Category: AI/ML

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.


Why Data Science Projects Fail: Key Takeaways for Success

87% of data science projects never make it beyond the initial vision into any stage of production. Even some that pass-through discovery, deployment, implementation, and general adoption fail to yield the intended outcomes. After investing all that time and money into a data science project, it’s not uncommon to feel a little crushed when you realize the windfall results you expected are not coming.

Yet even though there are hurdles to implementing data science projects, the ROI is unparalleled – when it’s done right.

Looking to get started with ML, AI, or other data science initiatives? Learn how to get started with our Data Science Readiness Assessment.

Opportunities

You can enhance your targeted marketing.

Coca-Cola has used data from social media to identify its products or competitors’ products in images, increasing the depth of consumer demographics and hyper-targeting them with well-timed ads.

You can accelerate your production timelines.

GE has used artificial intelligence to cut product design times in half. Data scientists have trained algorithms to evaluate millions of design variations, narrowing down potential options within 15 minutes.

With all of that potential, don’t let your first failed attempt turn you off to the entire practice of data science. We’ve put together a list of primary reasons why data science projects fail – and a few strategies for forging success in the future – to help you avoid similar mistakes.

Hurdles

You lack analytical maturity.

Many organizations are antsy to predict events or decipher buyer motivations without having first developed the proper structure, data quality, and data-driven culture. And that overzealousness is a recipe for disaster. While a successful data science project will take some time, a well-thought-out data science strategy can ensure you will see value along the way to your end goal.

Effective analytics only happens through analytical maturity. That’s why we recommend organizations conduct a thorough current state analysis before they embark on any data science project. In addition to evaluating the state of their data ecosystem, they can determine where their analytics falls along the following spectrum:

Descriptive Analytics: This type of analytics is concerned with what happened in the past. It mainly depends on reporting and is often limited to a single or narrow source of data. It’s the ground floor of potential analysis.

Diagnostic Analytics: Organizations at this stage are able to determine why something happened. This level of analytics delves into the early phases of data science but lacks the insight to make predictions or offer actionable insight.

Predictive Analytics: At this level, organizations are finally able to determine what could happen in the future. By using statistical models and forecasting techniques, they can begin to look beyond the present into the future. Data science projects can get you into this territory.

Prescriptive Analytics: This is the ultimate goal of data science. When organizations reach this stage, they can determine what they should do based on historical data, forecasts, and the projections of simulation algorithms.

Your project doesn’t align with your goals.

Data science, removed from your business objectives, always falls short of expectations. Yet in spite of that reality, many organizations attempt to harness machine learning, predictive analytics, or any other data science capability without a clear goal in mind. In our experience, this happens for one of two reasons:

1. Stakeholders want the promised results of data science but don’t understand how to customize the technologies to their goals. This leads them to pursue a data-driven framework that’s prevailed for other organizations while ignoring their own unique context.

2. Internal data scientists geek out over theoretical potential and explore capabilities that are stunning but fail to offer practical value to the organization.

Outside of research institutes or skunkworks programs, exploratory or extravagant data science projects have a limited immediate ROI for your organization. In fact, the odds are very low that they’ll pay off. It’s only through a clear vision and practical use cases that these projects are able to garner actionable insights into products, services, consumers, or larger market conditions.

Every data science project needs to start with an evaluation of your primary goals. What opportunities are there to improve your core competency? Are there any specific questions you have about your products, services, customers, or operations? And is there a small and easy proof of concept you can launch to gain traction and master the technology?

The above use case from GE is a prime example of having a clear goal in mind. The multinational company was in the middle of restructuring, reemphasizing its focus on aero engines and power equipment. With the goal of reducing their six- to 12-month design process, they decided to pursue a machine learning project capable of increasing the efficiency of product design within their core verticals. As a result, this project promises to decrease design time and budget allocated for R&D.

Organizations that embody GE’s strategy will face fewer false starts with their data science projects. For those that are still unsure about how to adapt data-driven thinking to their business, an outsourced partner can simplify the selection process and optimize your outcomes.

Your solution isn’t user-friendly.

