Why Your Company May Not Need a Data Catalog
The Apption Data Podcast - Episode 1
There is a large amount of information out there about data catalogs, usually written by data catalog providers themselves, about why your company needs a data catalog. In many cases it is accurate, though often a fully-fledged data catalog might not be the best choice for your company’s data journey.
Experts at Apption, Erik Putrycz, CTO, and Adam Joe, Director of Strategy, weigh in on why or why not your company needs a data catalog. We discuss DataOps, data agility, and great data catalog alternatives for companies who are on the fence.
What is a Data Catalog?
At its core, a data catalog serves as an organized inventory of all the data assets within an organization. Data catalogs play a crucial role in both deriving value from data and ensuring proper data governance. At Apption, we like to say that the goal is finding the right data at the right time and at the right quality.
“Data governance is finding the right data at the right time and with the right quality.” - Adam Joe
When Should You Implement a Data Catalog?
Implementing a fully-fledged data catalog becomes crucial when an organization deals with a substantial number of datasets, making it impractical to manage them manually. This need arises especially when tracking sensitive data or critical fields within datasets.
For example, as soon as data is being acquired and used by multiple teams in your organization - HR, Sales, IT, etc. – it is time to consider a data management strategy, whether that be a traditional data catalog or a more agile solution. Across different teams, we see analysts and managers asking for and sending each other datasets instead of being able to find the required data in a single location. This is validated by the now famous IBM statement that businesses spend 70% of their time looking for data and only 30% using the data. Constantly searching for the right data, adds significant overhead for companies that likely already have strained resources.
6 Benefits of Investing in a Data Catalog
A data catalog offers several benefits, including:
Streamlined data discovery
Semantic search over metadata
Controls around data privacy
Improved data quality
Enhanced data governance
Efficient use of data assets
For example, take an organization working with forestry data. A manager there should be able to go into a central repository and find all the relevant data to a particular tree species if they want to analyze the health of that species in a particular region. Or a sales director should be able to find all the sales data for the company's stores in western Canada for a certain quarter. The ability to access data companywide without having to go through multiple channels is essential for the efficiency of any organization.
However, the first step is to evaluate whether these benefits align with your organization's goals and priorities, which leads us to the downsides of a fully-fledged data catalog.
The Downsides of Data Catalogs
While data catalogs offer significant advantages, they may not be suitable for every organization.
Fully-fledged data catalogs require a considerable upfront investment in terms of time, money, resources, and expertise, which may not be feasible for smaller companies or startups. Also, rigid cataloging processes can impede agility and innovation, slowing down the pace of data-driven initiatives.
Many traditional data catalog solutions today are targeted towards Fortune 500 companies with heavily-funded data management programs, causing them to fall short for leaner organizations.
Why Data Catalogs Fall Short for Many Companies
Data catalogs may fall short for many companies due to the challenges associated with managing data agility, scalability, and diversity. Additionally, the complexity of implementing and maintaining a comprehensive catalog can outweigh its benefits, especially for organizations with limited resources.
Traditional approaches to managing data involve complex processes such as ETL and maintaining data warehouses. However, these processes are difficult to scale, leading organizations to explore more agile solutions like DataOps. IBM defines DataOps as a process-oriented approach to help manage data that creates, shortens, and amplifies feedback loops and allows for continued experimentation to learn from mistakes and achieve mastery.
“Today people want to look at implementing DataOps and have something that's more agile … you want to achieve this speed and flexibility to meet the demands of your business.” – Erik Putrycz, CTO
The goal is to achieve speed and flexibility to meet the ever-changing demands of businesses. This entails bringing in new datasets, ensuring data quality, and avoiding the need for overly formalized processes. While documenting data structures is ideal, as is practiced in traditional data management, it often becomes impractical due to resource constraints, hindering data agility within the organization.
Data agility is crucial for accurately documenting key items like KPIs and financial data, ensuring clarity in business discussions and decision-making processes. The overall size and depth of data within every business is rapidly growing and requires firms to be able to instantly search, use, and understand their data to make everyday decisions. Many existing tools are not agile and require a thorough process and policy development, such as a business glossary and detailed column definitions. Companies such as Apption are aware of the need for a more agile solution than the traditional data catalog.
An Alternative Approach: The Role of Datahunter
When assessing alternatives, your organization should be prioritizing:
Time-to-value: An agile solution should provide immediate value to your data teams with minimal integration.
Ease of use: Data users and owners are the focus. A prohibitive learning curve will limit adoption and efficacy.
While working with our long-term clients, Apption developed a solution to fill the gap in the data catalog market.
Apption’s Datahunter offers this alternative approach to traditional data catalogs, focusing on agility and ease of use. By leveraging AI algorithms and automation, Datahunter enables organizations to quickly assess their data landscape, identify valuable data assets, and facilitate data sharing and collaboration without the need for extensive manual cataloging efforts.
For more information about Datahunter, check out the website here.
3 things to Assess Within Your Organization First
Before diving into a data management strategy, it is crucial to assess your organization’s:
Short and long-term goals
Data requirements
Resource constraints
Consider factors such as data volume, diversity, governance needs, and organizational culture to determine the most suitable approach for managing your data assets.
Looking Ahead: The Future of Data Catalogs
As organizations continue to evolve in their data management practices, the future of data catalogs lies in striking a balance between comprehensiveness and agility. Solutions like Datahunter aim to bridge the gap by providing a flexible and user-friendly platform that empowers organizations to harness the full potential of their data assets while adapting to changing business needs.
In summary, while data catalogs offer significant benefits, they are not a one-size-fits-all solution for every organization. By evaluating your data management requirements and exploring alternative approaches, your organization can make informed decisions that align with your company objectives and value from your data assets.
Listen to the first Apption Data Podcast: Do you really need a data catalog? below where the team discusses the pros and cons of modern data catalogs, and why in some cases, they may be overhyped.