Business Topics
Anti-Ageing for Data

Anti-Ageing for Data

Stop the gradual ageing of customer data - with professional data quality management

In today’s society “anti-ageing” collectively refers to products and procedures claiming to slow down the effects of biological ageing of human beings, and to improve the quality of life and lengthen lifespan. The market for anti-ageing products has boomed in recent years, with a survey by Information Resources GmbH (IRI) revealing that in Germany, a turnover of more than EUR 202 million was reached in 2015 with premium articles in the anti-ageing segment. 

Companies should give the same level of importance to facing the challenge of slowing the ageing of their customer data to a minimum, as people attach to their “fight against ageing”. Today this is possible - with professional data quality management.

„Forever young” – Why this is so important for customer data

„Data is the raw material of the 21st century” – this quote favoured by German federal chancellor Angela Merkel is becoming increasingly significant in business practices with the background of rapidly advancing market digitalisation. Without a valid data basis, it will become increasingly difficult for many companies to achieve their strategic business targets and orientate their sales and business models to the new circumstances of a digitalised market. The keyword here is “digital transformation”, whereby companies are increasingly turning to so-called “predictive analytics” prognoses, and use data collected in the past to derive prognoses for the future development of their business. Here are a couple of examples:

  • „Credit scoring” used by banks for risk evaluation before approving a credit request.
  • „Predictive analytics” as an important component of innovative “smart factory” concepts for optimizing turnover and needs planning
  • „Predictive analytics” for campaign planning in the retail branch, as well as for travel and tourism planning
  • „Predictive maintenance” for introducing preventive maintenance and repair work.

However, when considering that – independent of operational scenario – predictive analytics analyses are usually based on just ten percent of the historic data available in a company, it soon becomes clear what this means for the data quality. Only when data is carefully maintained and kept completely up to date (i.e. “young and fresh”) will prognoses based upon it be accurate. And only accurate prognoses will ensure that the process adaptations based upon them (i.e. planning measures and campaigns) will function correctly. Otherwise, a dangerous chain reaction can result, and this could prove fatal for a company. Poorly maintained data means that analyses will be inaccurate; activities and measures will fail, and incorrect business decisions will be made.

It starts with just a couple of wrinkles: Gradual ageing of data 

The most visible external sign of a human being ageing is when the skin condition starts to deteriorate. It mostly starts after a person has turned 25 years of age when wrinkles start to appear in the skin. Unfortunately, the “wrinkles” in customer data are not so clearly evident. For many companies, the gradual ageing of their data is difficult to identify, and deficiencies only become apparent when it is already too late to implement countermeasures.

As a rule, companies are mostly unable to keep step with the more than eight million address alterations that occur annually due to relocations and bereavements; or the more than 500,000 changes of name following marriage and divorce; or the innumerable changes in companies due to employees joining, leaving, or changing position; as well as bankruptcies and company takeovers. A study performed by beDirect regarding data quality in German companies came to the shocking conclusion that, on average, probably every fourth address dataset is incorrect.

When account is also taken of errors made during the input of addresses, as well as multiple entries of the same data being made at different locations within a company, it soon becomes clear that customer data is subject to constant change, and its quality will gradually deteriorate when no measures for controlling and optimizing are implemented.

Botox treatment alone does not help: Sustainable measures for data quality are necessary

To keep with the analogy of the topic of anti-ageing, there are several possibilities to introduce ad-hoc and one-off initiatives for optimizing data quality in a company. For example, a data quality firewall (first time right - FTR) can ensure that only high quality (i.e. correct) datasets are collected. But it is far more important to optimize the processes for data collection and management on a sustainable basis, and then use them to optimize the system landscape. Another success factor is to make company employees aware of the importance of data quality, with appropriate training being provided, and long-term consciousness and culture created for maintaining optimal data quality.

At the same time, everybody involved has to clearly understand that this process is not simply a “one-way street”, but that the measures for optimizing data quality must be integrated within a “closed loop”. This begins with an analysis of the status quo, and continues through the processes for optimizing the data quality; the results of which are collected and documented and thus serve as the foundation for a renewed analysis of the data quality created.

Data Quality Cycle

Ground Truth: Fundamental trust in your data

The target of all anti-ageing measures for data quality is – as the name suggests - to hinder gradual ageing of data. They must guarantee that customer data collected by a company has the highest quality level possible, and that it is maintained on a sustainable and continuous basis. Only then is a foundation for creating an up-to-date, complete, and precise 360-degree view of the customer possible. This 360-degree customer view delivers provides e.g. a truly reliable foundation for predictive analytics processes. This means that in future, prognoses will no longer take place in a “vacuum”, but instead, that they have the necessary solid basis for delivering truly valid data for creating and implementing successful business measures and processes.

Ground Truth is a comprehensive solution and process methodology developed by Uniserv. Its multi-stage approach helps a company to create a golden profile of each individual customer, whereby address data, purchasing behaviour, interests and preferences, as well as communication and interaction with the company, are all aggregated to form a central dataset. Also, the traces a customer leaves behind in the internet and social networks are integrated to form a golden profile. In other words, the master data of each individual customer (golden record) as well as the dynamic (transaction and interaction) data is consolidated (golden profile). Ground Truth also ensures continual updates of this data, as well as synchronising with all different data sources. 

Ground Truth

In cooperation with Stuttgart Media University (Hochschule der Medien Stuttgart – HdM), Uniserv has developed a prototype based on Ground Truth especially for predictive analytics, to emphasise the importance of the data quality as a critical success factor for the quality of prognoses.

For the first time, this connection has been empirically examined within the framework of a Bachelor thesis. The author is Paul Titze, a student of the faculty of economic informatics and digital media at the HdM, where he is studying information and communication. With the help of different test scenarios in which analyses of master data with different quality levels were performed, the connection between high-quality master data and the results of the analysis via supervised machine learning were examined. Conclusion: in comparison to machine learning with untreated datasets, the basis of high quality data provided by master data management considerably improved predictions, especially in the case of supervised learning, where master data forms the basis for learning the algorithms.

Conclusion: As in real life, “anti-ageing” treatment for data does not simply consist of a short-term, one-off action to improve condition and appearance. Instead, it describes of a series of measures affecting processes, systems, employees etc., all of which help to keep the quality of a company’s customer data at the highest level – and entirely up to date - at all times. But it also requires understanding of the causes of gradual ageing, as well as knowledge of the methods necessary to counteract it.