The user experience is often an overlooked aspect of viable data science projects. Organizations do all the right things to create an analytics powerhouse customized to solve a key business problem, but if the end users can’t figure out how to use the tool, the ROI will always be weak. Frustrated users will either continue to rely upon other platforms that provided them with limited but comprehensible reporting capabilities, or they will stumble through the tool without unlocking its full potential.

Your organization can avoid this outcome by involving a range of end users in the early stages of project development. This means interviewing both average users and extreme users. What are their day-to-day needs? What data are they already using? What insight do they want but currently can’t obtain?

An equally important task is to determine your target user’s data literacy. The average user doesn’t have the ability to derive complete insights from the represented data. They need visualizations that present a clear-cut course of action. If the data scientists are only thinking about how to analyze complex webs of disparate data sources and not whether end users will be able to decipher the final results, the project is bound to struggle.

You don’t have data scientists who know your industry.

Even if your organization has taken all of the above considerations into mind, there’s still a chance you’ll be dissatisfied with the end results. Most often, it’s because you aren’t working with data science consulting firms that comprehend the challenges, trends, and primary objectives of your industry.

Take healthcare, for example. Data scientists who only grasp the fundamentals of machine learning, predictive analytics, or automated decision-making can only provide your business with general results. The right partner will have a full grasp of healthcare regulations, prevalent data sources, common industry use cases, and what target end users will need. They can address your pain points and already know how to extract full value for your organization.

And here’s another example from one of our own clients. A Chicago-based retailer wanted to use their data to improve customer lifetime value, but they were struggling with a decentralized and unreliable data ecosystem. With the extensive experience of our retail and marketing team, we were able to outline their current state and efficiently implement a machine-learning solution that empowered our client. As a result, our client was better able to identify sales predictors and customize their marketing tactics within their newly optimized consumer demographics. Our knowledge of their business and industry helped them to get the full results now and in the future.

In conclusion, implementing successful data science projects can be challenging, but the potential return on investment is unparalleled when done right. By addressing common hurdles such as analytical maturity, goal alignment, user-friendliness, and industry expertise, you can increase your chances of achieving meaningful results. Don’t let a failed attempt discourage you from harnessing the power of data science. Take the next step towards success by partnering with 2nd Watch.

Schedule a whiteboard session with our experienced team to explore how we can help you navigate the complexities of data science, align your projects with your business goals, and deliver tangible outcomes. Don’t miss out on the opportunity to unlock valuable insights and drive innovation in your organization. Contact us today and let’s embark on a data-driven journey together.


Strategies for Data Science ROI: Business Preparation Guide

Enhanced predictions. Dynamic forecasting. Increased profitability. Improved efficiency. Data science is the master key to unlock an entire world of benefits. But is your business even ready for data science solutions? Or more importantly, is your business ready to get the full ROI from data science?

Let’s look at the overall market for some answers. Most organizations have increased their ability to use their data to their advantage in recent years. BCG surveys have shown that the average organization has moved beyond the “developing” phase of data maturity into a “mainstream” phase. This means more organizations are improving their analytics capabilities, data governance, data ecosystems, and data science use cases. However, there’s still a long way to go until they are maximizing the value of their data.

Looking to get started with ML, AI, or other data science initiatives? Learn how with our Data Science Readiness Assessment.

So, yes, there is a level of functional data science that many organizations are exploring and capable of reaching. Yet if you want to leverage data science to deliver faster and more complete insights (and ROI), your business needs to ensure that the proper data infrastructure and the appropriate internal culture exist.

The following eight tips will help your machine learning projects, predictive analytics, and other data science initiatives operate with greater efficiency and speed. Each of these tips will require an upfront investment of time and money, but they are fundamental in making sure your data science produces the ROI you want.

Laying the Right Foundation with Accurate, Consistent, and Complete Data

Tip 1: Before diving into data science, get your data in order.
Raw data, left alone, is mostly an unruly mess. It’s collected by numerous systems and end users with incongruous attention to detail. After it’s gathered, the data is often subject to migrations, source system changes, or unpredictable system errors that alter the quality even further. While you can conduct data science projects without first focusing on proper data governance, what ends up on your plate will vary greatly – and comes with a fair amount of risk.

