In today’s competitive and data-driven landscape, the quality of your data can make or break your business. High-quality data is not just a technical necessity—it’s a strategic asset that drives informed decisions, operational efficiency, and long-term growth. However, many businesses struggle to gain stakeholder buy-in or data experts struggle to get the business to invest in data quality initiatives, even as the financial stakes continue to rise.
The cost of poor data quality is staggering. According to Gartner, organisations attribute an average of $15 million per year in losses to poor data quality, with nearly 60% unaware of the full extent of these costs because they simply do not measure them. Similarly, Forrester Research has revealed that low-quality data hampers productivity, forcing business leaders to spend valuable time verifying data accuracy. I’m not even surprised when I say, less than 0.5% of all data is ever analysed and used, yet even a modest 10% increase in data accessibility could generate over $65 million in additional net income for a typical Fortune 1000 company.
This blog will explore the significant risks of poor data quality—from lost revenue to damaged reputation—and outline practical strategies to demonstrate the ROI of data quality investments to stakeholders, ensuring your business remains ahead of the curve.
The Essential Role of Data Quality
Data quality is essential for any business to thrive and grow in this dynamic environment, with data growing at an exponential rate. Having high-quality data enables business leaders to make informed decisions that drive growth and improve the organisation’s bottom line. It enhances employee productivity and organisational efficiency by providing accurate, authorised, and timely data access. Organisations in highly regulated industries must maintain high-quality data to comply with regulations and standards, saving them from financial and reputation repercussions.
The benefits are extensive, so why are business leaders reluctant to invest in data quality initiatives?
Why Business Leaders Hesitate
Some business leaders may be unaware of how their data looks and may not have experienced the negative consequences of poor-quality data due to not having a strong data culture. Some may not realise how inaccurate data would impact their ability to make the right decisions while others may not be willing to invest if they do not see immediate benefits and do not have clear ROI visibility. Data quality initiatives may take time, and businesses might not be willing to wait for the benefits when quick decisions are necessary.
Securing support from key stakeholders is crucial for successful data quality initiatives. Without their backing, these initiatives may find it challenging to obtain the resources, attention, and momentum they need.
So, how can we persuade business leaders to invest in implementing Data Quality? Here are some steps that can help you to get the sponsors for your Data Quality initiative.
Essential Steps to Effectively Gaining Stakeholder Buy-In and Driving Project Success
It’s important to educate stakeholders that data quality isn’t just an issue for IT or the business – it’s everyone’s responsibility. The problem is multifaceted, involving people, processes, data, and technology.
Business leaders do understand the importance of “data quality” because they have heard about it too many times, but still, it is not prioritised. To overcome this situation, it is crucial to ask the right questions to get comprehensive answers from business and technical perspectives.
Key Questions to Ask:
- What do you need to do but don’t because you don’t trust the data?
- What could you achieve if you had the right data?
The first question aims to uncover the pain points of data consumers, such as the challenges they face with the current state of data and the reasons behind their lack of trust in the available data. It helps to pinpoint specific tasks and decisions that are being hindered.
The second question is designed to help identify areas for improvement within the organisation. For instance, consider a company that spends extra time cleansing and validating data for multiple reports. This practice slows down the reporting process, leading to delayed business decisions and missed opportunities. Consequently, this inefficiency results in increased operational costs and, in some cases, non-compliance with regulatory requirements, leading to fines and legal issues.
These questions will highlight some of the gaps that could be filled and opportunities that could be leveraged to convince the stakeholders to prioritise data quality.
To engage potential stakeholders and secure their commitment, it’s essential to present a compelling business case that highlights your project’s value.
Here are four key steps to crafting a business case that effectively persuades stakeholders to invest.
1. Building a Business Case:
To build a strong business case, the most important step is to identify the right stakeholders who will support you in creating the business case and approve the business case these could be the data consumers such as Data Analysts, Data Stewards, and C-level executives. These stakeholders will help you identify the historical issues with the data, and their day-to-day challenges with the data and give you some insight into the process of fixing the data issues.
Let’s take the example of a company that uses manual processes such as Excel for compliance reporting. However, data inconsistencies and errors often lead to non-compliance with regulatory standards, risking penalties and reputational damage. One of the business priorities of the company could be to produce accurate and up-to-date regulatory reports. So, to get the buy-in of the stakeholders it is crucial to tie the business case with a business objective and to have a defined scope.
It is important to emphasise business performance metrics and quantify the impact of poor-quality data rather than focusing only on the results of Data Quality dimensions or metrics.
2. Assessing Current Data Quality
Once you have defined the scope, the next step would be to identify the Data Assets for data profiling using a tool.
Let’s go back to the previous example where the company is struggling to generate accurate reports. One could identify the tables used to produce certain types of reports and initiate data profiling. Data profiling results can reveal issues such as tables with outdated data, duplicate values in a column instead of unique values, invalid formats, and values that do not match with the reference table such as country codes, currencies, etc.
These results will help you demonstrate the current state of data, the areas that need to be improved and how it will impact the performance of the business.
3. Communicate the Results and their Implications:
The results of the data profiling can be communicated to the key stakeholders using statistics from the DQ tool and presenting them in the form of interactive dashboards, charts, or graphs, to highlight the patterns in the data and problems found in the data.
The ability to quantify these results into penalties or fines could be one way to get the attention of business leaders, as that is their primary concern. This may also provide a clear indication of whether they are complying with specific regulations and the potential adverse consequences in the future if they fail to take action to improve the quality of their data.
4. Recommending Actions and Gathering Feedback
Finally, please share some suitable actions that can be implemented to enhance the quality of the data and show how it can contribute to achieving business objectives and the overall impact it will have on the business. It is also crucial to quantify the results that will be obtained from the investment. Additionally, it is vital to gather feedback from the stakeholders and engage with them throughout the process.
Final Thoughts
While the benefits are clear – such as improved decision-making, operational efficiency, etc – the path to achieving these outcomes often hinges on gaining the trust and support of key stakeholders. Securing stakeholder’s buy-in for data quality initiatives is not just about highlighting the benefits of clean, reliable data – it is about addressing the specific concerns and priorities of stakeholders.
The key takeaway is that investing in data quality is not just a technical necessity but a strategic move that requires clear communication, alignment with business goals, and ongoing stakeholder engagement. By proactively addressing concerns and demonstrating the tangible value of good data, you can secure the support needed to drive meaningful, data-driven transformations in your organisation.
It is important to note that it is not a one-time activity, it is an ongoing journey.
When stakeholders recognise the critical role that data quality plays in achieving business success, they are more likely to champion these initiatives, ensuring long-term impact.
At Nephos, we combine technical expertise and the strategic business value of traditional professional service providers to deliver innovative data solutions. Whether you’re looking to improve data quality or need guidance on persuading stakeholders about its time to value, our Data Quality Quick Start service ensures your data is accurate, relevant, and consistent, leading to quick wins and high data accuracy.
