Data Security Management

Essential data governance initiatives for leaders to implement

Published on July 4, 2024

Table of Contents

Picture this scenario: A financial manager walks into a client or investor meeting to present what they think is correct data-based insights, only to discover after the meeting that the data was wrong. Not only does the manager look inept, but the customer and investor lose trust, affecting the partnership and potentially future business.

Inaccurate financial reporting can affect any business sector, and poor data management can lead to not only errors in fiscal reporting, but also inadvertently misleading investors, regulatory non-compliance, and leading to legal repercussions if not managed appropriately.

Poor data governance has led to a data crisis

Reuters defines data governance as the “meticulous management and safeguarding of a company’s data resources.” However, there is compelling evidence organisations are struggling with their data, highlighting that inadequate data governance has led to a severe data crisis. According to a Harvard Business Review (HBR) study, only one in six executives trust the data they use every day.

To avoid becoming part of this alarming statistic, it is clear leaders must prioritise data governance, but what should they prioritise and where to start?

The top 5 priorities leaders should embrace within their data governance framework

It is apparent that agility and efficiency are lacking in the conventional method of data governance. However, to meet modern digital business requirements Gartner suggests key aspects of a roadmap leaders can utilise to begin building towards unlocking valuable insights and the power of data:

  1. Establish an appropriate governance foundation defining data critical priorities and highlighting key stakeholders, data gatekeepers, establishing accountability and building trust.
  2. Design a practical and efficient governance structure with developed data quality standards signed off, supported and implemented by team leads.
  3. Align business goals with governance policies and standards.
  4. Plan continuous assessments to analyse and enhance performance.
  5. Implement processes that uphold data accuracy through a culture of continuous improvement and learning.

Once these items are added to a data governance roadmap, the next step is to act, but analysis of data must be a central aspect of integration.

Data is valuable but dangerous if not analysed

Data is a critical asset that can be valuable if managed appropriately, making data governance essential to collectively ensure that data is accurate, secure, and used ethically and effectively. In contrast, the HBR suggests that it can also become dangerous if not analysed, and to ensure that data provides insights, leaders must take the time to understand its properties.

Attempting to fix data inputs within operational and analytical storehouses without first considering where the bad data is coming from is the “equivalent of bailing a boat without knowing where the holes are” asserts Stewart Bond, research vice president, Data Intelligence and Integration Software research at International Data Corporation (IDC).

He suggests that instead uncover the source first and then work towards plugging the hole. “Dirty data is often a symptom of deeper people, process, and technology issues that impact the quality of the data, but getting to the root cause can be difficult and time-consuming and may shed light on less-than-optimal processes and practices,” he said.

Data governance support through trusted partners

In order to improve data quality and ensure organisations are indeed ready to meet the demands of modern business, leaders must ensure their data governance and policies are in order, allowing them to unlock profits and maintain a competitive edge. By utilising a stringent data governance framework and by fostering a people-centric approach in relation to quality data input and output, businesses can not only improve their data, but also align with business objectives, uphold long-term data integrity, and improve organisational efficiency. Effective data governance also underpins sustainable and scalable AI deployment implementations by providing a solid foundation of high-quality, well-managed data.

A sense of urgency has also emerged surrounding data governance, driven by data breaches and regulatory demands, leading to a shift from centralised to decentralised practices, where individual business domains oversee their own data governance independent of IT.

Supporting this shift, the next generation of data governance tools has been designed for seamless implementation and ongoing management, catering to business needs with as-a-service consumption, high integration, and open-source software architecture. As a trusted partner, Experteq can support you through your data governance transformation. Contact us today to augment your company’s digital value.

Table of Contents

Featuring
Steven Bolland
Data Transformation Lead at Experteq
Related posts

Enter your details to subscribe

Receive exclusive thought leadership, insights, the latest trends and customer spotlights from Experteq, delivered straight to your inbox.

Subscriber form
Acceptance

Please enter your details to download

Web download
Acceptance