Create a compelling business case in just five steps.

For most successful enterprises today, data has asserted itself as a valuable asset and wielding it is a true differentiator. Making effective use of that unique pool of information is recognized as the key to drawing out ahead in a highly competitive digital world.

Yet data has an Achilles heel – inaccurate data.

Poor data quality destroys business value. According to Gartner, organizations can haemorrhage on average over $15 million per year in losses due to poor data quality – a wound that cannot heal itself.

Where is all that loss coming from? The answer – everywhere. Salespeople waste time dealing with erred prospect data; service delivery people waste time correcting flawed customer orders received from sales. In a survey conducted by CrowdFlower and reported by Forbes, surveyed data scientists spent about 60% of their time just cleaning data, IT expends enormous effort lining up systems that “don’t talk”. Senior executives hedge business decisions because they don’t trust the numbers.

As the information age grows increasingly complex, the impact of poor data quality only becomes more severe. Industry figures estimate the growth of corporate data is in the region of 40% per year.

Despite this, many organizations and data controllers are struggling to successfully propose a business case for sustainable data accuracy improvement. Proposals often lack an understandable connection between data accuracy improvement and business outcomes or strategy, and undercut the crucial role that data accuracy plays.

Five Steps to Building a Case for Data Accuracy

  1. Assess the Data Landscape

In order for your business case to be well-received and taken seriously, it’s got to walk the walk and talk the talk of the business and speak to the critical and specific priorities of key stakeholders.

Get well acquainted with the business goals of your organization and who will be sitting in the boardroom. Having your finger on the pulse of what makes them tick will help you identify the senior-level support that will be your rallying champion.

2. Focus on the Big Four

This may come as a shock, but most business cases for data accuracy improvement fail because they focus on – you guessed it – data accuracy. But if you’re going to see in the eyes of the stakeholders, you have to address the key components that factor into business goals. Link your data quality initiatives to what we like to call The Big Four: financial performance, operations & productivity, legal & regulatory compliance, and customer experience.

Think of it this way: Every year, sales departments lose a whopping 550 hours in selling time as a result of poor CRM prospect data. That’s the equivalent of 27% of their selling time they could be using to close leads and nurture customers.

3. Put Some Numbers Behind It

Profiling isn’t cool in criminal justice, but it’s absolutely integral to data quality. Data profiling enables you to continually prove the impact of improved data accuracy on The Big Four.

You’re going to need metrics from the get-go that benchmark the state of data quality as it exists before improvement initiatives begin. Performed early and often, this enables you to demonstrate positive (or negative) implications and justify ongoing funding for initiatives.

Did you know? The average company has a data-error rate as high as 25 percent, according to SiriusDecisions.

4. Envision the Future

Business leaders sometimes struggle with drawing a connection between data quality and the end goal, oftentimes confusing a data strategy with a “one and done” project. Describe the target state to achieve business improvements – How do we know when we’ve achieved “quality” or “accurate” data? At what point can we say the program was effective?

Don’t forget the first two steps – by speaking the language of your stakeholders and defining the target state in terms of critical business metrics (The Big Four), you’ll successfully align expectations and get the boardroom excited over what’s to come.

5. Estimate Financials

It all comes down to this. Nearly all business proposals hinge on the dollars behind it, not just in terms of cost but in what it can do for the company. In order to close the deal, you’ll be tasked with putting tangible and quantifiable figures behind your data quality initiative that identify lip-smacking benefits.

Here’s an example to make them sit up and listen: Using the popular 1-10-100 rule, we know it is far more cost-efficient to prevent data issues than to resolve them. Say your company has 500,000 records and 30% are inaccurate, then it would cost the business $15 million versus $150,000 to put solutions in place to improve the accuracy across the business. 

As you form your business case and engage stakeholders, be sure to impress upon them this one simple fact: Data accuracy is a journey, not a destination. In order to see the kind of returns data is capable of delivering, organizations need to treat data as a strategic business asset. That means developing a flexible strategy that evolves with the organization and maintains data to the highest degree of quality possible.

Need more inspiration? We’ve got some great resources to pass around the boardroom. This one on how 360Science helped DirectMailers realize a 360-view of leads while reducing processing time by 98% is a great place to start.