The Biggest Data Management Mistake Chief Data Officers Make

And What They Can Do Instead

When you follow the steps we've outlined, you’ll have an integrated plan that includes business initiatives, the shared data needed to support those initiatives, and the capabilities and best practices to manage the data effectively.

Avoid these big data management mistakes

Many companies have serious challenges with their data management programs but are looking in the wrong places for the solutions. IT managers may sometimes ask, “How do I get the business excited about data management?” The answer is simple: You can’t. Don’t even try. You can only get them excited about their own goals and how you can help them achieve those goals.

Some of these companies have a general sense that things are just not moving in the right direction—there may be morale issues, cost overruns, a poor relationship between IT and the business, and other issues. Sometimes the situation is more serious. For example, in some cases, data and analytics programs of various kinds — such as data warehouse implementations and data governance programs — have cost millions, only to be suddenly cancelled without much argument from the people in the business who were supposed to obtain value from these programs.

Most advice to Chief Data Officers in these situations comes down to this: Ensure that your data strategy provides business value — e.g., increasing revenue and improving cost control — and risk management – e.g., inclusive of compliance and privacy. While this may seem like the right advice, it puts the onus on the CDO to propose business value to the rest of the C-suite instead of supporting the initiatives in which leaders already have invested.

These business initiatives require data and analytics that the CDO can provide. But if CDOs initiate their own projects and separate business value propositions, the existing business initiatives are often left without the data management platform they require. This results in a divergence of projects that don’t support each other: the business initiatives will continue to generate data while the CDO builds a “foundation” of data, creating yet another silo.

The difference between proposing and supporting business value may seem subtle, but it’s actually profound. Most IT leaders running enterprise database management today are building up programs that have value independent of major business initiatives. When each IT program’s value isn’t directly connected to a top-level strategic goal, the C-suite no longer depends upon IT as a load-bearing wall that’s necessary for business success.

Here’s the hard truth: CDOs are playing it too safe. Perhaps they’re hesitant to create initiatives whose failure could mean broad negative consequences for them and for their IT organization — projects whose cancellation would mean other dependent initiatives would have to be cancelled too.

It’s time for CDOs to commit to getting on the critical path to business success when it comes to data management. While there is more risk with this approach, more risk brings more reward and more recognition. A CDO can take the following steps to align to the business’ strategic objectives:

1. Be proactive about aligning to top-level business objectives

Reach out to other C-suite leaders to learn about what’s on their plates for the quarter, half, and year. Their strategic priorities, from optimizing the supply chain to increasing customer engagement through digital marketing, will very likely depend on data. Using these conversations as a starting point, identify the highest priority goals and develop a comprehensive plan to provide the clean and standardized data that these initiatives require.

By being proactive, you’ll set your team up as the keystone of the mission-critical initiatives at your company. You’ll be creating a dependency relationship with your organization’s leaders and ensuring that your work has an impact.

2. Develop an analytic roadmap linked to business initiatives

With sponsorship and some specific initiatives to consider, develop a detailed program plan. Thoroughly examine the current and future business initiatives and determine which data is most important to deploy and in what order. Consider which applications will leverage the data, what systems (infrastructure) is needed, and what capabilities will be required. The criteria do not have to be rigorously scientific but should include the timing and value of the initiatives, the quality and integrity of source data, and the number of initiatives requiring the same data.

When the analytic roadmap is created, it is crucial to keep the link to the business initiatives. Remember it is not enough to simply use the information collected here to get funding to deploy key data domains. The initiatives must be used to scope the projects, so that you are not just delivering customer data, for example, simply because you have shown that it is really important. Instead, you are delivering customer data to support initiatives like One-to-One Marketing, Call Center Optimization, and the Social Media Sentiment Analysis, assuming these initiatives are linked to direct business value and are already approved and funded.

3. Align data management capabilities with the analytic roadmap

In this step, you determine which capabilities are needed and for what purpose. Some capabilities—at least at a minimal level of maturity—will be needed regardless of the initiatives and business goals. For example, data profiling will be needed to evaluate and analyze the quality and demographics of data from identified sources. However, now that you know your business goals, you can determine which data quality issues are most important so the data profiling can have some direction.

You will not solve all data quality problems. Because of the focus on business initiatives, you now know that good quality data means the data is suitable for the targeted initiatives. A data quality issue is defined as one that will have an adverse effect on the initiatives, not just that the data isn’t as it should be. A reporting application that produces financial reports will have a different set of data quality requirements than a sales forecasting application for automated inventory replenishment. Focus on the quality you need, when you need it.

4. Implement the roadmap and ensure a sustainable program

Now you should implement the roadmap you created. Make sure to manage the dependencies between the data initiatives and the corresponding business initiatives sponsored by the other CxOs very closely. (In fact, if there are no dependencies like this to manage, you’ve got yourself a problem and need to go back to step 1.)

To ensure that your program is sustainable, create linkages to the broader organization’s strategic planning, operations, and execution processes. For example, as the company is developing or revising the overall business strategy, you should participate so that you can propose data initiatives right along with any new or modified strategic plans proposed from other areas. The funding process is another great place to identify projects that could benefit from data delivery projects sponsored by the CDO. And embed data management practices — such as data profiling, data quality monitoring, data stewardship, and so on — directly into your organization’s project delivery methodology. All of these steps that you take will help data management planning and delivery become a natural part of the way the organization goes about its business.

When you follow these steps, you’ll have an integrated plan that includes business initiatives, the shared data needed to support those initiatives, and the capabilities and best practices to manage the data effectively. And everything in your plan will be linked to real business value that would be hard to dispute because you are supporting the initiatives that have already run the gauntlet required for funding approval. You also have a basis for making good decisions on the appropriate systems or infrastructure projects to include on the roadmap, based on planned applications, data types, and basic business volumetrics — not just guesses and assumptions.

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