Building a business case for data quality

Hi everyone and thank you for joining us today. My name is Erin Haselkorn and I manage the market research here at Experian Data Quality. Today we are going to talk about some new research we just conducted on building a business case for data quality. We have heard from many of our customers over the years that this is a challenge so we conducted a research study to look at the state of data maturity today, the current process for building a business case and some of the common challenges people face.

To do that, I am joined by my colleague Sean Coombs from our marketing team who is going to review some of the challenges and tips from our professional services team on how to overcome these obstacles.

Here is the agenda we have today. We’ll talk about the study; get into how to overcome common roadblocks and then wrap-up with a Q&A.

Before we get into the data, I want to give you a quick overview of the methodology.

This summer, we conducted a global survey of 402 management-level professionals to understand how they are developing business cases for data quality. This study looks at the current state of data quality in organizations, the tangible impacts of bad data, the challenges businesses face when quantifying the effects of bad data, and how successful organizations develop a proposal for sustainable data quality.

Respondents were chosen based on their visibility or knowledge about their organization’s customer or prospect data management practices, and if they have built or presented a business case to justify an investment in data quality or if they have evaluated the impact of bad data on their business.

Here is a breakdown of the organizations we surveyed. They came from a variety of industries including IT, telecommunication, manufacturing, retail, business services, financial services, healthcare, public sector, education, utilities, and more. Of the 402 total respondents, 25 percent represent the financial services sector.

Within these organizations, we also surveyed managers from a variety of departments, including IT, finance, general administration, operations, marketing, compliance, and several others.

Now let’s get into the study. Let’s start with a quick note on why data quality matters and what it means for businesses.

At Experian, we believe that data is at the heart of every organization -- and the quality of that data is critical to sustained business success. While most organizations indicate that data supports their business plans, on average, organizations believe a third of their data is inaccurate, which can undermine their ability to make strategic decisions.

Actions taken by employees or by customers create a wealth of information that organizations can collect. Savvy organizations are the ones that are able to turn these insights into action through initiatives like business intelligence, workforce optimization, predictive analytics, or targeted marketing. Data affects a lot of different areas of the business.

In fact, 79 percent of organizations we surveyed say data clearly ties into their business objectives. Over the years that I have been doing these types of studies, that has certainly increased. Early on, it was about cost savings and operational efficiency. While some of that still hold true, the level of work being done around business intelligence is incredible.

This next chart shows what initiatives businesses are tying data to. Master data management and regulation top the chart. But, following closely behind are things like improved decision-making, data security, customer experience, and customer loyalty.

These really divide into two categories: those that deal with internal processes and then those that deal with the customer. We did not ask companies to only pick one, which is why you can see the percentages as being so high. However, you can see a lot of people are using data to improve their own operations, which should also lead to an improved customer experience.

But as we talked about before, there is a high level of inaccurate information that is hurting efforts

Our survey found that 83% of organizations say that poor data quality hurts their business initiatives – a scenario that only undermines the confidence in the organization’s data.

And there is a big lack of trust in information. Our study showed that only 2% of businesses have complete trust in their data.

So why is there a lack of trust in information? The chart I just put up shows the biggest challenges to achieving trust in data.

41 percent indicate that the growing volume of information is the biggest challenge to achieving trust in their data. You can also see data standardization, the variety of data, human error, and disparate data sources are also identified as key challenges to establishing trust in data.

What does this tell us?

Organizations have a lot of data, but the sheer volume and breadth of information they collect renders it untrustworthy.

In addition, data standardization, the variety of data (which dovetails with the standardization issue), human error, and disparate data sources are also identified as key challenges to establishing trust in data.

So to build trust in your data, you’ll to improve the quality and regulation around data. For a lot of businesses, that involves putting data quality processes into place. The accuracy of data is really the foundation to being able to leverage it.

Business look to data quality initiatives for many reasons, and these are the main drivers at the organizations we surveyed. As you can see, regulatory compliance, business intelligence, adding value to business initiatives, and marketing efficiency are all driving forces for data quality program.

But where are people today? How sophisticated are their data quality strategies?

Well to be honest, a lot of businesses fall short. We at Experian look at the current approaches to data quality in terms of people, processes and tools. You can see those three areas and the maturity level represented.

From the data, we see that we see that only around 1 in 5 companies is operating at the most sophisticated level in any of these pillars. That is fairly low. Businesses are most mature around their processes, around the ownership of data and how it is monitored. The tools falls a little bit, but there is still a lot of good technology being used and most businesses are not relying on manual processes. But when we start to look at people, we see a big gap. Most companies do not have the data management staff required to monitor data, regulate it and make sure that it is well defined across the business. That is the biggest gap that people need to make-up today.

