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 data quality investment that is both sustainable and provides ongoing value. Proposals often lack an understandable connection between what is data quality, oftentimes undercutting how the smallest increase in accuracy can pay divided when it comes to the business.
How to Make the Right Data Quality Investment
Assess the current state of your data quality
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. Match data quality to business-impact
This may come as a shock, but most business cases for a particular data quality investment fail because they focus on – you guessed it – data quality. 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 investment proposal to things like financial performance, operations & productivity, legal & regulatory compliance, and customer experience to really appeal to the powers that be.
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. Document the current state of data quality
Data profiling absolutely integral to data quality, enabling you to continually prove the impact of improved data quality in relation to (again): financial performance, operations & productivity, legal & regulatory compliance, and customer experience
Gather metrics early-on that benchmark the state of data quality as it exists before data quality 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. Form an ongoing data quality strategy
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 the financial impact of not investing in data quality today
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 quality of data across the business.
As you engage stakeholders and approach higher-ups regarding your preferred data quality investment, be sure to impress upon them this one simple fact: Data quality 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.