The Four Stages of Data Maturity

Navigate the path to enterprise data governance, and improve the health of your organization’s data

By Tony Fisher

As companies collect more and more information about their customers, products, suppliers, inventory and finances, it becomes increasingly difficult to accurately maintain that information in a usable, logical framework. This can severely complicate workflow because the information within applications and databases – data pertaining to customers, products, employees, suppliers and financial transactions – provides the foundation for improved customer relationships or an optimized supply chain.

The data management challenges facing today’s businesses stem from the way that IT systems have evolved. Enterprise data is frequently held in disparate applications across multiple departments and regions. To address the spread of data and eliminate silos of corporate information, many companies implement enterprisewide data governance programs, which attempt to codify and enforce best practices for data management across the organization.

Data governance encompasses the people, processes and technology that are required to create a consistent enterprise view of a company’s data. Companies are embracing data governance as a way to bring order to the chaos of their IT infrastructures. By concentrating on the health of the data, companies address the lifeblood of their enterprises, helping create better data to support any business initiative.

The road to data governance 
Like many enterprise projects, data governance programs often start small before finding the sponsorship and support needed to transcend organizational boundaries. For most companies, data governance takes on a slow but steady evolution as the company matures in its management and control of enterprise data.

Through an established Enterprise Data Maturity Model, organizations can identify and quantify precisely where they are – and where they can go – to create an environment that delivers and sustains high-quality information. An organization’s growth toward this ultimate goal invariably follows an understood and established path. There are four stages in the Enterprise Data Maturity Model:

1. Undisciplined
2. Reactive
3. Proactive
4. Governed

Within the model, each stage requires certain investments, both in terms of internal resources and third-party technologies.

Progressing through the Enterprise Data Maturity Model
The Enterprise Data Maturity Model examines the technology being used, along with the people and policies associated with the governance initiative, to ascertain the level of data governance sophistication within that enterprise. In the first stage, the Undisciplined phase, an organization has few defined rules and policies about data quality and data integration. The same data may exist in multiple applications, and redundant data is
often found in different sources, formats and records.

The danger for Undisciplined companies is the real and constant threat that the underlying data will lead to bad business decisions that may, in turn, result in missed business opportunities and decreased customer satisfaction. Often, it takes a cataclysmic failure to shake the organization out of complacency.

At the next stage, the Reactive phase, a company begins to organize a data governance program, either through grass-roots efforts or, more likely, through an executive-driven effort fueled by an earlier failure. At the Reactive stage, organizations try to reconcile the effects of inconsistent, inaccurate or unreliable data as bad records are identified. Here, the gains are often seen on a departmental or divisional level, but the company is starting to establish some best practices for data governance.

The move to the next stage, the Proactive phase, is not an easy one. After years of investing time and resources in complex enterprise applications (such as customer relationship management, or CRM, systems), a Proactive company understands that a more unified view is necessary if the organization wants to derive any real value from its information. Applications like CRM often become silos of data, and to progress to a unified view and workable format, the organization needs to extend the reach of that data through the checks and balances of the maturity model technology to clearly manage the data and achieve master data management (MDM).

At the Proactive stage, the data governance program becomes cross-functional and has explicit executive support. To build a single view of a customer, for example, every part of the organization – sales, marketing, shipping, finance – has to agree on what attributes make up a customer record.

The final stage, the Governed phase, is where data is unified across data sources according to business rules established by an enterprise data governance team. In this final phase of the model, a company has achieved a sophisticated data strategy and framework, and a major cultural shift has occurred. Instead of treating issues of data quality and data integration as a series of tactical projects, these companies have a comprehensive program that elevates the process of managing business-critical data.

Although individual applications are still in use by a Governed company, the data that users access comes from a single repository that is propagated across the IT infrastructure. This provides the ultimate in control for the enterprise because all reports and dashboards pull from the same pool of information.

Using the maturity model to ascertain a reasonable IT approach
The Enterprise Data Maturity Model helps organizations understand that they will not reach the highest levels of data management overnight. Rather, they should view the process as a journey, with a host of challenges and significant milestones along the way.

The technology associated with data governance typically encompasses the following capabilities:

• Data profiling – To inspect data for errors, inconsistencies, redundancies and incomplete information. 
• Data quality – To correct, standardize and verify data. 
• Data integration – To match, merge or link data from a variety of disparate sources. 
• Data enrichment – To enhance the use of information from internal and external data sources. 
• Data monitoring – To check and control data integrity over time.

With these applications in place, companies have the ability to improve the data throughout its life cycle. Through the data monitoring function, organizations can also encapsulate established business rules to flag data that does not meet pre-established criteria. For example, a healthcare company can set up a business rule that states if the gender field in a record reads “male,” then another record about that patient cannot have the condition code for “pregnant.” Every organization has these business rules in place; data monitoring simply helps establish and automate them.

One customer that has embraced the Enterprise Data Maturity Model is a large nonprofit organization headquartered in the US. The organization wanted to build a more unified view of its donor network, which had data splintered in dozens of applications across the country. The IT department decided on an MDM program and began to seek technology vendors to support the effort.

The organization found that although its intentions were good, the people and processes were not ready for that move. Political battles broke out to determine who “owned” the data. No single group was responsible for determining what constituted a good record. The bare necessities of data governance were not in place, and initial trials were gridlocked.

Once the organization understood that it was an Undisciplined company according to the Enterprise Data Maturity Model, it developed a plan to progress to the next level. Business analysts within the company used data profiling technology to identify bad or incomplete data. The management team established a data stewardship group, which started to codify business rules for data quality and data integration efforts. Within months, the organization had attained the Reactive stage – and the goals of MDM were now within reach.
 
Conclusion 
The path to data governance is not easy, but organizations can get there. More importantly, data governance is a necessary response to the dozens or hundreds of disparate data sources within most organizations. By understanding the continuum of data governance, organizations can progress toward the ultimate goal: a single, unified view of the enterprise.

* This article was originally published in DM Review magazine.

Bio: Tony Fisher is President and General Manager of DataFlux, a SAS subsidiary.