The COVID-19 Data Governance Gap | A CDO perspective on data governance framework

The COVID-19 Data Governance Gap | A CDO perspective on data governance framework

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • Post-Covid-19
  • Emerging Technologies
  • Digital Transformation

Publication | Update: Oct 2020

When COVID-19 turned into a pandemic, companies found that they needed different data.

According to Winston Thomas of CDO Trends, before the pandemic, big data analytics and the virtues of a data-driven culture were already hot topics for the C-suite. Now C-suite is asking for more business transparency. They also saw the need to do what-if analyses more frequently. They also wanted to speed up the analytics process to guide their operational decisions that now directly impacted their business survival.

Bad data is a perennial problem for companies. But in a climate where the underlying business environment is shifting fast, bad data can trip companies into insolvency.

Companies need to own up to a stark reality: that it is a business issue, and not just an IT problem, said Praveen Kumar Chandrashekhar, senior vice president, and general manager for the Asia Pacific at ASG Technologies.

“The data owner has to be the business owner or someone at the top; it’s a corporate responsibility. IT might say we are data governed, but you step out of it, you find out that there are many aspects that are overlooked within the enterprise.”

Business owners and department/division heads are already under pressure to deliver on business metrics during the pandemic. Adding data governance on top of these can be a tall ask.

COVID-19 is changing mindsets on the value of data governance.

As demand stalls, competition heightens, and the market demand evolves fast, companies see value in having the right data. Bad data can now directly impact business survival.

Joe DosSantos, chief data officer at Qlik, contests that in his case, information was hidden in HR and other department data silos. "Very quickly, people wanted to know what the office closure policy is, the work-from-home support, the technologies that they need, etc. — so, a lot of concern about employee data."

For example, companies wanted to know whether they should lay off their employees, furlough them, or reduce their salary. Or which product lines to focus on, and which to stop, said DosSantos.

One consequence of COVID-19 was that the C-suite began to appreciate data operations (data ops). The pandemic also caught out those who did not have one. "Because the time that you need the data is not the time to ask where it is," said DosSantos. 

"For example, a lot of retailers needed to figure out how to get the right [products] into the retail channels. And so they are really trying to get the data for action," said DosSantos

As companies started to ask more what-if analysis questions, data workers began worrying about the richness of data.  It meant adding more valuable data, including third-party data, for analysts to understand and offer more insights.

As a result, companies are now changing the conversation about data governance.

For example, Chandrashekhar noted how a CDO at an Australian insurance firm talked about reducing data deduplication within his company to save about 30% of costs. While the issue is about data governance, he noted that it can be a tough sell. But equating to actual savings gets senior management and business owners on the same page.

Companies are also starting to realize the importance of understanding what data they have on the technology front.

“This whole concept of Master Data Management (MDM), which people keep selling, is very difficult to implement because of the variety of applications that an enterprise has. These applications actually store data in different forms that are built over a period of time. Moving all of that to a central MDM is a Herculean task. And that is a lot of money to be spent with no return that can be visible over the next maybe 36 months,” said Chandrashekhar.

He is also forecasting that the use of Data Intelligence to get a clear data landscape will play a more prominent role post-COVID-19. It is when companies will start to merge or acquire each other by taking advantage of low valuations. “Because the cost of enforcing data governance in an organization post-acquisition will be just too enormous.”

Data ops success, depends on the right data governance framework. And this raises some hard questions on data ownership.

When one department or division does not see the advantage of sharing their data, it creates both a data gap and analyst rift.

It boils down to alignment. "I think you need to start out with highlighting what the alignment is," he said, including offering clear incentives for sharing data. He noted that getting this alignment is also becoming critical as companies try to find their feet in the post-pandemic landscape. "One thing that the pandemic has actually shown us is the importance of the time value of data.”

An internal center of excellence or small data-driven projects can help, but ultimately it needs a strong corporate political will to shape old habits.

DosSantos felt this could be difficult but can be done successfully. He also suggested answering four questions to get data ownership right:

  • Who's responsible for defining the data?
  • Who has access to that data?
  • Who is entering the data?
  • Who is stewarding the data?

There is no single answer or blueprint for answering these questions. Every company will have its challenges.

For example, DosSantos argued B2C companies are doing better in entering data because it is usually captured by POS machines, lies within behavioral pattern reports and entered using standard forms. B2B companies are a different story. "You have data that is often entered by sales reps. And those are not the best people to enter them." 

No matter how difficult, companies need to address all four questions to create a proper data governance framework. Such a framework will then allow everyone in an organization to have a single source of truth.

It also frees up time for data scientists and analysts to do the work they were employed to do, and not waste time cleaning up bad data or wrestling with erroneous entries. 

This is already pushing a lot more companies to look into cloud-based data lakes. "They can make data available in a more real-time kind of construct. And if you combine that with the semantics and the ability to ask any kind of question that you want it's really where you're going to start to get some noticeable difference."

But without strong data governance, these decisions will be fraught with erroneous assumptions, bias, and security issues.

Source: CDO Trends

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This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.

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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
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Forecasts, Data modelling and indicator normalisation

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The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

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The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

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