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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
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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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Objectives and Study Scope

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.

The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges. The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period. The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the  market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players.  This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.

Research Process & Methodology

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We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.

We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.

Research Portfolio Sources:

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Global Business Reviews, Research Papers, Commentary & Strategy Reports

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M&A and Risk Management | Regulation

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  • CoreCompensation

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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.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.

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Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.

Revenues

Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates

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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.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.

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The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticities
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
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.

Prices
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.

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