Much like everything else, technology advancements and the growing proliferation of data have made sure that data warehouse modernization is on the enterprise priority list.
Data warehousing, which is essentially the process of storing data across the enterprise, needs modernization to access and analyze information and power real-time analytics.
Data warehousing modernization helps enterprises adapt to changing business needs, provides support to leverage new data sources, and aids product/solution development and improvement.
Why look at data warehouse modernization?
Legacy data warehouses cannot serve today’s business demands. Revamping and modernizing data warehouses become critical when enterprises want to leverage the benefits that new technologies bring and meet their constantly evolving business demands.
Data warehouse modernization induces agility in data processes and provides businesses with insights and reports in real-time, something that traditional, legacy data warehouses are not designed to do. To drive competitive capacities, good business outcomes, and organizational resilience in a world where the volume, variety, and velocity of data are exploding, modernizing the data warehouse becomes critical for business success.
Data warehouse modernization:
- Allows organizations to value data as an asset since the volume of data is growing exponentially.
- Enables organizations to understand the different types of data being generated, the frequency of data generation, its uses, and how often it needs to be retrieved to generate valuable business insights.
- Drives digital transformation initiatives and allows organizations to leverage the benefits of modern technologies and systems.
- Provides fast, reliable, and serviceable business insights.
- Saves costs and delivers a profitable RoI by removing the expenses associated with legacy system maintenance.
- Provides compatibility with unstructured data sources, social media, and advanced analytics provides deeper intelligence, and enables laser-focused decision-making.
Data warehouse modernization helps organizations manage the spiraling data management costs and delivers scalability, performance, and quality of service. These are mainly driven using technologies such as:
- Cloud: The cloud provides enterprises with a scalable and cost-effective infrastructure for increased agility. Data warehouses in a multi-cloud environment help enterprises modernize their data architecture and deliver better user experiences to data-driven applications and services.
- Big Data Analytics: Modern data warehouses come armed with powerful big data analytics tools and allow organizations to gather insights from historical as well as real-time data. New-age technologies such as advanced machine learning and AI further boost automation and facilitate deeper and hidden insights and correlations.
- Business Intelligence Dashboards and Reporting Tools: Modern data warehouses consist of many subsystems, relational databases, and ETLs that help transform data. User-friendly dashboards and advanced reporting tools facilitate data-driven decision-making and operationalize insights in specific business processes.
How to modernize the data warehouse?
Enterprises have a lot of choices when it comes to how they source, store, and manage their data as it continues to flow from different channels and sources. The focus on user experience and the need for multi-dimensional visualizations have also increased. Enterprises, as such, need to use both structured and unstructured data and capably consolidate information from data whether it is in motion or at rest, and offer clear, deep, and cost-effective insights irrespective of the diversity of data.
As enterprises look to decouple storage from computing to power new analytics ranging from traditional BI to machine learning, here are a few key considerations when embarking on the data warehouse modernization journey.
- Creating a robust the data warehouse modernization strategy
Modernizing the data warehouse and moving to the cloud doesn’t need to be a multi-year lift-and-shift process. It, however, needs clear planning and a well-defined modernization roadmap to meet tangible business needs at the right cost.
Conducting readiness assessments, technology selection sessions, data architecture assessments, and identifying if there would be any disturbances or downtime during the modernization process become critical for success.
Mapping existing legacy systems’ entity relationships, data types, partitioning strategies, indexing, and strategies with target schema are important to manage the native data warehouse assets appropriately to deliver the benefits of data warehouse modernization.
Identifying whether the migration will be manual, semi-automated, or entirely automated is an important consideration at this stage. Enterprises need to ensure that logic, which includes event-based error handling, writing, data cleansing, and reloading the processed data back to the data warehouse is correct to ensure that there is no error in transforming the existing code.
Creating the right data governance model also becomes an important part of the modernization strategy to make sure that infrastructure agility is not compromised.
- Choosing the data warehouse
Enterprises need to identify whether to use a cloud-based data warehouse or build and maintain an on-premises system during the data warehouse modernization journey. An on-premises data warehouse provides complete control of data and allows enterprises to implement strict data security policies. It can, however, require significant effort on the development front, making scaling a painful exercise, especially when there is a need for higher capacity only for a smaller time.
Cloud-based data warehouses provide immense scalability, are easy to deploy and maintain, and provide enterprises with all the functionalities needed to operate the data warehouses' web APIs.
The data warehouse modernization effort has to ensure that the enterprise turns data into an asset than a liability to maintain your big data systems.
- Building connectivity
Choosing the right data warehouse is one part of the modernization puzzle. Making sure that the data warehouse is connected to distributed data, apps, and disparate data sources is essential for the ROI of modernization initiatives.
Modernization initiatives thus have to leverage the right connectors spanning across the cloud and on-premises to ensure that enterprises can quickly access the data necessary for their data warehouse use cases. Enterprises need to connect the entire data ecosystem such that they can send that data in real-time not only to the data warehouse, but also to any application, microservice, or data store that needs it.
Leveraging modern ETL tools also become important since traditional ETL tools make it hard to build data warehouses and data marts fast. ETL automation, creating the right ETL pipelines and factoring in data regulations and compliance demands become key considerations as well.
- Managing Technical Debt
Enterprises need to ensure that their technical debt is under check during the data warehouse modernization process. Procrastination in data warehouse design and coding defects can lead to huge technical debt if not handled in time. The longer these defects are stacked, the higher the technical debt to manage.
Identifying the interdependent workloads at the data level, determining an effective partitioning strategy either by cluster, columns, distributing by keys, etc., or doing the same using the number of buckets are important for the low technical load.
Evaluating the nature of the database storage, and verifying how it’s loaded, processed, and analyzed to optimize schema objects and proper end-to-end Metadata Management are important considerations to ensure low technical debt and optimized data warehouse performance.
In Conclusion
Data warehouse modernization is now critical for growth across any enterprise. However, maturity and expertise in ETL tools and systems, database types, and platform-network mix and experience in identifying methods to standardize data along with knowledge in process optimization and automation are important consideration points. Domain knowledge also comes in handy to ensure all regulatory, compliance, and industry norms are adhered to.
Modern Data Warehouses are the enablers of the modern enterprise which is powered by immense new opportunities brought by big data. The shift towards modernization, however, must take an agile approach instead of the big bang approach. This is because enterprise needs evolve with time and sometimes differ significantly from the initial requirements. An agile approach allows for a more personalized strategy for data warehouse modernization and accounts for important factors such as vertical, industry, and business requirements, and helps businesses manage their modernizing efforts with greater assurance.
