What is a data catalog and what is it used for?
Learn all about how a data catalog works and how to use it
Companies are increasingly turning to data-driven applications as they digitize and automate processes. The foundation for such data-driven applications is a functioning data catalog: This is a type of centralized information registry that contains information about all company data, enabling efficient data management, categorization and use within the company.
Learn more about the basic functionality, the added values and get to know the specific use cases of data catalogs. We'll introduce you to the typical user roles of the data catalog and give you a practical step-by-step guide to creating a data catalog.
Learn more about the basic functionality, the added values and get to know the specific use cases of data catalogs. We'll introduce you to the typical user roles of the data catalog and give you a practical step-by-step guide to creating a data catalog.
Table of content
What is a data catalog and what added value does it bring?
The creation and use of data takes place across departments in all areas of the company. To provide employees with a uniform and consistent database for these purposes, it is essential to classify and structure the data in a central location. This structuring is usually done in companies with the help of so-called metadata.
Metadata is data that contains information about a specific set of data - in simple terms, "data about data". Metadata comes in a wide variety of forms, such as technical, business, operational, administrative, and terminological data, as well as governance metadata and context metadata. One of the central tasks of a data catalog is to document this metadata precisely and unambiguously and to make it visible and thus also available.
The data catalog therefore acts as a centralized information registry that contains all relevant information about existing data and associated sources, access to the data, quality, and content. In addition, the data catalog usually includes historical data such as usage history and relationships.
The task of a data catalog is also often described by the term data curation: Data curation enables structured searching, retrieval, and quality assurance of data. On the one hand, users of a data catalog can register data and determine who is allowed to use which data in which way. On the other hand, the targeted retrieval of data for the evaluation and analysis of questions is possible.
The use cases of data catalogs in the enterprise are versatile and range from data management and analysis (data discovery) to compliance with internal standards and guidelines (data governance) to data assessment, administration and application in the context of machine learning/artificial intelligence.
Metadata is data that contains information about a specific set of data - in simple terms, "data about data". Metadata comes in a wide variety of forms, such as technical, business, operational, administrative, and terminological data, as well as governance metadata and context metadata. One of the central tasks of a data catalog is to document this metadata precisely and unambiguously and to make it visible and thus also available.
The data catalog therefore acts as a centralized information registry that contains all relevant information about existing data and associated sources, access to the data, quality, and content. In addition, the data catalog usually includes historical data such as usage history and relationships.
The task of a data catalog is also often described by the term data curation: Data curation enables structured searching, retrieval, and quality assurance of data. On the one hand, users of a data catalog can register data and determine who is allowed to use which data in which way. On the other hand, the targeted retrieval of data for the evaluation and analysis of questions is possible.
The use cases of data catalogs in the enterprise are versatile and range from data management and analysis (data discovery) to compliance with internal standards and guidelines (data governance) to data assessment, administration and application in the context of machine learning/artificial intelligence.
These are the added values of using a data catalog
A data catalog brings order to corporate data and enables the structured, efficient and security-compliant creation and use of data by stakeholders in the company. The overarching goal of a data catalog is to support data-driven processes to increase performance, efficiency, and data quality, and to enable transparent decision-making.
The added values of the data catalog at a glance:
The added values of the data catalog at a glance:
Efficiency
Data catalogs are the basis for efficient processes in the company. As an "efficiency catalyst", data catalogs also reduce the workload of data managers and create free capacity for other tasks. According to the Forrester-Forbes report, data scientists spend 75 percent of their time finding and understanding data. So-called "data wrangling" - harnessing raw data for analysis - is a time-consuming and costly pain point for many companies.
Data catalogs are the basis for efficient processes in the company. As an "efficiency catalyst", data catalogs also reduce the workload of data managers and create free capacity for other tasks. According to the Forrester-Forbes report, data scientists spend 75 percent of their time finding and understanding data. So-called "data wrangling" - harnessing raw data for analysis - is a time-consuming and costly pain point for many companies.
Performance
A data catalog can be used to accelerate processes across the company, reduce costs, and identify new business areas. The use of structured data enables a significant increase in performance in all areas of the company.
A data catalog can be used to accelerate processes across the company, reduce costs, and identify new business areas. The use of structured data enables a significant increase in performance in all areas of the company.
Cost reduction
Thanks to the significant increase in efficiency and the elimination of data redundancies, it is possible to noticeably reduce costs in the company. In addition to measurable costs, a data catalog also has an impact on other areas of the company. In addition, communication between employees is optimized, errors are reduced and data is made more readily available.
Thanks to the significant increase in efficiency and the elimination of data redundancies, it is possible to noticeably reduce costs in the company. In addition to measurable costs, a data catalog also has an impact on other areas of the company. In addition, communication between employees is optimized, errors are reduced and data is made more readily available.
