8 Steps to Build a Business Case
for Data Analytics Quality

1. Assess the Current State of your Data Analytics

Data Analytics can provide golden opportunities for organizations because it gives insights and ensures the best decision-making. However, the “shiny” dashboards and visualizations will only get you so far. The Data Analytics presented may look good but it all comes down to one question: can the data that I see through reports, dashboards, or KPIs be trusted? Given the current adoption rate is 25% for BI and Analytics tools, clearly, there’s a problem, and even more so when you realize that data trust is the number 2 barrier for user adoption. Therefore, the first thing you need to do when building the business case is to analyze the current state of your Analytics and answer these questions:

• How is data analytics consumed today across your organization and by whom?

Identify the use cases where data is business-critical. This will help you determine the business leaders that will be most impacted by this investment and that you will need to convince.

• Are your business users satisfied with the BI and Analytics solutions deployed in the organization? Are they complaining about the accuracy, availability, or performance of the system?

• What is currently in place to ensure Data Analytics Quality? Who is in charge of this? How is this documented and monitored? 

This one is key because our customers often say that nobody is in charge of this even though everyone would say that the quality of the last mile of the data journey is critical.

• What level of trust is granted by your users when consuming Analytics? 

If you have a trust score, then you can easily use it to justify the investment and to prove the progress over time. Be careful with this indicator though, as a good score doesn’t mean there aren’t any problems. Data consumers usually never realize there is a problem with quality, only when it’s too late; which brings us to the next question;

• What historical mistakes or failures have occurred due to bad Data Analytics? 

This one is complicated as nobody wants to make these failures heard. But, errors in data happen every day and can have huge consequences. Investigate your company’s history and use them as reference case studies.

• Is your data subject to industry regulations? Can you ensure end-to-end data quality?

Lots of organizations are compelled to provide reports to external rulers and auditors. This business case is critical as you need to ensure the data is always accurate or you’re running the risk of getting fined.

2. Assess The Current Risk To The Organization

You then need to explain the implications of continuing like this so business leaders understand what problems of poor Data Analytics Quality could cause further down the line. Data Analytics is used to make decisions but if the Analytics can’t be trusted, how can users be sure the decisions made are the right ones? Equally, how can decision-makers reput their trust in Analytics when they’ve already been presented with a dashboard that is wrong? 

One risk of poor Analytics governance is the increase of silos between departments. Analytics governance requires effort from the whole organization in order to drive value to the business and silos will only decrease that. Without a unified approach, organizations will fail to capitalize on business opportunities and will not see an increase in user adoption. You need to trace the information on who and what is impacted by poor data quality across your business. Combining a detailed data assessment (e.g. lead times, costs) along with the human impact (e.g. user satisfaction) will help strengthen your case. 

According to Gartner, most organizations solely focus on data quality improvement which unfortunately holds organizations back further along the data journey. The last mile of the data journey is the BI and Data Analytics layers and to be successful in ensuring good governance and adoption, you need to prove that data quality is no longer enough. In order to deliver the promise of business value with your case, your Analytics needs to be of the highest quality. We’ve written an article explaining why you can’t simply stop at data quality and expect it to be the same once the data arrives in your Analytics solution.

3. Position Data Analytics Quality As An Ongoing Process

When presenting the business case, Analytics quality needs to be seen as a project with no finish line. Even if you achieve your desired outcomes, it must be a continuous effort by everyone. If Data and Analytics leaders and CDOs want to provide continual value to their organization, they must plan ongoing improvement assurance in their Analytics quality business case.

4. Understand What Data Is Critical For Decision-Making

Decision-making is at the heart of Data Analytics but it’s important to know which data is the most critical for decisions and that will create positive outcomes. You need to therefore engage with key business leaders (CDO, CAO, LoBs) to explore where trusted, high-quality data is critical for business success.

5. Identify The Nature Of Solutions Needed To Overcome The Problem Of Trust

Overcoming the issue of trust requires high data Analytics quality and achieving this requires specific technologies to make sure dashboards perform as users expect them to do. Your business users and leaders need to know the Analytics they consume are 100% accurate at all times. The integrity of your Analytics is the integrity of your business.
Start by exploring the different categories of products that are available on the market to help you enhance the quality of your Analytics. Then list the benefits of each of them and their impact on the quality.