Metrics Rich, Yet Information Poor!

Recently, I was in a meeting with the D&A head of a telecommunication giant, where he voiced a complaint that I have been hearing a lot.

“My team is constantly asked to build new reports but the very teams who are asking for it forgets to use them after a while. And before we know, another team is standing up yet another dashboard, a slightly different version of the same “Same Day Sales Report” that of course also becomes obsolete fairly quickly. It is like we are metrics rich, but information poor “ the D&A head said.

His frustration led me to ask my fellow big wigs from the tool companies as well as senior data science professionals what they are seeing in the market and how are they addressing it.

Much to my excitement, the discussion thread—thronged by my friends, colleagues, and professionals—picked up and people shared their varying views. With different opinions and solutions, one idea stood out for me: the importance of inculcating Data Culture in organizations.

As pointed out by many commenters, including Impacsis Analysis Director, Charles Sutton, Chief Data Strategy Officer of ThoughtSpot, Cindi Howson, and BI Analytics Lead at Patties Foods Pty, Tim Ta, there is a need to insert data in day-to-day decision-making process at every level to increase the adoption and in turn enable better decision making. Aryng always has emphasized these key components or the way I like to put it—the 4D’s of Data Culture.

The first of the 4D’s is Data Maturity, which means that there is a single source of truth for data, i.e. people can access data with ease and that data is accurate.

In the thread, Cindi, pointed out that one size does not fit all. “I have longed believe in the right tool for the right user and use case - it's a chapter in both my Successful BI books and have an updated paper that will come out in the next couple weeks. One sized does not fit all.[sic]”

I agree with her and others, including Business Intelligence Manager at Nationwide Building Society Marc Price who said the tools have to be dependent on the use cases. There is a need to incorporate data governance and standardization so that the people have access to data which they can trust, as pointed out by Gokula Mishra, Senior Director, Global Data & Analytics, Supply Chain, McDonald’s.

The second of the 4D’s is Data-Driven Leadership, which means that leaders of an organization understand the power of data as well as analytics and have a strong motivation to lead by numbers.

As Charles pointed out: “One champion creates more champions.” The same was reiterated by Cindi who said that success can be found in driving Data Culture by “using champions to evangelize,” among other ways. If leaders “champion” the nuances of Data Culture, they can be an example and “evangelize” others to follow suit.

I strongly believe if the leaders start leading with data and holding their team accountable, it will cascade down within the organization. For example, when one of our clients hired a new Vice President (VP) of Marketing, he restructured the marketing initiative planning process by introducing zero-based budgeting and implemented the following guideline: every project will be funded based on the expected Return on Investment (ROI). Due to this, marketing managers had to think through their targeting and segmentation strategy to understand and estimate the expected ROI. Only after proper evaluation, the managers would present their strategy to have a budget allotted for their respective projects.

Moving on, 3rd D is Data Literacy. It means that both the analytics and the non-analytics side of the organization need to have an appropriate level of Data Literacy.

In the favor of Data Literacy, Waleed Allen, the Head Customer Insights & Analytics at Batelco, called for suitably employing use cases to “drive the tools”. “The use cases at the end of the day drive the tools as long as there’s a positive outcome, however, it needs to be governed properly to avoid costs in terms of resources, time to market and financials while still keeping an open eye on what technology has to offer.”

And to use tools judiciously, Data Literacy is a must.

Similarly, Mark Price, who was direct in listing Data Literacy as a solution, said: “It really doesn't matter which tool you implement in an organization, its how you implement it that makes the difference. Based on your use case you may choose one tool over the other, but a tool on its own is not going to solve all of the issues faced by an organization. Data Literacy and how to plan adoption/uptake needs a lot of thought.[sic]”

Not everyone needs to be a data scientist, but they should possess the skills at the right level to carry out their job using data and make effective decisions. Having done this for the past eight years, I can tell you that just giving people training on tools or courses on statistics doesn't cut it. The training should be tied to their work; it should not only give them data skills but the ability to solve business problems with equivalent decision science skills to navigate through the business world. Additionally, they need to be mentored and be hand-held initially toward improving the metrics they are responsible for. This way they will start using it in their day-to-day decision-making process once they start getting quick wins using the data and the tools to make decisions.

Lastly, the 4th is the Data-driven Decision-Making Process, which implies that there exists a mechanism to make decisions in the organization, aligned with the key drivers of the business so that everyone understands how their work moves the key metrics, thereby adding value to the company and that data is part of that mechanism.

While the decision-making process is one of the salient features of developing Data Culture, it got overlooked in the discussion thread. I, however, consider it as important as the other features, and without it, developing a Data Culture may not bear the same results.

I believe that it is necessary to have a planning and look back mechanism. There should be a project prioritization mechanism based on ROI. Everybody should be held accountable for some KPI’s and some driver metrics that only they are held accountable for. There should be a clear understanding of KPI’s and driver metrics.

All of this together will form a data-driven culture and incorporate data-based decision making which in turn will improve the adoption of the tools significantly. What do you think?

If you wish to learn more about building a Data Culture and Aryng’s 4D’s approach to implement it, here are some actionable resources for you.