Improve data quality in your business

With more data being collected and analysed, the room for error is growing. With this analysis so heavily relied upon, errors can be extremely costly. Explore the most common data quality problems and they can be resolved.

5 minutes read | by Carly Enright | 6 October 2020

Why is data quality important?

As businesses embark upon journeys of digital transformation, they reap many benefits such as rich data sets, which can be analysed to identify any problem areas or improvements that can be made to enhance performance. However, analysing large data sets is only beneficial if that data is correct. Ted Friedman, vice president at Gartner, stated that “As organisations accelerate their digital business efforts, poor data quality is a major contributor to a crisis in information trust and business value, negatively impacting financial performance.“

Data is becoming an ever more present player within business. Across industries, companies rely on data to know where they are and where they are going. The knowledge gained from this data is utilised to make business decisions, with huge implications.

With more and more data being collected and analysed, the room for error is growing. With this analysis so heavily relied upon, errors can be extremely costly. Gartner research has found that poor data quality is responsible for an average of $15 million per year in losses.

To understand how such losses are occurring we need to look at the most common forms of data quality problems.

Data quality issues

Inconsistencies: 

Often the same data is inputted into many different systems. For example, John Smith may be recorded as such in your billing system, but in your marketing system he may have been recorded as J. Smith. If the same data is stored in different formats it can make it difficult for systems to recognise that they belong to the same customer. This can not only make running analysis on each customer difficult, but it can prove challenging to pull together all the information on a customer when requested to do so. Not only can you end up with inaccurate result and potential GDPR nightmares, but it can also result in duplicate data records potentially causing poor customer experience.

As is the case with formatting, sometimes differences in language, script or units of measurement can create difficulties. The most famous example of this is the NASA Mars satellite, which crashed because its navigation software was programmed in imperial, instead of metric, units. Ensuring that your formats are consistent or can be translated when read into another system is, therefore, key to any successful operation.

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Duplicates: 

Duplicate data is a challenge that every company deals with. It is estimated that 15% of leads contain duplicated data1. Often it occurs because of siloed processes spanning departments, with multiple systems recording the same information. When completing analysis on company wide data, this can significantly skew the results leading to ill informed decisions.

It is an issue that may even directly impact upon your customers. Customers could receive duplicates of marketing material if they have been recorded in the CRM twice. No one likes to be bombarded with marketing material, but definitely not the same material. In addition, it may make it difficult to deal with customers’ requests. If there is a problem somewhere in a customer’s account, but they have multiple records, it becomes a much lengthier process to find and correct the issue. Such problems lead to lower levels of customer satisfaction – something no company wants.

Incomplete information: 

Having data fields that have not been filled in can prove a significant issue. For example, if a customer’s postcode is not filled in then bills may go missing, or not be sent at all. This can also become an issue for analytical analysis, as it lacks important geographical information to help you spot trends.

Inaccurate data:

62% of organisations rely on marketing and prospect data that’s up to 40% inaccurate2. Attempting to contact customers using inaccurate data is a blatant waste of resources, while running analytics on inaccurate data can lead to costly decisions being made.

There are many reasons why data may be inaccurate. For example, it may be due to a customer service operate making a typo while taking a customer’s details or entering the information into the wrong field.  While analysing a spreadsheet all it takes is for an extra digit to be entered in somewhere and suddenly you have a whole different set of results.

What can you do?

It is important to be proactive when it comes to data quality. Data quality issues accumulate; not managing them or managing them through manual intervention with no automatic error correction is costly and libel to error. You don’t want to wait for costly issues to arise, before realising there is a problem.

Resolving data quality issues in multiple systems can be a time consuming and costly task. However, Low-Code platforms, like PhixFlow, can be utilised to quickly implement the process across an entire business. Using a combination of pre-built connectors and simple, drag and drop configuration, such platforms allow you to connect all your data sources, analyse and reconcile your data, automatically correct data quality issues and enable your operational teams to make intelligence led decisions based on a clear, unified view of data from across all IT systems.

Furthermore, you can reduce, or eliminate, room for data quality issues. Low-Code application development platforms can create applications to replace solutions such as spreadsheets, where human errors are inevitable. They can also be used to automate processes where human errors occur and ensure that key fields are filled in, and checked, before records are created.

For more information on how to ensure you are getting the most from your data, please request a demo.

Sources: 

1 https://discover.integrate.com/hs-fs/hub/419314/file-2405295672-pdf/Integrate_Indices+Guides/IntegrateIndices_DataQualityB2BTechIndustry_v1.1.pdf?t=1492441358121

2 https://blog.zoominfo.com/b2b-database-infographic/

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