The path to data nirvana can be tortuous. Once executives get zealous about data analytics, they want to quickly move up the maturity curve. But they often get sidetracked by their own urgency and biases.

Rather than put together a comprehensive data strategy that lays a sturdy organizational and technical foundation for success, many race ahead without a clear plan. The results are predictably sub-optimal.

In my work with companies of all sizes, I’ve seen four paths that organizations follow on their data analytics journey. Only one path leads to success. Companies embarking on a data modernization strategy need to get a good map, follow the right route, and avoid unnecessary detours.

MATURITY MODEL

We use a visual maturity model to help organizations recognize their current data analytics state and plot a steady course for future improvement. The maturity model has four dimensions or axes [1]: 

1. Analytical Maturity. This covers the spectrum of analytic capabilities, from reporting (static and interactive) to analysis (Excel, OLAP, and visual discovery) to dashboard and scorecards (key performance indicators with targets and thresholds in every department) and prediction (analytical models built using statistics, machine learning, and artificial intelligence techniques).
   
2. Data Maturity. This addresses the methods by which organizations consolidate and integrate data for analytic consumption. This ranges from spreadsheets and personal databases to independent data marts and data warehouses that are departmental and disconnected to an enterprise data warehouse that consolidates operational data for an entire organization or business unit to a big data ecosystem that consolidates all data, regardless of its volume, variety, or velocity.
   
3. Analytic Culture. This represents how top executives perceive the value of data and analytics, ranging from a cost center to a tactical resource (tools for power users to mission critical, with data analytics playing an operational role, to a strategic resource, where data analytics enables the organization to gain a competitive advantage or increase market share.
   
4. Scale and Scope. This defines the scope of a data analytics program. It ranges from individuals working in isolation to a departmental program that meets the needs of a workgroup to an enterprise program designed to support an entire organization to an enterprise+ program that delivers data and analytics to customers and suppliers.
   

QUADRANT THEMES AND PATHWAYS 

Each quadrant in the maturity model represents a different state of data analytics capabilities. Most of our clients (not all) reside in the bottom left quadrant and want to move out of it. Where they move next is determined by the quality of their strategy and their discipline in following it. (See figure above.) 

Most clients reside in the bottom left quadrant and want to move out of it. Where they move next is determined by the quality of their strategy and their discipline in following it.

QUADRANT: FLYING BLIND. Here, business executives make decisions without data or consistently defined data. Every decision is a crap shoot because no one is certain about the current reality, let alone how a change might impact it. This is symptomatic of organizations that run on spreadsheets and have a lot of analysts who spend most of their time gathering data and mashing it together, rather than analyzing and predicting the impact of decisions.

Pathway: False Starts. Many companies find themselves perpetually in the flying blind quadrant. Every five years or so, there is a concerted effort to industrialize the data analytics program. But there is usually insufficient leadership and funding to sustain the initiative and it collapses of its own weight. To get out of the “Flying Blind” quadrant, organizations must invest substantial funds (an order of magnitude more than they do currently) and assign top talent to oversee and drive the program.  

QUADRANT: POCKETS OF ANALYTICS. Organizations that end up in this quadrant have business executives who have an epiphany about data analytics and want to become a best-in-class industry leader overnight. Most embrace the newest technology and fund projects to get the ball rolling. For instance, today, they hire teams of data scientists and deploy Hadoop clusters to mine big data. Or they might broadly deploy self-service analytics tools so every user can create their own reports and data sets. A decade ago, they may have built executive dashboards on tablet devices or deployed reporting extranets to support customers and suppliers.  

Pathway: High Risk. However, this is a high-risk strategy. Without an enterprise data platform and coordinated analytics program, these quick hits fade over time since there is no comprehensive foundation upon which to grow. To sustain the initiatives, companies need to backfill an enterprise data platform behind the quick wins and bolster it with a robust governance program to ensure high quality, consistent data and a federated analytics organization to foster self-service in a governed manner.

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QUADRANT: ANALYTIC POTENTIAL. Here, IT executives get religion about data analytics and build a massive data infrastructure with all the bells and whistles. They often hire a large systems integrator to provide a standard industry model and help design and deliver the data analytics environment. Because IT has been blamed for being too slow, they race ahead with the implementation, forgetting to get sufficient business involvement. Rather than learning and iterating as they go, the IT team builds what it thinks the business needs.

Pathway: High Cost. This is a high-cost strategy. I’ve seen IT departments spend tens of millions of dollars on a new data and analytics infrastructure that no one uses. IT blames the business for not engaging, and business blames IT for being deaf to their real needs. Finger pointing ensues and everyone gets a bad taste about data analytics, and the term becomes taboo for years hence. In this scenario, IT needs to turn over the program to the business. This means convening a bipartite analytics council with cross-functional representation to oversee the program, review and approve the strategy, and prioritize the development portfolio, among other things. 

QUADRANT: ANALYTIC COMPETITOR. The top-right quadrant is where everyone wants and needs to be. Here, an organization ingests all types of data and makes it available to all types of users in a tailored manner so each gets exactly what they need, when they need it, and how they want it, including customers and suppliers. There is a dashboard in every department for monitoring KPIs and a vibrant self-service environment in which authorized users create reports and dashboards for themselves and colleagues to answer new and unanticipated questions. Behind all of this is an enterprise data ecosystem that consolidates and integrates data and makes it available in right time.

Pathway: Straight and Narrow. To move from the bottom-left quadrant to the top-right, organizations need a well-constructed data strategy, sustained executive sponsorship, experienced data analytics leaders, and a cross-functional analytics council that oversees the data analytics program. It also needs skilled, full-time data analytics professionals, both at corporate and in the field, who work together to develop processes and standards and actively engage with the business to proactively harness data to address business issues. The analytics program needs to be run like a business with a board of directors (i.e., analytics council) and active sales, marketing, and support to increase adoption and analytical and data literacy inside the organization.

It’s not easy becoming an analytic competitor, and it’s easy to backslide once there. But organizations that follow the “straight and narrow” path have the best chance of making good on their investments in people, processes, and technology and harnessing data for real business value. If you would like help finding the straight and narrow path, please contact us!

See the prior part in this series.

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[1] In client engagements, we measure and score each dimension independently.  The quadrant chart links opposing axes together, which is not ideal, but it usually works. A better way to visualize individual capabilities is a spider chart, but that would make it harder to show progression over time through quadrant themes, as I do below. However, we use spider charts in our Rate My Data quantitative assessment tool.