The Value of Data: Investigating the Analytics Value Chain

What does it mean to be data-driven? Does it mean that an algorithm is running your business for you? Does it mean a rules engine is making every decision? While the answer is clearly no, it is still essential to identify a desired state. At Brooklyn Data, we believe the optimal state is that you can approach every decision with knowledge and insight, reducing the opacity of the world around you. However, this doesn’t happen overnight and often doesn’t happen uniformly across your organization.

The Data on Data

The value of being data-driven isn’t idle speculation or a theoretical exercise; it can be (and has been) quantified. Econometric analysis from MIT’s Sloan School of Management, Stanford, and the University of Pennsylvania determined that large publicly traded companies adopting data-driven decision-making outperform their peers by 5–6% on average. For companies worth billions of dollars, this can represent a sizable return. This is just as important for smaller firms as they seek competitive advantages in their respective markets. Additional research has revealed that, on average, for every $1 spent on analytics, companies saw a return of $13, an incredible ROI that increased 22% over the three years of study.

The Analytics Value Chain

What is the secret to unlocking this latent value? We believe the key is recognizing that each step of the data journey offers benefits, and that exponential ROI comes from the opportunities that emerge as organizations mature their data capabilities. The journey to unlock data as a driving force in an organization has been described as the analytics (or insight) value chain.

A useful thought experiment in order to visualize how an organization uses data is to trace the path data must travel to generate value. A dashboard that shows the most popular items sold at a Direct-to-Consumer site can power any number of actions from targeted marketing and advertising campaigns, possible sales initiatives, customer personalization (Velir, our parent company, is a leader in this space), all the way to supply chain management. Underlying all of these is a functional data platform that enables this; the dashboard maker can find and access these curated data sets because of functional data processes that are enabled by instrumenting the data correctly to make it available.

The Data Journey

The first step of the data journey is the proper instrumentation and the deliberate and careful consideration of what data will be collected and available. Next comes decisions around storage and retention of high-quality data. Data is refined and made usable for individuals across the business to interact with through data transformation. This means aggregating and enriching data sources into more usable states, combining various data sources, or deriving new metrics from the available ones. These curated datasets are then accessed in the form of standard reports and dashboards or simple queries of data tables.

Centralizing and preparing data is often characterized as data engineering (and analytics engineering), which is the traditional remit of IT. More frequently, in the contemporary business world, these tasks are handled by specialized data teams. Decisions about structuring these teams, whether centrally or de-centralized by business function or department or in a hybrid federated design, often have implications for accessibility and usability for the rest of the organization. Such concerns also have various technological and procedural solutions to optimize the advantages and mitigate disadvantages.

The next step on the data journey is the creation of reporting. It is at this stage when companies can begin to answer simple questions such as “What happened?”, “Who, how many, how often, and where?” that the value of collecting and storing data becomes evident. Reducing the barrier to information through data catalogs or data discovery tools or even having a dedicated set of data analysts to consume and present information to others can expedite the time to value of data.

Beyond mere reporting are insights and analysis. This stage is differentiated from the earlier reporting step in that the information surfaced begins to answer “why” as well as “what will happen.” Deeper dives into the data by either subject matter experts or data scientists are often needed to unlock value at this stage. Machine learning can also be used.

The final stage of the journey is the crucial one. This is when the data gathered is used to activate a decision or action to drive maximum value. This could be anything from an email sent to remind a customer of an abandoned item in their shopping cart, an alert that a lightbulb needs to be replaced on a track switch, or a change in product ordering for a manufacturer based on predicted demand. Again, the purpose of data is to enable action because that is what has value. The commonality for all data driven actions is data that is organized, accessible, and intelligible. Even data-driven organizations need to continually maintain and invest in their data capacities and make a concerted effort to democratize and surface clean, reliable data.

Not every organization matures to the point of predictive modeling and forecasting. However, there is still plenty of value to derive from data in its more straightforward uses. The previous examples show that even a basic understanding of business can be extremely powerful. Crucially, the analytics value chain is multiplicative; the maximum value is greater than the sum of its parts. This is especially true when considering the impact of machine learning and AI and the returns they can generate, whether they be improvements to operational efficiency, revenue generation, or predictive capacity.

Taking Your First Step

Getting started on the data journey can be daunting. Even as most companies now collect and store vast amounts of data, most can still derive more value from their data. It’s also not hard to understand why. The journey can be daunting, with myriad decisions, approaches, and tools to be evaluated and decided on. These questions are universal across the steps of the analytics value chain and data journey.

Brooklyn Data has helped scores of companies through this process by setting up new infrastructure, reconfiguring existing systems, training teams, and providing data strategy and solutioning. We support your brand with best-in-class data engineering, data governance, analytics, reporting, and more. Together, we can unlock the (exponential) value of data and grow your business. Contact us to get started on your journey.