Keeper of the Golden Record

When it comes to efficiency enhancements and cost savings, have Company quickly open ears.

"And that's what master data management is all about."

defines CEO Monika Pürsing and thus sets the direction right at the beginning of the E-3 conversation.

Modern Data Management is not a technical discipline, but an entrepreneurial challenge for the entire Company. The SAP-The grassroots must be on board here, as well as the specialist departments.

Cause and effect

According to general estimates, the negative effect of poorer Data quality at eight to twelve percent of operating profit.

"Bad Data quality therefore costs real money",

Monika Pürsing also knows this and she names the causes:

"The reason for this is in an often completely inefficient ,Data Management' to look for, if one can speak of it at all. Historically wildly grown DataThe company's data is not managed in the same way as other companies, with silos of data that are isolated from one another, a confusion of competencies, time-consuming manual data maintenance, complex, cumbersome processes, regular fire fighting in the event of data problems, and no internal company guidelines for dealing with data. Data, no 'golden record' - to name a few of the key aspects."

Professional master data management is also becoming more important as a result of developments such as Big Data, the Internet of Things and Industry 4.0, predictive analytics etc.

"The Internet of Things, Industry 4.0, the digitalization of value chains are important current drivers for the topic of Data quality"

"Add to that business intelligence and Big Data analytics. For the effective evaluation and use of Data must be the Data quality agree. The use of processing and analysis methods can only generate monetary value from Big Data if the Data 'vote'. From 'bad' Data there can be no 'good' information. Evaluations or planning are prone to error if they are not based on complete, unambiguous and "correct" information. Data based."

The effect: The assumption that forecasts can only be as good as the Dataon which they are based sounds plausible.

As part of a bachelor's thesis at the Stuttgart Media University (HdM), this correlation has now been empirically investigated for the first time and confirmed using a concrete test series.

The author of the paper, Paul Titze, a student at the Department of Information and Communication of the study program Business Informatics and digital media at the HdM, reviewed the data analysis process with the help of various test scenarios using Master data of different quality were carried out, the connection between high-quality Master data and the results of the Analysis among other things by means of Machine Learning.

Result: Especially in the Machine Learning, where the Master data form the basis for the learning of the algorithm, significantly better predictions could be achieved with a data basis of high quality prepared by master data management than with the Machine Learning with an untreated data set.

"About Data quality has indeed been talked about for a long time" "But the pressure on the Company is growing more and more to really take the issue seriously and do something about it."

Data volumes are increasing rapidly. Only many Data bring the Company but little. Without recognizing connections, meanings and patterns, they are largely worthless.

According to a PwC study from 2014, 90 percent of all industries in the future are expected to Company convinced that the ability to efficiently analyze and effectively use big data will be critical to the success of their business model.

"In order to develop this capability and in the Company effective data governance is required, i.e. uniform processes and responsibilities for data entry, release and maintenance."

Algorithms and data structures

"Without data governance, i.e. controlled processes and clear responsibilities, it won't work. They form the basis on which we build with our master data solution ZetVisions SPoT" "This is not a data quality tool for short-term data cleansing, but a sustainably effective multi-domain MDM solution, with which Company a 'Golden Record' for example Customers, products and suppliers and be able to recognize interactions between these domains. You will get an all-round view of the Master data across all domains."

There is currently an exciting interplay between big data and algorithmic business. One cannot exist without the other - in both cases, it is always a matter of quality.

Already exSAP-Vishal Sikka, Chief Technology Officer, recognized that a fast database is not enough for Big Data, it also needs mathematics and Algorithms. SAP means that Master Data the "DNA" of a Company is. Is that right?

"I agree with that: Master data form the spinal cord of the Business processes" "On their basis decisions are made, they regulate the Business process. Master data are an important overarching component involved in operational processes and business decisions as well as data evaluation and analysis or products and services."

Disruptive digital Technologies like Algorithmsartificial intelligence, bots and chatbots are already changing the entire business world.

The IT-research and consulting firm Gartner predicts that the so-called algorithmic business will generate an even greater number of disruptive developments and thus create new industries.

In order to meet the new digital and Algorithms To support the digital business, CIOs need to develop and deploy a technology platform for the digital business.

"DataAnalytics and artificial intelligence are driving the algorithmic business so that it can continue to grow and develop. Company This growth is continuing unabated, as the market for the Algorithms enriching themselves from the inexorably increasing amount of data."

More than 500,000 new devices connect to the Internet every hour, and each one contributes to the inevitable data growth.

"Core business processes and Master data are closely interlinked""Only optimally coordinated with each other, they do not cause any additional costs in the entire Company. As the heart of any Company and as a basis for corporate decisions, reliable information has an Master data positive impact on process efficiency and provide cost savings."

Market researcher Lünendonk surveyed 103 management consultants, just under three quarters of whom said they provide analytical Software to use. Often, these are still classic tools such as Excel or standard tools from Microsoft or SAP, as well as in-house developments.

Agile and interactive Software-tools, which usually have a deeper Analysis are used by less than half of the consultants surveyed.

There is still a lot of catching up to do here, as 77 percent of consultants fully or to a high degree believe they can use analytical software and Algorithms To better recognize interrelationships and thus better implement consulting projects.

Predictive Analytics

"If predictive analytics is to deliver valid results, high Data quality mandatory requirement"

Here's an example from GE, described by Sokwoo Rhee, director of the Cyber-Physical Systems Program at the National Institute of Standards and Technology in the United States.

