Getting the governance go-ahead

Data governance need not be a grudge purchase, like insurance. It can go beyond that to improve operational and financial performance. But, with many stakeholders, and since it touches so many organisational elements and processes, it can be a difficult internal sell.

I've been involved in many of these projects with large companies and I've learned numerous lessons as a result. I rely on a 10-step process to develop the business case:

1. Create the strategy;
2. Identify value;
3. Leverage industry sources;
4. Assess the data;
5. Identify technology requirements;
6. Define the concept;
7. Determine necessary funds;
8. Determine the factors for success and areas of risk;
9. Document the business case; and
10. Present it.

It's also good to know that, when a company starts to sell data governance to its internal stakeholders, it's not walking virgin ground. Benefit from lessons learned by other companies and leverage the experience of industry thought leaders. Sources of information - such as research papers, industry studies, case studies, analyst findings and comment, practitioners (like me), and technology leaders - do exist. They can be available either by simple search and download off the Web, attending industry events, or literature, from articles to books authored by experts.

I constantly consume information from my peers and the industry sources I mentioned above, so I know, when I start a project, the strategy I use consists of similar combinations of a nine-step process. Steps range from developing top-down and bottom-up approaches to naming the programme, listing people and groups to influence, to creating a high-level project plan and including relevant third-parties.

But, one of the first questions a company is faced with when it begins talking about a data governance programme inside the business is what value it offers. At the highest level, there are five primary areas of the business to find that value: the programme itself, data management operations, projects, business operations, the organisation's strategy and policy.

It goes without saying that building a concept definition is a prerequisite to selling data governance to internal audiences. Concepts accumulate much informative legwork in a single resource to clearly map it into the initial plan. It will be used to demonstrate the benefits, goals, objectives, plans, business structure, interoperability, accountability, resources, and roadmap to various people affected or responsible for data governance.

Funds are the head-on-block component: if the bean-counters don't like the numbers and the business leaders can't prove ultimate revenue generation or cost reduction, then the programme will never take off.

Data governance costs are typically minimal compared with the value the programme brings to the company. If the company has done its homework in preparing for the pitch to the rest of the business, then the value will become apparent.

I've also found it advantageous to clearly demonstrate, at this point, how the business will leverage existing assets and resources since that is so closely tied to funds. Proper planning will also divulge the starting costs, including the cost of people, technology, education and third-party assistance, and the cost to maintain the programme thereafter.

Benefit from lessons learned by other companies and leverage the experience of industry thought leaders.

Determining requirements for success during the planning phase will also lead the company to see the likely risks. There's no point continuing the programme if the company simply doesn't have the funds. There are other factors to consider too in the context of risk:

* Span of control;
* Authority;
* Accountability;
* Legitimacy;
* Technology;
* Business alignment of leaders, IT personnel and operations; and
* Programme performance and expectations.

Data assessments are a great place to exert high-impact, low-cost activities. A company can validate data assumptions, learn all of its data challenges and opportunities, focus attention where it is needed most, tie data outcomes to business performance, provide tangible metrics to the organisation's leadership, and create compelling presentation content.

Technology will always form a fundamental component of any data governance exercise. By exploring the technology that exists in the company, it can be determined how the technology can be used to achieve the company's data governance goals by supporting listed activities.

The exercise will also reveal where the shortfalls lie so the company can plug the gaps with only necessary and relevant technologies, instead of a basket of expensive software.

Key technology groupings will include:

* Data integration;
* Data profiling;
* Data quality;
* Data remediation for exceptions;
* Enterprise performance monitoring;
* Metadata management;
* Data synchronisation;
* Data visualisation; and
* Master data management.

At some point, the team is going to have to present all its data to other people in the business. The documents created to do so will have the power to determine whether or not the green light is given. This makes it important to highlight all the aspects I've mentioned above, in an executive summary, which contains specific elements, the value proposition, the technology and funding requirements, success and risk areas, an investment summary and a roadmap of recommendations.