There is now a widespread agreement that a data catalog for data warehouse is necessary — they bring value in terms of data efficiency & contextual analysis. Significant amounts of money are already being invested by several businesses in Big Data, AI, and data-driven automation. According to Contiamo, statistics reveal that just 72% of such businesses are able to uphold a data culture, while others struggle to incorporate new technologies and processes since there aren’t enough organized data catalogs.
As businesses digitize and automate operations, they are turning more and more to data-driven solutions. A working data catalog serves as the basis for such data-driven applications: This kind of centralized data directory enables effective administration, classification, and utilization of data inside an organization by providing complete information about organization-wide data.
Learn more about data catalogs’ core capabilities, extra values, specialized use cases & how it helps your data and business teams get the most out of your data warehouse. We’ll provide you with a step-by-step procedure for implementing a data catalog for data warehouses as well as an introduction to the typical functions of the data catalog.
Table of Contents
- What is Data Cataloging?
- What is the Value Added by Data Catalog for Data Warehouse?
- Why do You Need A Data Catalog for Data Warehouses?
- Aspects to Consider While Deciding a Data Catalog for Data Warehouses
- How do Different Departments Benefit from Data Catalog for Data Warehouse?
- How to Set Up A Data Catalog for Data Warehouse?
- How OneMind Improved Data Availability Using Data Catalog?
- Final Thoughts
What is Data Cataloging?
Data cataloging is the practice of creating an ordered inventory of data. Your data and business teams use a data catalog (like a library’s card catalog) to keep track of where everything is stored after your data transfer & storage procedure is complete.
To gather, categorize, and store datasets, the data catalog employs metadata (also known as the information about your data). Your datasets might be kept in a master repository, data warehouse, data lake, or any other kind of storage facility. The majority of commercial businesses opt to store their data in the cloud-like a cloud data warehouse.
A well-organized data catalog for data warehouse provides you with quick access to information since your data is categorized and easy to find. You can easily search for the datasets you’re looking for, view all the ones that are accessible, and assess and analyze them swiftly and confidently using a data catalog for data warehouse.
When implemented correctly, data cataloging provides you with access to all of your data in a single source of truth for all of your data repositories.
It is a must if your business is constantly evaluating and using a growing repository of data.
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What is the Value Added by Data Catalog for Data Warehouse?
Data is created and used throughout a company’s departments and divisions. Data Catalog is crucial to classify and organize the data in one single area (lake or warehouse) so that your data and business teams have access to a standard and consistent database. In businesses, this classification is typically carried out with the use of so-called metadata.
Metadata, or “data about data,” is data that includes details about a particular set of data. There are many different types of metadata, including governance metadata and context metadata for technical, business, operational, administrative, and terminological data. One of the primary functions of a data catalog for data warehouse is to properly and unambiguously describe this metadata, and make it accessible and available to all.
As a result, the data catalog serves as a primary archive for information that compiles all relevant details about the data, its sources, its quality, and its content. Data catalog typically also contains historical information, such as usage and relationship histories.
The phrase “data curation” is also frequently used to describe the function of a data catalog: it facilitates organized data retrieval, searching, and quality control. Users of a data catalog can register data and control who is permitted to use which data in what ways. Furthermore, it is also feasible to retrieve data specifically for analyzing and evaluating questions.
Why do You Need A Data Catalog for Data Warehouses?
There are multiple reasons for you to have a data catalog for data warehouse:
- Existing Data Discovery Techniques are Overly Complicated: The conventional way to utilize a data catalog is ineffective, laborious, fragmented, and only useful to a technical group. Such scenarios ask for a “catalog of catalogs” since many businesses try to merge distinct data catalogs for various types of data repositories.
In this case, there is little chance of the company having real self-service data since conventional data catalogs are more tailored to data engineers and IT users who need SQL scripting to do data searches. Due to the complexity of current data catalogs and the lack of tools accessible to them, business analysts usually rely on already overworked IT for data requests.
- Loss of Information Assets & A Lack of Visibility: Data is rapidly changing and expanding. It is also present everywhere, whether it is local, cloud-based, SQL databases, or NoSQL stores, and in a variety of forms – structured, unstructured, and semi-structured. Finding and organizing this data clutter quickly, simply, and thoroughly is a tedious task.
- Business Analysts Lack the Element of Independence: Numerous analysts are dependent on IT to find the required data and grant access to them. However, this approach always introduces bias because the IT support staff member might not completely get what the analyst is searching for. Data browsing is usually just as important in order to come up with ideas or speculations.
