Increasing data quality without manual effort


Isn't it frustrating to have poorly maintained data that requires a lengthy manual update or remains incomplete due to the time and effort involved? This question will certainly cause some hands to go up in your team.

Manual maintenance of master data – a lot of potential for frustration

Incomplete data is not only a pain point for sales reps that fail to reach their customers by phone, but it is also an issue for the marketing department. Indeed, can we still consider a campaign to be successful with only a response rate of 30% due to incorrect contact data?

To ensure that a business progresses effectively without having to spend unnecessary time on data maintenance, it is essential to maintain a high level of data quality in the CRM – something that is probably obvious to all users.

Although this problem is so trivial, it is not so easy for many companies to solve it. According to a study by Experian Marketing Services, about three-quarters of all corporate decision-makers estimate that business success is significantly influenced by poorly maintained data and that about one in eight euros of corporate revenue is wasted due to deficient data quality.

What does data quality stand for?

A central question that always stands out when it comes to data quality is: How well is the recorded data able to meet the company's needs?

To better assess and evaluate data quality, the data stock should fulfill various aspects – just to start with: It is not enough to have all the relevant fields in the CRM filled in. To qualify as high-quality data among several sources, it should also be accurate, up-to-date, relevant, reliable, accessible, and consistent.

Data quality as the basis for Big Data

Regarding these aspects, only with the right data quality they can be useful for the company. However, this is not just about users rubbing their hands in glee at optimally maintained information from contacts and accounts. It is much more about the big picture for the company, it is about Big Data. By using large and well-prepared data volumes, it is possible to perform significantly better data analyses. In this way, insights are acquired based on better and possible corporate strategic decisions.

Therefore, it should be a central aim of all companies to cleanse the existing data records continuously and to enable reliable statements employing artificial intelligence.

The best artificial intelligence is useless if the database is incomplete

Especially in B2B business, working with and analyzing data using AI is becoming an increasingly important factor. To enable artificial intelligence to correctly predict cross-selling opportunities or even a churn risk, it is essential to have a reliable and broad database. Even the best-trained artificial intelligence will only deliver half-baked results if it is filled with poor data. In the worst case, incorrect conclusions are made, which do not (or cannot) contribute to the goal of improving the company's success.

Several studies confirm that AI has a revolutionary impact on sales activities and, ultimately, on the success of a company. For example, a study conducted by SAP and the Economist Intelligent Unit states that companies that have already established machine learning achieve a significant 48% higher profitability than competitors whose operations in this field are still underdeveloped.

How can I better generate data in the company?

Less is more

It is important to choose wisely the team that will be in charge of your CRM. The team should include all relevant user groups – but not be blocked by too many different interest groups.

After all, you will soon realize that each user has his or her requirements and expectations of the CRM. Of course, this increases the risk that the CRM will be overloaded with too many custom fields. And let's be honest: lots of those individual fields are never taken into account…

Therefore, always consider the interface of your CRM rather than a momentary snapshot. The CRM team should make it their business to take a critical look at custom fields and workflows at regular intervals. Ideally, not only new fields should be integrated, but old fields should also be consciously eliminated again and again. A clear, well-structured CRM with a user interface that is easy to understand will improve the quality of your data in the long term and, at the same time, increase user satisfaction.

Outsourcing the data processing

The implementation of a CRM represents a fundamental new start in every company. Take this opportunity to make a clean break and start with clean data.

Especially convenient is the fact that there are a large number of different providers on the market who will gladly take over this laborious task for you. Your IT department will certainly appreciate it, as you can thus counteract any potential overload.

The concept is simple: companies supply the "raw" inventory data record and the service providers take care of structured cleansing and processing.

TIP: All data cleansing providers work with databases in the background, from which they draw the missing data. But it's no secret that databases quickly become outdated, even with the best maintenance efforts. After all, this is one of the reasons to outsource your database cleansing to an external provider.

snapADDY Data Cleansing Service always gets the required information from current sources, such as Google Maps, legal notices, or business networks (Xing and LinkedIn). These are sources of information where companies are careful about displaying correct data – take advantage of this accuracy!

Providing options for optimizing data

Note: Optimizing master data through a service provider should not be the endpoint of data processing. Importing the cleaned data into the CRM always represents only a brief status quo, since data – as we all know from experience – is not set in stone.

Keeping data quality high in CRM should remain a permanent goal to provide optimal data quality for users and Big Data projects. Even everyday helpers help to structure the enormous flow of data and display it correctly in the CRM.

TIP: snapADDY DataQuality, for example, helps to keep an overview in the mailbox. In the background, all incoming e-mail signatures are tracked, checked for new information, and compared with the data records in the CRM – no further action is required by the user. The user ultimately decides which information is to be included in the CRM via a simple click and thus has the highest level of control. Sounds like a dream? You can test our "Swiss Army Knife" for sales here, fully and free of charge.

If you have special requirements for transferring data to your CRM, our experts will be happy to help you identify the ideal workflow to use with snapADDY DataQuality.

Clear responsibilities

As described at the beginning: The problem of poor data quality is known and evident to all CRM users. But to permanently eliminate the problem, you need to clearly define responsibilities. If it is unclear who is in charge of data quality, a data set will not be optimally maintained in the long term.

The first step is to assign contacts and accounts to the responsible sales staff. They must be aware that they are responsible for maintaining their data set.

Besides, a central CRM team is ideal for regularly checking the global data quality of the CRM. If necessary, this team can pull the ripcord, point out bottlenecks, and prevent incorrect use.

Conclusion – Data quality is not a one-off project

The topic of data quality has been and unfortunately still is treated neglectfully in many companies. Far too often it is associated with tedious, manual data entry. But as shown before, there are many automated ways to raise your master data to a better level and to keep the data quality permanently high. When looking for the right solution, always keep in mind that data quality should remain an ongoing issue. No matter how quickly a one-off data processing project is implemented, some of the data will already be out of date when the processed data is imported into your CRM system. That is why master data processing projects should always go hand-in-hand with a permanent solution that makes it easier for CRM users to enter data in their day-to-day work.

Eva Keller

Sales Manager at snapADDY GmbH

Eva Keller has been an expert for digital sales at snapADDY GmbH since 2017. Previously, she worked for a PR agency in Darmstadt where she was responsible for clients from the children and lifestyle segments. The political scientist started her career as PR manager for a member of the Bavarian parliament.