Surviving Your Second Year as CDO
As the Chief Data Officer (or Chief Analytics Officer) of your company, you manage a team, oversee a budget and a hold a mandate to set priorities and lead organizational change. The bad news is that everything that could possibly go wrong from a security, governance and risk perspective is your responsibility. If you do a perfect job, then no one on the management team ever hears your name.
The average tenure of a CDO or CAO is about 2.5 years. In our conversations with data and analytics executives, we find that CDOs and CAOs often fall short of expectations because they fail to add sufficient value in an acceptable time frame. If you are a CDO looking to survive well beyond year two, we recommend avoiding three common traps that we have seen ensnare even the best and brightest.
1) The Trap of Data Defense
Tom Davenport classifies data and analytics projects as either defense or offense. Data defense seeks to resolve issues, improve efficiency or mitigate risks. Data quality, security, privacy, governance, compliance — these are all critically important endeavors, but they are in essence, just enabling activities. You could think of data defense as providing indirect value.
Data offense expands top-line revenue, builds the brand, grows the company and in general puts points on the board. Using data analytics to help marketing and sales is data offense. Companies may acknowledge the importance of defense, but they care passionately about offense and focus on it daily. Data offense provides the organization with direct value and it is what gets CDOs and CAOs promoted.
The challenge for a CDO is that data defense is hard. A company’s shortcomings in governance, security, privacy, or compliance may be glaringly obvious. In some cases, new regulations like GDPR demand immediate action. Data defense has a way of consuming more than its fair share of attention and staff. If not put in perspective, data defense is a trap that can divert the CDO’s attention and resources away from offensive activities that create value for the organization.
2) The Trap of Deferred Value
Projects that implement new platforms and solutions can require months, if not years, of integration and oversight. If conceived as a waterfall project, with a big-bang deliverable at the end, these projects produce little to no value until they are complete. We call this the trap of deferred value, and it is possibly the main reason that many CDOs never make it past year three of their tenure.
In a fast-paced, competitive environment, an 18-month integration project can seem like the remote future. Also, success is uncertain until you deliver. Your C-level peers know that big software integration projects fail half the time. Projects frequently turn out to be more complex than anticipated, and they often miss the mark. For example, you may have thought you needed ten new capabilities, but your internal customers only really require seven, and two of them were not on your original list. The issue is that you won’t know which seven features are critical until around the time of your second annual performance review and by then it might be too late to right the ship.
3) The Trap of Data Valuation
Industry analysts and the media have long touted the strategic value of data. Following the advice of analysts, a CDO may decide to embark on a project to quantify the monetary value of the company’s data. This seems like a worthy endeavor that some say should attain a high level of visibility.
A data valuation project can take months of effort and consumes the attention of the CDO and her staff on what is essentially an internally-focused, intellectual exercise. In the end, you have a beautiful PowerPoint presentation with detailed spreadsheets to back it up. Your data has tremendous value that can and should be carried on the balance sheet.You tell everyone all about it — why don’t they care?
Don’t confuse data valuation with data offense. Knowing the theoretical value of data is not data offense. While data valuation may be useful and important in certain cases, it is often a distraction. All of the time and resources devoted to creating and populating the valuation model could have been spent on higher value-add activities.
Direct vs. Indirect Value
Investments in data analytics can create value either directly or indirectly. Sales growth is an example of direct value. Indirect value lays the foundation for future growth and productivity. In both cases, value is delivered either quickly or in a longer time frame. One common mistake is to focus too heavily on indirect-value, long-term projects. See figure 1.
That’s not to say indirect or long-term projects don’t have their place. They can be important and worthwhile. For example, a CDO may wisely invest in employee training or building technical infrastructure. It’s essential to create the right mix, investing in enough indirect value-creators to support long-term growth and enough direct-value and short-term projects to maintain a high level of visibility.
DataOps Accelerates Value Creation
The trick is to reorganize the data and analytics teams to be responsive and adaptable to the needs of internal customers and users. DataOps can help here. DataOps subscribes to an Agile, iterative approach. Deliver something of value in a few weeks and build on that in successive intervals. DataOps combines Agile with DevOps and lean manufacturing methods to provide a data and analytics team with the processes and tools needed to accelerate the value-creation cycle. Raise a glass to year two!
Want to learn more about this topic? Download the Second Edition of the DataOps Cookbook or visit DataKitchen.io