Think about going to a library and asking a librarian to help you find a book about a specific subject. If the librarian doesn’t have a catalog of books, there is a lack of a systematic list that is needed to directly find the book, and it will take the librarian hours to find your desired book.
Extend the same example to the business universe. An analyst, in a similar situation, can decide not to investigate a hunch or theory because it would take hours or it would be extremely difficult to find, obtain, and evaluate the necessary data.
Aspects to Consider While Deciding a Data Catalog for Data Warehouses
Overcoming these difficulties is easy with the use of an enterprise data catalog. The technology, features, and advantages offered by various data catalog systems can however vary.
Your assessment criteria for data catalog solutions should be based on the following ideas:
- Pick a Tool that can Connect to Many Data Sources
- The Data Steward should be Able to Ensure Data Compliance
- Pick an AI-Powered Data Catalog for Data Warehouse
- Data Catalog Creates & Monitors Data History
1. Pick a Tool that can Connect to Many Data Sources
No matter where your data is located—in the cloud, a data warehouse, or a data lake—an enterprise data catalog should be able to operate on structured or semi-structured data.
Additionally, this data catalog must be capable of cataloging all corporate data, not just selected subsets of it. Choose a provider that facilitates numerous different types of data sources and has native connectors for these sources. This is essential if data needs to be moved later to support data transformation; your data catalog should support bulk offload APIs to minimize any operational impact the data movement may have.
It is important to make a checklist of all the data sources that you currently use and then match it to the merchant’s list of sources and apps. It’s also crucial to understand how a provider links to a data source; some demand that query tracking code be put into each data source in order to be cataloged; although this has benefits, it creates issues for cloud apps like Salesforce, ServiceNow, Workday, etc.
2. The Data Steward should be Able to Ensure Data Compliance
It’s crucial to have good data governance and security. Higher degrees of control and visibility over the data, who is accessing it, and how it is being used must be provided by the data catalog solution you choose. For business analytics, you need the data and business analysts to have access to correct data, but not at the expense of transgressing data compliance laws. Data & business users should be able to request access to data through a data catalog, and that access should be revocable.
3. Pick an AI-Powered Data Catalog for Data Warehouse
Data should be automatically profiled in an AI-assisted data catalog. It should automatically inform you of the locations of Personally identifiable information (PII) data kept across your company, which is a crucial first step in compliance.
In addition, it can identify widespread problems with data purification, including duplications, statistical outliers, and numerical normalization, all of which provide important new information throughout the discovery phase.
AI can reveal information about your data that has previously been considered “tribal wisdom.” The easiest approach to effortlessly transfer tribal understanding or any unwritten knowledge within a company that is not widely known to the business user is through AI if you want to enable your business users to locate and use data.
4. Data Catalog Creates & Monitors Data History
If you want to know where the data came from, how and when it was integrated, and the data’s general history and development, have a look at a data catalog solution that displays the data lineage. Through data lineage, users are able to achieve increased clarity, and they are able to determine the data’s origin, reliability, and evolution.
One of the most crucial elements of your self-service data journey will be how this fosters trust around the data. Previously, IT provided information, and the outcomes were frequently blindly believed. There needs to be a trust element now that your users are discovering data and utilizing it in reporting and analysis.
How do Different Departments Benefit from Data Catalog for Data Warehouse?
- Marketing Teams: The marketing teams, more often than not, rely on data to make decisions on target marketing and paid marketing campaigns. A common problem they face is how to find the relevant data and derive customer insights. A data catalog for data warehouses helps with this concern by making data discovery significantly easier by monitoring the data lineage.
- Sales Teams: Sales teams have to consistently monitor multiple KPIs to evaluate their progress. To make sure that the communication between their members is crystal clear, a well-defined business glossary is extremely important. This business glossary makes identifying new KPIs as well as monitoring current ones a lot easier since the data is far easier to interpret with a data catalog.
- IT Teams: While with their technical prowess, the IT team might not struggle with finding and understanding data, a lot of their time is wasted in providing the relevant data to the other teams. Because of countless requests to find some data and grant access to others, they lose a lot of their precious time not working on more important projects at hand.
How to Set Up A Data Catalog for Data Warehouse?
Stage 1: Pick An Appropriate Catalog
Analyzing the data model needs of your business is the first stage. Define your use cases, aims, and objectives for the catalog, design a clear and concise data strategy, and incorporate all appropriate organizational stakeholders. It is critical to collect many proposals and evaluate them in light of the company’s unique requirements when picking a competent provider.
