Building A Business Case For Data Quality

White Paper

While organisations see the value in data quality, the process of making it a permanent program can prove more challenging than it first appears. To help you expedite the process and avoid common roadblocks, read the results of this survey of more than 400 professionals around the globe to find out how they built a business case for data quality, what challenges they faced, and where they saw success.

In this report, we discuss our findings from this unique study, including:

  • The current state of data quality maturity
  • The impact of bad data on the business
  • Common practices when building a business case for data quality
  • Measuring outcomes and success of the business case

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1. Introduction

The concept of writing a business case sounds simple – put forward a recommendation of a change you want to make, identify the investment required and the resulting benefits for your business and share this with senior management.

The reality, however, appears to be a sizable challenge for many businesses when the business case is about data quality. Over half of respondents (54%) said they have problems when building a case, mainly because of two key factors; their ability to quantify the impact of poor quality data and the clarity of strategic ownership for data quality programmes.

14% of respondents stated they could get a business case approved and implemented in less than a year, but this only equates to one in seven organisations. Furthermore, one in ten approved business cases are still not implemented two years later. These results are disheartening, particularly in a world where technology, data and consumer attitudes are rapidly changing. In two years, the rapid advancements in technology alone could mean that an investment request is no longer viable as proposed solutions are out of date and ever increasing consumer expectations have shifted again. Therefore, even those who are successfully having business cases approved (10%) could still be falling short of their original objectives.

This discussion paper explores the challenges respondents raised and identifies key aspects organisations should consider when writing a business case for data quality investment. Derek Munro, Head of Product Strategy at Experian Data Quality offers his insight based on 30 years’ experience working in the data industry.

2. How does your organisation perceive data quality?

The research asked respondents what they perceived their biggest challenges were when building a business case, see figure 2. The top challenge identified by respondents was a “lack of budget and funding.” However, a lack of funding is an effect, not a cause. To understand why approvals for investment don’t succeed, we need to consider how data quality is perceived across most organisations. The research uncovered two common trends.

1. Quantifying the impact of data quality

A key finding from this year’s research cited that only 10% of companies believe bad data has impacted their business to a great extent, suggesting that business don’t see or feel the impact of bad data. This correlates with the majority of respondents (78%) citing that they believe their data is trustworthy. Interestingly, 40% of respondents hadn’t even been able to quantify how much bad data is costing them. From my experience I would assume that if they haven’t quantified bad data it can’t be a key business concern, and therefore data quality is only perceived as a small issue.

Despite this, 70% of businesses claim to have processes in place to fix data quality bad data impacts them to a great extent.

Against this background, perhaps it’s not surprising that the majority of businesses don’t see the value of investing in data quality. They don’t see the major impact of poor data quality and believe they have processes in place to fix any issues. However, if we continue to dig deeper and explore the processes businesses have in place to fix data quality, nearly half (49%) cited they rely on desktop tools such as Microsoft Excel, to find and correct data quality issues.

In my opinion, manual processes like these are not effective, and commonly businesses struggle with human errors, duplication of effort, and managing large volumes of data all of which waste valuable time and money. When asked to identify the biggest challenges in achieving trust in their data the research identified volume of data, standardisation and human error as the major contributors.

2. Understanding who is responsible for data quality

Only 17% of organisations have a central data role, yet the vast majority of respondents (77%, rising to 97% in Financial Services) believe that the “Data Management” department is responsible for data quality. Related to this is the challenge that 55% of businesses are trying to build enterprise-wide business cases, which is a very challenging thing to do. Therefore, is it a coincidence that 54% have problems building a business case if so many are stretching enterprise-wide and there is no one owning data management?

I believe that the majority of businesses are trying to be too ambitious when setting out to create an enterprisewide business case and, due to the complexity and lack of ownership, they miss the point of relating to business objectives and focussing on specific impacts to the business.

Interestingly, 60% of businesses believe that data quality needs are the same for every department. I would disagree. Whilst I believe there are similarities between departments, their unique needs are going to vary dramatically. For example, a marketing department might say that the accuracy of an email address is essential. However, for a product delivery department, an email is not critical, but an accurate postal address is. Therefore, in my opinion, it would be much more effective to start small, add value and then showcase this back to business to drive further investment for affected areas.

