Cloud Data & Analytics | 2nd Watch Blog

Data & 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.

Think you’re ready to get started? Find out with 2nd Watch’s data science readiness assessment.

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.

-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.

3 Data Priorities for Organic Value Creation

Organic value creation focuses on a few main areas, including improving current performance (both financial and operational) of your companies, establishing a pattern of consistent growth, strengthening your organizational leadership team, and building the potential for a brighter future through product and competitive positioning. All of these are supported by and/or partially based on the data foundation you create in your companies. At exit, your buyers want to see and feel confident that the created organic value is sustainable and will endure. Data and analytics are key to proving that.

Companies that solely focus on competition will ultimately die. Those that focus on value creation will thrive. — Edward De Bono

To organically create and drive value, there are a few key data priorities you should consider:

A starting point is data quality, which underpins all you will ever do and achieve with data in your organization. Achieving better-quality data is an unrelenting task, one that many organizations overlook.
Data monetization is a second priority and is also not top-of-mind for many organizations. The adage that “data is the new oil” is at least partially true, and most companies have ways and means to leverage the data they already possess to monetize and grow revenue for improved financial returns.
A third data priority is to focus on user adoption. Having ready data and elite-level analytical tools is not sufficient. You need to be sure the data and tools you have invested in are broadly used – and not just in the short term. You also need to continue to evolve and enhance both your data and your tools to grow that adoption for future success.

Data Quality

Data quality is a complicated topic worthy of a separate article. Let’s focus our data quality discussion on two things: trust and the process of data quality.

If you are organically growing your companies and increasing the use of and reliance upon your data, you better make sure you trust your data. The future of your analytics solutions and broad adoption across your operational management teams depend on your data being trustworthy. That trust means that the data is accurate, consistent across the organization, timely, and involved in a process to ensure the continuing trust in the data. There is also an assumption that your data aligns with external data sources. You can measure accuracy of your portfolio company’s data in many ways, but the single best measure is going to be how your operating executives answer the question, “How much do you trust your data?”

Data quality is never stagnant. There are always new data sources, changes in the data itself, outside influences on the data, etc. You cannot just clean the data once and expect it to stay clean. The best analogy is a stream that can get polluted from any source that feeds into the stream. To maintain high data quality over time, you need to build and incorporate processes and organizational structures that monitor, manage, and own the quality of your company’s data.

One “buzzwordy” term often applied to good data governance is data stewardship – the idea being that someone within your enterprise has the authority and responsibility to keep your data of the highest quality. There are efficient and effective ways to dramatically improve your company data and to keep it of the highest quality as you grow the organization. Simply put, do something about data quality, make sure that someone or some group is responsible for data quality, and find ways to measure your overall data quality over time.

A leading equipment distributor found new revenue sources and increased competitive edge by leveraging the cloud data warehouse that 2nd Watch built for their growing company to share data on parts availability in their industry. Using the centralized data, they can grow revenue, increase customer service levels, and have more industry leverage from data that they already owned. Read this private equity case study here.

Data Monetization

Organic value creation can also come from creating value out of the data your portfolio companies already own. Data monetization for you can mean such options as:

Enriching your internal data – Seek ways to make your data more valuable internally. This most often comes from cross-functional data creation (e.g., taking costing data and marrying it with sales/marketing data to infer lifetime customer value). The unique view that this enriched internal data offers will often lead to better internal decision-making and will drive more profitable analytics as you grow your analytics solutions library.

Finding private value buyers – Your data, cleansed and anonymized, is highly valuable. Your suppliers will pay for access to more data and information that helps them customize their offerings and prices to create value for customers. Your own customers would pay for enhanced information about your products and services if you can add value to them in the process. Within your industry, there are many ways to anonymize and sell the data that your portfolio companies create.

Finding public value buyers – Industry trade associations, consultancies, conference organizations, and the leading advisory firms are all eager to access unique insights and statistics they can use and sell to their own clients to generate competitive advantage.

Building a data factory mindset – Modern cloud data warehouse solutions make the technology to monetize your data quite easy. There are simple ways to make the data accessible and a marketplace for selling such data from each of the major cloud data warehouse vendors. The hardest part is not finding buyers or getting them the data; it is building an internal mindset that your internal data is a valuable asset that can be easily monetized.

User Adoption

Our firm works with many private equity clients to design, build, and implement leading analytics solutions. A consistent learning across our project work is that user adoption is a critical success factor in our work.

