Understanding the information held by your organization and determining how to wield it for your good is pivotal to the realization of your strategic objectives. To effectively do that you have to answer several vital questions. How and where is your data stored? How can you vouch for its accuracy, currency and timeliness? Is the data trustworthy? In the sea of information your organization handles daily, which one is high priority? These questions seem straightforward but conclusively answering them isn’t easy.
You cannot tackle these questions without differentiating data governance vs data management. While the two terms are regularly used interchangeably by technology vendors and IT professionals, they refer to two distinct aspects of data handling. Organizations that have a clear understanding of data governance vs data management have greater odds of getting their data strategies right. We’ll take a closer look at the two terms in order to dispel the most common inaccurate assumptions. But first, let’s see why data is so valuable.
Data as an Asset
If there’s one thing that defines the most successful businesses today, it’s their ability to capture, analyze, understand and leverage data. In fact, some analysts argue that data is the new oil. When you look at the list of the world’s largest companies by market cap 30 years ago and what the list looks like today, there’s indeed merit to this argument. For example, Alphabet (Google’s parent company) has nearly three times the market cap of oil behemoth Exxon Mobil. Google’s business is largely built around the organization of information on the internet.
Data has a number of characteristics that give it an advantage over traditional assets like oil. First, it is non-depletable. A single data packet can be distributed and reused countless times. Second, it’s non-degradable. As long as the data is secured and maintained as it should, it retains its characteristic indefinitely and doesn’t wear out. Third, data assets are durable. They can generate value for years.
With that said, business data has no intrinsic value until it’s prudently applied to the realization of business objectives. A surprising number of companies do not have a clear idea of what data they possess, how competent/skillful their employees are at wielding data, and how data can be deployed to the maximum as a weapon of supporting their data strategies. A clear distinction of data governance vs data management is the first step toward getting business data right.
What is Data Management?
Data management is the set of policies, procedures, processes and programs that allow you to control, organize and execute on your data in a way that makes it reliable, accessible and current whenever users require it. Technology teams charged with data management will often rely on a wide range of tools that streamline and automate the collection, validation, storage, organization, protection, processing and maintenance of company information.
In the earliest days of computing, data management was a simple process that involved punching cards to capture values. Today, the world of computing is far more complex and features a wide range of data capturing and manipulating devices including desktop computers, laptops, smartphones, tables and a plethora of IoT (Internet of Things) gadgets. This has made data management increasingly complicated.
Without effective data management, organization information could become unusable, inaccessible and outdated. Data management covers the entire lifecycle of information—from its creation to its retirement. It supports the data requirements of customers, partners, employees, shareholders and regulators.
What is Data Governance?
It is the overarching framework that guides every aspect of data handling. It’s the strategic decision-making, enforcement and monitoring business program or body that has legal authority over data assets. Putting it differently, while data management dwells on the logistics of data handling, data governance is devoted to the strategy of data handling. Therefore, data governance is less technology driven.
In this sense, data governance decisions occur at higher levels of the organization than data management. It’s about deciding what you are going to do with company data then following through to ensure it happens. At the heart of it is data ownership, planning, process, accountability and performance management.
Data is a highly political asset and is regularly at the center of inter-departmental conflicts within the organization. Various business units will take strong unwieldy positions around the data they consider they hold, even when such data is in conflict with that held by another department. Such clashes and rivalry can become a major cause of inefficiency and call for a higher authority within the business to arbitrate and resolve. This is one of the key objectives of data governance.
For example, if there are 30 distinct data repositories in the company and you want to collapse them into a single master repository, then tough decisions must be made. These can only be done at the highest management level where unpopular decisions can be pushed through as long as they are in the best strategic interests of the company.
Data governance seeks to answer the questions around how the organization can reap the financial benefits of good data while mitigating against or avoiding the risks of bad or poor data. Good data governance entails determining the attributes of acceptable data including where it’s collected, how it’s used, how accurate it is, what rules it follows, who is in charge of it and who is allowed to handle it.
Given the broader role of data governance vs data management, governance goes beyond the technology department and ropes in stakeholders drawn from different arms of the organization. This ensures the consistent application of data governance policies and principles across the enterprise.
In the absence of such a centrally coordinated approach to data governance, each department and business unit could adopt their own approach which would end up creating silos and precipitating chaos. Data governance brings together multiple distinct elements of data handling including usability, security, privacy, compliance and integration.
Benefits of Data Governance
Data governance has multiple benefits such as:
- Increasing the value of enterprise data
- Decreasing data management costs by focusing resources on the most relevant and high priority information assets
- Increasing overall enterprise revenue through better use of company data
- Standardization of data policies, procedures and systems
- Satisfying regulatory and compliance procedures
- Resolving problems with the data itself such as inconsistency and corruption
- Promoting transparency in data handling
- Establishing a training and awareness program around the handling of data assets
Best Practices of Data Governance and Data Management
We have defined data governance and data management, however there’s plenty of common ground between the two especially as pertains to best practices. Here are three of the most important.
Recognize That It’s Not Exclusively an IT Burden
We mentioned this in the data governance section, but this is in fact something that also applies to data management. Whereas data management is more tech-centric, it too cannot succeed if there’s no buy-in from the very top of the organization’s leadership as well as the active participation and support of other business units.
IT teams may execute on their data management roles seamlessly but that could all come to naught if the rest of the enterprise isn’t on the same page. Business input is also crucial because techies won’t always know which data is the most valuable (since this is something the respective business departments would have a far better understanding because they are the actual users of the data).
IT departments could come up with excellent technical systems for data management. Yet, without an understanding of the bigger picture, they won’t be adding real value to the business’ data goals.
Get External Perspective
Consultants are often derided as an unnecessary expense. Many business leaders believe that consultants do little except tell them what they already know. However, when you are setting up a data governance and data management framework, you are better off contracting an expert data consultant to guide you through the way.
Hiring a data management or governance specialist to run this process internally may seem like the logical thing to do (and is in fact a good thing). Even then, the external consultant can provide invaluable inputs on what will work and what doesn’t. While no two organizations are the same, there are a lot of common principles that are applicable across the board. Since a data governance and data management consultant has probably worked with a wide range of organizations, they have deep knowledge that can help you avoid the most common data governance and data management pitfalls.
Be Proactive AND Reactive
The mantra of business management for years now has been, “Be proactive not reactive.” A reactive approach is often depicted as a bad thing. However, no matter how much effort and resources an organization puts into mitigating against associated risks, that’s no guarantee that a risk or threat will not materialize.
While proactive data governance and data management is the ideal, there must be a clearly laid down mechanism for putting out fires when they do occur. Organizations must balance proactive and reactive strategies to ensure all their bases are covered in the worst-case scenario.
Recognizing that data is an asset is an important first step in an organization’s mission to get an optimal return from the volume of information they possess. By employing the right combination of data governance and data management practices and tools, they can make data the fuel that drives them to desirable business outcomes.
This is the second post in a five-part blog series on data governance. Make sure you check out the rest of the series to learn more about:
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