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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 optimised supply chain. Companies should navigate a path to enterprise data governance, and improve the health of the organisation’s data.

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 enterprise-wide data governance programs, which attempt to codify and enforce best practices for data management across the organisation.

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.

Like many enterprise projects, data governance programs often start small before finding the sponsorship and support needed to transcend organisational 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, organisations 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 organisation’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.

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 organisation 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 organisation out of complacency.

At the next stage, the reactive phase, a company begins to organise a data governance program, either through grass-roots efforts or, more likely, through an executive-driven effort fuelled by an earlier failure. At the reactive stage, organisations 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 organisation 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 organisation 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 organisation — 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.

The Enterprise Data Maturity Model helps organisations 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.

Conclusion

The path to data governance is not easy, but organisations can get there. More importantly, data governance is a necessary response to the dozens or hundreds of disparate data sources within most organisations. By understanding the continuum of data governance, organisations can progress towards the ultimate goal: a single, unified view of the enterprise.

*Tony Fisher is president and general manager of DataFlux, an SAS subsidiary.

 

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