding session with one of our private equity analytics consultants.

How 2nd Watch can Help

At 2nd Watch, we can assist you with value-focused due diligence by providing comprehensive cloud cost analysis and optimization strategies. Here’s how we can help:

  • Cost Analysis: We conduct a thorough evaluation of your existing cloud infrastructure and spend. We analyze your usage patterns, resource allocations, and pricing models to identify areas of potential cost savings.
  • Optimization Strategies: Based on the cost analysis, we develop customized optimization strategies tailored to your specific needs. Our strategies focus on maximizing value and cost-efficiency without sacrificing performance or functionality.
  • Right-Sizing Recommendations: We identify instances where your resources are over-provisioned or underutilized. We provide recommendations to right-size your infrastructure, ensuring that you have the appropriate resource allocations to meet your business requirements while minimizing unnecessary costs.
  • Reserved Instance Planning: Reserved Instances (RIs) can offer significant cost savings for long-term cloud usage. We help you analyze your usage patterns and recommend optimal RI purchases, enabling you to leverage discounts and reduce your overall AWS spend.
  • Cost Governance and Budgeting: We assist in implementing cost governance measures and establishing budgeting frameworks. This ensures that you have better visibility and control over your cloud spend, enabling effective decision-making and cost management.
  • Ongoing Optimization: We provide continuous monitoring and optimization services, ensuring that your cloud environment remains cost-efficient over time. We proactively identify opportunities for further optimization and make recommendations accordingly.

By partnering with 2nd Watch, you can conduct due diligence with a clear understanding of your cloud costs and potential areas for optimization. We empower you to make informed decisions that align with your business goals and maximize the value of your cloud investments. Visit our website to learn more about how we can help with value-focused due diligence.


Data and AI Predictions in 2023

As we reveal our data and AI predictions for 2023, join us at 2nd Watch to stay ahead of the curve and propel your business towards innovation and success. How do we know that artificial intelligence (AI) and large language models (LLMs) have reached a tipping point? It was the hot topic at most families’ dinner tables during the 2022 holiday break.

AI has become mainstream and accessible. Most notably, OpenAI’s ChatGPT took the internet by storm, so much so that even our parents (and grandparents!) are talking about it. Since AI is here to stay beyond the Christmas Eve dinner discussion, we put together a list of 2023 predictions we expect to see regarding AI and data.

1. Proactively handling data privacy regulations will become a top priority.

Regulatory changes can have a significant impact on how organizations handle data privacy: businesses must adapt to new policies to ensure their data is secure. Modifications to regulatory policies require governance and compliance teams to understand data within their company and the ways in which it is being accessed. 

To stay ahead of regulatory changes, organizations will need to prioritize their data governance strategies. This will mitigate the risks surrounding data privacy and potential regulations. As a part of their data governance strategy, data privacy and compliance teams must increase their usage of privacy, security, and compliance analytics to proactively understand how data is being accessed within the company and how it’s being classified. 

2. AI and LLMs will require organizations to consider their AI strategy.

The rise of AI and LLM technologies will require businesses to adopt a broad AI strategy. AI and LLMs will open opportunities in automation, efficiency, and knowledge distillation. But, as the saying goes, “With great power comes great responsibility.” 

There is disruption and risk that comes with implementing AI and LLMs, and organizations must respond with a people- and process-oriented AI strategy. As more AI tools and start-ups crop up, companies should consider how to thoughtfully approach the disruptions that will be felt in almost every industry. Rather than being reactive to new and foreign territory, businesses should aim to educate, create guidelines, and identify ways to leverage the technology. 

Moreover, without a well-thought-out AI roadmap, enterprises will find themselves technologically plateauing, teams unable to adapt to a new landscape, and lacking a return on investment: they won’t be able to scale or support the initiatives that they put in place. Poor road mapping will lead to siloed and fragmented projects that don’t contribute to a cohesive AI ecosystem.