Just because we have more accurate data, or more timely data, or more enriched data won’t necessarily increase the adoption of advanced analytical solutions in your portfolio companies. Not all of your operating executives are data driven nor are they all analytically driven. Just because they capably produce their monthly reporting package and get it to you on time does not mean they are acting on issues and opportunities that they should be able to discern from the data. Better training, organizational change techniques, internal data sharing, and many other ways can dramatically increase the speed and depth of the user adoption in your companies.

You know how to seek value when you invest. You know how to grow your companies post-close. Growing organically during your hold period will drive increased exit valuations and let you outperform your investment thesis. Focus on data quality and broad user adoption as two of your analytics priorities for strong organic value creation across your portfolio.

Contact us today to set up a complimentary private equity data whiteboarding session. Our analytics experts have a template for data monetization and data quality assessments that we can run through with you and your team.

What Is the Difference Between Snowflake and Amazon Redshift?

The modern business world is data-centric. As more businesses turn to cloud computing, they must evaluate and choose the right data warehouse to support their digital modernization efforts and business outcomes. Data warehouses can increase the bottom line, improve analytics, enhance the customer experience, and optimize decision-making.

A data warehouse is a large repository of data businesses utilize for deep analytical insights and business intelligence. This data is collected from multiple data sources. A high-performing data warehouse can collect data from different operational databases and apply a uniform format for better analysis and quicker insights.

Two of the most popular data warehouse solutions are Snowflake and Amazon Web Services (AWS) Redshift. Let’s look at how these two data warehouses stack up against one another.

What is Snowflake?

Snowflake is a cloud-based data warehousing solution that uses third-party cloud-compute resources, such as Azure, Google Cloud Platform, or Amazon Web Services (AWS.) It is designed to provide users with a fully managed, cloud-native database solution that can scale up or down as needed for different workloads. Snowflake separates compute from storage: a non-traditional approach to data warehousing. With this method, data remains in a central repository while compute instances are managed, sized, and scaled independently.

Snowflake is a good choice for companies that are conscious about their operational overhead and need to quickly deploy applications into production without worrying about managing hardware or software. It is also the ideal platform to use when query loads are lighter, and the workload requires frequent scaling.

The benefits of Snowflake include:

Easy integration with most components of data ecosystems
Minimal operational overhead: companies are not responsible for installing, configuring, or managing the underlying warehouse platform
Simple setup and use
Abstracted configuration for storage and compute instances
Robust and intuitive SQL interface

What is Amazon Redshift?

Amazon Redshift is an enterprise data warehouse built on Amazon Web Services (AWS). It provides organizations with a scalable, secure, and cost-effective way to store and analyze large amounts of data in the cloud. Its cloud-based compute nodes enable businesses to perform large-scale data analysis and storage.

Amazon Redshift is ideal for enterprises that require quick query outputs on large data sets. Additionally, Redshift has several options for efficiently managing its clusters using AWS CLI/Amazon Redshift Console, Amazon Redshift Query API, and AWS Software Development Kit. Redshift is a great solution for companies already using AWS services and running applications with a high query load.

The benefits of Amazon Redshift include:

Seamless integration with the AWS ecosystem
Multiple data output formatting support
Easy console to extract analytics and run queries
Customizable data and security models

Comparing Data Warehouse Solutions

Snowflake and Amazon Redshift both offer impressive performance capabilities, like scalability across multiple servers and high availability with minimal downtime. There are some differences between the two that will determine which one is the best fit for your business.

Performance

Both data warehouse solutions harness massively parallel processing (MPP) and columnar storage, which enables advanced analytics and efficiency on massive jobs. Snowflake boasts a unique architecture that supports structured and semi-structured data. Storage, computing, and cloud services are abstracted to optimize independent performance. Redshift recently unveiled concurrency scaling coupled with machine learning to compete with Snowflake’s concurrency scaling.

Maintenance

Snowflake is a pure SaaS platform that doesn’t require any maintenance. All software and hardware maintenance is handled by Snowflake. Amazon Redshift’s clusters require manual maintenance from the user.

Data and Security Customization

Snowflake supports fewer customization choices in data and security. Snowflake’s security utilizes always-on encryption enforcing strict security checks. Redshift supports data flexibility via partitioning and distribution. Additionally, Redshift allows you to tailor its end-to-end encryption and set up your own identity management system to manage user authentication and authorization.

Pricing

Both platforms offer on-demand pricing but are packaged differently. Snowflake doesn’t bundle usage and storage in its pricing structure and treats them as separate entities. Redshift bundles the two in its pricing. Snowflake tiers its pricing based on what features you need. Your company can select a tier that best fits your feature needs. Redshift rewards businesses with discounts when they commit to longer-term contracts.