Data security
Against the backdrop of increasingly stringent data protection standards and security requirements, data catalogs enable adherence to internal company compliance and legal regulations. In particular, the data catalog also helps eliminate shadow IT and prevents unnecessary data copying.
Against the backdrop of increasingly stringent data protection standards and security requirements, data catalogs enable adherence to internal company compliance and legal regulations. In particular, the data catalog also helps eliminate shadow IT and prevents unnecessary data copying.
Data access & agility
Through data catalogs, companies make their data accessible across the enterprise, opening up whole new possibilities for teams. Thanks to the elimination of data silos, it is possible to develop new use cases and thus also open up new sales markets. At the same time, agile projects are being promoted in terms of data initiatives. Around 60 percent of agility projects in the company currently fail due to a lack of data culture.
Through data catalogs, companies make their data accessible across the enterprise, opening up whole new possibilities for teams. Thanks to the elimination of data silos, it is possible to develop new use cases and thus also open up new sales markets. At the same time, agile projects are being promoted in terms of data initiatives. Around 60 percent of agility projects in the company currently fail due to a lack of data culture.
Decision making
Data-driven decision making is becoming more and more important in companies. Data enables transparent, traceable and objective decisions based on trustworthy data.
Data-driven decision making is becoming more and more important in companies. Data enables transparent, traceable and objective decisions based on trustworthy data.
Data quality
Those who set up a data catalog are naturally concerned with the quality of the existing data and want to identify missing of incorrect data. By establishing a data catalog, it is possible to optimize data quality in general and identify data-associated problems. Improved data quality also increases employee confidence in the data: The data catalog becomes the "central point of truth" and enables self-service analyses.
Those who set up a data catalog are naturally concerned with the quality of the existing data and want to identify missing of incorrect data. By establishing a data catalog, it is possible to optimize data quality in general and identify data-associated problems. Improved data quality also increases employee confidence in the data: The data catalog becomes the "central point of truth" and enables self-service analyses.
Balance
With a data catalog, responsibilities are defined and managed. In this way, it is possible to ensure a sustainable balance between agility and governance and to adapt data management in the company to regulatory guidelines and market requirements.
With a data catalog, responsibilities are defined and managed. In this way, it is possible to ensure a sustainable balance between agility and governance and to adapt data management in the company to regulatory guidelines and market requirements.
Tip: According to surveys, 40% of all departments would like faster response times from IT. The introduction of a data catalog can reduce the strain on IT resources and allow that department to devote itself to tasks more efficiently.
Experience shows: Creating an enterprise-wide data catalog requires additional effort in the first step. For the process, it proves beneficial to set a good focus. Data sets that are very important and used a lot should be cataloged as early as possible. However, the investment usually pays for itself very quickly thanks to efficiency and performance improvements as well as noticeable cost reductions.
CHeck out the contiamo data catalogUse and roles of a data catalog
Once the data catalog is set up and all relevant data is cleanly entered, it opens up far-reaching possibilities for data-driven project development, support for self-service analyses, and strategic development of new markets. Most companies have recognized the potential of data catalogs: 85 percent of organizations see data catalogs as a solution to the challenges of today and tomorrow.
In the following, we briefly present the most important use cases of data catalogs and address the different roles of data catalog users.
In the following, we briefly present the most important use cases of data catalogs and address the different roles of data catalog users.
Project support:
In operational business, data catalogs enable a significant increase in efficiency. Around 70 percent of the time in data projects is spent searching for and preparing data - data catalogs enable efficient and successful execution at the project level.
In operational business, data catalogs enable a significant increase in efficiency. Around 70 percent of the time in data projects is spent searching for and preparing data - data catalogs enable efficient and successful execution at the project level.
Self-service analyses:
With self-service analyses, companies make their data treasure trove available even to "laymen" who are not familiar with IT, thereby enabling the broad use of data in all departments. Self-service business intelligence enables synergy effects between IT and business operations and opens up completely new possibilities for end users in terms of data preparation and analysis.
With self-service analyses, companies make their data treasure trove available even to "laymen" who are not familiar with IT, thereby enabling the broad use of data in all departments. Self-service business intelligence enables synergy effects between IT and business operations and opens up completely new possibilities for end users in terms of data preparation and analysis.
