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July 26, 2019

Not All Data is Created Equal: Why Companies Need a Data Governance Strategy to Succeed

Rahul Nair

We live in a data-driven culture – no doubt about it. From smartphones to tractors, almost everything around us generates some form of data. To make matters more challenging, data is not only expanding in size and volume, but also in complexity. New forms of data are appearing overnight, and companies are struggling to keep up.

Forward-thinking companies are already turning to data governance to make sense of their data and to stay ahead of the game. But data governance is not something that you can implement at the touch of a button and early adopters are already widening the gap.

How Much Data Is Being Generated Worldwide?

According to the latest “Data Never Sleeps” report, in every single minute of the day Amazon sends over 1 thousand packages, users send 2 million snaps on Snapchat, and Americans use over 3 million GB of internet data. By 2020, for every person on earth, 1.7 MB of data will be created every second.

IDC’s “Data Age 2015” whitepaper reveals that businesses and consumers will have the capacity to consume 175 Zettabytes by 2025 (one zettabyte equals approximately one billion terabytes).

And these numbers just keep growing – making it increasingly difficult for companies to store, manage and protect their data.

How Is Data Becoming More Complex?

Soon, if your company is producing data (and it almost certainly is), then it will need to be able to address not only the data you have had with for years, but also new types of data.

That’s because it’s not just the volume of data that is expanding – it’s also the level of detail.

With the proliferation of IoT technology, machine learning and artificial intelligence, new types of data are appearing every day, and their complexity keeps growing.

For example, machine learning algorithms in e-commerce are becoming increasingly reliant on the use of extremely detailed data, gathered from every point in the customer journey. But not many companies have the necessary processes put in place to handle this level of data complexity. Data accuracy and consistency can quickly become a challenge for organizations without a data management framework.

How Are Companies Dealing with These Challenges?

Sensing the power of data and recognizing the need to control it, some companies are already starting to capitalize on the power of data by using data governance.

And that might be the smartest thing to do.

A 2018 McKinsey report revealed that high-performing companies were more than twice as likely to have a strong data governance strategy and twice as likely to have a clear and well-understood data strategy overall. The same report reckoned that the gap between the high performers and the pack was growing rapidly.

It’s easy to assume that time is of the essence if you’re a company that wants to exploit active data and to make that data work for you, not the other way around.

Moreover, for some of these companies, data governance is not a matter of choice or competition. Financial services organizations, for example, are compelled to put in place data governance best practices because of the nature of their business.

(Peshkova/Shutterstock)

FinServ companies need a solid data governance strategy because of the high level of regulatory oversight. Add other facets of the industry – like data security and the protection of sensitive data – and it’s no wonder FinServ companies are expected to be at top of the list of companies that want to implement data governance.

What Is Data Governance and How Is It Different from Data Management?

We already know that the understanding, use, and strategy of a company’s data are necessary for its success in the business world. But how does data governance fit into all of this?

Data governance combines elements of both strategy and execution and is usually described as the framework surrounding the data management processes. A data governance framework certifies that data is available, reliable, usable and consistent throughout the organization.

Data management, on the other hand, comes in handy when trying to identify sources, owners and users of data. Data management integrates data from multiple sources, centralizes, cleans, and streamlines it to make it available for use in other business initiatives. Additionally, core data management touches upon the architecture of data across the organization.

Data management aims to bring forward the financial benefits of good data practices and to reduce the risks related to deficient data practices.

To make the leap from data management to data governance, the business and IT sides of an organization need to come together and define the rules that will govern data across the enterprise.

Not All Data Is Created Equal

While collecting all kinds of data can bring added value to most companies, sales data is that one instance in which data governance really shines through.

Most of the data that companies generate can be used in directional analysis. Think about marketing data, for instance. When a company is trying to decide which e-mail program is most effective, it will look at indicators like opens, clicks, and conversions. Everybody knows that these metrics are not 100% accurate, but they can still be used directionally, because they’ll help you decide which campaigns are performing better. This data doesn’t need to be 100% accurate to be used in this type of directional analysis.

However, sales data used in compensation calculations is a different matter. Sales data needs to be as accurate as possible, because employees are being paid based on these records. When it comes to sales data, companies need to make sure data sets are error-free and complete to be exploitable. And that’s a fairly difficult thing to achieve.

A data set pertaining to sales data requires that information from many different sources (CRM, HRIM, ERP, etc.) be brought together and normalized through data transformation, with the aim of making it more easily available, usable and consistent. Having reliable and consistent sales data in all parts of the organization eliminates the risk of disconnects between departments.

Unlike other types of data, sales data reflects the actual performance and success of an organization. Having clean sales data available for use across the organization (preferably with access to historical data) can and should drive strategy and actions throughout the enterprise.

Looking Towards the Future

Companies that understand the value and complexity of data are slowly turning to data governance to manage data related risks and errors and to build a data-driven strategy. However, the key word here is “slowly.”

Yes, data governance might be the next step in a completely digitalized business world – but we’re not there just yet.

According to a survey from Syncsort, data governance is only third in a top of IT initiatives respondents identified for 2019. The first two in the winner’s circle are still cloud/hybrid computing and modernizing IT infrastructure.

The reality is that a lot of companies use, and will use, for the foreseeable future, a hybrid form of data governance – one that combines element of both data management and data governance.

Data governance is a complex endeavor – a cultural shift that impacts all areas of an organization, and a lot of companies still have a long way to go before taking the leap.

The good news is that, if you’re thinking of putting in place a data governance framework and you’ve already got data management as a core business process, you’re more than a step closer to achieving your goals.

About the author: Rahul Nair is the director of strategic engagement at Optymyze. During his time at Optymyze, Rahul has worked with clients to increase accuracy, timeliness, automation, efficiency and transparency in the sales organization. He holds a BS in Mechanical Engineering from Pune University. Contact Rahul at [email protected]

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