Data Governance

Do you consider your organisation ‘data-driven’? It’s hard to imagine an industry where data isn’t a prevailing force. But with great volume comes great responsibility.

If you don’t keep on top of your data throughout its entire lifecycle, you run the risk of poor data quality, integrity and security. This is bad news for innovative, forward-thinking businesses, especially in industries where meeting compliance regulations can make or break your reputation.

Fortunately, that’s why the process of data governance exists. But what does the term ‘governance’ actually mean and how can you make it a reality in your organisation?

That’s what we’re here to cover. In this guide, we’ll run you through everything you need to know about the data governance process, including an explanation of why it’s important, a look at the challenges you’ll face along the way, and a step-by-step approach to successful implementation.

Ready? Let’s get started.

What is Data Governance?

In many ways, data governance is a negotiation between your organisation and your data.

It’s a set of universally defined policies, processes and technologies that optimise the availability, usability, integrity, and security of all enterprise data. 

Without a strong data governance framework, most businesses fail to manage their data efficiently. As a result, the quality suffers and dirty, unstructured data begins to clog up your systems. At this point, you can’t rely on the data to provide valid insights or inform key decision-making processes.

While overall control and visibility is the driving force behind data governance, the discipline breaks down into several, interconnected practices:

data-governance-practices

  • Data architecture
  • Data modeling and design
  • Data storage and operations
  • Data security
  • Data integration and interoperability
  • Documents and content
  • Reference and master data
  • Data warehousing and business intelligence
  • Meta-data
  • Data quality

Mastering these practices makes it possible to standardize, integrate, protect and store your data in line with strategic, operational, and regulatory expectations.

Blog: 5 Data-Driven Steps to Keeping your Regulators Happy

Sounds like a simple enough concept. But, in practice, you’re likely to encounter a number of challenges. While this is part and parcel of any data management strategy, with a proactive and iterative approach to data governance, you can overcome all business and technical roadblocks.  

Why Should Data Governance Matter to my Organization?

If you’ve led data management initiatives for a particular department or project, the term ‘governance’ will probably be familiar to you. Most teams implement some kind of informal rules that dictate what happens to their data throughout its lifecycle. But this isn’t the same as a systematic framework.

As your organisation scales, it becomes more difficult to implement cross-functional tasks efficiently. For example, if you're integrating millions of financial records across a network of banking systems, you can’t rely on hastily defined definitions and policies to ensure you meet regulatory standards.

It’s just not possible to keep track of your data that way.

At this stage, you need a formal data governance strategy to ensure your organisation continues to meet its desired outcomes. These might include:

  • Increasing consistency and confidence in decision-making
  • Decreasing the risk of regulatory fines
  • Improving data security or transparency of data lineage
  • Maximising the revenue potential of your data
  • Eliminating the need for rescoping and project extension
  • Optimising staff productivity or process efficiency

Strong data governance is also a vital part of business innovation and process transformation. Remaining responsive and open to new data capabilities is the key to fending off fierce competition. But relying on outdated or backward-thinking structures can harm your chances of success.

The following data innovations will have a huge impact on your current business processes:

In each case, a lack of data governance can prove costly, resulting in poor data quality, insurmountable business silos, and complex and time-consuming reconciliation projects.  

The Challenges of Implementing True Data Governance

Despite the push to build company-wide data cultures, 69 percent of organisations are still failing to become ‘data-driven’. And the use of this terminology is a core part of the problem.

The word ‘data-driven’ implies that you take a backseat to your data’s fate – the exact opposite of what data governance best practices preach.

The ultimate goal is to retain complete control of your data, no matter where it sits in your enterprise architecture. You’re the one behind the wheel.

Of course, implementing this data governance philosophy is easier said than done.  

Let’s look a few key obstacles you’re likely to face along the way.

Challenge #1: A closed organisational culture

People are just as integral to the success of your data governance program as the technology you use. If your workplace culture is resistant to change, it’s very difficult to successfully launch and sustain new governance projects. Real problems occur when there is no defined data quality standards or clear ownership. In this situation, datasets begin to diverge, data silos emerge, and there is no single definition of what the data actually represents. This is a tricky challenge to overcome and requires careful negotiation. But it’s also an important step in your overall strategy. To eliminate the need for expensive reconciliation processes, you need every department, function, and employee working within the same data governance framework.

Challenge #2: Limited stakeholder buy-in

While securing the enthusiasm of the wider company is one thing, engaging stakeholders is an entirely different beast. Low-level, non-threatening changes to data management are often welcomed by senior execs. But bigger shifts to the status-quo are harder to get across the line.