3. AI technologies, like Document AI (or information extraction), will be crucial to tap into unstructured data.

According to IDC, 80% of the world’s data will be unstructured by 2025, and 90% of this unstructured data is never analyzed. Integrating unstructured and structured data opens up new use cases for organizational insights and knowledge mining.

Massive amounts of unstructured data – such as Word and PDF documents – have historically been a largely untapped data source for data warehouses and downstream analytics. New deep learning technologies, like Document AI, have addressed this issue and are more widely accessible. Document AI can extract previously unused data from PDF and Word documents, ranging from insurance policies to legal contracts to clinical research to financial statements. Additionally, vision and audio AI unlocks real-time video transcription insights and search, image classification, and call center insights.

Organizations can unlock brand-new use cases by integrating with existing data warehouses. Finetuning these models on domain data enables general-purpose models across a wide variety of use cases. 

4. Data is the new oil.

Data will become the fuel for turning general-purpose AI models into domain-specific, task-specific engines for automation, information extraction, and information generation. Snorkel AI coined the term “data-centric AI,” which is an accurate paradigm to describe our current AI lifecycle. The last time AI received this much hype; the focus was on building new models. Now, very few businesses need to develop novel models and algorithms. What will set their AI technologies apart is the data strategy.

Data-centric AI enables us to leverage existing models that have already been calibrated to an organization’s data. Applying an enterprise’s data to this new paradigm will accelerate a company’s time to market, especially those who have modernized their data and analytics platforms and data warehouses. 

5. The popularity of data-driven apps will increase.

Snowflake recently acquired Streamlit, which makes application development more accessible to data engineers. Additionally, Snowflake introduced Unistore and hybrid tables (OLTP) to allow data science and app teams to work together and jointly off of a single source of truth in Snowflake, eliminating silos and data replication.

Snowflake’s big moves demonstrate that companies are looking to fill gaps that traditional business intelligence (BI) tools leave behind. With tools like Streamlit, teams can harness tools to automate data sharing and deployment, which is traditionally manual and Excel-driven. Most importantly, Streamlit can become the conduit that allows business users to work directly with the AI-native and data-driven applications across the enterprise.

6. AI-native and cloud-native applications will win.

Customers will start expecting AI capabilities to be embedded into cloud-native applications. Harnessing domain-specific data, companies should prioritize building upon module data-driven application blocks with AI and machine learning. AI-native applications will win over AI-retrofitted applications. 

When applications are custom-built for AI, analytics, and data, they are more accessible to data and AI teams, enabling business users to interact with models and data warehouses in a new way. Teams can begin classifying and labeling data in a centralized, data-driven way, rather than manually and often-repeated in Excel, and can feed into a human-in-the-loop system for review and to improve the overall accuracy and quality of models. Traditional BI tools like dashboards, on the other hand, often limit business users to consume and view data in a “what happened?” manner, rather than in a more interactive, often more targeted manner.

7. There will be technology disruption and market consolidation.

The AI race has begun. Microsoft’s strategic partnership with OpenAI and integration into “everything,” Google’s introduction of Bard and funding into foundational model startup Anthropic, AWS with their own native models and partnership with Stability AI, and new AI-related startups are just a few of the major signals that the market is changing. The emerging AI technologies are driving market consolidation: smaller companies are being acquired by incumbent companies to take advantage of the developing technologies. 

Mergers and acquisitions are key growth drivers, with larger enterprises leveraging their existing resources to acquire smaller, nimbler players to expand their reach in the market. This emphasizes the importance of data, AI, and application strategy. Organizations must stay agile and quickly consolidate data across new portfolios of companies. 

Conclusion

The AI ball is rolling. At this point, you’ve probably dabbled with AI or engaged in high-level conversations about its implications. The next step in the AI adoption process is to actually integrate AI into your work and understand the changes (and challenges) it will bring. We hope that our data and AI predictions for 2023 prime you for the ways it can have an impact on your processes and people.

Why choose 2nd Watch?

Choose 2nd Watch as your partner and let us empower you to harness the power of AI and data to propel your business forward.

