The Business Case for Data Quality

In this chapter, I will focus on a key step of any data quality initiative – how to explain the need for it internally in the organization and get the support required to get started. Chapter 1 established that data quality initiatives do not always get assigned as much value as other initiatives. In this chapter, I will explore the reasons behind this in greater depth and what can be done to maximize the chances of a successful outcome.

I will consider what motivates decision-makers, how their decisions are typically made, and how to position data quality in this context. Then, I will explore the elements of a successful data quality initiative (people, processes, and technology) and how to estimate the costs. The chapter will also discuss how to create a picture of the likely benefits of your initiative in a way that convinces decision-makers to support you. It will give practical examples of successes and what contributed to these outcomes...

Activities, components, and costs

Quantitative business cases are essentially made up of two components – expected benefits and expected costs. We must now start to look at the components that will make up the costs of our data quality initiative. We are going to determine how to analyze those costs and decide what should be included or excluded.

Activities in a data quality initiative

Before it is possible to understand the components of cost for a data quality initiative, you need to understand the activities that are usually required from start to finish. This section lists the typical activities in phases.

Chapter 2 outlined the data quality improvement cycle.

In my experience, the organization iterates through this cycle continuously and each iteration requires funding. This chapter will examine a single journey through the phases and identify the expected costs and benefits associated with it.

The first iteration usually requires more significant funding...

Developing quantitative benefit estimates

As explained in Chapter 1, one of the most difficult challenges when getting a data quality initiative “off the ground” is quantifying the benefits. I have already said that it is not possible to identify a comprehensive set of benefits.

At the business case stage of an initiative, there are usually few (or no) data quality rules in place to measure a full population of data. This means the size of the problem is not known and therefore the benefits of fixing the problem are also not known.

On top of this, “fixing” the data quality issue does not in itself provide business benefits. The benefit is “one step removed” because the corrected data only provides benefit at the point that it is used in a successful business process or in a meeting where a better decision is made based on more complete reporting.

For anyone thinking that calculating the benefits of data quality improvement cannot be...

Developing qualitative benefits

Qualitative benefits are intangible and cannot be quantified with sufficient certainty to be included as quantitative benefits.

Most qualitative benefits relate to the avoidance of different types of risk. They cannot be easily quantified because they are only a probable risk rather than past events where the impact can be accurately measured. Here are some typical data quality risk examples:

• Impact on compliance risk.
• Reputational risk – including the perception of the brand and damage to your reputation in terms of customers, suppliers, and employees. Employee dissatisfaction, in particular, can lead to impacts on efficiency in general, and staff retention.
• Risk of challenges to the delivery of future projects/activities.

This final example could be quantified project by project, and where this is possible, the benefits can be included in the quantified benefits area of the business case. In general, though, the final...

Anticipating leadership challenges

The time has come to present your business case to a board – a set of senior leaders who have a limited budget and what they feel is a “never-ending” set of projects trying to take up that budget.

It is a hard position for them to be in. They have to disappoint some of the presenters and deny their requests, and they know that people have worked extremely hard in most cases to produce a strong business case.

To make quality decisions and accept the projects and initiatives that will have the greatest impact on the organization, leaders have to ask challenging questions. They have to ensure assumptions are truly valid and do not “fall apart” under the slightest exploration.

This means that any presenter must expect challenges. Our intention in this section is to prepare you as well as possible for these challenges and give you the best chance of approval. We will cover the most common challenges to a data...

Summary

Data quality business cases are a very challenging area – and as many initiatives will fail at this stage as those that will succeed.

This chapter gave a clear message: that it is critical to be well-prepared. It is typically really important to share that preparation with the members of an approval board in advance. As mentioned previously, data quality business cases often differ significantly from what people are accustomed to seeing, and at first glance, they may not seem as competitive as others.

If you can explain one-on-one (or in small groups), you often have the chance to answer challenging questions before the decision-making session and get stakeholders to give a fair hearing to your initiative.

A lot of effort will go into the process of getting the approval that you need. As soon as that approval is given, the initiative will begin. Sometimes people spend so much energy in the preparation of the business case that they are not ready to start the...