Click to learn more about author Piyanka Jain.

If you are anything like our Fortune 500 client on a journey to increase their data culture quotient (DCQ), then you are a brand that consumers recognize and trust. You have been around at least a few decades and probably a lot more. You have employees with tenure of at least a decade or more, and in the last 7-8 years, everything in your world has gone digital. As a result, your technology stacks have become outdated, your processes have become outdated, and your employees (at least most of them) feel overwhelmed with the digital world around them.

To deal with this, in the last five years, you have been part of a massive digital transformation movement. You are investing in new technology stacks, new ways of information flow, new processes, new data warehouses, and new shiny BI tools. You also have new leadership wanting to become more data-driven to thrive in this digital world and, yet, the journey seems to have just begun.

If this is you, you are not alone. You have started working on one of the four D’s of building and scaling a culture of data — namely, data maturity (DM). As a CDO or somebody from the CDO’s office, you bear responsibility for your organization’s Data Strategy, Data Governance, security, policies, data lifecycle management, and making sure people in your organization have the right level of access to the right data with ease (i.e., you deliver on data maturity for your organization).

But many CDOs stop here. And they shouldn’t.

As Gartner’s 2017 “Understanding the Chief Data Officer Role” propounds, the CDO office is also responsible for creating business value from the data — i.e., data monetization. Because at the end of the day, data is useless unless it can be leveraged effectively to drive decisions and thus deliver value.

So, as CDO, how can you enable effective monetization of the data? You do not own the product roadmap or the marketing or any of the monetization channels. But you can still deliver on your role — how?

You can do this by developing a surround sound culture of data (DC), so every individual decision-maker:

1. Can easily access data they need (i.e., you have data maturity or DM)

2. Is supported and held accountable by their management and leadership when meeting goals (i.e., you have a data-driven leadership or DDL)

3. Knows how to turn that data into insights to potentially move the metrics they are responsible for (i.e., the right level of Data Literacy or DL)

4. Can use those insights to drive decisions through a structured process (i.e., a data-driven decision-making process or DDM)

The above are the four D’s of developing a culture of data. As a CDO, you are very familiar with #1. But, in the end, #2, #3, and #4 are paramount for that data to create business value.

Before we discuss how you can develop DDL, DL, and DDM, let’s talk about how you can measure where you are in your data culture today. That measurement is called your data culture quotient (DCQ). There are many frameworks to assess DCQ. The ones we use are mapped to the four D’s of data culture with 30 underlying dimensions. Whatever framework you follow, make sure your DCQ assessment gives you a clear picture of the gaps followed by an actionable plan to address those gaps.

Let’s look at this journey through a customer case:

ALBC (this is a pseudonym to preserve privacy) is a large mature healthcare insurance organization, which has had significant issues with declining member CSAT and increasing claims payout. The CEO identified the lack of a culture of data as one of the reasons for this state and gave the mandate to his CDO and the L&D head to chart out how to make the organization data-driven.

We started ALBC’s journey by assessing their data culture quotient (DCQ) via these steps:

1. The first step in DCQ is understanding the executive’s view of their data culture, their leadership style and strength, and their vision of the data culture and Data Literacy for the organization. We do that through the process of the Voice Of Executive and a series of structured interviews.

2. The second step is to then understand every individuals’ perception of the data culture of the organization and their level of Data Literacy. We do that through two enterprise-wide surveys on DC and DL each.

3. The last step is a DCQ readout and making a plan for addressing the gap. We do that in conjunction with executives through a process called 3-Key Questions, during which we map the top metrics of the organization to the top projects that would move the metrics and the top champions who would work on those projects. This enables us to identify top use cases through which we would build the DCQ for the organization.

This is how the DCQ looked for ALBC (see the image below). As you can see, ALBC was 54 percent progressed on their data culture journey, and that was largely driven down by the lack of Data Literacy within the organization. Their decision-making process was also hampered by this, and some parts of data maturity and leadership needed help as well.

The readout is accompanied by a detailed plan of action. Once the plan is agreed to by all the key stakeholders, the next phase of the DCQ rollout would begin. This phase includes four major steps.

1. Design of Learning Path: ALBC had significant challenges in Data Literacy (only 12 percent of the organization was at the right level). We needed to lay out a detailed plan of mapping 24 DL skills and competency to each Data Literacy persona and create a detailed plan of how to get them from their respective start points to respective DL endpoints. For example, all claims analysts need to be at least a “citizen analyst” level. So, based on the DL survey, we understood the current levels of literacy for a claims analyst and, based on the leadership goal, what their learning path should include in order to become a “citizen analyst.” This exercise should then need to be repeated for every persona.

2. Upskilling Champions While They Work on Top Projects: Next, we use the BADIR framework (a data-to-decisions framework) to skill up the champions at the right level of their persona, using in class, face-to-face training. This training would be followed by champions working in teams on their respective projects, leveraging the BADIR framework, and getting mentored by our team of data scientists.

At ALBC, 25 champions were working on seven projects which had the highest chances of driving up CSAT and lowering claims payout. The team cumulatively found high-value detractors to CSAT (for example, tickets were staying too long in the system due to a particular loophole and contributing to lower CSAT, etc.) and found fraud and other opportunities to decrease claims payout by 15 percent or so. The champions then started executing changes that resulted in a 7 percent improvement in CSAT and lowering claims payout by 12 percent (whoa).

3. Scaling to Enterprise-Wide Literacy: While our champions are executing changes to their product and processes, we team up with the CDO office and L&D to design online cohorts to take the rest of the organization through their respective learning path via WBT.

At ALBC, we trained two D&A heads and two 2 L&D coaches to lead the project mentoring for the remaining 3000 odd in the organization who needed Data Literacy.

4. Continued Support for a Data-Driven Leadership and Decision-Making Process: While efforts on Data Literacy continue, leadership often needs to be continued to be coached to create an environment of data-driven decision-making along with specific changes to the decision-making process that enables transparency and accountability for decision-making.

At the end or middle of this phase, you will want to run the DCQ assessment again to see where the organization has moved.

By the end of 9 months, for those at ALBC who had already gone through the program, DCQ had moved to 7.4. This work continues today as the rest of the cohorts get skilled up and catch up with the rest of the organization. DCQ assessment is now part of ALBC’s process and done every three months to fine-tune the DC journey.

Surprisingly, many CDOs and L&D leaders believe that this kind of culture shift is a 3-5-year process. For ALBC, this was a 15-month process, and I have seen many organizations make major strides in less than 12 months. If you start by first measuring the DCQ and then systematically and systemically addressing the gap, as I have shown above, you will find that not only can you improve DCQ quickly but will also have positively impacted the top KPIs for your organization.

All the best!