Introduction
For modern data driven businesses, data catalogs are at the centre of their data journey. This is because of the fact that these businesses aim to become a data first company and in order to become one, implementing a data catalog solution is extremely important. Data Catalog contributes immensely to the success of any data analysis process. In simple terms, just like how we have a catalog in the library that helps readers discover the book of their choice, a data catalog can provide an overview of all the business data.
Before starting the journey of a data catalog, we need to understand what is a data catalog ? What is its significance ? and then we can look at the steps involved in building a data catalog.
In this article, we try to answer these questions and lay down a roadmap for aspiring data driven businesses to build a data catalog.
What is a Data Catalog ?
A data catalog is a collection of all business metadata along with tools that helps users to locate the data required for the analysis. In short, a data catalog serves as an inventory for all data, where users can always refer before entering any evaluation process. A data catalog not only lists out the data but also explains it to the users. Everything that we find in a database schema are also an integral part of the data catalog but there are a few major differences.
For example, schema is applicable to data from a single source. Like, data from Google Analytics. When we move the data from data sources to data warehouses like Google Big Query, Amazon Redshift, Snowflake to name a few, we need to map the schema, unless we are using Sprinkle, which automatically maps the schema.
In contrast to this, a data catalog reflects all the data that is present in the organisation across sources and repositories.
In the age of self served analytics, for managing metadata, data catalogs have become a standard. The data catalog connects the datasets, which is the primary focus of a data catalog, with the people who work with the data.
Key Functions of a Data Catalog
In today’s modern age, it's not practical to build or even try to manually build a data catalog. It is very essential that the existing data and the new data that is generated are automatically discovered and indexed. This is where tools like Sprinkle, come into the picture which not only helps in collecting data from multiple sources but also helps in tagging them as well.
Some of the other key functionalities of a data catalog are as follows:
- Searching Dataset : A modern data catalog must be capable of searching the data sets through business terms, keywords and natural language. This is particularly required to empower semi-technical or non technical users. Advanced data catalog must also rank the searches based on relevancy.
- Evaluating a Dataset : Before proceeding for data analysis, it is important for the user to evaluate a particular dataset so he/she can decide the fitment of the dataset as per the use case. Therefore, it becomes very important for a data catalog to provide a preview of the dataset (without the need of downloading the data), a picture of the other users comments, user ratings and also the quality of the data.
- Accessing the Data : Users should be able to access the data in an organisation without facing many hindrances. At the same time, it is equally important for the data catalog to not expose all the data to all users or in other words the data catalog must understand the hierarchical structure of the data in an organisation.
Benefits of Data Catalog
Some of the key benefits of a data catalog are as follows:
- Data Democratisation: For modern age businesses, building a data first company is at the core of every data strategy. Moreover, modern businesses want to establish relationships between multiple datasets and drive business insights and decision making. A data catalog not only helps in data discovery but also helps in building a data first company.
- Personalisation of the Data : Intensive users of data or the ‘power users’ can use the catalog to mix and match data across data sets and get a personalized view. By just glancing over the data, an user must be able to comprehend major characteristics like quality, relevance, owner and content of the data set.
- Easy Onboarding of New Data : Data lying in silos, like standalone departmental servers or individual computers, could be a big headache for an organization looking to collaborate all data at one place. Through a data catalog individual users can index the data based on pre approved workflows and security permissions.
- Providing a Holistic View of the Data : A well documented data catalog provides a holistic view of the complete data of the organization. It helps users to comprehend the data in a dataset, understand the meaning of the data as per business use cases, view relationships, sources and issues across datasets.
- Ease of Collaboration across Organizations : With a data catalog in place collaboration across organizations is made smooth. Also, users can now collectively work to improve the data quality and track which dataset was used by whom and for which specific purpose.
- Increase in Speed of Business : Research shows 75% of the total time spent in analysing any data is used for data wrangling. Data catalog makes it simple for the users to trace the data, tie it with the proper business use case and enhance the process of decision making.
- Maximisation of the Data Value : As a data catalog helps different stakeholders of an organisation, from a data analyst to a Chief Data/Analytics Officer, the value out of a particular data set is used at all levels of an organization. Hence the use of the data increases which otherwise could have been lying in silos across the organization.
Steps of Building a Data Catalog
The different steps involved in building a data catalog are as follows :
- Collection of Metadata : Collecting metadata of all the present data is the first step in building a data catalog. A data catalog crawls across all the data present in the repository and copies the metadata to the catalog. This metadata is used in identifying datasets, data tables and files.
- Construction of a Data Dictionary - After the metadata has been collected, the next step is to build a data dictionary which contains the description of all the metadata. There are multiple softwares available which facilitate building a data dictionary or simple excel could be used for building one.
- Profiling the Data - The next step involved in building a data catalog is to profile the available data which helps stakeholders to visualise and comprehend the data.
- Marking Out the Relationship among Data - Marking the relationship between datasets is critical as this helps stakeholders to easily understand the relationship of the data across data sources. This relationship could be marked out manually by individuals or by advanced systems, which learn from queries made across datasets or similar values.
- Lineage Building : A visually represented lineage is very helpful in establishing the path of the data. From the origin to the destination. The different processes that are involved in the data flow are explained through the lineage. This enables stakeholders to find the root cause of an error very easily by just simply following the lineage.
- Data Organization : Data inside a file or a table is present in a technical way. This may or may not make sense as per the business requirements. Therefore, manual efforts are required to organise the data in a way so that they can be easily comprehended and trusted by business users. Some of the ways by which this can be done include Tagging the data, organising the data based on usage and user role and through automation.
- Ease of Accessibility : For greater use of the data catalog it should be easily accessible within the data stack. If you use Sprinkle, you’ll be able to use the data catalog within the website which increases the usability of the data catalog.
- Security : As the data catalog holds the overview of all the data of an organisation, it is very important to adhere to the security standards in an organisation. A data catalog must have role level security, knowledge on who used what data and at what time, auditing and encryption.
Conclusion
Data management in the modern age is a challenging task. Also, a complicated data stack doesn't help either. Multiple tools for ETL, data catalog and visualisation makes it even more complicated. Also, as discussed manually building a Data Catalog could be challenging and time taking. Therefore, tools like Sprinkle, which has all the above mentioned features in one platform becomes a necessity.