Stage 2: Proof-of-Concept
The proof-of-concept stage aims to determine whether the existing data catalogs are appropriate for your data warehouse, demands, and objectives. The manner in which you collaborate with the seller also matters. The supplier is crucial to both the operation of the data catalog and its later deployment, thus the “chemistry” must be proper.
Early data and business teams’ involvement is crucial at this stage, as is using “actual” use cases to boost users’ adoption of the data catalog. The many business divisions, IT, compliance teams, and business intelligence teams are some of the stakeholders in the data catalog. It is a good idea to arrange practical workshops with your data catalog for data warehouse merchants to understand and work upon two or three frequent use cases during the proof-of-concept stage.
Different data sources should be linked to the data catalog as soon as possible to maximize data catalog use within your business. It is important to build a unified “phrasing” and promote the new instrument as a “single source of truth” by creating similar definitions and technical words. At the same time, it’s also essential to improve the tool’s exposure throughout the organization through open communication.
Stage 3: Initialization
It’s crucial to win over data & business users and obtain widespread acceptance of the data catalog during the launch phase. It is advised to provide thorough training and to gradually add more users to the data catalog using an iterative process.
In order to guarantee a high degree of usefulness straight away, it is feasible to link the data catalog to all pertinent tools at an early stage. The data catalog is then regularly adjusted and enhanced based on the experience obtained (see proof-of-concept stage).
Note: Even after deployment, it’s crucial to keep an eye on how the data catalog is received by the workforce and what value it adds.
Stage 4: Proper Application
Use the data catalog to address your business use cases that were discovered during the planning phase or in the early phases of deployment. It helps the team members to reap the benefits of the new technology right away.
Current users are now responsible for highlighting their accomplishments to other team members and igniting interest in new use cases as they become more accustomed to the data catalog.
As you expand the usage, you must set up the right responsibilities and procedures to guarantee high-quality data as soon as possible by the data catalog. The implementation process might take too long if the data catalog is not rapidly filled with pertinent data, which lowers its perceived value.
To avoid this, designate data stewards early on. By defining responsibilities for the adoption team, stakeholders are created who feel responsible for the catalog’s success. Teams have gatekeepers when data stewards are appointed. During the first phases, these people are in charge of data accuracy and documentation.
Stage 5: Share Business Results & Advantages
The act of implementing a data catalog is continual. The data catalog needs your constant social support. Share company successes, insights, and overall advantages. The more you keep track of your implementation progress, the more motivated data users will be to use the catalog.
Communication is intended to reduce tension spots. Be transparent with your team about how to effectively develop the data catalog to meet business goals while taking the demands of data consumers into consideration.
How OneMind Improved Data Availability Using Data Catalog?
OneMind is a real estate company that offers data to many other real estate agencies, including MLS and Zillow, as well as insurance firms, governmental bodies, NGOs, and other similar entities.
OneMind desired a solution for business customers to have simple access to reliable sources. OneMind collects data from many different data sources into its data warehouse to assemble curated data on hundreds of real-estate variables. The business struggled with data overload and intricate dashboards or Excel files that frequently contained too much information or outdated, erroneous data to provide easy answers.
The organization equipped its business customers with OneMind Natural Language Queries for quick, precise data insights after putting in place a data catalog.
A corporate user may, for instance, enter the following inquiry into the Data Catalog: “What is the current median housing price for a single-family home in San Mateo County, California?” The OneMindTM-powered search on the data platform broke down the question into its component parts > The OneMindTM-powered search on the data platform will break down the question into its component parts.
- “Current Median”: A statistic that is calculated based on input from numeric data
- “Single-family homes”: Specific type of property
- Right Now: Latest trusted data
The business user receives the information they require to ask the next question and reach the next conclusion as soon as the data is returned.
The executives and analysts of the organization now have access to a cutting-edge Natural Language Query solution that gives them instant access to reliable information through AI-powered enterprise data catalogs. OneMind has saved countless hours of coding and manual searching by utilizing an AI-powered Data Catalog for data warehouse.
A well-organized data catalog for data warehouses puts a clearer, quicker, and more transparent analysis at your hands. Your data catalog should enable your data and business teams to gain deeper data insights and make sound decisions. Your business will then be well on its path to being data-driven.
The process of choosing and implementing a corporate data catalog solution doesn’t have to be complex. Changing demands of today’s business analysts in terms of data discovery is not compatible with outmoded methods of metadata management. Using modern data catalogs, the business user will be able to find and utilize the data they require independently, without the assistance of IT, while the data steward may continue to uphold data governance, security, and compliance. The entire analytical process is made more effective, accurate, and perhaps more motivating for the business user with a data catalog.
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