A New Way to Look at Data ROI – “Return on Data Investments”
Digital disruption is causing a furor in the business world.
With people now connected with things 24×7, and things connected with smart products and digitized services, enterprises are realizing the importance of data, and the value generated from it. While many are reaping substantial gains from their data investments, many are realizing they have a long way to go.
Calculating the data ROI or Return on Data Investment is critical; if you can’t justify the value from your data investment, there is bound to be resistance across the organization, causing you to miss out on opportunities and lag behind your competition.
But how do you overcome the data RoI challenge? Is there a new way to look at data ROI? Let’s find out!
The challenges of data ROI calculation
Investments in data are at an all-time high. Enterprises are jumping on the bandwagon, and investing in expensive data infrastructure, tools, and teams to unearth critical insights about their business, customers, and market. Data is allowing them to achieve tremendous benefits: from improved employee productivity to more efficient processes, shorter sales cycles to improved revenue per customer, quicker time-to-market, and improved cost savings, among others.
There’s a lot that businesses can gain from data, which is why there is an exponential increase in the investments that are being made. However, given that businesses invest in data for different reasons, and are executing different projects at different levels of maturity. When it comes to calculating data RoI, there is little understanding of where to start and how to go about it.
How do organizations measure the value from data investments? How do they quantify the benefits?
The Data Innovation Maturity Curve
When it comes to Return on Data Investments, there are many challenges and roadblocks that restrict businesses from seeing real value. However, a new way of looking at Data RoI is to assess your position on the Data Innovation Maturity Curve. The higher you are on the maturity curve, the more returns you can achieve from your data investments.
That said, here are 4 stages that typically define the Data Innovation Maturity Curve:
- Organizations in the early stage are those evaluating the various data tools, developing a business case, and looking at the various emerging technology options. Although they are highly dependent on IT, they lack robust data maturity roadmap. Only a few identified or designated users – defined by their functions and levels – enjoy limited success with data investments. These users, primarily analysts, who have little contact with day-to-day operations, rely mostly on MIS and monthly reports that provide limited insights. However, such legacy reporting and MIS environments have spreadsheet anarchy and involve manual and long consolidation cycles.
- Organizations at the emerging stage have enterprise data and information strategy in place and have already begun with roadmap and migration planning. They start to see the real benefits of data from their multiple transactional applications. CXOs of such organizations begin to realize that having certain metrics could help them improve the outcomes from their data investments. However, since the data they work offers only a few limited indicators, organizations are not able to fully reap the benefits from their data investments.
- Organizations in the maturity stage are able to successfully correlate data coming from transactional, social, and external sources. They derive insights for future business with predictive analytics, and by applying advanced algorithms and data science techniques. Top management is able to get a deeper view of important trends from the market, and the enterprise as a whole is able to compete on data innovation. KPIs typically include customer, economic, and strategic focus areas, and RoI from data management and data science becomes clear. These organizations deploy mature analytics practices with a set of executive dashboards, business insights, and scenarios for better decision-making based on operational and social data.
- Organizations in the leadership stage are those who have reached a stage of data maturity. By accessing and leveraging industrial, textual, and relational data, they are able to carry out real-time monitoring and make accurate predictions. As ROI compounds, a greater number of users and use cases emerge, offering deeper product, customer, and market insight. Metrics are quantitative as well as qualitative and are offered through a simple and accessible user interface. Such organizations are disrupting their business models and are usually market leaders with the drive to constantly be ahead of the competition.
Drive real data innovation
The benefits from data have been stated on countless occasions. And that’s exactly why organizations are putting faith in data teams and investing billions of dollars in data tools and infrastructures.
Yet, many organizations are not seeing a return on investment, and are unable to prove its worth in terms of time, and money. Without numbers pointing to success, it is difficult for CIOs to continue to invest in data efforts. However, a good way of looking at Returns on Data Investments is to assess where your organization is on the Data Innovation Maturity Curve. The higher you are, the more positive your returns will be. So, get on the data bandwagon, and drive real innovation and value with your data.