5 minute read 9 May 2023
working in data center

If data's the new gold, how do you use it to create value?

Esther van Laarhoven-Smits
By Esther van Laarhoven-Smits

Partner, Data & Analytics Lead | EY Switzerland

Experienced Data & Analytics professional who gets excited by supporting companies to improve decision making resulting in e.g. better performance, more satisfied customers and employees.

5 minute read 9 May 2023
Related topics Data and decision intelligence
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Investing in data governance is key to safeguarding reputation, avoiding costly mistakes – and securing a competitive edge.

In brief
  • Legacy systems and inconsistent practices across business units often result in a lack of transparency on data within an organization
  • Business leaders should establish good data governance to extract the value from data – and protect the organization from data risks
  • We propose five phases on the transformation journey toward being a data-driven organization

In our tech-driven world, data has become the most important asset for companies. Managed well, data oils the business engine, facilitating the improvement of processes, providing a sound basis for reporting and insights, as well as enabling complex automation.

Without an accurate, up-to-date data foundation, key performance indicators are unreliable and the cascade of potentially negative effects can be hard to predict. Despite its important role, data governance is all too often neglected. Business leaders can struggle to ensure that data is standard and consistent across business units, especially if they have inherited a patchwork of legacy systems as a result of e.g., mergers and acquisitions, siloed operations. A lack of integration across platforms and applications is a major source of resource inefficiency – and potential error – as time and effort is spent manually transferring and reconciling data sets.

Against this background, organizations may have a limited understanding of where their data comes from and how they can efficiently protect and use it. The situation is compounded by the complex data protection landscape, which continues to grow as new regulations enter into force. Poorly managed data can make it difficult to track personally identifiable information, potentially leading to gaps in – or outright breaches of – data privacy. Some legislation, like the EU General Data Protection Regulation (GDPR), is associated with hefty financial implications in the event of non-compliance, which can lead to fines of around EUR 20m or 4% of a company’s annual turnover.

  • Case study: The damage of duplicate data

    Recently, a large supplier of building materials delivered an order worth CHF 600k to the wrong client. The roots of this costly error were traced back to an error in the master data, which contained duplicate customer entries. This short but high-impact example illustrates the need for good quality data – and one of the consequences of data mismanagement.

  • Case study: Bad data management, bad for business

    A large global organization was witnessing undesirable effects like slower speed to market, manual workarounds, product scrapping and returns as well as delayed payments due to bad data management. Financially this translated into a business impact of CHF 80m+ due to incorrect pricing and CHF 35m+ lost sales due to product delisting.

The case studies above show what can happen when data management and governance is ignored. But there are other good reasons why data governance belongs on the board agenda. Data quality issues and misalignment can create rework, cause miscommunication and impact decision making. People are sometimes surprised to learn that even large, publicly listed companies have these issues with their data, despite elaborate IT setups. The problem is often a lack of transparency, which contributes to silo thinking as data tells a different story in each department or unit of an organization. Without a common understanding of data and shared terminology, you inevitably end up getting wires crossed somewhere along the way.

Without a solid data foundation, organizations risk falling even further behind their data-savvy peers.

Failure to practice good data hygiene is emerging as a competitive disadvantage, while good data governance gives organizations an edge. In the absence of data governance, companies miss out on the benefits of consistent, clean data across the organization. Data quality issues impact business leaders’ ability to make decisions and leave them vulnerable to financial and reputational penalties resulting from data gaps. What’s more, leading organizations are already embracing data analytics and artificial intelligence to drive business growth and evolve the business model. Without a solid data foundation, organizations are badly placed to do this and risk falling even further behind their data-savvy peers.

Understandably, companies are keen to embrace better data governance. This desire to change is a good start, but it’s important to acknowledge that there is no quick fix to issues that have been accumulating in the infrastructure for decades. Data governance is not about finding “the” technical solution, but about embarking upon a transformation project that spans organizational, process and technology changes. Given the scale and reach of a company-wide data governance policy, it’s important to have strong executive sponsorship. Decision-makers often seek hard facts about the expected return on investment (RoI), especially when the spend is significant. This can be assured by connecting the objectives of the data governance program to the organization’s strategic goals, e.g., customer satisfaction, market agility, competitive positioning and increased revenue.

A broader view also reveals the potential gains of good data governance. Reliable data and powerful AI “on top” can support a company by providing the basis for strategic, evidence-based decisions – but only if you have large volumes of accurate, trustworthy data. It’s this combination that makes data valuable.

With executive buy-in established, it’s time to start your transformation journey covering organizational, process and technological changes. Although individual work streams will vary depending on the nature and needs of the organization, there are five key phases to consider:

  • Current state analysis

    To reach your objectives, you first need to understand where you stand and where you are going. A current state analysis, assesses your levels of capability and maturity within the organization. Looking at areas like data management strategy, governance, quality, operations, platforms and architecture and supporting processes, you can identify current strengths and weaknesses. An external expert can help by asking the right questions to establish the maturity of each category and highlight limiting factors which are holding you back from being a data-driven organization. EY, for example, has been a significant contributor in the design of industry-standard frameworks for data governance maturity evaluation. We also apply our experience and expertise to design specific, customized data governance frameworks for more bespoke cases.

  • To-be definition

    Once your current state view has been created, you can move on to your desired state: the to-be. In this phase, you should define the target state for the same areas as assessed in the current state analysis and define key areas for improvement within your organization.  Industry benchmarking from a trusted third party can help you identify your targets and priorities.

  • Roadmaps

    Having defined your as-is and to-be states, you can start creating end-to-end roadmaps for your data management and governance program, with prioritized work packages based on your specific needs and objectives. At this stage, consider effort per work package, who will be responsible for  the different work streams and allocate resources appropriately for the short-term initiatives.

  • Implementation

    The implementation phase should focus on the three core aspects: people, processes and technology. This stage is about putting into action what you have defined so far. As each organization is different, this will depend on your individual roadmaps. The more common work packages include organization definition and operating model creation, definition of roles and stakeholders who are responsible, accountable, consulted and informed, process definition and technology solution implementation. Here, again, an external provider can offer valuable support. At EY, we offer a broad array of technology solutions, assets and accelerators.

  • Embedding

    Data governance is not a task, it’s a transformation that happens by embedding the program in your operations. It means change management will play an important role in its success following rollout at the company. The embedding stage is also when you start to scale up solutions and establish your dashboards and reporting setup.

For companies still wondering whether their data governance needs polishing, a couple of questions can help clarify the need for action:

  • Do you ever have inconsistent data and reporting across systems and geographies?
  • Do you make use of data when making strategic business decisions, i.e., are you a data-driven organization?
  • Is data owned and accessible by the right roles within the organizations, and protected elsewhere?
  • Are the financial figures you report on completely relevant, useful and accurate?

We believe that data governance is the prerequisite for becoming a truly data-driven organization. EY can help you start your journey today.

Summary

In today’s tech-driven world, data is the new gold. Strong data governance is essential if organizations are to extract the value from this precious asset – and prevent it from becoming a risk. Establishing good governance is not a task but a transformation that requires executive sponsorship at each stage of the journey.

Acknowledgment

We thank Alexandru Bortnic and Oliver Mohajeri for their valuable contributions to this article.

About this article

Esther van Laarhoven-Smits
By Esther van Laarhoven-Smits

Partner, Data & Analytics Lead | EY Switzerland

Experienced Data & Analytics professional who gets excited by supporting companies to improve decision making resulting in e.g. better performance, more satisfied customers and employees.

Related topics Data and decision intelligence
Upvote