The Big Data revolution has ushered in a new era of data management. While data-driven decision making has been prevalent for the past thirty years or so, never has data been more important and central to a business than today. In the 1990’s, businesses saw the emergence of data warehouses, a central data repository that was fed by numerous databases, source systems, and applications to enable companies to get an enterprise-wide view of their customers, sales, supply chains and operations etc.
While back in the day the data warehouses were approximately a terabyte in size, today’s data warehouses are much larger and measured in petabytes. As data got bigger, enterprises realized the need to upgrade and augment their data warehouse capabilities for business benefits with technologies such as the in-memory process that supported these warehouses to process the vast data workloads of today which are almost 1000 times larger than before. Along with becoming bigger, data warehouses also needed to become faster with the growing requirement of real-time data analytics and new data types. These warehouses had to serve a much wider scale of business-critical functionalities and deliver actionable insights anytime, anywhere. This ‘datafication’ of businesses needed a modern data warehouse, one that was agile, smart and helped in making better decisions at lower costs. Clearly, just Data Warehousing needed an automation upgrade.
Data Warehouse Automation
Data Warehouse Automation or DWA is revolutionizing traditional data warehousing with the obvious financial gain, increased speed, data accuracy, and efficiency. The idea behind Data Warehouse Automation is to automate all those parts of the Data Warehouse that allow automation so that the project team can focus their energies on those part of the Data Warehouse and BI processes that need intellectual input. Rather than having one team write every piece of the ETL code by hand and take months to build a data warehouse, DWA simplifies the capture of the Data Warehousing design and automates almost the entire Data Warehousing lifecycle, minimizes manual code writing and automates the repetitive, time-consuming and labor intensive tasks. This ultimately makes BI implementations almost five times faster.
Data Warehouse Automation not only helps the Data Warehouse development process but is also capable of handling changing business requirements real-time without impacting the delivery schedules of projects. However, Data Warehousing Automation is not the silver bullet that can replace the complete thought process that goes into designing an analytical environment. Data Warehouse design, architecture, and implementation of analytical components are the art that needs intellectual inputs from sound technical teams. Hence, in order to be successful with their DWA efforts, enterprises need to leverage the technical skills of their teams to automate the tricky bits of data with discipline and focus. To ensure that the DWA process does not turn into an issue these are a few good thumb rules to follow:
* Define an end-to-end and step-by-step automation strategy that involves all the stakeholders
* Identify the right analytical information and engage it with the correct design process for faster implementation
* Identify the right technology depending on the teams’ skill sets and budgets
* Start the DWA process in small, bite-sized pieces to ensure perfect implementation.
Tools and Technologies
Choosing the right DWA tool becomes the next critical thing to consider. Since the market is flooded with choices today, how can enterprises ensure that they have made the right tool choice? Here’s what an ideal DWA tool should do:
* It should allow automation of everything without requiring additional mapping or ETL coding and data modeling
* It should be simple enough to be managed by an in-house team
* It must allow for automation of model design and code and allow teams to choose between Third Normal Form and Data Vault along with Star-Schema
* It should not be restrictive to one target database platform and also should offer future migration options
* It should be able to understand data dependencies and then automate all the required processes and finally run them in the correct order.
* It should simplify analysis for optimized and streamlined data discovery
When it comes to Data Warehouse Automation, identifying the problem is just ‘Step 1’ of the entire process. Having done so, instead of putting the problem of broken processes on roller blades, it’s best to take a structured approach to DWA to ensure the effectiveness of the Data Warehouse to lead to business profitability through productivity.