3. What’s wrong with your business case?

Getting the right stakeholders involved

According to the research, 80% of respondents say there are too many stakeholders involved in building a business case. This in itself could be a problem, as multiple stakeholders bring a variety of different and possibly conflicting requirements to the table. To reduce this complexity, who should be involved in the creation of a business case?

Figure 3 demonstrates who commonly assists in building a business case. The role involved most often is a Data analyst. This is a good start because in general this role, along with Data stewards work closely with the business and data.

IT staff are the second most likely group of people to be involved in building a business case. It’s surprising that they are ranked so highly as it’s unlikely that IT are involved in the day to day use of data in the same way that a business user might be. They will be responsible for the architecture, but it’s unlikely they would understand the full extent of how data is used by the business or the typical pain points it causes. In order to get this vital information, business users need to be more prominent in the process. These are the people who use data every day and find it critical to carry out their job. Therefore, whilst IT should definitely be involved and will no doubt add value, they are not best placed to lead this.

According to the research, the first tangible involvement from business users is the Marketing team, ranking fifth place. These are the right people, but with only 30% of business case having this representation, it isn’t enough. So why is it that IT are commonly involved over business users?

The research highlights that the most popular way to quantify the cost of bad data is to ask IT (56%). It’s likely that business users rely on IT to quantify the cost of data because either the tools required are too technical or they don’t have access to the relevant tools to cope with the volumes and complexity of data. In my opinion, the reason we see IT so heavily involved is because technology is a perceived barrier. In order to improve results, IT need to work with the business to put appropriate technology in the hands of the users. With nearly half of businesses (49%) using manual processes such as Microsoft Excel to fix data quality issues, business users are eager to act. However, these users need to be empowered with easy to use technology to support them.

Who is most influential to sign off a business case?

When exploring the challenges businesses face getting approval for investment, it’s important to understand who the decision makers are in this process. Research shows that the most influential people in signing off a business case are in fact business users themselves, see figure 4. Business users are best placed to communicate the value of quality data and make this relatable to other business users involved in signing off the investment.

Relating a business case to company objectives

Business objectives are a set of key business priorities. A business case that helps achieve these is likely to succeed over one that doesn’t. When asked what organisational objectives their data quality initiatives apply to, the results varied. The majority weren’t business objectives at all, see figure 5. Commonly cited were tactics such as Master Data Management and improved decision-making, both of which aren’t business objectives. They might be a strategic initiative that could support the business in achieving an objective, but they are still one step removed. Associating investment in data quality with non-business objectives will undoubtedly create a weaker business case.

Business cases that don’t relate to business objectives fail to connect their audience. We know the majority of those most influential in gaining approval are business users themselves, and nearly half of these are C-level representatives. Their success is directly judged on their ability to meet strategic objectives, so relevance is essential.

Positively, regulatory compliance and data security rank second and fourth. These are tangible business objectives about avoiding risks, potential fines or reputational damage. As such, they make a good motivation for a business case. However, these types of objectives come with their own challenges. Often the monetary amounts associated with fines or the perceived impact of reputational damage are subjective. The majority of businesses never think it’s actually going to happen to them. So whilst presenting a business case related to this is useful, this shouldn’t be the only driver.

The approach to quantify the cost of bad data

Figure 6 shows how businesses commonly quantify the cost of bad data, but I believe three of the four approaches aren’t reliable.

Compliance penalties do feature as a method, but as we saw earlier, they come with drawbacks as a justification method on its own. If you pair this with the 17% who openly recognise that they are quantifying the cost of bad data as a ‘rough estimate’, more than 50% are essentially guessing. It’s positive to see that 55% of businesses are using technology to quantify the cost however, whilst the tools remain in the hands of IT this is an unreliable tactic as it’s disconnected from business challenges.

In the next section, I’ll provide some suggestions to propel your case using the full mix of people, process and technology. As we concluded previously, technology in the hands of IT is not best placed to quantify the business cost, and opportunity cost is usually an unreliable rough estimate anyway.

4. Advice on building the business case

First and foremost, when building a business case for data quality, it is important to remember that businesses shouldn’t try to justify data quality in itself. In its simplest form, building a business case for data quality must help to achieve one or a few key business objectives. Our research has helped us understand the common challenges and perceptions of data quality, as well as the key points to demonstrate the need for action.

What is apparent from the research is that the struggles to get a business case approved are not necessarily those directly observed by our respondents, such as budget.