Achieve strategic goals and open up new markets:
Today, data catalogs also play an important role in strategic business alignment. In a rapidly changing business landscape, building a data catalog helps companies adapt to new market developments and achieve strategic goals. These goals include:
Today, data catalogs also play an important role in strategic business alignment. In a rapidly changing business landscape, building a data catalog helps companies adapt to new market developments and achieve strategic goals. These goals include:
Improving the value proposition of products through a sound data basis
Generating new fields of application for existing products
Opening up new sources of revenue
Digitization of processes in the sense of Industry 4.0 based on improved data processing
Deriving data-driven, well-founded insights for strategic business alignment
Support of business intelligence (BI) approaches for the provision of analytical content
Structured management of continuously growing data volumes
Establishing data-driven technologies and trends such as Big Data, cloud hosting, automation, machine learning or self-service analytics in the enterprise
There is now a broad consensus regarding the need for and added value of data catalogs. Many companies are already investing large sums in Big Data, artificial intelligence and data-driven automation. However, statistics also show that only 72 percent of these companies maintain a data culture - many companies face challenges in successfully implementing new technologies and processes due to a lack of structured data catalogs.
The typical roles of data catalog users:
A clear allocation of roles is required for the sustainable and successful implementation of a data catalog. This involves describing the tasks of the various stakeholders and, at the same time, their relationship to each other.
Typically, the following three roles are defined in a data catalog:
Typically, the following three roles are defined in a data catalog:
Data owner: Data owners are the "owner" of the data. They are responsible for the data and its maintenance. The quality of the data catalog depends largely on the work of the data owner.
Data steward: They maintain the metadata in the catalog and ensures the data quality. They are particularly involved in the creation of the data catalog, enriches existing metadata and ensures the maintenance of the catalog. In doing so, they work closely with the data owner, but also with the business users, to ensure correctness.
Data user: Data users actively use the data catalog for his work. They search for data in a targeted manner and use datasets for analysis and evaluation.
Data steward: They maintain the metadata in the catalog and ensures the data quality. They are particularly involved in the creation of the data catalog, enriches existing metadata and ensures the maintenance of the catalog. In doing so, they work closely with the data owner, but also with the business users, to ensure correctness.
Data user: Data users actively use the data catalog for his work. They search for data in a targeted manner and use datasets for analysis and evaluation.
From a corporate strategy perspective, the goals of the data catalog can also be described with the following four catchy keywords:
Data discovery: applications within Business Intelligence (BI) that enable the targeted identification of patterns and the retrieval of data according to user permissions and the elimination of in-house data silos.
Data transparency: Obtaining a correct overview of the entire data landscape including data sources and relationships.
Data analytics: The use of raw and cross-source data for analysis purposes.
Data compliance: The adherence to applicable guidelines and compliance requirements in the company.
The functions of a data catalog
The data catalog is the data technology heart of the company. This is where metadata is read from databases and managed, data is structured, organized and made available, and roles and authorizations are assigned. The most important functions of the data catalog are briefly presented below.
Metadata management
Data organization
Data governance
Data analysis
Access to and management of metadata
For a data catalog to function, it must collect descriptive information about all data. This is the metadata. The metadata later enables the user to find the desired data quickly and efficiently on the basis of certain characteristics. The data catalog accesses the customer's databases for this purpose. These can be CRM systems, ERP systems, data warehouses, data lakes, databases or a master data repository, for example. These can be stored either "on premise" or in a cloud and can be accessed either via a direct database connection, via APIs or via ingest databases.
In addition, the data catalog can also contain other data information types such as data reports with visualizations as well as APIs, data lineages and relationships between data. Basically, a data catalog distinguishes between two types of metadata:
In addition, the data catalog can also contain other data information types such as data reports with visualizations as well as APIs, data lineages and relationships between data. Basically, a data catalog distinguishes between two types of metadata:
Manually added data
This metadata is usually in a business context and therefore cannot be extracted automatically. They must be manually added to the data catalog.
This metadata is usually in a business context and therefore cannot be extracted automatically. They must be manually added to the data catalog.
Automatically extracted data
This metadata is derived solely from technical information and analysis of actual data sets, such as machine learning methods.
This metadata is derived solely from technical information and analysis of actual data sets, such as machine learning methods.
Data organization and management
A data catalog is responsible for structuring and documenting data. To do this, the data catalog analyzes data sources based on metadata, tags, annotations, similarities, the respective context, or the data origin. It does not matter whether the data is already structured or still unstructured, or what type of data it is.
In analyzing the data for structuring, the data catalog makes use of modern IT methods: with the help of artificial intelligence (AI), machine learning (ML), semantic interference, tags, patterns or relationships, it succeeds in systematically scanning databases and automatically deriving the required information.
By classifying and linking metadata to terminologies and processes within an organization, business glossaries or data dictionaries can also be created to facilitate the use of the data catalog.
The centralized business and technical documentation of the data assets in the data catalog offers a decisive advantage: a "single source of truth", a central point of truth, is thus created within the company.