GE used to sell aircraft engines at a uniform price. Today, they give the engines away for free under a subscription model and charge a monthly or annual usage fee that comes with a warranty.

Customers not have to worry about repairs or anything like that. You can trust the motors to start and run when you flip the switch.

For this model to work, GE needs to know exactly when the engines are at risk of failing. If they are replaced too soon, the Company money, because the engines could have generated even more revenue.

If, on the other hand, they wait too long, the consequences can be disastrous.

To find out exactly when the engines are at risk of failing, GE collects the Data from hundreds of sensors and evaluates them using big data analytics.

"Master data are a critical success factor in gaining these analytical and predictive insights" "Of course, the topic should be Data quality all in the Company existing Data include"

In addition, not only the ERP Master databut also, for example, the supply chain Managementcustomer relationship management, marketing, sales, HR, accounting, controlling. In this respect, the topic Master data the ERP-Predictive analytics is just one part of this, albeit an increasingly important one.

"Individual solutions for the various master data domains do not make sense, as you end up with diverse data silos again" "In a master data project, it makes perfect sense to start with just one domain - customer master data, for example."

Company should, however, make sure to implement a multi-domain solution from the outset that can cover all relevant master data domains. Such a solution offers the possibility to successively implement the entire Master Centralize data management in one place.

And Monika Pürsing explains that ZetVision's SPoT can be provided with content for Customers- and supplier master data, product master data and financial master data. In addition Company Master data that cannot be assigned to another master data domain.

This can be, for example, organizational structures or reference data such as regional or country hierarchies.

ZetVision's SPoT is just one product that provides the same functionalities such as validation framework, requests, data quality dashboard, data transfer, ad-hoc reporting, hierarchies for all domains.

There are no different roadmaps; functional improvements are available to all domains, such as mass data maintenance or creating from templates.

Data quality or MDM

"Data quality and MDM are not the same thing"

Master data management is the compilation of all data stored in the Company information and data into a coherent whole, the "golden record", for example for Customers, products, suppliers.

Only in this way can interactions between these domains be identified, and one obtains an all-round view of the Master data across all domains. A Master Data management thus creates the "one truth" for different master data domains across the entire Business process.

A side effect of this is improved and sustainable data consistency and Data quality over the entire Business process across. Above all, process efficiency and performance are improved through cross-divisional collaboration.

Data quality on the other hand, says something about the correctness, consistency, reliability, completeness, accuracy and timeliness of Data - not only from Master data. Data quality is therefore one aspect of master data management.

"MDM is not an issue for the IT. It can be used from the IT driven, but must be supported by the departments concerned. Data responsibility should also lie with the departments."

It's never too late to get to grips with Data quality is the firm opinion of Monika Pürsing:

"When bad Data quality leads to the fact that a Company business or money, however, it's high time to address the issue. Not in the distant future, but right now."

This is then the so-called Compelling Event, which gives a clear answer to the question "Why now". Developments such as the Internet of Things, Industry 4.0, etc. are driving the topic of Data quality more.

There is no room for postponing and putting off. And there is something else that is important to CEO Pürsing:

"Whether Data are the new oil has been the subject of much philosophizing for the past ten years or so, ever since this dictum first appeared. In the end, it is not decisive whether oil and data differ more than they have in common. What is decisive is to keep reminding ourselves that Data are a valuable lubricant for companies and the economy in the digital economy. Data are the central raw material of the digital transformation. This realization alone should be enough to make dealing with Data quality not to put it on the back burner."

Furthermore, responsibilities must be defined, roles such as Master Data Expert and Data Steward - Master data management goes beyond mere data management and distribution.

"From our perspective, MDM and MDG go hand in hand. Data governance is essential for effective master data management."

Pürsing explains.

A professional software solution then provides the technical support. MDM alone - without data governance - could also be just a HUB or the consolidation of Master data be - without the definition of rules for dealing with Data and the adaptation of organization, structures and processes.

"Master data-Governance is primarily an organizational issue, Technology is only supportive",

knows CEO Pürsing.

And from a business perspective, the issue is relevant in that Company without internal guidelines for dealing with DataWithout standardized processes and responsibilities for data entry, release and maintenance, there will be a problem with their data quality in the long term. Data quality have

"And bad Data quality costs money"

knows the ZetVisions CEO.

Data governance creates the necessary regulatory and control framework as the organizational basis for master data management in the Company to be able to introduce

The relevant roles, responsibilities and processes must be defined here. For example, the relevant policies should specify who is responsible for specific Data and compliance with quality standards; what the roles are of the employees involved with Data and how they deal with Data standards according to which they have to deal with Data be recorded (here it should be defined which minimum requirements for Data must be observed); and finally, which safety rules must be observed.

To effectively put data governance into action, a data governance office can serve as an institutionalized Data-Authority can be installed, which allows the use of Data within the Company sets.

The Office consists of representatives from the relevant business units; it can be divided according to specialist focus (central/central sales functions, finance, HR, IT) or geographical focus (all functions of a country office).

And finally, from ZetVisions' perspective, what are the biggest challenges in striving for high Dataquality?

"There must be a willingness to change, a willingness to question what has been handed down. Whoever wants to Management from Master data If a company wants to take a professional approach, it cannot avoid breaking up old processes and intervening in previous "territories. There are some 'sensitivities' that need to be addressed. New, standardized processes must be defined and data managers named."