So what can make a positive difference and create a convincing case? Outlined below are a number of steps you should take to ensure that your business case is successful.

Involve the right people

From the very beginning it is vitally important to have the right people involved. Make sure you have business stakeholders who understand the extent to which data quality issues can affect the business. This group will also be influential in determining what to priotise to assess the full value, and further communicate it. It is also useful to identify a champion who has previously been affected by data quality issues. Ideally, this person sits within the business and has had a business case approved in the past. Utilise this knowledge to improve and communicate your own business case.

Highlight the impact of poor data quality over time

A powerful technique to support your business case is to show a data quality trend over time and link it to a business challenge. 63% of organisations claim that they document the impact of data quality errors, so analyse these issues and link to where common problems have arisen. Understand how much time business users are spending working around these issues, particularly the time spent using manual tools. With the help of a business stakeholder, establish what the knock-on impact is, all the way up to the strategic business objectives.

Understand competitor shortfalls and showcase relevant industry examples. If you can demonstrate industry-wide problems and not just random events, approvers cannot so readily dismiss your case. For example, you can mention sanctions imposed on organisations in the same industry or cite an occasion a competitor was fined. Use this as an advantage to show how you can generate positive attention and succeed where competitors have failed.

Relate to business objectives

In order to make your case compelling think about how you can articulate the business value. Avoid “onestep-removed” justifications and remember that having good quality data is not in itself a business objective. Relate it to corporate objectives. Be realistic. Data issues are often found to be annoying but not actually a hindrance to doing business. Do not make broad claims of benefit which you cannot support with evidence

89% say the “customer experience” is a critical driver for data quality. This provides a very solid foundation for your business case and it will undoubtedly form part of a strategic objective. Speak with those directly responsible for the customer experience and gain an understanding of their issues and challenges. Use this to your advantage. You will be more likely to achieve a good response from those reviewing your business case.

Utilise compliance as a driver

Through the research we found regulation to be the number one business driver for producing a business case (48%). The ability to quantify potential fines will certainly help your business case. Speak to your Compliance Officer, find out what is on the horizon, and look at data that is relevant to specific regulations to support your business case. With changes in data regulation coming in to force, the stakes—which have always been high—are only increasing. It goes without saying that all organisations need to take great care when handling personal data and by factoring in compliance this will strengthen your business case.

Use consistent business language

Always keep your business case as simple as possible and use business language, not complex data terms. Your key approvers are business users. They may not understand data quality lingo so avoid your case being lost in translation. Make sure your language is consistent, unambiguous and meaningful to the people approving it.

“This might seem obvious, but when writing the business case talk about business issues and business impact; explain what it means to the business if data is bad. Don’t talk about data, data quality or anything technical or readers will switch off. If you make it too technical it will get pushed over to IT again and become irrelevant.”

Empower business users and profile your data

Businesses struggle to find and quantify data quality issues because they don’t have the right tools. Technology doesn’t need to be a barrier. It’s important to highlight to business users that desktop tools are an inadequate fix. However, you need to provide them with tools that quickly and easily allow users to investigate and assess their own data themselves, across multiple systems, regardless of the data volumes. Look for easy to use, business friendly software to ensure users don’t revert back to manual processes.

To help gather evidence for your business case focus on the business scope, quantity of occurrences and the impact poor data quality has. Experian Data Quality offers a free data profiling product that will help you provide a value. You’ll be able to download, install and request a license key within minutes. To find out more, see page 12.

Start small and publicise successes

Many make the mistake of trying to implement an enterprise-wide solution. This is extremely difficult to do and can create a fragmented end goal. If you are able to show that adding value in one area, although small, can have a positive impact on the business as a whole, you are more likely to have your business case accepted. Shout about early success and initiatives which have had immediate impact on the business.

Never name and shame

When writing your case, never name and shame or place heavy blame on a particular department. This can only lead to conflict and a negative culture long term. We recommend educating people as to why they have to take care of data quality by explaining the impact it has on them. For example, you can mention the adverse effect inaccurate data may have on customer experience and the knock-on impact this could have to bonuses or share price. If your colleagues can understand this impact they are more likely to recognise issues earlier and want to fix them.

“Do not try and attribute blame for issues and don’t make out that people ‘don’t care’ or ‘didn’t do what they should’. People don’t deliberately create problems. Focus on the improvements that can be made.”

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