Which data warehouse is best for my business?

To determine the best fit for your business, ask yourself the following questions in these specific areas:

Do I want to bundle my features? Snowflake splits compute and storage, and its tiered pricing provides more flexibility to your business to purchase only the features you require. Redshift bundles compute and storage to unlock the immediate potential to scale for enterprise data warehouses.
Do I want a customizable security model? Snowflake grants security and compliance options geared toward each tier, so your company’s level of protection is relevant to your data strategy. Redshift provides fully customizable encryption solutions, so you can build a highly tailored security model.
Do I need JSON storage? Snowflake’s JSON storage support wins over Redshift’s support. With Snowflake, you can store and query JSON with native functions. With Redshift, JSON is split into strings, making it difficult to query and work with.
Do I need more automation? Snowflake automates issues like data vacuuming and compression. Redshift requires hands-on maintenance for these sorts of tasks.

Conclusion

A data warehouse is necessary to stay competitive in the modern business world. The two major data warehouse players – Snowflake and Amazon Redshift – are both best-in-class solutions. One product is not superior to the other, so choosing the right one for your business means identifying the one best for your data strategy.

2nd Watch is an AWS Certified Partner and an Elite Snowflake Consulting Partner. We can help you choose the right data warehouse solution for you and support your business regardless of which data warehouse your choose.

We have been recognized by AWS as a Premier Partner since 2012, as well as an audited and approved Managed Service Provider and Data and Analytics Competency partner for our outstanding customer experiences, depth and breadth of our products and services, and our ability to scale to meet customer demand. Our engineers and architects are 100% certified on AWS, holding more than 200 AWS certifications.

Our full team of certified SnowPros has proven expertise to help businesses implement modern data solutions using Snowflake. From creating a simple proof of concept to developing an enterprise data warehouse to customized Snowflake training programs, 2nd Watch will help you to utilize Snowflake’s powerful cloud-based data warehouse for all of your data needs.

Contact 2nd Watch today to help you choose the right data warehouse for your business!

4 Data Principles for Operational Resilience

Scaling your portfolio companies creates value, and increasing their native agility multiplies the value created. The foundation of better resilience in any company is often based on the ready availability of operational data. Access to the data you need to address problems or opportunities is necessary if you expect your operating executives and management teams to run the business more effectively than their competitors.

Resilience is the strength and speed of our response to adversity – and we can build it. It isn’t about having a backbone. It’s about strengthening the muscles around our backbone. — Sheryl Sandberg

You need and want your portfolio companies to be operationally resilient – to be ready and able to respond to changes and challenges in their operations. We all have seen dramatic market changes in recent years, and we all should expect continued dynamic economic and competitive pressures to challenge even the best of our portfolio companies. Resilient companies will respond better to such challenges and will outperform their peers.

This post highlights four areas that you and your operating executives should consider as you strive to make yourself more operationally resilient:

Data engineering takes time and effort. You can do a quick and dirty version of data engineering, also called loading it into a spreadsheet, but that won’t be sufficient to achieve what you really need in your companies.
Building a data-driven culture takes time. Having the data ready is not enough, you need to change the way your companies use the data in their tactical and strategic decision-making. And that takes some planning and some patience to achieve.
Adding value to the data takes time. Once you have easily accessible data, as an organization you should strive to add or enrich the data. Scoring customers or products, cleaning or scrubbing your source data, and adding external data are examples of ways you can enrich your data once you have it in a centrally accessible place.
Get after it. You need and want better analytics in every company you own or manage. This is a journey, not a single project. Getting started now is paramount to building agility and resiliency over time on that journey.

Data Engineering can be Laborious

Every company has multiple application source systems that generate and store data. Those multiple systems store the data in their proprietary databases, in a format that best suits transactional systems, and likely redundantly stores common reference data like customer number and customer name, address, etc. To get all that data, standardize it, scrub it, and model it in the way that you need to manage your business takes months. You likely must hire consultants to build the data pipelines, create a data warehouse to store the data, and then build the reports and dashboards for data analysis.

On most of our enterprise analytics projects, data engineering consumes 60-70% of the time and effort put into the project. Ask any financial analyst or business intelligence developer – most of their time is spent getting their hands on the right, clean data. Dashboards and reports are quickly built once the data is available.

