High Noon for Master Data Management

It is very similar to the Hollywood classic with the topic of master data quality. For years, the lack of data quality and inadequate master data management in many companies has been pointed out again and again.

Here are some examples:

According to the "European biMA Study 2012/13" presented by Steria Mummert Consulting, 34 percent of companies have no regulated process for data quality management.

Effective master data management is not in place at all in 38 percent of companies. In 2013, according to a study by the Fraunhofer IAO, 51 percent of the companies surveyed stated that poor production data quality made short-term interventions in production control necessary to a great/very great extent.

In 2014, 36 percent of companies surveyed for a Barc study said they were very dissatisfied (7 percent) or somewhat dissatisfied (29 percent) with data quality.

In the answers to the question "What are currently the biggest problems in operating the data warehouse?", data quality is in third place with 50 percent.

According to an Aberdeen Group paper published in January 2015, 49 percent of companies surveyed cited too many data silos and 47 percent poor data quality as top challenges driving data management activities.

The same findings over and over again for years. The causes of poor data quality are manifold, but generally the same everywhere: lack of embedding master data management in the corporate strategy, no clearly defined processes and responsibilities for data entry, release and maintenance, and a proliferation of data sources and systems.

All not funny!

Because now companies are also facing the challenges posed by digital transformation. According to a recent study by Bitkom Research, many companies still lack awareness of the disruptive nature of digitization.

Only 50 percent of German companies see digitization as an important or very important goal. The transformation affects the entire company: Business models, products and services, customer segments, channels, business processes and jobs.

Against this background, it is not good news at all when, according to the Lünendonk study "Revival of Master Data" published in December 2016, numerous companies in Germany are currently not sufficiently equipped to meet the challenges of digital transformation.

Only about one in seven (15 percent) of the companies surveyed consider themselves to be in a good position when it comes to the important foundation of master data management. 72 percent rate themselves as "mediocre" and 13 percent as "poor".

Although data quality has improved significantly over the last five years, companies of all sizes are not satisfied. 40 percent say their data quality is currently very good (16 percent) or good (24 percent).

But still, 60 percent emphasize only mediocrity here. To put it bluntly: mediocrity is not bad. But mediocrity is not enough to meet the challenges of digital transformation!

In addition to poor data quality and data timeliness, the volume of data to be managed will grow exponentially in the future. Big Data, the Internet of Things, and Industry 4.0 mean that managing the enormous data streams will become a huge challenge for companies.

Let's hold on:

Data is of great importance to companies, its quality is already often inadequate, and at the same time its quantity is growing dramatically. In a figurative sense, one can only say, "Houston, we have a problem." And not a small one!

It is high time to do something. Master data may not be particularly "sexy," but there is no way companies can avoid professional master data management.

You must first define effective data governance to regulate tasks, roles, access rights and responsibilities around information processes. Only then can IT support be provided by a professional standard solution for master data management.

Professional master data management costs money. Poor data quality and non-existent or "home-made" master data management cost even more money!

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