Why do companies go for Data Warehouse Modernization?
4 Feb, 2017
In this era of rapid technological advancements, evolution is essential in almost every facet of life. In real life applications, especially in business, finance, healthcare and other industries, there is a persistent need for decision makers to align their business goals to stay ahead of their competitors.
Hence, Enterprise Data Warehouse, which has been recognised as the heart of the decision support systems, also continues to evolve to meet new business requirements and technology challenges. This process can be termed as Data Warehouse Modernization or Data Warehouse Augmentation.
Need for Data Warehouse Modernization
Enterprise Data Warehouse is, obviously, not dying but evolving. Data Warehouse architectures have been experiencing a relatively intense evolution in recent years, and this trend is expected to continue in predictable future; the simple reason being its capability to mirror the business it serves. Those days have gone where top database vendors were advocating the necessity of maintaining a massive enterprise Data Warehouse by consolidating data from operational systems for the benefit of analysis and tracking of key performance indicators. With the unprecedented growth of data in the form of semi-structured and unstructured data such as videos, images, e-mail messages, audio files, presentations, webpages and several other kinds of business documents, companies have realized the difficulty of storing everything into a single massive Data Warehouse.
Key Business Drivers for Data Warehouse Modernization
With the traditional Data Warehouse failing to meet new business requirements, it is significant for an enterprise to modernize their Data Warehouse
environment to keep it competitive and responsive to the market place. The key business drivers in modernizing Data Warehouse are
To accommodate massive data sets comprising unstructured, semi-structured and structured data, organizations tend to go fornew data storage and management architectures for Big Data to leverage them to gain business advantages.
The driving force for enterprises to switch to advanced analytics like ad-hoc statistical analysis, predictive modelling, data mining, text analytics, entity analytics, optimization, real-time scoring, machine learning, etc. to derive better business insights pave the way for modernization of Data Warehouse.
Real time operations
Modern data-driven organizations need real time access to their data to enable cutting-edge features such as self-service data access
and similar techniques to their analytic features which is achievable with Data Warehouse modernization.
Approaches to Data Warehouse Modernization
Data Warehouse modernization can be brought to realization by combining many of these approaches - from server upgrades and restructuring of data models to adding additional data platforms into the existing Data Warehouse environment to replacing the exiting primary DW platform. Thus, DW modernization can utilize one of these two strategies depending on the organization’s requirements and budget constraints.
Extend existing Data Warehouse’s primary platform
This is the most adopted trend in data warehousing today. It augments the existing Data Warehouse’s primary platform by adding additional data platforms, thereby moving towards multiplatform Data Warehouse environments. This approach is comparatively less expensive and non-disruptive as it preserves existing investments in data warehousing.
Replace existing Data Warehouse’s primary platform
Organization which have outdated data warehousing platforms that are no longer a good fit to satisfy business and technology goals tend to move towards replacing the existing Data Warehouse platform with sophisticated data platforms. This approach can be quite expensive and disruptive for businesses.
Organizations can also use a combination of both these approaches. Whichever approach is taken, the modernization can extend, remodel, consolidate, and improve data.
Top Vendor Platforms for Data Warehouse Modernization
There are several vendors offering Data Warehouse modernization platforms. The top players in this area are
IBM offers a suite of products for modernization such as IBM InfoSphere Big Insights for Apache Hadoop, Pure Data System for Analytics, IBM dashDB for a managed Data Warehouseservice on the cloud, DB2 with BLU Acceleration for building a software-defined Data Warehouseand InfoSphere Streams to process Streaming data and other forms of real-time data.
Pentaho provides a commercial open source platform that tightly couples data integration and business analytics with products such as Pentaho Data Integration. It also supports portability with other major Big Data Distribution platforms such as Cloudera, Hortonworks, Amazon Web Services, and MapR and integrates with NoSQL Databases.
SAP offers a broad set of solutions for big data, analytics applications, rapid deployment solutions, advanced analytics tools, analytics databases, data warehousing solutions, and information management tools.
SAS provides big data management and advanced analytics solutions to help customers make better faster decisions.
Benefits of Data Warehouse Modernization
There are many benefits to Data Warehouse modernization which help organizations to
- Bring in streaming and other structured and unstructured data sources to existing Data Warehouse environments
- Optimize Data Warehouse storage and provide archiving capability
- Streamline the Data Warehouse for better simplicity and reduced cost
- Provide improved query performance to support advance and complex analytical applications
- Deliver better-quality business insights to operations for real-time decision-making
- Securely govern and access data with centralized metadata environment
- Experience substantial productivity gains