Consider this hypothetical example of predictive analytics in manufacturing. A medium-sized manufacturer wants to use predictive maintenance to help lower the risk and cost of an avoidable machine breakdown (which can easily amount to $22,000 per minute). But first, they need to train a machine learning algorithm to predict impending breakdowns using their existing data. If the data’s bad, then the resulting detection capabilities might result in premature replacements or expensive disruptions.

Tip 2: Aim to create a single source of truth with your data.
Unifying data from assorted sources into a modern data warehouse or data mart simplifies the entire analytical process. Organizations should always start by implementing data ingestion best practices to extract and import high-quality data into the destination source. From there, it’s critical to build a robust data pipeline that maintains the flow of quality data into your warehouse.

Tip 3: Properly cleanse and standardize your data.
Each department in your organization has its own data sources, formats, and definitions. Before your data can be data science-ready and generate accurate predictions, it must be cleansed, standardized, and devoid of duplicates before it ever reaches your analytics platform or data science tool. Only through effective data cleansing and data governance strategy can you reach that level.

Tip 4: Don’t lean on your data scientist to clean up the data.
Sure, data scientists are capable of cleaning up and preparing your data for data science, but pulling them into avoidable data manipulation tasks slows down your analytical progress and impacts your data science initiatives. Leaning on your data scientist to complete these tasks can also lead to frustrated data scientists and increase turnover.

It’s not that data scientists shouldn’t do some data cleansing and manipulation from time to time; it’s that they should only be doing it when it’s necessary.

Tip 5: Create a data-driven culture.
Your data scientist or data science consulting partner can’t be the only ones with data on the mind. Your entire team needs to embrace data-driven habits and practices, or your organization will struggle to obtain meaningful insights from your data.

Frankly, most businesses have plenty of room to grow in this regard. For those looking to implement a data-driven culture before they forge deep into the territory of data science, you need to preach from the top down – grassroots data implementations will never take hold. Your primary stakeholders need to believe not only in the possibility of data science but in the cultivation of practices that fortify robust insights.

A member of your leadership team, whether a chief data officer or another senior executive, needs to ensure that your employees adopt data science tools, observe habits that foster data quality, and connect business objectives to this in-depth analysis.

Tip 6: Train your whole team on data science.
Data science is no longer just for data scientists. A variety of self-service tools and platforms have allowed ordinary end users to leverage machine learning algorithms, predictive analytics, and similar disciplines in unprecedented ways.

With the right platform, your team should be able to conduct sophisticated predictions, forecasts, and reporting to unlock rich insight from their data. What that takes is the proper training to acclimate your people to their newfound capabilities and show the practical ways data science can shape their short- and long-term goals.

Tip 7: Keep your data science goals aligned with your business goals.
Speaking of goals, it’s just as important for data-driven organizations to inspect the ways in which their advanced analytical platforms connect with their business objectives. Far too often, there’s disconnection and data science projects either prioritize lesser goals or pursue abstract and impractical intelligence. If you determine which KPIs you want to improve with your analytical capabilities, you have a much better shot at eliciting the maximum results for your organization.

Tip 8: Consider external support to lay the foundation.
Though these step-by-step processes are not mandatory, focusing on creating a heartier and cleaner data architecture as well as a culture that embraces data best practices will set you in the right direction. Yet it’s not always easy to navigate on your own.

With the help of data science consulting partners, you can make the transition in ways that are more efficient and gratifying in the long run.

Conclusion

In conclusion, data science holds immense potential for businesses to gain enhanced predictions, dynamic forecasting, increased profitability, and improved efficiency. However, realizing the full ROI of data science requires careful preparation and implementation. It is crucial for organizations to ensure they have the proper data infrastructure, a data-driven culture, and a solid foundation of accurate and standardized data.

By following the eight tips outlined in this article, businesses can optimize their machine learning projects, predictive analytics, and other data science initiatives. These tips emphasize the importance of data governance, data cleansing, creating a data-driven culture, training the entire team on data science, aligning data science goals with business objectives, and considering external support when needed.

