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Artificial Intelligence, blockchain, predictive analyses, etc. – companies are increasingly turning to new technologies and processes. They promise a competitive advantage, greater efficiency and better decisions. All of this depends on the quality of the data. But what does data quality mean? What are the criteria? And why is high data quality so important in order to ensure long-term competitiveness? This article provides the answers to these questions.
Data costs money. That’s an undeniable fact. High-quality data costs even more. But what’s the alternative? Leave it be? And then make decisions based on incorrect or obsolete information? Put up with laborious processes in the company? Land yourself with penalties due to non-compliance? Or even take on major financial risks?
Poor-quality, incorrect and incomplete data has the potential to cause a lot of damage. According to Gartner, the losses can run into millions. Ignoring data and its quality is therefore not an option. Admittedly, projects and initiatives aimed at improving and maintaining data quality are complex and expensive, not to mention daunting. But the costs of simply doing nothing are much higher.
According to Gartner Research, poor data quality is responsible for 9.7 million dollars worth of damage in companies every year. (https://www.gartner.com/smarterwithgartner/how-to-create-a-business-case-for-data-quality-improvement)
To be competitive and to position yourself as a successful enterprise, it is absolutely crucial to have control of your data and to ensure high quality. Not surprisingly, data quality is the top priority of many companies and CEOs. Providing complete, correct and current data in a company is the key to long-term success and ensures that money is not needlessly wasted.
In this article we take a closer look at data quality. We focus on the following three aspects:
Let’s examine why data quality is so vital to success in the business world. Providing the right data with the right level of quality opens up a host of advantages and possibilities.
The higher the data quality, the more confidence employees have in the decisions that they make. This reduces the level of risk and increases output as well as efficiency.
High data quality leads to higher productivity. Instead of having to validate and correct errors, employees have more time for their actual work.
High data quality is often the difference between compliance with regulations and penalties running into millions.
Correct data makes accurate targeting and personalised communication possible. This is something that is becoming increasingly difficult, yet at the same time highly important, in today’s omnichannel environment.
We now know why it is so important to have high-quality data. Let’s look at what constitutes good quality. The four criteria for data quality are described below.
It is self-evident that data and information must be correct. A database should not contain incorrect information. Correct data ensures that mailings actually reach their intended recipients and – better still – that they reflect corporate hierarchies and affiliations.
However, accuracy also depends on the context and situation. For example, a company might have 10 different phone numbers listed. If you call the Marketing department, you’re probably not going to get any information about financials. When we talk about the accuracy of data, there is no single “truth” that determines whether information is correct or not. Accuracy has many nuances. Information that is useful in one situation might be completely useless in another.
Data and records can only be considered complete if they reflect all available information. For example, if only two-thirds of all purchases made by a customer are recorded, this record does not reflect the customer’s true value, but actually undervalues them. The record may remain this way for a long time. In other words, the company fails to capitalise on the customer’s true potential. It would obviously be extremely frustrating for the company to miss out on a lot of potential sales.
Standardised data is very important for users. It makes it easier to synchronise data. Take address data, for example. Addresses in Tokyo look different to addresses in Switzerland or the United States. Clearly defined standards are crucial for ensuring that new records are automatically imported correctly and for identifying and eliminating duplicates.
Data sources must be reliable and credible and serve a specific purpose. Only when data meets these criteria and is reliable will it be used by users, thereby facilitating the making of well-informed, accurate decisions.
You now know why data quality is so important and what the criteria are. So, how can you improve the quality of your data?
Before you begin cleaning up data, you must have a clear idea as to which business case you want to solve. All stakeholders must be on board.
The data is mostly stored in different silos, in other words in various CRM systems, Excel lists, sometimes even on paper.
Percentage of top-performing companies that have made leadership in data and analytics a strategic priority. “2016 Global CEO Outlook”, KPMG International.
After completion of the data inventory comes an assessment of current data quality. This also includes processes and tools.
This involves identifying the causes of poor data quality. It may well be the case that employees are constantly entering information incorrectly.
Given that data quality is an ongoing issue, permanent or periodic monitoring is essential.