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Author: Chethan Laxman

Chethan Laxman is Data Transformation Executive at Apexon, a digital engineering professional services company. A seasoned and accomplished senior executive, he has more than 28 years progressive experience leading firms across multiple functions, including Operations, Business, Technology, Team Development, Sales and Relationship Development, all achieved in diverse industries and geographical locations. He was the head of client engagement at Gathi Analytics before that company was acquired by Infostretch, which in turn merged with Apexon in 2022.

Why Business Agility Starts with Strong Data Governance

Data is now one of the most valuable assets corporations own, yet, it is still often treated as a waste product.

As data increasingly guides decision-making and digital advances automate business processes, so the integrity of data assets becomes more important. All organizations, regardless of size or sector, need to shift from viewing data as an inconvenient cost center to an asset that is reliable, accessible, secure and usable. To ensure organizations can proactively manage their data in all its disparate guises and locations, they need a structured approach with defined rules and policies governing how it is handled and stored.

The Case for Data Governance

Bad governance is costly. Poor data management impairs an organization’s ability to conduct business, whether that involves managing customers, delivering timely products, spotting market opportunities or operating as efficiently as possible.

Gartner predicts that through 2025, 80 percent of organizations looking to advance their digital business will fail because of their outdated approach to data and analytics governance. If that estimate is correct, businesses are actively missing opportunities and putting themselves at risk.

What Barriers Do Businesses Face?

Faced with increased internal demand for answer to complex questions combined with external pressures to demonstrate value and ROI, organizations are turning to data for the answers. However, harnessing data to deliver actionable insights starts with improved data stewardship. Furthermore, amid a rapidly changing regulatory climate, data management and governance are also critical to ensure businesses don’t fall foul of their regulatory obligations.


Establishing Good Data Governance

Good data governance is about creating a system of data management that addresses all these challenges. Few data and analytics leaders would disagree with the push factors for data governance, but market unpredictability and the sheer pace of innovation has made it harder to benchmark data governance efforts and establish best practice. Having invested in data and analytics, business and IT leaders now urgently need to ensure good governance in order to meet their goals of operational efficiency, increased growth or improved CX.

Establishing a data governance framework is a multidisciplinary exercise. It requires strategic input, data engineering, cloud management, data privacy and compliance expertise. Whether a business is setting up data governance systems from scratch or amending existing processes, there are four guiding principles that can help them wherever they are on their digital maturity journey.

  1. Align Data Governance to Business Goals

The most common pitfall for data governance projects is the failure to match the mission statement to business goals. It may sound obvious but quantifying the business value of the any information transformation required is essential to success. Too often data governance has been perceived as something carried out by the data people, far removed from the rest of the business. However, when business and IT leaders fail to align their thinking at the strategy and goal-setting stage, they risk ending up with tactics and metrics that do not reflect business goals.

Example: metrics that measure data transformation (“X data points de-duped, Y data points corrected”) rather than linking data quality or data management to customer experience.

  1. Establish a Data Governance Blueprint

Does the business have a holistic view of the information requirements by each business process and stakeholder? Amid shifting regulatory requirements and growing data volumes, it is easy for a data governance framework to get outdated. Focus on establishing clear stewardship of data, information, business and security risk. Once data requirements are understood, identify a data management roadmap to organize, maintain and sustain the underlying data so it can deliver the capabilities identified in your blueprint.

  1. Leverage Technology and Automation

Improving data integrity at speed is achievable through smart tool selection. In the burgeoning RegTech sector, for example, there are many examples of solutions that address the need to comply with regulations such as GDPR, KYC and anti-money laundering (AML); bolster security using biometrics, 2FA, blockchain and AI; and automate unnecessary, expensive manual interventions in the data management lifecycle.

  1. Embrace a Cross-functional Approach

Formalizing cross-functional teams for data governance is an important first step to strong data stewardship. Balanced decision-making, which involves weighing up (IT, security, compliance) risks against business opportunities, is the hallmark of true multidisciplinary collaboration, but it only happens if everyone’s committed to the same goals. Training and education are good for raising awareness of issues around data integrity, but don’t go far enough. The next step should be to involve teams in data accountability, transparency and ethics discussions and foster a more data-centric, collaborative culture as a result.


Compliance and Competitiveness Drive Governance

In industries such as healthcare and BFSI, strong data governance has become an imperative in order for these organizations to adapt to changing regulatory environment. Regulatory volatility has made it much harder for these organizations to improve data integrity in a timely manner across business functions. Pioneering organizations in these sectors have led the way with strategies that prioritize an active data governance approach, leveraging cross-functional decision-making and the latest automation technologies to adapt to changing environments fast.

Compliance has been a key driver for data governance until recently but now companies are beginning to understand the importance of data stewardship for value generation as much as risk mitigation. Any business that is committed to its digital transformation goals around improving data-driven decision-making  – indeed any business that has invested or is planning to invest in data and analytics – should make data governance a major focus for 2022.