Measure Your Data Analytics Capability

There is a famous business concept ‘Managing by Measuring’, and nowhere does this concept apply more than Data Analytics. In fact, it would be odd if analytics, which aim at measurement and analysis, is not measured itself.

Data Analytics is all about driving transformational change, and the impact of that change should be known, so you know what works, what doesn’t, and where you should improve and focus next.

You are not just measuring the raw power of your analytics platform, but your overall approach to analytics and the maturity level of your Data Analytics practice. For more detail, read my blog The Data Analytics Maturity Model. 

Data Analytics done right transforms your entire business, so to measure its value you are tracking many aspects of your organization, from the analytics themselves to the performance of key aspects of the enterprise.

When many enterprises think about analytics, they tend to think about how much these solutions cost and how much the people that run them make each year. In short, they view analytics as an expense. That is short-sighted. If you are to make optimum use of your data, you have to view data as an asset rather than an expense, an asset that drives deep value and returns.

With the mindset of analytics being an expense, the enterprise doesn’t understand the value. It fails to devote true attention to the data analytics capabilities, doesn’t invest in training, doesn’t  invest in data professionals, and doesn’t get optimum results. When an organization shifts to seeing the value of data and analytics, the commitment to optimizing Data Analytics solutions increases immeasurably.

The returns for a committed organization are huge. “Defying typical enterprise software ROI cycles, business analytics increase return on investment substantially as the solution matures and is extended to handle ‘big data’ beyond the firewall including social media and partner ecosystems. A Nucleus Research analysis of 60 deployments shows that an average ROI of 188 percent in the initial automation phase grows to an average of 1,209 percent in the later predictive phase,” Nucleus Research argued. “With increasing rates of return on investments in analytics, companies are benefiting from even small initial deployments and are using analytics to drive continuous improvements to business process and decision making. Few investments, financial or otherwise, generate a higher return with increasing levels of investment. Analytics should be a top priority for future investments for most organizations,” said Hyoun Park, principal analyst at Nucleus.

The Value of Invention

Let’s start at a high level. Many of today’s hottest inventions and coolest new services are a direct result of Data Analytics. The benefit, therefore, is the value of the new invention or service. The initial value is the early revenue, which can start off slow. Then there is the future value (FV), revenue from improvements, and the benefits of applying automation and streamlining to product marketing, management, support, etc.

Value of Data-Driven Decisions

A data-driven approach has a marked impact on productivity and decision-making. Research from the MIT Sloan School of Management shows that “Those that adopted ‘data-driven decision making’ achieved productivity that was 5 to 6 percent higher than could be explained by other factors, including how much the companies invested in technology,” said a New York Times article When There’s No Such Thing as Too Much Information.

Using data this way earned its own acronym, Data-Driven Decision Making or DDDM. The data is not just collected, but gathered based on the use case, KPIs, metrics or other measurable goals, and verified. Here, smart organizations look for insight into patterns that are leveraged to drive new strategies, services, business processes, etc.

Project-Based ROI

Measuring return on Data Analytics across the entire enterprise is a massive endeavor. Measuring a project is more manageable. The key here is choosing KPIs that match the use or business case of the project. Then decide what your goals are and what you consider success.

There are direct and indirect values. While it is great to measure value holistically, it is easier to quantify direct values.

Often your organization is already measuring business case performance via metrics. Now you are looking at how your Data Analytics and a more advanced dedicated data approach moves that KPI needle.

Process Improvements: Your business or use case already revolves around a particular operational area with existing processes that are either improved or replaced by new, more efficient approaches – driven by analytics.

Once you have applied improved analytics, how have key processes changed? Are there measurably better results, is productivity improved with processes made easier, done quicker or with greater precision or accuracy?

Once you have seen gains in certain areas, ask how you can boost results even further? For areas that haven’t gotten better, ask if these are critical, why haven’t things improved, and what can you do to give them a boost?

Once good measurement for the data is if the analytics is providing meaningful data, and how the end-users are employing and leveraging the data. Some of this operational information is gathered by the analytics solutions themselves in the form of operational metrics, which include:

  • How many users are utilizing your data platform?
  • Where they are using it from?
  • What value streams are they focused on?
  • Is data analytics embedded in processes and which processes are they?
  • How are these processes delivering value?

Other operational questions to ask include: Are you operationalizing internally, developing a new business model, and implementing new or improved services? If so, you have effective Data Analytics.

Analytics – the Ultimate KPI Weapon

While much of this blog focuses on using metrics to measure the value of analytics, the flip side is also important: how analytics improves your overall ability to apply KPIs to your organization.

Think about marketing. Without metrics, it’s like playing pin the tail on the donkey. With metrics, marketing decisions are made with precision, leads and sales go up, and costs are efficiently parceled.

Here are some metrics your marketing organization would love to collect:

  • Number of new customers
  • Cost of customer acquisition
  • Average spend per customer
  • How many customers are no longer active?
  • Which marketing channel generates the most leads?
  • How are leads converted into sales?

Now imagine using deeper Data Analytics, including third-party industry data and social media dynamics to better target customers. You can apply these approaches with Data Analytics, and measure the results with … that’s right, Data Analytics!

Use Cases

Analytics are most often applied to use or business cases, or what the enterprise wants to achieve. A use case could show how valuable the data is today, and the estimated revenue would be future value creation you can obtain.

If your specific use case is to reduce churn, and you apply a data analytics lens and reduce churn by 50%, that’s your indicator – the KPI is what you’ve achieved in terms of reducing churn. Those KPIs are likely already throughout the organization, you are simply applying analytics to accelerate those efforts and get to that desired value.


Return on investment analysis always starts with the cost of the solution driving a return. Lowering the cost without reducing the benefit increases ROI. While it is important to not overspend on your Data Platform solution and associated training and employee costs, Data Analytics is such a fundamental game-changer and value driver that most of the ROI emphasis is truly focused on value delivered.

That said, a careful analysis of all Data Analytics costs, such as cloud services, storage, monthly SaaS costs, etc. is always warranted. You always want to get what you pay for, and stop paying for things you don’t need or are overpriced.

Where ROI really plays into costs is discovering where you need to focus and invest more, especially in people, to make even more great things happen.

Opportunity Costs

Opportunity costs are what you miss out on if you don’t spend on something. What do you lose if your competitor spends wisely and smartly adopts Data Analytics – and you don’t?

How much better off would your company be if data drove amazing new inventions, powerful new approaches to marketing, and dramatic changes in operations?

Next Steps

If you’re looking to monetize, drive innovation and radically boost ROI with your data, ACTS empowers you to unleash that value. We deliver solutions for the entire data journey map from data collection to data management to downstream analytics, to cognitive AI and Machine learning.  Get started with our Modern Data Maturity Assessment  or contact us directly at [email protected] for a personalized consultation.

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David Baldwin

Practice Manager – Data & Insights, ACTS, Inc.

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