2nd Watch, with its team of experienced data management, analytics, and data science consultants, offers comprehensive support and expertise to guide businesses through their data science journey. From building the business case to data preparation and model building, our customized solutions are designed to deliver tangible results and maximize the value of your data.

Partner with 2nd Watch and harness the power of data science to drive enhanced predictions, dynamic forecasting, increased profitability, and improved efficiency for your business. Schedule your data science readiness whiteboard session now and take the first step towards unlocking the full potential of your data.

Schedule a data science readiness whiteboard session with our team and we’ll determine where you’re at and your full potential with the right game plan.


How Insurance Fraud Analytics Protect Businesses from Fraudulent Claims

With your experience in the insurance industry, you understand more than most about how the actions of a smattering of people can cause disproportionate damage. The $80 billion in fraudulent claims paid out across all lines of insurance each year, whether soft or hard fraud, is perpetrated by lone individuals, sketchy auto mechanic shops, or the occasional organized crime group. The challenge for most insurers is that detecting, investigating, and mitigating these deceitful claims is a time-consuming and expensive process.

Rather than accepting loss to fraud as part of the cost of doing business, some organizations are enhancing their detection capabilities with insurance analytics solutions. Here is how your organization can use insurance fraud analytics to enhance fraud detection, uncover emerging criminal strategies, and still remain compliant with data privacy regulations.

Recognizing Patterns Faster

When you look at exceptional claim’s adjusters or special investigation units, one of the major traits they all share is an uncanny ability to recognize fraudulent patterns. Their experience allows them to notice the telltale signs of fraud, whether it’s frequent suspicious estimates from a body shop or complex billing codes intended to hide frivolous medical tests. Though you trust adjusters, many rely on heuristic judgments (e.g., trial and error, intuition, etc.) rather than hard rational analysis. When they do have statistical findings to back them up, they struggle to keep up with the sheer volume of claims.

This is where machine learning techniques can help to accelerate pattern recognition and optimize the productivity of adjusters and special investigation units. An organization starts by feeding a machine learning model a large data set that includes verified legitimate and fraudulent claims. Under supervision, the machine learning algorithm reviews and evaluates the patterns across all claims in the data set until it has mastered the ability to spot fraud indicators.

Let’s say this model was given a training set of legitimate and fraudulent auto insurance claims. While reviewing the data for fraud, the algorithm might spot links in deceptive claims between extensive damage in a claim and a lack of towing charges from the scene of the accident. Or it might notice instances where claims involve rental cars rented the day of the accident that are all brought to the same body repair shop. Once the algorithm begins to piece together these common threads, your organization can test the model’s unsupervised ability to create a criteria for detecting deception and spot all instances of fraud.

What’s important in this process is finding a balance between fraud identification and instances of false positives. If your program is overzealous, it might create more work for your agents, forcing them to prove that legitimate claims received an incorrect label. Yet when the machine learning model is optimized, it can review a multitude of dimensions to identify the likelihood of fraudulent claims. That way, if an insurance claim is called into question, adjusters can comb through the data to determine if the claim should truly be rejected or if the red flags have a valid explanation.

Detecting New Strategies

The ability of analytics tools to detect known instances of fraud is only the beginning of their full potential. As with any type of crime, insurance fraud evolves with technology, regulations, and innovation. With that transformation comes new strategies to outwit or deceive insurance companies.

One recent example has emerged through automation. When insurance organizations began to implement straight through processing (STP) in their claim approvals, the goal was to issue remittances more quickly, easily, and cheaply than manual processes. For a time, this approach provided a net positive, but once organized fraudsters caught wind of this practice, they pounced on a new opportunity to deceive insurers.

Criminals learned to game the system, identifying amounts that were below the threshold for investigation and flying their fraudulent claims under the radar. In many cases, instances of fraud could potentially double without the proper tools to detect these new deception strategies. Though most organizations plan to enhance their anti-fraud technology, there’s still the potential for them to lose millions in errant claims – if their insurance fraud analytics are not programmed to detect new patterns.

In addition to spotting red flags for common fraud occurrences, analytics programs need to be attuned to any abnormal similarities or unlikely statistical trends. Using cluster analysis, an organization can detect statistical outliers and meaningful patterns that reveal potential instances of fraud (such as suspiciously identical fraud claims).

