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A proposal for data quality: 5 key takeaways

Businesses today rely on tremendous amounts of data to do everything from improving their customers’ experience, to streamlining operations, to reducing risk to the organization. Data is everywhere, and countless individuals interact with it throughout its life cycle. While this provides an incredible opportunity, organizations need to ensure that the quality of that data is upheld to ensure that it is fit for purpose.

Why is this important?

Data is frequently created by business users within specific departments. For instance, customer contact information is often created by a customer service representative in a call center. That information is then leveraged by sales, marketing, and finance teams to perform additional functions, such as upselling, targeted promotions, and collections. In addition, this data is usually stored by the IT department in servers or up in the cloud.

Although many users access the data for different needs, it’s up to the customer service department to determine whether or not the data is still accurate. With too many cooks in the kitchen, it’s no wonder why business users can lose sight of the data they’ve created. Due to vaguely defined data governance roles and a nebulous understanding of responsibilities for data quality, the business’s valuable customer data can quickly turn bad.

Luckily, senior leaders at organizations understand the importance of data quality.

Not so luckily, the process of putting together a proposal to make it a permanent part of the company’s data management program is often quite difficult and lengthy. That’s why we conducted a global study to understand how organizations have gone about building a business case for data quality. We looked at the challenges they faced, what stakeholders were involved, who the decision-makers were, how long it took them to implement a program, and how they monitored success. Here are five key takeaways from our report:

  1. 80 percent say the process of building a business case for data quality tends to involve too many stakeholders and takes far longer than it should. Often, the process of building a business case for data quality tends to become over-engineered, so keep it simple. Build your business case by bringing together select IT stakeholders and business SMEs who can provide quantifiable impacts of data quality (or lack thereof) on business objectives. According to our study, getting a business case approved and implemented can take upwards of 18 months, so keeping it simple is a sure way to speed up that timeline.
  2. 20 percent of respondents indicate that the responsibility for data quality is held at the department level. Further, a majority of the responsibility for data quality lies with IT, which helps to explain why building a case for data quality is often such a challenge. As the IT department isn’t necessarily using the data, they are not in a position to explain the business impacts of data quality (without consulting with business users first). In an ideal world, the departmental responsibility for data quality should be much, much higher.
  3. 66 percent say that bad data quality has negatively impacted their organization in the last twelve months. Bad data can take a toll on your business, resulting in lost sales opportunities, wasted time from inefficient processes, diminished relationships with customers and prospects, and a negative cultural impact on employees. Even worse, a lot of the negative consequences of bad data tend to be anecdotal, making it hard to present a proposal to solve for them.
  4. 43 percent of organizations say that they struggle to quantify the cost of bad data in their organization. They just can’t put their finger on it. However, our study shows that success can be achieved by tracking compliance penalties tied directly to bad data, analyzing the cost of lost business opportunities, and using technology tools to quantify the cost.
  5. 47 percent of the most influential decision-makers are at the C-level. What does this mean? In order to win their support, you’ll need to make data quality interesting to them. When developing your proposal, it’s even more essential to tie the impact of data quality back to business objectives, such as operational performance, financial performance, customer experience, and regulatory compliance. By speaking the same language as the C-level decision-makers, you can position your data quality proposal as a business imperative.

In our report, you can learn more about the challenges that organizations face when building a business case for data quality and tips for achieving success. We’ve also included a useful worksheet to help you begin to quantify some of the intangible consequences of bad data, and the positive business impacts of remedying it, in order to help you build a strong case. We hope that it helps you to think in a way that articulates the tangible benefits of a data quality program to your business’s leadership.

Read the report