The dirty data dilemma (and how to avoid it)

Data hygiene isn’t sexy, but it is an essential part of your business process, and if neglected, it can cause unthinkable damage. On average, dirty data – data that is inaccurate, incomplete or inconsistent – costs a business 15% to 25% of revenue, and the US economy over $3 trillion a year. Quite simply, with bad data you’re leaving money on the table.

Data informs nearly every facet of industry decision-making, deeply affecting our lives – from why groceries aren’t selling and how people move through airports to the unique order of our Netflix feeds and the music Spotify serves. If you consider that companies around the world believe 26% of their data is inaccurate or corrupt, or that only 16% of business executives are confident in the accuracy that underlies their business decisions, the dilemma of dirty data (or rather the missed opportunities) becomes crystal clear.

In addition to the revenue loss, dirty data impacts businesses in more dangerously subtle and stealthy ways. When you can’t rely on your own data, something quickly needs to be done to increase your data accuracy and reliability.

What makes data dirty?

Human error accounts for more than 60% of all dirty data – which should come as no surprise. The human brain is simply inept at mastering fault-free manual inputs. The other 40% is a combination of inaccurate records and poor data strategy, which often circles back to human error anyway.

Dirty data is also commonly the result of departmental miscommunication – different teams feeding the system with related data from separate silos, without co-operation or internal data logic. It’s the old adage of the left-hand versus the right-hand. Internal bureaucracy can mean dirty data goes unchecked or unnoticed for years. In fact, it’s reported that over 57% of companies only discover dirty data when it’s reported by the customer or prospects.

How can you get it clean?

Data cleansing or data cleaning is the (often painstaking) process of detecting and correcting corrupt or inaccurate records. This involves identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, editing or deleting the dirty entries until you’re left with crisp, clean, useful data.

The challenge is, there are many tools that can identify the source of your dirty data, but few can actually fix the problem. As SAP data experts and official Build Partners, we realised that our clients needed a solution within the SAP framework that identified their data challenges and solved them simply, affordably and permanently. So, we built one.

Welcome a revolutionary in-SAP tool that cleans your dirty data

SimpleData Management (SDM) is a master data management tool embedded within SAP. It’s the first product of its kind to rapidly identify and solve your data challenges, proactively setting up structures to prevent them from reoccurring.

One of the most comprehensive SAP data solutions, SDM offers in-built data governance and management that’s simple, affordable and sustainable. It makes dirty data clean and drives a culture of high-quality data in your organisation.

Understanding dirty data isn’t just helpful, it’s essential. The good news is, anyone can master data management with the right data management tools. Explore our best in class tool.