CASE STUDY

The CEO of a large manufacturing company wanted to radically increase the level of data-driven decision-making in his company. Working with his executive team, we quickly realized that functional silos, prior lack of easy data access, and ingrained business processes were major inhibitors to achieving their vision. 2nd Watch incorporated extensive organizational change work while we built a new cloud-based analytics warehouse to facilitate and speed the pace of change. Read the full case study.

A Data-driven Culture needs to be Nurtured and Built

Giving your executives access to data and reports is only half the battle. Most executives are used to making decisions without the complete picture and without a full set of data. Resiliency comes from having the data and from using it wisely. If you build it, not all will come to use it.

Successful analytics projects incorporate organizational change management elements to drive better data behaviors. Training, better analytics tools, collaboration, and measuring adoption are just some of the best practices that you can bring to your analytics projects to drive better use of the data and analysis tools that will lead to more resilience in your portfolio companies.

Data Collaboration Increases the Value of your Data

We consistently find that cross-functional sharing of data and analytics increases the value and effectiveness of your decision-making. Most departments and functions have access to their own data – finance has access to the GL and financial data, marketing has access to marketing data, etc. Building a single data model that incorporates all of the data, from all of the silos, increases the level of collaboration that lets your executives from all functions simultaneously see and react to the performance of the business.

Let’s be honest, most enterprises are still managed through elaborate functional spreadsheets that serve as the best data source for quick analysis. Spreadsheets are fine for individual analysis and reporting, and for quick ad-hoc analytics. They are not a viable tool for extensive collaboration and won’t ever enable the data value enhancement that comes from a “single source of truth.”

Operating Executives need to Build Resilience as they Scale their Companies.

Change is constant, markets evolve, and today’s problems and opportunities are not tomorrow’s problems and opportunities. Modern data and analytics solutions can radically improve their operational resilience and drive higher value. These solutions can be technically and organizationally complex and will take time to implement and achieve results. Start building resiliency in your portfolio companies by mapping out a data strategy and creating the data foundation that your companies need.

Contact us today to set up a complimentary whiteboarding session. Our analytics experts will work through a high-level assessment with you.

A Developer’s Guide to Power BI

There are many options when it comes to data analytics tools. Choosing the right one for your organization will depend on a number of factors. Since many of the reviews and articles on these tools are focused on business users, the 2nd Watch team wanted to explore these tools from the developer’s perspective. In this developer’s guide to Power BI, we’ll go over the performance, interface, customization, and more to help you get a full understanding of this tool.

Why Power BI?

Power BI is a financially attractive alternative to the likes of Tableau and Looker, which either offer custom-tailored pricing models or a large initial per-user cost followed by an annual fee after the first year. However, don’t conflate cost with quality; getting the most out of Power BI is more dependent on your data environment and who is doing the data discovery. Companies already relying heavily on Microsoft tools should look to add Power BI to their roster, as it integrates seamlessly with SQL Server Analysis Services to facilitate faster and deeper analysis.

Performance for Developers

When working with large datasets, developers will experience some slowdown as they customize and publish their reports. Developing on Power BI works best with small-to-medium-sized data sets. At the same time, Microsoft has come out with more optimization options such as drill-through functionality, which allows for deeper analytical work for less processing power.

Performance for Users

User performance through Power BI Services is controlled through row-level security implementation. For any sized dataset, the number of rows can be limited depending on the user’s role. Overviews and executive dashboards may run somewhat slowly, but as the user’s role becomes more granular, dashboards will operate more quickly.

User Interface: Data Layer

Data is laid out in a tabular form; clicking any measure column header reveals a drop-down menu with sorting options, filtering selections, and the Data Analysis Expressions (DAX) behind the calculation.

User Interface: Relationship Layer

The source tables are draggable objects with labeled arrows between tables denoting the type of relationship.

Usability and Ease of Learning

Microsoft Power BI documentation is replete with tutorials, samples, quickstarts, and concepts for the fundamentals of development. For a more directed learning experience, Microsoft also put out the Microsoft Power BI Guided Learning set, which is a freely available collection of mini courses on modeling, visualization, and exploration of data through Power BI. It also includes an introduction to DAX development as a tool to transform data in the program. Additionally, the Power BI community forums almost always have an answer to any technical question a developer might have.

Modeling

Power BI can easily connect to multiple data sources including both local folders and most major database platforms. Data can be cleaned and transformed using the Query Editor; the Editor can change data type, add columns, and combine data from multiple sources. Throughout this transformation process, the Query Editor records each step so that every time the query connects to the data source, the data is transformed accordingly. Relationships can be created by specifying a from: table and to table, the keys to relate, a cardinality, and a cross-filter direction.