  • Expertise: With years of experience in cloud optimization and data analytics, we have the expertise to guide you through the complexities of AI implementation and maximize the value of your data.
  • Comprehensive Solutions: Our range of services covers every aspect of your AI and data journey, from cost analysis and optimization to AI strategy development and implementation. We offer end-to-end solutions tailored to your specific needs.
  • Proven Track Record: Our track record speaks for itself. We have helped numerous organizations across various industries achieve significant cost savings, improve efficiency, and drive innovation through AI and data-driven strategies.
  • Thoughtful Approach: We understand that implementing AI and data solutions requires a thoughtful and strategic approach. We work closely with you to understand your unique business challenges and goals, ensuring that our solutions align with your vision.
  • Continuous Support: Our commitment to your success doesn’t end with the implementation. We provide ongoing support and monitoring to ensure that your AI and data initiatives continue to deliver results and stay ahead of the curve.

Contact us now to get started on your journey towards transformation and success.


Modern Data Warehouses and Machine Learning: A Powerful Pair

Artificial intelligence (AI) technologies like machine learning (ML) have changed how we handle and process data. However, AI adoption isn’t simple. Most companies utilize AI only for the tiniest fraction of their data because scaling AI is challenging. Typically, enterprises cannot harness the power of predictive analytics because they don’t have a fully mature data strategy.

To scale AI and ML, companies must have a robust information architecture that executes a company-wide data and predictive analytics strategy. This requires businesses to focus their data application beyond cost reduction and operations, for example. Fully embracing AI will require enterprises to make judgment calls and face challenges in assembling a modern information architecture that readies company data for predictive analytics. 

A modern data warehouse is the catalyst for AI adoption and can accelerate a company’s data maturity journey. It’s a vital component of a unified data and AI platform: it collects and analyzes data to prepare the data for later stages in the AI lifecycle. Utilizing your modern data warehouse will propel your business past conventional data management problems and enable your business to transform digitally with AI innovations.

What is a modern data warehouse?

On-premise or legacy data warehouses are not sufficient for a competitive business. Today’s market demands organizations to rely on massive amounts of data to best serve customers, optimize business operations, and increase their bottom lines. On-premise data warehouses are not designed to handle this volume, velocity, and variety of data and analytics.

If you want to remain competitive in the current landscape, your business must have a modern data warehouse built on the cloud. A modern data warehouse automates data ingestion and analysis, which closes the loop that connects data, insight, and analysis. It can run complex queries to be shared with AI technologies, supporting seamless ML and better predictive analytics. As a result, organizations can make smarter decisions because the modern data warehouse captures and makes sense of organizational data to deliver actionable insights company-wide.

How does a modern data warehouse work with machine learning?

A modern data warehouse operates at different levels to collect, organize, and analyze data to be utilized for artificial intelligence and machine learning. These are the key characteristics of a modern data warehouse:

Multi-Model Data Storage

Data is stored in the warehouse to optimize performance and integration for specific business data. 

Data Virtualization

Data that is not stored in the data warehouse is accessed and analyzed at the source, which reduces complexity, risk of error, cost, and time in data analysis. 

Mixed Workloads

This is a key feature of a modern data warehouse: mixed workloads support real-time warehousing. Modern data warehouses can concurrently and continuously ingest data and run analytic workloads.

Hybrid Cloud Deployment

Enterprises choose hybrid cloud infrastructure to move workloads seamlessly between private and public clouds for optimal compliance, security, performance, and costs. 

A modern data warehouse can collect and process the data to make the data easily shareable with other predictive analytics and ML tools. Moreover, these modern data warehouses offer built-in ML integrations, making it seamless to build, train, and deploy ML models.

What are the benefits of using machine learning in my modern data warehouse?

Modern data warehouses employ machine learning to adjust and adapt to new patterns quickly. This empowers data scientists and analysts to receive actionable insights and real-time information, so they can make data-driven decisions and improve business models throughout the company. 