Even beyond the above automation example, your organization can use data discovery to find hidden indicators of fraud and predict future incidents. Splitting claims data into various groups through a few parameters (such as region, physician, billing code, etc., in healthcare) can help in detecting unexpected correlations or warning signs for your automation process or even human adjusters to flag as fraud.

Safeguarding Personally Identifiable Information

As you work to improve your fraud detection, there’s one challenge all insurers face: protecting the personally identifiable information (PII) of policyholders while you analyze your data. The fines related to HIPAA violations can amount to $50,000 per violation, and other data privacy regulations can result in similarly steep fines. The good news is that insurance organizations can balance their fraud prediction and data discovery with security protocols if their data ecosystem is appropriately designed.

Maintaining data privacy compliance and effective insurance fraud analytics requires some maneuvering. Organizations that derive meaningful and accurate insight from their data must first bring all of their disparate data into a single source of truth. Yet, unless they also implement access control through a compliance-focused data governance strategy, there’s a risk of regulatory violations while conducting fraud analysis.

One way to limit your exposure is to create a data access layer that tokenizes the data, replacing any sensitive PII with unique identification symbols to keep data separate. Paired with clear data visualization capabilities, your adjusters and special investigation units can see clear-cut trends and evolving strategies without revealing individual claimants. From there, they can take their newfound insights into any red flag situation, saving your organization millions while reducing the threat of noncompliance.

Are you ready to take your data science initiatives to the next level? Partner with 2nd Watch, the industry leader in data management, analytics, and data science consulting. Our team of experts will guide you through the entire process, from building the business case to data preparation and model building. Schedule a data science readiness whiteboard session with us today and unlock the full potential of data science for your business. Don’t miss out on the opportunity to enhance fraud detection, uncover emerging criminal strategies, and remain compliant with data privacy regulations. Get started now and experience the transformative power of insurance fraud analytics with 2nd Watch by your side.

Check out our insurance analytics solutions page for use cases that are transforming your industry.


Supply Chain Industry Using Predictive Analytics to Boost Their Competitive Edge

Professionals in the supply chain industry need uncanny reflexes. The moment they get a handle on raw materials, labor expenses, international legislation, and shipping conditions, the ground shifts beneath them and all the effort they put into pushing their boulder up the hill comes undone. With the global nature of today’s supply chain environment, the factors governing your bottom line are exceptionally unpredictable. Fortunately, there’s a solution for this problem: predictive analytics for supply chain management.

This particular branch of analytics offers an opportunity for organizations to anticipate challenges before they happen. Sounds like an indisputable advantage, yet only 30% of supply chain professionals are using their data to forecast their future.

Want to improve your supply chain operations and better understand your customer’s behavior? Learn about our demand forecasting data science starter kit.

Though most of the stragglers plan to implement predictive analytics in the next 10 years, they are missing incredible opportunities in the meantime. Here are some of the competitive advantages companies are missing when they choose to ignore predictive operational analytics.

Enhanced Demand Forecasting

How do you routinely hit a moving goalpost? As part of an increasingly complex global system, supply chain leaders are faced with an increasing array of expected and unexpected sales drivers from which they are pressured to determine accurate predictions about future demand. Though traditional demand forecasting yields some insight from a single variable or small dataset, real-world supply chain forecasting requires tools that are capable of anticipating demand based on a messy, multifaceted assembly of key motivators. Otherwise, they risk regular profit losses as a result of the bullwhip effect, buying far more products or raw materials than are necessary.

For instance, one of our clients, an international manufacturer, struggled to make accurate predictions about future demand using traditional forecasting models. Their dependence on the historical sales data of individual SKUs, longer order lead times, and lack of seasonal trends hindered their ability to derive useful insight and resulted in lost profits. By implementing machine learning models and statistical packages within their organization, we were able to help them evaluate the impact of various influencers on the demand of each product. As a result, our client was able to achieve an 8% increase in weekly demand forecast accuracy and 12% increase in monthly demand forecast accuracy.

