3 Ways to Improve Data Quality

May 19, 2021

When you are investing in your firm or planning your next big move, choosing how to best spend your time and money can be stressful. To decrease that stress, the best option is to do your research and increase your knowledge. Find the best product based on mountains of data, condensed down to what is most important to you.

A Glut of Bad Data

But how can you trust that the data you based your decision on is correct?

Recent studies by Gartner found that poor data quality has companies losing up to $10 million a year, with IBM calculating that in the US alone, the lost revenue due to data errors total over $3.1 trillion. These costs come in many forms:

• Added work correcting bad data
• Shipments delivered to the wrong address
• Reports with incorrect information leading to poor conclusions
• Lost inventory
• Poor worker usage
• Etc.

You may still be wondering how these figures could be possible. This is because an error in one record can impact your entire production chain. As a matter of fact, research from the Harvard Business Review states that only 3% of company data meet basic quality standards.

Data scientists have a name for this ongoing production of errors: the hidden data factory. The hidden data factory can lead to an eroding sense of trust in your data.

Examples of Wasted Data

• Defects – data entered with errors, like typos or data entered in the wrong field.
• Overproduction – entering data in duplicate, like entering customer data into the CRM and shipping systems.
• Waiting – waiting for data to be processed, or delivered.
• Non-Utilized Talent – staff not trained to enter data into a certain system or in a certain way.
• Transportation – transferring data from one physical location to another.
• Inventory – the data you keep. Having an excess of inactive or inaccurate records.
• Motion – moving data between systems, like entering data into multiple systems.
• Extra-processing – doing more than what is required. Like unnecessary reports.

For more information on Lean and Downtime, see our white paper on Cutting Waste with LEAN Thinking.

A Leaner Way Forward

While it isn’t easy to decrease the root cause of the waste, applying a lean methodology to data entry and processing will improve your data quality.

I – Identify the problem

Bad data comes from communication errors, which can be summed up as:

• Receipt errors. For example, when receiving information from the customer, the email or phone call was unclear, so you couldn’t understand what the customer wanted and didn’t follow-up correctly.
• Input errors. For example, a typo or entering a value in the wrong field.
• Transfer errors. For example, two applications that don’t communicate well with one another generate issues or errors.

II – Map the process

Once you discern where the problem areas are you now have to map what is being affected by those errors and how to resolve them.

Receipt and input errors can be resolved by adding confirmation to the data, or you can bypass the possibility of human errors by looking to implement direct electronic communication, or EDI, between you and your customers, vendors, and banks.

The third problem area often requires 3^rd party plug-ins, or other services to ensure the applications can communicate with one another.

A better solution to all three areas is to implement a single point data system, like OnRamp, where all the data is contained within one program and one database that quickly and efficiently allows communication between all your business units and supports EDI.

III – Create flow

With the process mapped and implemented, you now have to ensure your staff are using the new flow.

This will generate less waste, just by having better data available.

IV – Establish pull

With errors decreasing, you will see your costs go down, meaning you can pass some of those savings onto your customers. Implementing a single point system allows your customers to create requests that are automatically viewed by your planning team, thus shortening the amount of time you require to get your goods to the customer.

V – Seek perfection

With one problem solved and your flow and pull running well, you can run the process again on the same data set or a new one to keep improving your processes and removing waste.

Lean Data is Good Data

The above process has been found to help you close hidden data factories, leading to more productivity, and most importantly, more informed decisions.

As you know, there are multiple data points for you and your team to work through. For example: Customers, Vendors, Employees, Parts, Work Centers, and Maintenance.

For most firms, it is impossible to clean all this up in one go. Instead, focus on your small data initiatives by trying to implement the following practices:

1. Eliminate hidden data factories
2. Reduce transport and movement wastes
3. Simplify handoffs

The next steps are:

1. Define your subject matter experts, or SMEs.
2. Open communication channels between the SMEs.
3. Have your SMEs train your teams.

Conclusion

Bad data creates non-value-add expenses in the form of hidden data factories that decrease your competitiveness and decision making. While there is money and business lost because of bad data in every modern industry, that can be mitigated with better systems, more automation, and smarter people management. A single point, single database ERP system, such as OnRamp, helps you better manage your data, decrease data waste, and helps you better manage your workers in all departments.

Staying ahead of your bad data ensures your firm is nimble and ready to capture more market share and make you a leader in your field.