Customization

In terms of data transformation, Power Query is a powerful language for ensuring that your report contains the exact data and relationships you and your business user are looking to understand. Power Query simplifies the process of data transformation with an intuitive step-by-step process for joining, altering, or cleaning your tables within Power BI. For actual report building, Power BI contains a comprehensive list of visualizations for almost all business needs; if one is not found within the default set, Microsoft sponsors a visual gallery of custom user-created visualizations that anyone is free to explore and download.

Permissions and User Roles

Adding permissions to workspaces, datasets, and reports within your org is as simple as adding an email address and setting an access level. Row-level security is enabled in Power BI Desktop; role management allows you flexibly customize access to specific data tables using DAX functions to specify conditional filters. Default security filtering is single-directional; however, bi-directional cross-filtering allows for the implementation of dynamic row-level security based on usernames and/or login IDs.

Ease of Dev Opp and Source Control

When users have access to a data connection or report, source and version control are extremely limited without external GitHub resources. Most of the available activities are at the macro level: viewing/editing reports, adding sources to gateways, or installing the application. There is no internal edit history for any reports or dashboards.

Setup and Environment

Setup is largely dependent on whether your data is structured in the cloud, on-premises, or a hybrid. Once the architecture is established, you need to create “data gateways” and assign them to different departments and data sources. This gateway acts as a secure connection between your data source and development environments. From there, security and permissions can be applied to ensure the right people within your organization have access to your gateways. When the gateways are established, data can be pulled into Power BI via Power Query and development can begin.

Implementation

The most common implementation of Power BI utilizes on-premises source data and Power BI Desktop for data preparation and reporting, with Power BI Service used in the cloud to consume reports and dashboards, collaborate, and establish security. This hybrid implementation strategy takes advantage of the full range of Power BI functionality by leveraging both the Desktop and Service versions. On-premises data sources connect to Power BI Desktop for development, leading to quicker report creation (though Power BI also supports cloud-based data storage).

Summary and Key Points

Power BI is an extremely affordable and comprehensive analytics tool. It integrates seamlessly with Excel, Azure, and SQL Server, allowing for established Microsoft users to start analyzing almost instantly. The tool is easy to learn for developers and business users alike, and there are many available resources, like Microsoft mini-courses and community forums.

A couple things to be aware of with Power BI: It may lack some of the bells and whistles as compared to other analytics tools, and it’s best if you’re already in the Microsoft ecosystem and are coming in with a solid data strategy.

If you want to learn more about Power BI or any other analytics tools, contact us today to schedule a no-obligation whiteboard session.

A Developer’s Guide to Tableau

Why Tableau?

Tableau gets a good reputation for being sleek and easy to use and by bolstering an impeccable UI/UX. It’s by and large an industry leader due to its wide range of visualizations and ability to cohesively and narratively present data to end users. As a reliable, well-established leader, Tableau can easily integrate with many sources, has extensive online support, and does not require a high level of technical expertise for users to gain value.

Want better Tableau dashboards? Our modern data and analytics experts are here to help. Learn more about our modern cloud analytics solutions with Snowflake and Tableau.

Performance for Developers

One of the easiest ways to ensure good performance with Tableau is to be mindful of how you import your data. Utilizing extracts rather than live data and performing joins or unions in your database reduces a lot of the processing that Tableau would otherwise have to do. While you can easily manipulate data without any coding, these capabilities reduce performance significantly, especially when dealing with large volumes of information. All data manipulation should be done in your database or data warehouse prior to adding it as a source. If that isn’t an option, Tableau offers a product called Tableau Prep that enables data manipulation and enhanced data governance capabilities.

Performance for Users

Dashboard performance for users depends almost entirely on practices employed by developers when building out reports. Limiting the dataset to information required for the goals of the dashboard reduces the amount of data Tableau processes as well as the number of filters included for front-end users. Cleaning up workbooks to reduce unnecessary visualizations will enhance front-end performance as well.

User Interface: Data Source

After connecting to your source, Tableau presents your data using the “Data Source” tab. This is a great place to check that your data was properly loaded and doesn’t have any anomalies. Within this view of the data, you have the chance to add more sources and the capability to union and join tables together as well as filter the data to a specific selection and exclude rows that were brought in.

User Interface: Worksheet

The “Worksheet” tabs are where most of the magic happens. Each visualization that ends up on the dashboard will be developed in separate worksheets. This is where you will do most of the testing and tweaking as well as where you can create any filters, parameters, or calculated fields.