But hiring those people or getting those resources to ensure quality data isn’t always easy. While most businesses have an understanding of data and know it needs to be accurate, making data quality a permanent program isn’t always easy. In order to make data quality a part of your organization, it’s likely that you’ll need to build a business case for it.

According to our study, this is where a lot of businesses see trouble. While there is plenty of word-of-mouth evidence of data quality issues, hardly any of it is tied back to a financial impact. Without a cost to associate with data quality (or a lack thereof), getting approval for an investment in data quality can be challenging.

In fact, 54% of those we surveyed say that building a business case for data quality is actually quite difficult. The main reasons for this are pretty straight forward.

1. They lack the hard evidence or data to show that there is a real problem with the data. And I am not talking about completeness of fields. I mean showing that poor data is actually hurting a business initiative.
2. There are too many people involved. 80% of respondents say there are too many stakeholders involved. That is dragging out approval times and making it difficult to come to a consensus.
3. There is also a lack of skills. There may be a lot of people involved, but as we saw before, there is a lack of data professionals in many businesses. They need the skills to do solid analysis and present data in the right way.

Now I am going to turn things over to Sean to talk about some of the common challenges we saw from the data and some advice on how to avoid those.

Thanks, Erin. In the next section, we will discuss some of the common challenges we’re seeing, as well as provide our advice for overcoming these challenges.

To help you develop a successful proposal for data quality, we sifted through the results of our survey to understand what works and what doesn’t to provide you with proven strategies that will help expedite the process of having your business case approved and implemented.

One of the challenges we see among those developing a business case for data quality is that it’s hard to separate “facts” from “emotions”, which can make it very difficult to articulate value back to business leadership.

For example, managers hear about certain issues from their teams, but they don’t necessarily have hard evidence of the impact these issues are having on the business. What they need to do is to think more broadly about the effect on the business and its resources. One way, is to document not only the time that is wasted, but also how your staff could invest that time into something more useful for the business. This way you take that anecdotal claim related to poor data quality, and turn it into a monetized business impact.

According to our study, almost half of organizations struggle to actually put a cost on their bad data. And this is a problem. Without a clear cost associated with your bad data, securing funding for a program to fix it will be much more challenging.

Sometimes quantifying the intangible requires a little creative thinking. We found that businesses are able to quantify the effects of bad data in terms of lost sales opportunity, wasted time, diminished customer relationships, and a negative cultural impact on employees.

How did they measure this?

By and large, they used technology tools that quantify the cost, they add up the sum of compliance penalties, or they determine the cost of lost opportunities to the business. We also found that almost 1 in 5 organizations rely on rough estimates to determine the impact of their bad data.

A successful business case for data quality should contain quantifiable evidence. If you’re stuck, ask yourself the following questions:

1. Can data quality issues be linked to wasted time? If so, measure the time taken by inefficient data processes. Can this be reduced by implementing change?
2. Do data quality issues directly impact the resources who work with data? If so, map the resources that are either required to manage data quality or are dependent on good quality data to correct the problems.
3. Do data quality issues cost the business today, or do they have the potential to do so down the line? If your answer is ‘yes’, try to understand the negative impact that current data quality processes (or the lack of) can have on your financial bottom-line.
4. Do data quality issues make your business processes inefficient or unachievable? If so, then you’ll want to try mapping data quality issues to your business processes, identifying specific workarounds that are costing the business.
5. Can data quality issues prevent the start of or completion of strategic projects the business has planned or already begun? If this is a problem for you, you’ll want to map data quality issues that may directly impact the success criteria of any strategic initiative, either directly or indirectly related to data assets held by the business.

Another challenge that we have identified is that the stakeholders involved in developing a proposal for data quality tend to be in IT-related roles, and therefore, they don’t necessarily represent the interests -- or needs -- of business users.

Our research shows that 87% of respondents indicate that data analysts are involved in the process and another 69% indicate that IT staff members are involved. While data analysts potentially sit across areas of the business, only around 30 percent of respondents indicate involvement of specific departments like marketing or finance.  Furthermore, when we look at departmental responsibility for data quality, the heavy role of IT and operations – and the smaller involvement of departments like finance and marketing -- becomes even more apparent. It’s also interesting to note that c-level executives are involved in the creation of a business case for only 10% of businesses.

While it’s not surprising to see data- and IT-related roles assisting in building a business case, the much smaller involvement of departments such as finance (at 33 percent) and marketing (at 30 percent) may help to explain why developing a business case is often so challenging. The business users who are most likely impacted by bad data quality, and who could offer tangible and quantifiable impacts, tend to be left out of the conversation.

