Masterdata Management is the no.1 trend in today’s Business Intelligence industry surveys. This trend follows from the increasing decentralization of data with the rise of micro-services, unstructured data, external data, and the increased popularity of self-service analytical applications.
These developments may negatively impact data quality, resulting in a degradation of trust in the data. In order to become (and stay) a data-driven organization data quality has to be ensured.

To solve this issue organizations implement single trusted applications that are the master of a data entity, for example customer data. This is often referred to as Masterdata Management (MDM) and a way to enable a ‘Single version of the truth’.

The cusbi data analytics platform is designed around the use of master data, because it makes it much easier and effective to create great business insights. Cusbi can also help organizations to use master data, by advising and providing support in this transformation, and facilitate in creating master data management applications.

Masterdata Management with cusbi

With cusbi you control the data in the machine.

Before getting into Masterdata Management, a quick look back how this became such an important trend in the Business Intelligence industry.

The democratization of business data

To keep competitive in today’s data-driven world people need answers fast for their business challenges. This becomes harder in today’s connected world where data is becoming more decentralized. A data entity such as customer information might be sourced from a multitude of systems. This can include unstructured and external data sources such as social media channels, suppliers and distributors.

IT departments have a hard time to meet these changing demands and environments and at times are left somewhat behind due to the pace of change.

One of the solutions to solve this issue is the rise of self-service data analytics and visualization tools. Organizations are putting these user-friendly applications directly in the hands of end-users and give them the power to solve their business problems. Surveys indicate that currently around 55% of organizations report adopting self-service BI tools (1).

Tablet with data analytics

Self service analystics and visualisation tools have empowered end-users (Photo by Burak K from Pexels)

At cusbi we encourage and support this democratization of data, allowing organizations to more effectively reach their business goals. But we also recognize some of the new challenges this brings..

Distress due to data decentralization

With the larger variety in data sources, in combination with the rise of self-service tools, the decentralization of data results in several new challenges including:

  • The decentralized data make it harder to exchange data between applications and organizations. This could be due to limits in tooling, processes or sometimes deliberate political choices of holding on to data.
  • The implementation costs of self-service BI tools are often higher than expected. With data present in different silo’s, integration is needed to achieve the insights required by the business which drives up costs.
  • Different users applying different definitions to the same data entity, causing incomparable and inconsistent insights.
  • Users might be introducing a bias into their analysis and thereby highlighting or disregarding certain data.

The overall effect is that data quality is degrading. The democratization and decentralization thus results in less trust in the information by the users. This has led to poor information reliability being reported as the biggest obstacle for businesses to monetize data (with data silo-ing also coming in the top 5). (2)

The concerns about data quality have changed the subjects currently on top of mind in our industry. The self-service and data visualization tools that were for long a hot topic for BI platforms are now seen as a commodity (3).

The biggest challenge now is how to get a ‘Single version of the truth’ in organizations.

Bring back the trust – the focus on Masterdata Management

Implementing Masterdata Management (MDM) solutions is one of the best ways to improve your data.

Classic examples of master data are customer or product data. The goal of Masterdata Management is to create a single data master that is then shared across the organization. A customer master for example ensures that the definitions and the quality controls around that entity are all the same and will behave the same wherever it is used.

The importance of Masterdata Management in solving the data quality challenges is reflected in the industry surveys. Masterdata and Data Quality Management are now the no. 1 trend for the few last years. (4)

BI Trends 2021 - BARC

 Extract from infographic: “Data, BI & Analytics Trend Monitor 2021” (

Adopting Masterdata Management

The goal of Masterdata Management is to create a single master, consisting of clear and consistent data entity definitions. Key elements to achieve this are removing duplicate data and combining multiple (incomplete) data records to create one clear data entity. This applies to both existing data in an organization and new data being generated.

The reference master data needs to integrate this with multiple systems in the organization. This is often the one of the bigger challenges, especially when dealing with legacy systems or systems that are a black box to the organization.

But Masterdata Management is more than only technology and architecture, it requires a more all-encompassing approach. Successful Masterdata Management goes hand-in-hand with establishing good Data Governance in organizations, such as ownership and policies for the various data sources. Furthermore, data quality should be assured by implementing best practices such as the Data Quality Cycle.

Masterdata Management with cusbi

The cusbi team and cusbi platform can help you to create an excellent Masterdata Management solution. Some of the elements provided in our solution:

  • Masterdata Management functionality is provided standard in our platform. We publish them using the OpenAPI standard. (5)
  • Build a clear data entity by removing duplicate data and combining multiple (incomplete) data records.
  • A user-friendly GUI allows users to directly manage master data.
  • Our platform performs data quality audits on data sourced from various systems.
  • Our team has extensive experience with Masterdata Management and know that a key success factor is getting the definitions right. Integral part of deploying the cusbi platform is supporting organizations in establishing a Master Data Model.

Cusbi Masterdata Management in use

One of our clients uses the cusbi platform to perform audits for dozens of supermarkets which are themselves part of various supermarket formulas. The accounting packages from each supermarket are unified to the same data entity definitions. From this unified accounting scheme, the audit reports can be produced which are customized for each supermarket. The users of the accountancy firm have direct control of master data to map the various data sources and reports for each supermarket with the unified data entities.

Just get started!

The all-encompassing approach of adopting Masterdata Management in an organization can be intimidating and causes many to hold off. It impacts the technology, governance and processes in an organization. Masterdata Management is difficult, but the rewards are great. The savings in resources, the quality of your information and simplification of your administration by being able to manage data from a single point are tangible.

Our experience has shown that successful Masterdata Management adoptions are the result of “just getting started”, even if your datasets are relatively small.
We at cusbi are ready to help you in your adoption. Reach out to us at and let’s get you started!


(1) Self-Service BI: An Overview (
(2) Data trust pulse survey results 2019 – PWC.
(3) Magic Quadrant for Analytics and Business Intelligence Platforms – February 2020, Gartner.
(4) Data, BI and Analytics Trend Monitor 2021 – November 2020, BARC
(5) OpenAPI Initiative (