User Interface: Dashboards

In the “Dashboard” tab, you bring together all of the individual visualizations you have created. The drag-and-drop UI allows you to use tiles predetermined by Tableau or float the objects to arrange them how you please. Filters can be applied to all of the visualizations to create a cohesive story or to just a few visualizations to break down information specific to a chart or table. It additionally allows you to toggle between different device layouts to ensure end-user satisfaction.

User Interface: Stories

One of the most unique Tableau features is its “Stories” capability. Stories work great when you need to develop a series of reports that present a narrative to a business user. By adding captions and placing visualizations in succession, you can convey a message that speaks for itself.

Usability and Ease of Learning

The Tableau basics are relatively easy to learn due to the intuitive point-and-click UI and vast amount of educational resources such as their free training videos. Tableau also has a strong online community where answers to specific questions can be found either on the Help page or third-party sites.

Creating an impressive variety of simple visualizations can be done without a hitch. This being said, there are a few things to watch out for:

Some tricks and more niche capabilities can easily remain undiscovered.
Complex features such as table calculations may confuse new users.
The digestible UI can be deceiving – visualizations often appear correct when the underlying data is not. One great way to check for accuracy is to right-click on the visualization and select “View Data.”

Modeling

Unlike Power BI, Tableau does not allow users to create a complicated semantic layer within the tool. However, users can establish relationships between different data sources and across varied granularities through a method called data blending. One way to implement this method is by selecting the “Edit Relationships” option in the data drop-down menu.

Data blending also eliminates duplicates that may occur by using a function that returns a single value for the duplicate rows in the secondary source. Creating relationships among multiple sources in Tableau requires attention to detail as it can take some manipulation and may have unintended consequences or lead to mistakes that are difficult to spot.

Customization

The wide array of features offered by Tableau allows for highly customizable visualizations and reports. Implementing filter actions (which can apply to both worksheets and dashboards), parameters, and calculated fields empowers developers to modify the source data so that it better fits the purpose of the report. Using workarounds for calculations not explicitly available in Tableau frequently leads to inaccuracy; however, this can be combated by viewing the underlying data. Aesthetic customizations such as importing external images and the large variety of formatting capabilities additionally allow developers boundless creative expression.

Permissions and User Roles

The type of license assigned to a user determines their permissions and user roles. Site administrators can easily modify the site roles of users on the Tableau Server or Tableau Online based on the licenses they hold. The site role determines the most impactful action (e.g., read, share, edit) a specific user can make on the visualizations. In addition to this, permissions range from viewing or editing to downloading various components of a workbook. The wide variety of permissions applies to various components within Tableau. A more detailed guide to permissions capabilities can be found here.

Ease of Dev Opp and Source Control

Dev opp and source control improved greatly when Tableau implemented versioning of workbooks in 2016. This enables users to select the option to save a history of revisions, which saves a version of the workbook each time it is overwritten. This enables users to go back to previous versions of the workbook and access work that may have been lost. When accessing prior versions, keep in mind that if an extract is no longer compatible with the source, its data refresh will not work.

Setup and Environment

With all of the necessary information on your sources, setup in Tableau is a breeze. It has built-in connectors with a wide range of sources and presents your data to you upon connection. You also have a variety of options regarding data manipulation and utilizing live or static data (as mentioned above). Developers utilize the three Tableau environments based primarily on the level of interactions and security they desire.

Tableau Desktop: Full developer software in a silo; ability to connect to databases or personal files and publish work for others to access
Tableau Server: Secure environment accessed through a web browser to share visualizations across the organization; requires a license for each user
Tableau Online: Essentially the same as Tableau Server but based in the cloud with a wider range of connectivity options

Implementation

Once your workbook is developed, select the server and make your work accessible for others either on Tableau Online or on Tableau Server by selecting “publish.” During this process, you can determine the specific project you are publishing and where to make it available. There are many other modifications that can be adjusted such as implementing editing permissions and scheduling refreshes of the data sources.

Summary and Key Points

Tableau empowers developers of all skill levels to create visually appealing and informative dashboards, reports, and storytelling experiences. As developers work, there is a wealth of customization options to tailor reports to their specific use case and draw boundless insights for end users. To ensure that Tableau gleans the best results for end users, keep these three notes in mind:

Your underlying data must be trustworthy as Tableau does little to ensure data integrity. Triple-check the numbers in your reports.
Ensure your development methods don’t significantly damage performance for both developers and end users.
Take advantage of the massive online community to uncover vital features and leverage others’ knowledge when facing challenges.

