<img height="1" width="1" style="display:none" src="https://www.facebook.com/tr?id=155514168445921&amp;ev=PageView&amp;noscript=1">

Becoming a Data-Driven Organization

Data is the “oil” for all digital transformation initiatives including big data analytics, AI, Machine Learning, and more. Many of these initiatives intend to relentlessly improve customers’ experience with your business. However, failure can occur when computing with inaccurate data resulting in lower ROI, costly overruns, decreased productivity, and loss of trust. IT executives, business managers, and knowledge workers all have a shared responsibility to implement the best processes and technology to eradicate bad data. The negative impact of data inaccuracy can be widespread throughout organizations.

Using the “oil” analogy, an automobile that has dirty oil can have a significant, negative impact on the performance of your car. Left unaddressed, bad oil can destroy your engine, costing thousands of dollars to replace, or could ultimately render the car undriveable. Even worse, if the car were to break down unexpectedly, you and your passengers’ safety could be seriously jeopardized.

This same scenario applies to your company’s data. While many companies feel they have a good handle on their data’s quality, studies by leading analysts and companies like IBM suggest otherwise. An Experian report found that 88% of companies see a direct effect of inaccurate data on their bottom line, costing an average of 12% of yearly revenue. In a similar study by Database Marketing, organizations estimate they could increase sales by nearly a third (29%) with corrected customer data.

A recent article in InformationWeek stated that data quality is hardly a new concept, but the need for it is vital, not only to ensure more accurate analytics, but also to ensure that AI training data is reliable. Enterprises need further prioritize data quality or suffer the consequences since "garbage in, garbage out" can erode brand reputation and customer trust (Morgan 2018).

 

The 1-10-100 Rule.

The 1-10-100 rule is a quality management concept developed by G. Loabovitz and Y. Chang that is used to quantify the hidden costs of poor quality. In simple terms, the rule suggests that one dollar spent on prevention will save ten dollars on correction and 100 dollars on failure costs. The further bad data moves through your business processes, the costlier it becomes to fix. Decision makers, managers, knowledge workers, data scientists, and others must accommodate to the inconvenience of bad data in their everyday work. This is a time-consuming and expensive use of company resources. The data needed has plenty of errors, and in the face of a critical deadline, many individuals may make corrections in order to complete the task at hand. They don’t have time to reach out to the data creator, explain their requirements, and eliminate root causes. Poor quality data also impacts our ability to operate. If we invoice the incorrect amount, payment is stalled. If we deliver to the wrong address, we have higher shipping costs. If we provide the wrong risk assessment, we increase our chance of a bad debt.

It’s apparent that if organizations want to become data- driven to minimize costs, the focus needs to be on prevention, not remediation.

The Naveego Solution.

Naveego is a cloud-based data accuracy management service that identifies and continually monitors your data problems at the source BEFORE bad data creates inaccuracies in your applications, reports, dashboards, or other decision-making tools. Additionally, their integrated Master Data Management solution compares data in all your systems and delivers one version of the truth by making sure data alterations in one system are reflected in other systems containing the same data.

 

Leave a Comment