Why Data Intensity is the new KPI for CIOs

Using data analytics is crucial to gaining a competitive advantage in today’s fast-paced world. Data KPIs allow you to know why your sales are growing or declining, what the market dynamics are, how your customer expectations are evolving, and (through predictive modeling) what you can do to get ahead of the competition. A new buzzword in the data world is data intensity. What does it really mean? Can it be measured? What can data intensity KPIs enable you to achieve?

Is data utilization a benchmark for success?

At the beginning of the discussion, participants were asked if data utilization was seen as a benchmark for success in their organizations. A CIO of a non-profit healthcare organization said that they collect health data from over 150 countries, and it's key to their success. A head of technology added that they have a lot of data-driven initiatives within their company. They often use data analysis to make informed pricing decisions. A development manager told the audience that they have a data warehouse that helps them model buyer preferences and product consumption. Lastly, a product management executive remarked that data enables them to automate processes, monitor mechanical operations, and develop tools for customer assistance.  

What is data intensity?

An attendee said that data intensity signifies how much a business can use its data to generate actionable insights and gain a competitive advantage. Data intensity and data maturity are similar terms.  Another attendee chimed in to say that data intensity KPIs are metrics you need to run your business and provide your customers with a delightful experience. In the past, businesses would use opinions and hunches to make crucial decisions. Today, data-first companies use data analytics and modeling to drive all their decisions.

The importance of KPIs

KPIs allow us to track and measure success continuously. It’s important to know which KPIs are the right fit and how they may evolve. You must strive to ensure that all relevant stakeholders are on the same page regarding the KPIs that are the best measures of success. For example, if we talk about data intensity, it’s a measure of the impact of data on your day-to-day business— but what should be its unit? Should it be several data visualizations, or should it be a dollar figure? If you don’t truly know what the KPI is or don’t understand the key factors that may affect it, it won’t be of any tangible use to you.

Start with “what does your business need?”

An executive talked about the importance of answering the right data questions at the right time. Before you start collecting data, ask yourself, “What does the business need?” “What’s your goal? Is it to grow? If yes, what should be the indicators for growth?” Only when you have identified what your end goal with data is will you know what kind of data you want to collect. Knowing the right answers upfront allows you to create a reliable, well-formatted data store, and avoid data silos.  

Validating data and removing bias

One contributor mentioned that data must be validated against a source. For example, if you are dealing with healthcare data, you can validate incoming data against a standardized data dictionary. It’s crucial to define and constantly refine a validation and governance process, ensuring that data from different sources is cleaned and formatted before it is loaded into the system.

While answering a question about data bias, a participant said that bias typically arises from incomplete information because you could not factor in all the relevant environment variables. To remove bias, sometimes you just have to zoom out, broaden your perspective, and look at more data points to paint a more descriptive picture.

Moving data exercises from the cost center to the profit center

A speaker encouraged the audience to think about moving data exercises from the cost center to the value-generating center. It is high time to make people realize that data exercises drive revenue, empower executives to make prudent decisions, and fast-track a business’ journey to success. This ideological change will make it much easier for the higher management to approve investments in data engineering.

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