If you have any questions on Tableau or need help getting better insights from your Tableau dashboards, contact us for an analytics assessment.

Here’s Why Your Data Science Project Failed (and How to Succeed Next Time)

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.

Is your organization equipped to achieve meaningful results through data science? Secure your success by working with 2nd Watch. Schedule a whiteboard session with our team to get you started on the right path.

5 Ways Insurance Companies Are Driving ROI through Analytics

Insurance providers are rich with data far beyond what they once had at their disposal for traditional historical analysis. The quantity, variety, and complexity of that data enhance the ability of insurers to gain greater insights into consumers, market trends, and strategies to improve their bottom line. But which projects offer you the best return on your investment? Here’s a glimpse at some of the most common insurance analytics project use cases that can transform the capabilities of your business.

Want better dashboards? Our data and analytics insurance team are here to help. Learn more about our data visualization starter pack.

Issuing More Policies

Use your historical data to predict when a customer is most likely to buy a new policy.

Both traditional insurance providers and digital newcomers are competing for the same customer base. As a result, acquiring new customers requires targeted outreach with the right message at the moment a buyer is ready to purchase a specific type of insurance.

Predictive analytics allows insurance companies to evaluate the demographics of the target audience, their buying signals, preferences, buying patterns, pricing sensitivity, and a variety of other data points that forecast buyer readiness. This real-time data empowers insurers to reach policyholders with customized messaging that makes them more likely to convert.

Quoting Accurate Premiums

Provide instant access to correct quotes and speed up the time to purchase.

Consumers want the best value when shopping for insurance coverage, but if their quote fails to match their premium, they’ll take their business elsewhere. Insurers hoping to acquire and retain policyholders need to ensure their quotes are precise – no matter how complex the policy.

For example, one of our clients wanted to provide ride-share drivers with four-hour customized micro policies on-demand. Using real-time analytical functionality, we enabled them to quickly and accurately underwrite policies on the spot.

Improving Customer Experience

Better understand your customer’s preferences and optimize future interactions.

A positive customer experience means strong customer retention, a better brand reputation, and a reduced likelihood that a customer will leave you for the competition. In an interview with CMSWire, the CEO of John Hancock Insurance said many customers see the whole process as “cumbersome, invasive, and long.” A key solution is reaching out to customers in a way that balances automation and human interaction.

For example, the right analytics platform can help your agents engage policyholders at a deeper level. It can combine the customer story and their preferences from across customer channels to provide more personalized interactions that make customers feel valued.

Detecting Fraud

Stop fraud before it happens.

You want to provide all of your customers with the most economical coverage, but unnecessary costs inflate your overall expenses. Enterprise analytics platforms enable claims analysis to evaluate petabytes of data to detect trends that indicate fraud, waste, and abuse.

See for yourself how a tool like Tableau can help you quickly spot suspicious behavior with visual insurance fraud analysis.

Improving Operations and Financials

Access and analyze financial data in real time.

In 2019, ongoing economic growth, rising interest rates, and higher investment income were creating ideal conditions for insurers. However, that’s only if a company is maximizing their operations and ledgers.

Now, high-powered analytics has the potential to provide insurers with a real-time understanding of loss ratios, using a wide range of data points to evaluate which of your customers are underpaying or overpaying.

Are you interested in learning how a modern analytics platform like Tableau, Power BI, Looker, or other BI technologies can help you drive ROI for your insurance organization? Schedule a no-cost insurance whiteboarding strategy session to explore the full potential of your insurance data.

How a Dedicated Data Warehouse Yields Better Insight than Your CRM or ERP

What percent of your enterprise data goes completely untapped? It’s far more than most organizations realize. Research suggests that as much as 68% of global enterprise data goes unused. The reasons are varied (we can get to the root cause with a current state assessment), but one growing problem stems from misconceptions about CRMs, ERPs, EHRs, and similar operational software systems.

The right operational software systems are valuable tools with their own effective reporting functions. The foundation of any successful reporting or analytics initiative depends on two factors: on a centralized source of truth that exists in a unified source format. All operational software systems struggle to satisfy either aspect of that criteria.

Believe it or not, one of the most strategic systems for data-driven decision-making is still a dedicated data warehouse. Here is the value a data warehouse brings to your organization and the necessary steps to implement that enhance your analytics’ accuracy and insight.

