Eight Factors for a Successful Data Catalog Project

Eight factors for a successful data catalog implementation

A Data Catalog is a crucial foundation for managing data as an asset and for democratizing data within the company. Most importantly, it helps make data FAIR (findable, accessible, interoperable and reusable) and empowers employees to work with data.

While a data catalog offers a variety of benefits, one of the challenges is still to convince your company of its value and to get buy-in from business stakeholders. Therefore, the data catalog implementation team needs to identify the impacts of the data catalog functions on the business and communicate these to the appropriate stakeholders.

There are essentially two phases in a data catalog implementation project:

  • The preparation phase, which refers to the phase until an appropriate data catalog tool is selected, and
  • the implementation phase, which refers to the configuration and customization of a selected data catalog tool and the feeding of data into it.

In the following, we present eight success factors that turned out to be essential for planning and executing a data catalog project.

Data Catalog

The Competence Center Corporate Data Quality (CC CDQ), in collaboration with researchers from Fraunhofer ISST and many data management experts, developed a data catalog reference model and a market study. Data Catalog Reference Model & Market Study

Preparation phase

In the preparation phase of a data catalog project, a company understands the need of managing data as an asset and creating data transparency. The company eventually appraises the potential of data democratization and data-driven value creation. Based on this understanding, it designs a vision that outlines goals, scope and major use cases.

Business goals and a scope with respect to data domains, organizational scope and depth of the metadata shall be defined at the very beginning of the data catalog project. The scope can be extended later, but a clear initial base is essential for the first phase. The scope should be documented in a data catalog concept. The concept frames the project in this early stage and communicates the vision as well as the project road map.

The identification, documentation and prioritization of data catalog use cases is a key factor in this phase. The use cases help to address the needs and requirements of various stakeholders and potential sponsors within your company and to ensure you reap the benefits of the data catalog. In addition, the use cases are helpful in specifying the scope and evaluating different data catalog tools compared to these use cases while also helping you develop a step-by-step implementation plan.

The Competence Center Corporate Data Quality (CC CDQ) has created more than 20 use cases that can be used by CC CDQ members to jumpstart data catalog projects.

With various data catalog tools on the market, each having their very own advantages and unique features, the prudent selection of an appropriate tool has long-term implications. A detailed selection concept including goals, scope, use cases, functional requirements, meta model and basic implementation approach can help you make the right decision. This is also a good basis for reflecting the requirements to business stakeholders.

Implementation phase

The usage range of a data catalog might be difficult to envision for some business users during the early stages of the data catalog implementation. For a successful involvement of business users, it can help to gain their attention by means of mock-ups, proof-of-concepts and showcases. Business users should get a realistic impression of the information that is stored in the data catalog and how they can use it in their daily business activities.

By these means, they can participate in the data catalog initiative and provide valuable input. The project team can learn from these interactions and shape their models, meta models and customize the data catalog to meet their business needs.

It is advisable to follow an agile, flexible implementation approach alongside use cases. Continuous review sessions with users ensure that the data catalog meets the business needs, solves relevant problems and continuously delivers business value.

After an initial set of use cases is implemented, it is the task of the project team to promote their success and gain interest for new use cases. A data catalog implementation can be regarded as a continuous realization of business use cases.

Particularly at the beginning of the implementation phase, prioritize the use cases that provide a high potential for quick wins and business benefits. A data catalog implementation should start pragmatically with use cases that address urgent and relevant user needs and generate business value. For instance, many metadata management approaches have failed in recent years because of overly complex data models and too many initial meta-attributes. Instead, it is recommendable to start simple, e.g., with metadata from existing repositories.

When diving into data modeling activities, it is important to align the data models with the intended use. Whereas physical models are clearly defined by the technical reality, conceptual and logical models give the modeler a lot of creative freedom. Data models should have a clear goal, which guides the granularity of the model, its shape, modeling technique and many more modeling decisions.

Whereas a data catalog tool provides the comprehensive functionalities to match data supply and demand, it is delivered as kind of an empty shell. The success of a data catalog implementation tool depends a considerable amount on how well and how fast it is filled with life, i.e., loaded with relevant (meta)data. Therefore, a company needs to establish processes and roles (a data steward, for example) to provide content and document data in the data catalog during the preparation phase.

Market Study: Data Catalogs

Here you can find the article "Data Catalogs - Integrated Platforms for Matching Data Supply and Demand" of the Competence Center Corporate Data Quality (CC CDQ). Data Catalogs - Integrated Platforms for Matching Data Supply and Demand

Stakeholders should continuously grasp the benefits of the data catalog. Success stories, lessons learned, information about new datasets as well as tips and tricks about using the data catalog should be communicated in an appropriate manner.

The communication of implemented business use cases and resulting business benefits can help to create positive network effects and, therefore, convince and empower more and more people (data citizens) to use the data catalog.

Project Communication

Do great things and talk about it – How to effectively communicate your data management project. Tips from our data management experts. Data Management Project Communication

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Was this information helpful to you? We determined these eight success factors for project teams to create a quick and valuable business benefit with their data catalog initiative. We defined these factors based on our work within the data catalogs co-innovation session of the Competence Center Corporate Data Quality and several Data Management Consulting projects. If you are looking for additional support, please contact us.

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Competence Center Corporate Data Quality (CC CDQ)

The Competence Center Corporate Data Quality is your reliable partner for the scientific support of your Data Catalog projects. Competence Center Corporate Data Quality (CC CDQ)

Data Management Consulting

Data management capabilities are a key factor for business success! We are the leading consulting firm in data management in Europe, and we support our customers along their entire journeys towards data excellence. Data Management Consulting Services
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