In the world of digital marketing and media, data governance does not generate the most buzz. Frequently, companies view it as a “nice to have,” rather than an essential business function. That leads marketing executives to focus on it only after addressing other, hotter topics.
Data governance may be one of the least visible factors of effective data-driven approaches to marketing specifically and business in general. It is, however, one of the most impactful.
Essential to data quality, master data management and other initiatives, data governance is critical for being data-driven. Companies that get it right generate substantial and impressive benefits. These include significant cost and time savings, plus improved confidence in their data – which has knock-on benefits of its own.
In other words, data governance is a “must have.” Without it, an organization’s entire data strategy and digital marketing approach may rest on a shaky foundation.
What Is Data Governance?
First, a quick definition. Data governance means defining and managing different types and categories of data. For marketing departments, that data may include information about:
- Customers and accounts, including demographics, behavior, interactions
- Transactions and activities
- Reference data
Think of it as synonymous with “quality control” for your data. It enables companies to have reliable and consistent data sets. Reliable data, in turn, enables organizations to assess performance and make management decisions.
True, sophisticated, systematic governance involves much more than just storing, cleaning and consolidating data, however. It takes a framework of policies, business rules, and assets designed to not only manage data, but also to enable and activate it.
It’s critical to remember that effective governance is not a one-time exercise. Rather, governance should be a well-developed, repeatable process. That’s the only way to ensure ongoing compliance with corporate standards and requirements.
There is a common perception that governance is all about documented policies. But the most effective governance programs are active and ongoing. They consist of regular audits, reconciliations, compliance reviews and quality control activities.
The right governance program supports and complements your overall data strategy, and is key to driving value from data.
Five Reasons Why Data Governance Is Essential for Effective Data-Driven Marketing
In our experience, data governance matters to companies’ data-driven marketing efforts in five essential ways:
1. Saving (and making) money.
The most important reason to invest in data governance: ROI. Data governance saves money – and can even help to generate revenue, too.
Having a firm grip on how you define core metrics, as well as marketing exclusions, segments, and derived attributes like customer lifetime value, can help you to optimize your marketing campaigns. Optimized campaigns will generate more revenue while also saving money.
Data governance can save you money in other ways, too. Strong governance models reduce data duplication – and reduce duplicate data management efforts. With better quality data, companies also reduce the likelihood of errors. Fewer errors and greater understanding equals better data efficiency – which also brings costs down.
In addition to saving money, governance can also generate new revenue and value. It enables companies to better understand their customer data – which in turn means better understanding their customers. That’s key to successful data-driven marketing.
2. Ensuring data consistency, reliability, and repeatability.
You can’t get consistent, reliable and repeatable data without data governance.
Managing your data means defining consistent metrics across the organization. With consistent, shared metrics, everyone knows what KPIs like “conversion rates” or “unique visitors” mean.
Without documented standards around metrics, decisions may be made around false assumptions. Obviously, communication and reporting suffer in such situations (see point #3).
Governance also guarantees data integrity for future changes. Such changes may include evolving business challenges, emerging technologies, and new data flows.
There are any number of factors that can compromise your ability to rely on the data you already have. Rigorous governance models sustain data strategy and the marketing programs your data supports even as the business environment shifts.
3. Solving analysis and reporting issues.
When there is confusion about the meaning of data, or lack of clarity in reports, many organizations are quick to blame their tools or technology.
These challenges are most often data governance problems, not because of problems with technology. Typically, companies have not configured the tools and reports to clarify what various metrics mean. The technologies also have not been set up to align to specific goals. Nor have companies designed their tools to digest data provided by different systems.
As a result, companies end up ripping out and replacing functional systems. Don’t make that mistake. Instead, understand the importance of doing the necessary governance work to ensure your tools and reports work. (Preventing premature technology replacement is another way data governance saves you money – point #1).
This is an especially important consideration in environments with multiple analytics tools. With ChiefMarTec’s latest supergraphic listing over 5,000 vendors, complex martech stacks are common. Managing data effectively can make a disjointed technology environment function.
4. Guiding all other analytics activities.
Contractors would never build a house without clear blueprints. It’s the same for analytics teams, which need data governance to guide and structure their activities. Those governance-guided analytics activities include:
- Metric and key attribute definition
- Dashboard and report design
- Statistical models, machine learning, and artificial intelligence (AI)
- Risk management associated with data quality and the usage of data
- Analytical application development
Governance ensures that your data capture mechanisms are set up to collect what you need. It may also outline who is responsible for which analytics tasks or data.
Robust governance helps ensure clear alignment between your marketing organization’s analytics tactics and the company’s big-picture strategic goals.
5. Improving confidence and providing clarity.
Data governance helps you (and your boss) sleep at night.
It’s a nightmare trying to explain or reconcile conflicting data sets to skeptical executives. Anything less than 100% confidence in analytics data leads to headaches and second-guessing.
Effective governance eliminates those headaches by clarifying:
- What metrics mean
- Which metrics are the most important
- How internal numbers relate to outside ratings
- Why there may be gaps
- What is the risk of using the data
Greater clarity means more confidence in data-driven decisions. As a result of having greater confidence, you can increase the speed with which you can make data-related decisions and choices.
True data-driven marketing is about generating and acting on insights in real time. Data governance builds the confidence you need to get closer to that goal. And that means more peaceful nights for everyone.
There is also a compliance angle here. There are growing legal and financial reporting ramifications for how data is managed and consumed. Poor data quality can lead to penalties, fines, not to mention bad publicity.
Strong governance models ensure all your operations follow relevant privacy policies. The same goes for data security guidelines. Such governance models also enable compliance with consumer information regulations, especially around use of Non-Public or Personally Identifiable information.
The Bottom Line
Adopting a robust data governance model enables three key things: 1) data accessibility, 2) data confidence, and 3) data activation. Through governance, various stakeholders gain access to quality, trustworthy data they can use to make decisions, both internally and externally.
While data governance may be one of the least “sexy” aspects of being data-driven,. it is essential to data-driven success.
Editor’s note: This piece was originally published in 2011. It has been fully updated for accuracy and comprehensiveness.
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