Download Now: Modern Data Warehouse Comparison Guide [Snowflake, Redshift, Azure Synapse, and Google BigQuery]

CRMs and ERPs Are Data Silos with Disparate Formats

Operational software systems are often advertised as offering a unified view, but that’s only true for their designed purpose. CRMs offer a comprehensive view of customers, ERPs of operations, and EHRs of patient or member medical history. Outside of their defined parameters, these systems are data silos.

In an HBR blog post, Edd Wilder-James captures the conundrum perfectly: “You can’t cleanly separate the data from its intended use. Depending on your desired application, you need to format, filter, and manipulate the data accordingly.”

Some platforms are enabled to integrate outside data sources, but even that provides you with a filtered view of your data, not the raw and centralized view necessary to generate granular and impactful reports. It’s the difference between abridged and unabridged books – you might glean chunks of the big picture but miss entire sections or chapters that are crucial to the overall story.

Building a dedicated data warehouse removes the question of whether your data sets are complete. You can extract, transfer, and load data from source systems into star schemas with a unified format optimized for business users to leverage. The data is formatted around the business process rather than the limitations of the tool. That way, you can run multifaceted reports or conduct advanced analytics when you need it – without anchoring yourself to any specific technology.

Tracking Down Your Data Sources

In all honesty, organizations not familiar with the process often overlook vital information sources. There might be a platform used to track shipping that only one member of your team uses. Maybe there’s a customer service representative who logs feedback in an ad hoc document. Or it’s possible there’s HIPAA-compliant software in use that isn’t automatically loading into your EHR. Regardless of your industry, there are likely gaps in your knowledge well outside of the CRMs, ERPs, EHRs, and other ostensibly complete data sources.

How do you build a single source of truth? It’s not as simple as shifting around a few sources. Implementing a dedicated data warehouse requires extensive planning and preparation. The journey starts with finding the invisible web of sources outside of your primary operational software systems. Those organizations that choose to forgo a full-fledged current state assessment to identify those hidden sources only achieve fragmentary analytics at best.

Data warehouse implementations need guidance and buy-in at the corporate level. That starts with a well-defined enterprise data strategy. Before you can create your strategy, you need to ask yourself questions such as these:

What are your primary business objectives?
What are your key performance indicators?
Which source systems contribute to those goals?
Which source systems are we currently using across the enterprise?

By obtaining the answers to these and other questions from decision-makers and end users, you can clarify the totality of your current state. Otherwise, hunting down those sources is an uphill battle.

Creating Data Warehouse Value that Lasts

Consolidating your dispersed data sources is just a starting point. Next, you need to extract the data from each source system and populate them within the data warehouse framework itself. A key component of this step is to test data within your warehouse to verify quality and completeness.

If data loss occurs during the ETL process, the impact of your work and veracity of your insights will be at risk. Running a variety of different tests (e.g., data accuracy, data completeness, data transformation, etc.) will reduce the possibility of any unanticipated biases in your single source of truth.

What about maintaining a healthy and dynamic data warehouse? How often should you load new data? The answer depends on the frequency of your reporting needs. As a rule of thumb, think in terms of freshness. If your data has gone stale by the time you’re loading it into your data warehouse, increase the frequency of your data refresh. Opt for real-time analytics if it will provide you with a strategic advantage, not because you want to keep current with the latest buzzword.

Improving Your Results with an Outsourced Partner

Each step in the process comes with its own complications. It’s easy to fall into common data warehousing pitfalls unless you have internal resources with experience pinpointing hidden data sources, selecting the right data model, and maintaining your data warehouse post-implementation.

One of our clients in the healthcare software space was struggling to transition to a dynamic data warehousing model that could enhance their sales. Previously, they had a reporting application that they were using on a semi-annual basis. Though they wanted to increase the frequency of their reporting and enable multiple users to run reports simultaneously, they didn’t have the internal expertise to confidently navigate these challenges.

Working with 2nd Watch made a clear difference. Our client was able to leverage a data warehouse architecture that provided daily data availability (in addition to the six-month snapshot) and self-service dashboards that didn’t require changes or updates on their part. We also set them on the right path to leverage a single source of the truth through future developments.

Our strategies in that project prioritized our client’s people instead of a specific technology. We considered the reporting and analytics needs of their business users rather than pigeonholing their business into a specific tool. Through our tech-agnostic approach, we guided them toward a future state that provided strategic advantage and a clear ROI that might have otherwise gone unachieved.

Want your data warehouse to provide you with a single source of the truth? Schedule a whiteboard session to review your options and consolidate your data into actionable insight.