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.
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.
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.
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.
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.
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.
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.