This practice can be carried across the supply chain in any organization, whether your demand is relatively predictable with minor spikes or inordinately complex. The right predictive analytics platform can clarify the patterns and motivations behind complex systems to help you to create a steady supply of products without expensive surpluses.

Smarter Risk Management

The modern supply chain is a precise yet delicate machine. The procurement of raw materials and components from a decentralized and global network has the potential to cut costs and increase efficiencies – as long as the entire process is operating perfectly. Any type of disruption or bottleneck in the supply chain can create a massive liability, threatening both customer satisfaction and the bottom line. When organizations leave their fate up to reactive risk management practices, these disruptions are especially steep.

Predictive risk management allows organizations to audit each component or process within their supply chain for its potential to destabilize operations. For example, if your organization currently imports raw materials such as copper from Chile, predictive risk management would account for the threat of common Chilean natural disasters such as flooding or earthquakes. That same logic applies to any country or point of origin for your raw materials.

You can evaluate the cost and processes of normal operations and how new potentialities would impact your business. Though you can’t prepare for every possible one of these black swan events, you can have contingencies in place to mitigate losses and maintain your supply chain flow.

Formalized Process Improvement

As with any industry facing internal and external pressures to pioneer new efficiencies, the supply chain industry cannot rely on happenstance to evolve. There needs to be a twofold solution in place. One, there needs to be a culture of continuous organizational improvement across the business. Two, there need to be apparatuses and tools in place to identify opportunities and take meaningful action.

For the second part, one of the most effective tools is predictive analytics for supply chain management. Machine learning algorithms are exceptional at unearthing inefficiencies or bottlenecks, giving stakeholders the fodder to make informed decisions. Because predictive analytics removes most of the grunt work and exploration associated with process improvement, it’s easier to create a standardized system of seeking out greater efficiencies. Finding new improvements is almost automatic.

Ordering is an area that offers plenty of opportunities for improvement. If there is an established relationship with an individual customer (be it retailer, wholesaler, distributor, or the direct consumer), your organization has stockpiles of information on individual and demographic customer behavior. This data can in turn be leveraged alongside other internal and third-party data sources to anticipate product orders before they’re made. This type of ordering can accelerate revenue generation, increase customer satisfaction, and streamline shipping and marketing costs.

Conclusion

Incorporating predictive analytics into supply chain management can be a game-changer for businesses, providing them with a competitive edge in today’s dynamic and unpredictable market environment. With the expertise and support of 2nd Watch, a leading provider of advanced analytics solutions, organizations can harness the power of predictive analytics to drive better decision-making, optimize operations, and stay ahead of the competition.

By leveraging cutting-edge technologies and machine learning algorithms, 2nd Watch helps businesses enhance their demand forecasting capabilities, enabling them to accurately predict future demand based on a holistic analysis of key motivators and variables. This empowers supply chain leaders to make informed decisions and avoid profit losses resulting from the bullwhip effect, ensuring optimal inventory management and efficient resource allocation.

Moreover, 2nd Watch enables organizations to adopt smarter risk management practices by auditing every component and process within the supply chain. By leveraging predictive analytics, businesses can identify potential disruptions and bottlenecks, proactively mitigate risks, and maintain a seamless flow of operations. Whether it’s accounting for natural disasters in specific regions or evaluating the impact of geopolitical factors on the supply chain, 2nd Watch helps businesses stay resilient and agile in the face of uncertainties.

Additionally, 2nd Watch plays a crucial role in driving formalized process improvement within the supply chain industry. With its expertise in predictive analytics, the company uncovers hidden inefficiencies, identifies bottlenecks, and provides actionable insights for streamlining operations. By automating the process of seeking out greater efficiencies, organizations can create a standardized system for continuous improvement and innovation, ensuring they stay ahead in a rapidly evolving market.

Incorporating predictive analytics into supply chain management with the support of 2nd Watch offers numerous advantages, from optimized demand forecasting to smarter risk management and formalized process improvement. Don’t miss out on the transformative potential of predictive analytics. Contact 2nd Watch today to learn more about their advanced analytics solutions and unlock the full power of predictive analytics for your supply chain.