Let’s look at how this applies to the age-old question, “how do I get more customers?” We’ll discuss two different approaches to answering this common business question.

The first methodology is the traditional approach: develop a marketing strategy that appeals to a specific audience segment. Your business can determine the segment to target based on your customers’ buying intentions and your company’s strength in providing value. Coming to this conclusion requires asking inductive questions about the data:

  • What is the demand curve?
  • What product does our segment prefer?
  • When do prospective customers buy our product?
  • Where should we advertise to connect with our target audience?

There is no shortage of business intelligence tools and services designed to help your company answer these questions. This includes ad hoc querying, dashboards, and reporting tools.

The second approach utilizes machine learning within your data warehouse. With ML, you can harness your existing modern data warehouse to discover the inputs that impact your KPIs most. You simply have to feed information about your existing customers into a statistical model, then the algorithms will profile the characteristics that define an ideal customer. We can ask questions around specific inputs:

  • How do we advertise to women with annual income between $100,000 and $200,000 who like to ski?
  • What are the indicators of churn in our self-service customer base?
  • What are frequently seen characteristics that will create a market segmentation?

ML builds models within your data warehouse to enable you to discover your ideal customer via your inputs. For example, you can describe your target customer to the computing model, and it will find potential customers that fall under that segment. Or, you can feed the computer data on your existing customers and have the machine learn the most important characteristics. 

Conclusion

A modern data warehouse is essential for ingesting and analyzing data in our data-heavy world.  AI and predictive analytics feed off more data to work effectively, making your modern data warehouse the ideal environment for the algorithms to run and enabling your enterprise to make intelligent decisions. Data science technologies like artificial intelligence and machine learning take it one step further and allow you to leverage the data to make smarter enterprise-wide decisions.

2nd Watch offers a Data Science Readiness Assessment to provide you with a clear vision of how data science will make the greatest impact on your business. Our assessment will get you started on your data science journey, harnessing solutions such as advanced analytics, ML, and AI. We’ll review your goals, review your current state, and design preliminary models to discover how data science will provide the most value to your enterprise.

  • Data Integration: We help you integrate data from various sources, both structured and unstructured, into your modern data warehouse. This includes data from databases, data lakes, streaming platforms, IoT devices, and external APIs. Our goal is to create a unified and comprehensive data repository for your machine learning projects.
  • Feature Engineering: We work with you to identify and engineer the most relevant features from your data that will enhance the performance of your machine learning models. This involves data preprocessing, transformation, and feature selection techniques to extract meaningful insights and improve predictive accuracy.
  • Machine Learning Model Development: Our team of data scientists and machine learning experts collaborate with you to develop and deploy machine learning models tailored to your specific business needs. We leverage industry-leading frameworks and libraries like TensorFlow, PyTorch, or scikit-learn to build robust and scalable models that can handle large-scale data processing.
  • Model Training and Optimization: We provide expertise in training and optimizing machine learning models using advanced techniques such as hyperparameter tuning, ensemble methods, and cross-validation. This ensures that your models achieve the highest levels of accuracy and generalization on unseen data.
  • Model Deployment and Monitoring: We assist in deploying your machine learning models into production environments, either on-premises or in the cloud. Additionally, we set up monitoring systems to track model performance, identify anomalies, and trigger alerts for retraining or adjustments when necessary.
  • Continuous Improvement: We support you in continuously improving your machine learning capabilities by iterating on models, incorporating feedback, and integrating new data sources. Our goal is to enable you to extract maximum value from your modern data warehouse and machine learning initiatives.

With 2nd Watch as your partner, you can leverage the power of modern data warehouses and machine learning to uncover valuable insights, make data-driven decisions, and drive innovation within your organization. Our expertise and comprehensive solutions will help you navigate the complexities of these technologies and achieve tangible business outcomes.

-Ryan Lewis | Managing Consultant at 2nd Watch

Get started with your Data Science Readiness Assessment today to see how you can stay competitive by automating processes, improving operational efficiency, and uncovering ROI-producing insights.