The role of MDM and analytics as a catalyst for making sense of big data

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August 14 2019

In the internet’s infancy, before search engines and browsers, you had to know exactly where to look to find the information you wanted.

With the emergence of search engines, the entire wealth of information became available at our fingertips just by inserting a keyword. Google’s most significant achievement involved making sense of big data served up from over a billion websites globally according to user preferences, location and history. Google simply and elegantly integrated the world wide web to deliver information with speed and relevance for the user.

Today, big data does not only exist on the internet but within single companies and organizations who need to capitalize on that data. Searchability no longer suffices; businesses need advanced data analytics that can both accurately describe and predict behavior and movements.

That’s the power of analytics – to discover new correlations of different data sources and blend data so that meaningful patterns of information emerge.


Master Data Management (MDM) as the catalyst

MDM is the natural application hub for bringing together this diverse array of data in a way that provides structure and data confidence.

The reason: master data represents your most essential business assets, including products, customers, locations, suppliers and vendors, digital and physical assets, employees and more. Each of these data domains and their interrelationships can have hundreds of attributes and billions of records that need to be clean and consolidated. High-quality master data is the foundation to this – as well as any digital transformation initiative.

For many retailers, manufacturing companies and financial institutions, an MDM solution is a critical data management tool because it streamlines master data for running operations and provides reliable data quality for analytics across both structured and unstructured data sources.

Having a powerful analytics engine embedded in your MDM solution enables you to derive new kinds of insights from these sources when correlating them with your master data.

The value of analytics comes from being accessible and reliable

Data analytics, when embedded into the MDM solution, further fuels the multidomain efforts.

With embedded data analytics you can blend other types of data into the master data view and discover new connections in the landscape of big data.

Data analytics shows its full potential when it is not limited to a certain type of data but instead integrates data from a multitude of sources: IoT, transactions, social media, production workflows to name a few. Each of these sources creates data that flows into various data repositories and data lakes where you run the risk of having valuable data lying idly around because it is not easily accessible to your organization.

Benefits of MDM

An MDM solution facilitates developing a single source of truth for any desired data domain. The single source of truth, often labelled as the “golden record,” lays the foundation for:
  • Streamlining onboarding processes through data syndication
  • Creating omnichannel and customer-centric experiences
  • Enhancing collaboration through enterprise solutions
  • Agile business management capable of responding quickly to market trends
MDM helps to break down and connect data silos and prepares a true enterprise solution with global potential by enabling a rich ecosystem of interconnected data and processes.

The Role of MDM and Analytics as a Catalyst for Making Sense of Big Data

But having a powerful analytics engine embedded in your MDM solution enables you to derive new kinds of insights from these sources when correlating them with your master data.

Examples include:

  • Track sales performance and relate that to the quality and availability of your product data and customer demographic data. This enables you to increase the performance by improving specific data attributions based on your buyers.
  • Pinpoint bottlenecks of your onboarding process to discover that it’s not the amount of time spent on each work stream that prevents you from cutting time-to-market but instead the amount of review and rework cycles that add overhead. You can then apply targeted corrective measures and enhance workflow efficiency to significantly reduce your time-to-market.
  • Track customer value over time by seeing how frequency of customer interaction, recency of customer engagement, and amount of money spend by customer is changing over time as a result of targeted segment specific campaigns.

Actionable data

User-friendly visualizations of blended data embedded in your MDM application can provide quick answers to the questions:

  • What is happening (descriptive data)?
  • What is going to happen in the future (predictive data)?

Most importantly, having this insight at your fingertips motivates users to take action and improve decision making. Embedded data analytics democratizes data and empowers business users to become effective data analysts.


Summing up the basic requirements for maximizing value from data analytics:

  1. Mining for data from different sources provides insights and lead to cost savings and new business opportunities.
  2. A master data platform is uniquely suited to be the hub for merging disparate data due to its focus on the most central assets in your organization, also providing the single source of truth and trustworthy, high-quality data that is needed for successful analytics.
  3. Empowering business users to become data analysts using customizable dashboards and intuitive visualizations promotes intelligent decision making and effective business agility
How to Increase Business Performance with Embedded Data Analytics

Always having a love for technology and what it can do for global enterprises and individuals, Nils continues successful program deliveries in Manufacturing, Distribution, Retail, and Automotive Aftermarket.
Nils Pederssen war schon immer von Technologie und ihrem enormen Potenzial für Menschen und Unternehmen fasziniert. Ganz in diesem Sinne realisiert er heute bei Stibo Systems umfangreiche Stammdatenprojekte in den Bereichen Produktion, Distribution, Handel und Automotive-Ersatzteilmarkt.



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