Chief Data Officers Look Forward to 2016

Will this be the year of the CDO? It looks that way based on recent conversations we’ve had. The time has come for a data advocate in the C-suite.

I previously wrote about my 5 predictions for BI and Big Data in 2016, and the emergence of the CDO is at the top of my list.

There is no mistaking the ever greater demand for data — good and usable data for a variety of purposes. The demand is growing fast for data to sharpen focus on customers, help make internal processes run leaner, and to lend certainty to strategic decisions, to name a few. Yet too often the right data can’t be harnessed and remains untapped.

CDOs and higher revenue seem to go together. A study by the research firm Forrester, cited in Information Week, found that 54% of companies with 10% year-over-year revenue growth or more have a CDO, while only 33% of companies with less than 4% revenue growth have one. The causation may not be clear yet, but the correlation couldn’t be clearer.

 Under a CDO’s guidance, an organization can achieve the kind of agility to really make use of all its data, to make data a part of so many more decisions, all of which drives ROI.

But how will CDOs fulfill the promise? Our conversations turn up a consistent set of strategies.

Build a Results-Oriented Data Infrastructure

An effective data infrastructure is designed around the agile nature of companies to give maximum flexibility at every stage of the data supply chain, from storage to use in analytics tools or other business applications.

Hadoop alone cannot make a Big Data project successful, but agility in data storage is the foundation for scaling big. To get the most out of an organization’s data, the infrastructure must be designed with the end game in mind. Data needs to flow seamlessly into business intelligence and predictive analytics tools as well as custom business applications.

Forrester Research recommends integrating business intelligence and Big Data in a flexible hub-and-spoke architecture with components such as:

• Data Storage – Gather enterprise data and information into a flexible Hadoop or a Hadoop-like technology – the data hub of the architecture
• Data Preparation – Cleanse and transform the data into the right structure for downstream use
• Data Discovery Acceleration – Profile, identify, and unify all of the enterprise information with a self-service data source discovery system
• Predictive Analytics – Feed the data into an analytics engine and build predictive models
• Data Visualization – Create dashboards and reports with business intelligence and data visualization tools.

Understand the Information You Have

The storage layer is agile with Hadoop, and the data analytics and visualization tools have become easily accessible. It’s the data supply chain that needs attention — the flow of data sources to data consumers. It’s a slow, manual process.

The top-of-mind question for CDOS is: what is the cost of not fully understanding your organization’s data landscape?

The ability to find, understand, and correlate disparate data sources and provision applications with structured, semi-structured and unstructured information in an automated, efficient way changes the game.

Not only that, a solid understanding of the data landscape enables CDOs to distribute resources appropriately. Some data gets used every day and merits a place on expensive hardware. Other data gets called only now and then, which earns it only a spot on low cost storage.

Become a Hero to the Business

The promise of data democratization is nothing short of transformative. The sooner the data supply chain can be streamlined, and the data democratized for the waiting data consumers, the sooner that data becomes a strategic asset. When self-service data discovery becomes a reality, analytics will take a fraction of the time. Productivity will be transformed, and executives will be able to leverage the best information and act with certainty.