What should you do to ensure data quality is not an IT-only program?

Leverage individual stakeholders from other business areas to help secure funding and to provide the required subject matter expertise for the proposal. Our study revealed that ‘a lack of budget’ and ‘a lack of knowledge’ are cited as the top two challenges to implementing a data quality initiative. So by involving stakeholders from the business, you can possibly secure additional funding from their departments, as well as leverage the first-hand experience of business users to identify measurable impacts. After all, who better to identify the impact of poor data on a business initiative than the business user?

Another challenge we see is that those who are putting together a business case for data quality find it hard to convince their leadership teams that funding a data quality program will produce a return on investment. Calculating an ROI is about performing a cost-benefit analysis. Do the benefits to the business outweigh the costs associated with the investment in data quality?

Costs are relatively simple to determine. They’re definite, objective, hard numbers that can be calculated out based on set criteria. But when you’re talking about ‘benefit to the business,’ it’s critical to keep in mind who your audience is -- and who the ultimate decision-maker is. Think about what that benefit looks like to them. And, specifically, what kind of benefits will make them excited for your data quality program?

Through our study, we identified that a majority of influencers sit at the C-suite level, and 22% are specifically Chief Data Officers. So when you go to develop your proposal, it’s important to make it relatable to them. For c-level executives, this means embedding the details of your business case within strategic business initiatives. The most successful business cases will show that the two are so inextricably linked that the business cannot meet its objectives without addressing the quality of its data.

The other 53% of decision-makers sit at the department level. In this case, presenting the benefits of your business case in relation to the objectives of your department is essential. If you’re in customer service this could be achieving higher customer satisfaction numbers, in marketing it could be increasing market penetration, or in finance it could be reducing time spent on collections. Find out what’s important to your department’s decision-maker, and highlight it in your ROI analysis.

So, what should you do to make your ROI relevant to your leadership?

When framing the tangible impacts of your data quality business case, consider your audience and tailor your story to meet their interests. Identify who the influencers and decision-makers are, what departments they’re from, and what their goals are. Then, rather than going on about metrics showing the uniqueness and completeness of your database, show why that impacts the Finance department's ability to collect on invoices or your marketing team’s ability to reach their most valuable customers. If your decision-makers sit at the C-level, remember that they’ll want to see clear metrics relating your data quality program to broader objectives for the business, such as operational performance, financial performance, customer experience, and regulatory compliance.

Another challenge we identified through our research is that the timeline for having a proposal for data quality approved and implemented can take a fairly long time -- upward of 18 months in some case.

This chart shows the total time for getting a data quality initiative approved. While a majority of organizations say it took them up to 1 year to get approval, nearly half of those surveyed indicate that it took them over a year – and even over a year and half in 19% of the cases.

Likewise, implementing a data quality program can take a similarly long time. Just over half of respondents indicate that they were able to implement a program in under a year, but a large percentage of them indicate that it can take upwards of 18 months.

We believe that the number of stakeholders involved, and the time it takes to collect and quantify evidence, is what slows down most organizations. What’s important to remember is that the longer the process takes, the more people will lose interesting in what you’re doing. So expediting the process should increase your chances of implementing a program for data quality successfully.

How do you speed up the time it takes to secure approval and implement a data quality program?

Our hope is that you can utilize the strategies we have provided so far to expedite the process of building a business case. Begin by leveraging key stakeholders from areas of the business that have experienced data quality issues, and who would receive great benefit from improved data quality. Including the right people will ensure you are focusing on the right issues. Next, quantify the benefits in a measurable way. This can be done by measuring the impacts of poor data on the business today, and how the program will increase revenue or lower compliance penalties in the future. Next, link your business case to strategic business objectives and demonstrate that the two are mutually dependent. This will help you secure higher-level buy-in. Finally, maintain a constant line of communication with stakeholders to hold their interest throughout the process. This can be achieved through bi-weekly or monthly newsletters, webinar-style updates, or even by email. The point is to share progress updates on the program and to solicit feedback from stakeholders, keeping them actively involved with your program.

It’s our goal here at Experian to help you unlock the power of your information through good and reliable data quality. We hope that you are able to leverage our insights to build a business case of your own.

For further insights on developing a proposal for data quality, check the full report on our website. You’ll also be receiving a link to the report after this webinar ends. For more resources on data quality and data management, visit us online at: edq.com

Questions

This concludes our webinar. On behalf of the team here at Experian Data Quality, Erin and I want to thank you for joining us today.