In analyzing the data for structuring, the data catalog makes use of modern IT methods: with the help of artificial intelligence (AI), machine learning (ML), semantic interference, tags, patterns or relationships, it succeeds in systematically scanning databases and automatically deriving the required information.
By classifying and linking metadata to terminologies and processes within an organization, business glossaries or data dictionaries can also be created to facilitate the use of the data catalog.
The centralized business and technical documentation of the data assets in the data catalog offers a decisive advantage: a "single source of truth", a central point of truth, is thus created within the company.
Data governance
The data governance function is a core part of a data catalog. This function manages and documents user access to data. The data governance function assigns roles and permissions, identifies responsibilities for the data, and analyzes the quality of the data and the data flows. On the basis of functioning data governance, it is possible to adhere to the company's internal compliance guidelines while at the same time taking into account legal regulations such as the General Data Protection Regulation.
Info: 43% of analyses are held back due to governance concerns
Data analysis tools
Advanced data catalogs are characterized by extensive data analysis tools and thus provide the user with far-reaching options for further searching and analyzing data. For example, the data catalog can prepare and document data specifically for metrics, reports, KPIs or comparable evaluations. API interfaces make it much easier to output and evaluate analyses from the user's point of view.
The user interface of modern data catalogs is designed in such a way that it actively supports the user flow and offers an intuitive user interface with an integrated search function. To ensure that the data catalog can be flexibly adapted to changes and is largely scalable, it should have open interfaces to the outside world. This makes it possible to extract metadata to other applications or import data.
The user interface of modern data catalogs is designed in such a way that it actively supports the user flow and offers an intuitive user interface with an integrated search function. To ensure that the data catalog can be flexibly adapted to changes and is largely scalable, it should have open interfaces to the outside world. This makes it possible to extract metadata to other applications or import data.
Step by step to the right data catalog
Step 1: Selecting a suitable catalog
The first step is to analyze the company's requirements for the data model. It is important to involve all relevant stakeholders in the company, define goals and objectives for the catalog, and develop a coherent data strategy.
When selecting a suitable provider, it is important to obtain various offers and examine them with regard to the company-specific requirements. Some exemplary criteria are listed in step 2.
When selecting a suitable provider, it is important to obtain various offers and examine them with regard to the company-specific requirements. Some exemplary criteria are listed in step 2.
Step 2: Proof-of-concept phase
The goal of the proof-of-concept phase is to assess the suitability of the available data catalogs for the company-specific needs and goals. It is also important how the cooperation with the vendor works. The provider plays an important role in the subsequent implementation of the data catalog as well as in its operation, so the "chemistry" should be right.
In this phase, it is important to involve employees as early as possible and to increase acceptance of the data catalog with the help of "real" use cases. The stakeholders of the data catalog include the various business departments, IT, compliance teams, and also business intelligence teams. In the proof-of-concept phase, it is a good idea to organize hands-on workshops with the vendor to define and implement two or three common use cases.
To achieve a high degree of utilization within the company, the various data sources should be connected to the data catalog as early as possible. By establishing common definitions and technical terms, it is possible to establish a uniform "wording" and make the new tool known as a "single source of truth". At the same time, it is important to increase the visibility of the tool through transparent communication within the company.
In this phase, it is important to involve employees as early as possible and to increase acceptance of the data catalog with the help of "real" use cases. The stakeholders of the data catalog include the various business departments, IT, compliance teams, and also business intelligence teams. In the proof-of-concept phase, it is a good idea to organize hands-on workshops with the vendor to define and implement two or three common use cases.
To achieve a high degree of utilization within the company, the various data sources should be connected to the data catalog as early as possible. By establishing common definitions and technical terms, it is possible to establish a uniform "wording" and make the new tool known as a "single source of truth". At the same time, it is important to increase the visibility of the tool through transparent communication within the company.
Step 3: Introduction phase
During the introductory phase, it is important to get employees on board and achieve a high level of acceptance for the data catalog. It is advisable to carry out comprehensive training and to add further users to the data catalog step by step by means of an iterative procedure. By linking the data catalog to all relevant tools at an early stage, it is possible to ensure a high level of functionality right from the start. Based on the experience gained, the data catalog is then continually adapted and continuously improved (see proof-of-concept phase).
More and more companies are deciding to establish a central catalog for managing, structuring and monitoring all company data. To help you, we provide a practical guide to creating a data catalog with our step-by-step instructions.
Tip: Even after implementation, it is important to constantly monitor how the data catalog is accepted by the workforce and what added value it brings.
Experience Contiamo yourself
Get started in seconds and test our platform 14 days for free, no strings attached.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Follow us
