Using Data Repositories

Post Author – Mike Hester, Senior Data Architect, Prolifics

Businesses are expanding and collecting more data that is rapidly becoming more complex. The data being collected is commonly used for marketing purposes, helping to make the customer user experience better and will ultimately drive business decisions. This data will come from many disparate sources and will need to be stored in a consistent manner that can be used by everyone within the organization.

“According to the results of a survey on customer experience (CX) among businesses conducted in the United States in 2021, the main challenge affecting data analysis capability for CX is the lack of reliability and integrity of available data. Data security followed, being chosen by almost 46 percent of the respondents.”

J. G. Navarro, Main challenges affecting data analytics for CX in the U.S. 2021,U.S.: main challenges for data analytics CX 2021 | Statista, accessed on May 9, 2023

To address these challenges many companies are turning to data repositories. A data repository, also known as a data library or data archive, is an entity that will be isolated and is for long-term storage and storing data for analytic and reporting purposes. A data repository is large and is generally made up of many databases.

Some examples of data repositories –

Data warehouses store large amounts of aggregated data and are not always necessarily related.

Data lakes are large repositories which generally store unstructured raw data, with that data being classified and tagged with metadata. Raw or unfiltered data means it has not been filtered or structured and does not have a predetermined use case.

Data marts are a subset of a data repository and are more targeted for a particular type of business need or user. Data marts are more secure since the users can only access what they need and not the entire data repository.

Metadata repositories store data about data and databases. Metadata can generally explain the lineage of the data, where it was sourced and any additional information that may be important.

Benefits of a data repository

There are many advantages to storing large volumes of data in an isolated manner which allows the business to make informed, data driven decisions. Data repositories require large investments of money, resources and time.

• Storing multiple data sources in a single place makes it easier to manage, analyze and report
• Isolation allows for faster and less complex reporting or analysis since the data is clustered
• Workload for administrators is reduced due to isolation and compartmentalization of the data
• Data is preserved and archived

Disadvantages of a data repository

There are several vulnerabilities that exist in data repositories that corporations must manage effectively to mitigate potential risks, including:

• Growing databases and data sets may slow down corporate systems. Ensuring that database systems can scale with data growth is mandatory.
• When systems are isolated a system crash can affect all of the data. This can be mitigated by a solid backup strategy that limits and isolates access.
• In some cases, unauthorized users may be able to access all or large volumes of sensitive data more easily than if it was distributed across several locations.

Data repository vs a data warehouse

A data repository consolidates data sets from various sources and isolates them in order to make them easier to access and mine for business insights, reporting needs, or machine learning. It is a general term, whereas a data warehouse is a specific subtype of a data repository designed for collecting and storing structured data from multiple source systems across an enterprise.

A data warehouse is best suited for providing a broad, historical view of large data sets integrated from multiple sources to drive strategic decisions that affect the entire enterprise. Other types of data repositories are better suited for handling unstructured or complex data formats, analyzing data for different subsets of business operations, and other use cases.

Data repository best practices

1. Selecting the right/correct extract, transform, load (ETL) tools or applications to load the data repository is key to ensuring data quality throughout the data lifecycle.

2. Initially it is best to limit the scope and breadth of a data repository. Storing and maintaining smaller data sets and limiting the number of subject areas is beneficial. This can aid in maintaining quality. Over time growth will happen and more complexity and subject areas should be added.

3. Automating the loading of a data repository should be a priority. Manually running processes to load and maintain a repository will be too difficult as the system grows in both volume and complexity. Automated processes help with the management of schedules for things like source file receipt, ensuring proper data hierarchy, i.e., parent-child relationships, and process recovery.

4. Prioritize flexibility. A data repository should be scaled as new sources and targets are introduced, as well as different types of data, such as unstructured data. Incorporating a design that allows for growth without rework should be the goal of architecting a data repository.

If you’d like more information on data repositories or would like to discuss your data needs, click here.

About the Author:

Mike Hester is a Senior Data Architect at Prolifics with 36 years of experience in information technology, specializing in DSS/Data Warehousing. He has worked in various roles such as project manager, system analyst, technical architect, and developer, delivering information management solutions in industries like government, engineering, and ERP. Mike is familiar with operating systems like Mainframe, UNIX, VMS, and Windows, and has worked with databases including Teradata, Oracle, SQL Server, DB2 UDB, DB2 DPF, and Netezza.