Over the past decade, the idea of a Chief Data Officer (CDO) grew from a loosely-defined, pioneering role to a key partner and peer of the CIO. 

Recently Forbes shared a survey of Fortune 1000 companies where almost 70% reported hiring a full-time executive in that role. For most teams, before any strategic work could begin, the first priority was aligning business and technology resources around the collection and consolidation of a highly fragmented data infrastructure. 

Fortunately, for most companies these investments have been wildly successful. Another recent market survey found that enterprises were capturing structured data at an incredible rate. And it’s accelerating – enterprise storage of structured data is growing at a CAGR of 12.7% over a seven-year period. Most companies are now sitting on truly “internet-scale” data warehouses. In theory, this should mean that each of these organizations is now in a position to make data-informed decisions on a frequent basis. But while we’re incredibly effective at capturing this data, our ability to turn it into a competitive asset is falling behind. This is the next challenge facing the Chief Data Officer.

If the first phase of a data transformation is defined by the collection and consolidation of data assets, the hallmark of the second phase is descriptive analytics. We’ve democratized access to data through dashboards, reports, and other visualization tools, but thinking effectively about that data and putting it to use requires a completely different skillset. Armed with richer dashboards, business units are now aware of the data that describes what happened, but we still lack the ability to effectively understand why something has changed.

If we’re going to change the way teams make daily decisions, a CDO needs to change the way they access, interact with, and integrate data into their daily processes. To make data a competitive asset and accelerate a transformation towards Operational Analytics, CDOs need to focus on developing three critical skill sets across the organization.

More Diagnosis, Less Description

This first set of skills is a natural progression from the work CDOs and their teams have done to build data awareness and access. As teams incorporate dashboards and reports into their daily workflows, they naturally introduce increasingly detailed follow-up questions: “Why did transaction volumes decline over the last two weeks?” “What’s driving up conversion rates with our customers in Canada?” Or, “Which combinations of products are leading to increases in average transaction sizes?”

The problems most teams face is that as their key metrics change, the speed at which they can diagnose the cause and recommend clear action is the difference between success and failure. When response time is critical, it’s no longer effective to rely on a small centralized analytics team. Nor is it reasonable to expect that you can hire analysts fast enough to keep up with demand.

The key to unlock faster answers to these questions is twofold: first, teach stakeholders across the business effective ways to ask questions and explore metrics; and second, equip them with tools that augment their ability to ask those questions. 

Proper Use of Data

The second set of skills a CDO will need to disseminate across the organization involve the proper use and collection of data. This is an extension of the work security and compliance teams have spearheaded over the last two years in response to the GDPR, Japan’s APPI, and California’s new CCPA standards. 

Strategically, the effective use of data is a competitive advantage. Competitive moats in industries will be built and crossed by companies who can make data-driven decision processes the norm. However, like most weapons, when wielded inappropriately or without training, they frequently do more harm to the organization than good. Chief Data Officers should look to collaborate with security, compliance, and learning management teams to regularly disseminate data skills training across the team, and measure its impact.

These organizational skills must go beyond the basics of data collection, consent, and privacy by design, and extend into the realm of how information can be shared within an organization, what kinds of data can be used for business processing and analysis, and even appropriate ways to discuss the implications of a specific diagnosis. We’ve seen social media and advertising organizations struggle with these skills over the past few years, but even B2B operations needs to consider the appropriate use of data and analysis with their customers and society overall.

Optimizing for Impact

The culmination of these skills is helping teams across the organization prioritize and argue for their recommendations based on the impact they could have on the business overall. Too often, organizations get stuck in an endless back-and-forth around the utility of “insights” coming from data. Business teams feel like they can’t act on overly academic results from deep data analysis. Similarly, data experts are concerned about the statistical significance and rigor of analysis – independent of what the business can reasonably act upon.

The solution to this is two-fold. First, data leaders should drive both analysis and decision processes around the idea of Impact. Impact is a straightforward way to focus the discussion on a metric that matters – often a KPI like revenue growth, customer engagement, COGS, or yield – and an observation’s influence on that metric. 

Along with KPI-based alignment, the second component of the solution is to focus teams on the speed and feasibility of acting on an observation. We often advise data officers that when faced with a change in your metrics, “how you respond matters.” When the speed and clarity of your decisions matters most, your ability to get your organization to act with clarity is key. The benefit of this approach is that it aligns these different perspectives around a common goal: can we find facts in our data that we can reasonably act upon.

Ultimately, teams that align around a common language and criteria for impact will accelerate decision making and eliminate a lot of the unnecessary back-and-forth between analytics and business teams. Even better, this approach dramatically reduces the number of spurious “insights,” opinions, and correlations in the analytics process. 

Designing for Operational Analytics

By actively addressing these three skill gaps, CDOs can accelerate their organizations’ paths towards true Operational Analytics. As companies invest more in the infrastructure, automation, and role digitization, our processes for organizational decision-making must shift. In addition to new tools and platforms for digesting enterprise data, data leaders must teach their organizations new skills to help guide decision-making. 

This is the most critical function of a successful CDO: companies will need to hire and train new skills, adopt new tools, and transform decision-making processes at every level. And the only way to do so is to improve how every individual interacts with data on a daily basis. This is the core of the Operational Analytics concept:  everyone in an organization, regardless of their role needs to act more like an analyst. 

This idea is taking hold quickly. Whether you’re operating a chain of quick service restaurants or trying to disrupt the travel and hospitality industry, your ability to understand why your most critical metrics are changing will determine your ability to compete and survive.

Previous post

How can you be AI ready?

Next post

Why over one-third of AI and Analytics Projects in the Cloud fail?