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Questions to Determine Your Data Maturity

It's hard not to read an article these days about Big Data, Machine Learning, Artificial Intelligence, etc. and not get inundated with stats showing how important data is for the enterprise.

So we’re going to keep it light on stats and get to the important bits, other than stating: by 2020, ten percent of organizations will have a highly profitable business unit specifically for commercializing their information assets (Gartner). In other words, there is money to be made from managing your data, and the sooner your company reaches this pinnacle the better.

There are several stages of data maturity, by which we are referring to how mature your company is in managing its data, and awareness is the first step. Not only for the company as a whole but for individual business units as well, since data maturity requires all levels of the business to be engaged.

Levels of Data Maturity

Level One - Aware
At this level, awareness of challenges is the extent of data maturity, but companies lack the budget, resources, and/ leadership to make any meaningful steps forwards.

Level Two - Reactive
Companies at this level typically wait until information-related problems result in significant business losses or reduced competitiveness before addressing them.

Level Three - Proactive
Most enterprises today find themselves at this level, becoming proactive in addressing at least certain aspects of information management, and moving towards an enterprise-wide management approach. At this stage, the company likely has a handful of operational programs, but lack alignment between those programs and investments.

Level Four - Managed
These companies are the leaders in their industries when it comes to managing and leveraging their data assets. They have a managed approach to managing their information, with enterprise-level coordination with effective people, processes and technology.

Level Five - Optimized
Very few companies have gotten to this level of optimizing most, if not all, aspects of acquiring, administering and applying information as an actual asset.

The majority of companies sit in the reactive and proactive section of the bell curve, but the push to move up the data maturity ladder is going to be relenting. Between competitors in your industry taking on their own data initiatives, and new entrants into your market built from the ground up on data, the question won’t be whether to invest or not but whether to survive or not.

Applying the time and resources to manage and maintain your data across the enterprise, open up the future to take advantage of this valuable asset hiding in plain sight. Several components play a vital role in your data’s financial prospects, which leads us to several questions your organization needs to ask itself and have an answer. Note, however, your company will only migrate from one level to another without skipping a step.

Aspects of Data Maturity

First, has your company established a vision for data management? Bring together your IT and business leaders, along with your data and analytics leaders to agree upon and communicate the company’s approach and attitude towards information. Including things, like how information is to be managed and used for the benefit of the organization.

After establishing a vision, have you put together a strategy? Your data and analytics teams need to lay out an overall plan to manage information to support current and future planned business initiatives; while taking into consideration anticipated advances in data growth and information technology.

No major initiative can be successful without the proper metrics by which to monitor and evaluate. Make sure to tie budgeting and ROI metrics to enterprise goals and measures. How will your data management initiatives reveal themselves in company performance improvements?

While not the most exciting topic, governance is vital to successful data management. How will your data’s management and quality and use be monitored, measured and kept in-line with current and future business needs? Effective governance demands diverse business leadership and involvement; beyond just writing a stand-alone set of policies.

Who will be responsible for the key data management activities, and how will they be organized? It might come as a surprise, but higher levels of information management shouldn’t be a function of the IT department, but instead its own entity within the business. Lead by a CDO and comprised of roles such as infrastructure/architecture specialist, data scientists, information strategists, information architects, information product managers, and data curators, they support the entire information ecosystem in your enterprise.

Next, how will data move through and be available to the organization and eventually archived or disposed; in line with current and future needs? Analytics, data and application managers need to document and ensure the proper flow of information through its entire lifecycle.

And finally comes the matter of infrastructure, the components of which must support the current and future architecture and application needs for the organization. How will your information and infrastructure be selected and maintained? What are the strategies for upgrading them, and when?

Enterprise level data management is vast and complex, but starting by recognizing what level you are currently is an important first step. Make the commitment to treat your data as an asset as soon as possible, your competitors will be doing the same.