Large data governance projects require a careful balance of resources, budgets, and timescales. So, your bosses need to understand the business value of a complete data management overhaul.

Bringing transparency of data to the executive team - for example, by using data modeling tools and processes - is a good way to engage them more in the process and empower them.

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Challenge #3: Lack of standardization or maturity

In today’s competitive business landscape, it pays to be flexible and fast-moving. But this shouldn’t come at the expense of your data governance standards. There’s a fine line between responsiveness and poor standardisation – one that will quickly blur if the right processes aren’t in place.

A lack of alignment between data ownership and specific business divisions leads to a mess of black-box data processes. With no enforced standards, the movement and modification of your data quickly becomes a free-for-all – much like a bus station without a consistent timetable.

Over time, every business function ends up with its own interpretation of the same data. Institutional knowledge becomes fragmented and lost in a sea of scripts, transformations, and mappings. And, without a single version of the truth, it’s virtually impossible to track data lineage and ensure your data pipelines remain efficient and auditable.

Read more: How to Regain Control of Your Data Auditing Process (6 Best Practices)

8 Steps to Building an Effective Data Governance Strategy

While change of this magnitude certainly won’t happen overnight, building an effective data governance strategy can be the difference between success and failure.

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The good news is that you probably already have much of this strategy in place. But that doesn’t mean you can rest on your laurels. Following an established data governance framework will help you benchmark your current strategy against successful models. This will ensure you’re not overlooking vital policies or procedures.

Here are our eight essential steps to building an effective data governance strategy:

  • Step #1: Understand your corporate drivers and clearly define data management goals.

  • Step #2: Analyse the current state of your enterprise data management and prioritize areas for improvement.

  • Step #3: Maximize the availability of information by using data integration tools to consolidate data from disparate and diverse architectures.

  • Step #4: Create your new data governance policies and assign roles, responsibilities, and rules.

  • Step #5: Ensure data quality and integrity by profiling, parsing, standardizing, enriching, and monitoring all information.

  • Step #6: Establish accountability and assign ownership of each information asset.

  • Step #7: Convert to a master-data culture where a single ‘system of record’ provides a unified, consistent, and accurate view of all entities.

  • Step #8: Develop a feedback mechanism and promote continuous process improvement using real-time monitoring tools.

These steps become particularly important as your organisation and data projects scale. The proliferation of data across multiple applications, systems, and architectures makes a single version of the truth harder to obtain.

But it’s certainly not impossible to achieve. With the right data governance tools and policies, you can unite business objectives and technology initiatives and continue to make measurable improvements.

Choosing the Right Data Governance Tools

Many companies still rely on manual processes to profile, validate, and monitor their data. But for larger organizations, this is no longer an option.

Despite our best intentions, human error almost always creeps into data processes. These errors lead to false, fragmented or duplicated information. At small scales, this is easily spotted and corrected. However, as projects and ambitions grow, so does the complexity of data management.

Eventually, you end up in a situation where institutional knowledge is lost to a sea of different data transformations, scripts, and interfaces.

Fortunately, automated data governance tools eliminate this problem. When evaluating these tools, the following criteria will help you identify the best technology for the job:

  • Can it identify and track common create, read, update and delete activities for data elements?
  • Can it provide effective data quality management (i.e. rules, profiling, reporting)?
  • Can it perform data movement, data lineage views, and positioning?
  • Does it provide metadata support for document classification and document lifecycle management?
  • Does it assign and manage governance roles and responsibilities?
  • Does it define and monitor service-level agreements, issues and activity statuses?
  • Can you define and manage data management workflows and track progress of data governance activities?
  • Can you monitor business strategies and plans, and calculate the business value of data?

In many cases, you won’t find a single tool that meets all these criteria. You might instead opt for a series of connected data governance tools that provide a complete data governance pipeline – from data modeling, through integration and transformation, to reporting and visualization.

Webinar: Turn Data Models into ETL Jobs at the Click of a Button

Don’t Start With a Big Bang

Data is here to stay. And you’ll always need a way to stay on top of it. Competitive pressures, internal and external risk, and an ever-changing regulatory landscape mean that strong data governance is as much as business priority as cashflow or R&D.

While there is a clear need for rapid and consistent change, you can’t expect to reinvent the wheel in one afternoon. By taking a practical and measured approach to data governance, you can start to take back control of your data and move towards a single version of the truth.

But just because you’re not starting with a big bang, doesn’t mean you can’t make rapid progress. With the right approach and transformation tools, you can significantly reduce the time it takes to put your data governance strategy into action and supercharge your business operations.