Data automation can empower business users to make better quality decisions by providing instant access to pertinent data.
Data warehouse modernization is a hot topic these days, and for good reason. As the amount of data continues to grow exponentially each year, so does the number of data systems that manage all this data. With this sizeable growth comes a major responsibility by organizations to manage all the dimensions involving corporate data such as access, privacy, security, cost, upgrades, scalability, and much more. In short, managing your data enterprise can be downright overwhelming. To support this expansion of data and data systems, organizations are turning to data warehouse modernization.
If you haven’t considered a plan for data warehouse modernization, you might want to bump this initiative to the top of your technology to-do list. Doing so will help keep you in line with market forces and competitive options.
The central component for achieving data warehouse modernization is data automation. There are myriad ways that data automation can support your move to modernize your organization’s data management program.
For instance, data warehouse modernization can help you by quickly preparing your data for AI and analytics without writing code. It can better assist you in maintaining governance and compliance by automatically documenting your data (among many other tasks). There are many vendors and numerous technology options available that can help organizations accomplish data warehouse modernization with data automation.
Automation, of course, has been around for years. It revolutionized the production line and has been a linchpin in transforming business and industries. Today, we see technology automation taking place in numerous ways, such as business process automation, robotics, and IoT. Data automation has become the foundation for data warehouse modernization.
Data warehouse modernization is not a new industry trend. It’s actually been a strategy used by many organizations for more than 10 years. Recently, it has escalated in importance because of the rapid growth of the corporate data ecosystem. Given its importance, it’s a topic that has been analyzed by TDWI’s Upside over the last few years. In this article, TDWI senior analyst Philip Russom looks at how modernization projects, including data warehouse modernization, will be a priority in 2020. In this article, Russom considers why it’s essential to align data warehouse modernization with overall business modernization planning.
I first discovered the preeminence of data automation many years ago when I co-founded an IT consultancy. At the time, we were helping companies implement business intelligence on top of their existing ERP systems. I saw firsthand the resources required on projects. We had to consistently allocate talented coders to perform repetitive, manual coding tasks. I determined that there was a better way and that way was data automation.
When you utilize data automation as part of your data warehouse modernization plan, you can easily design, deploy, and manage a complete and single data management platform that will be agile, robust, and future-proof. This modern and automated data management platform can help you more easily address emerging business requirements, leverage new technology innovation, and meet requirements for regulatory compliance. With that in mind, let’s take a deeper look at why organizations are turning to a data warehouse modernization strategy that has data automation as the centerpiece.
Reason 1: Accounting for Growing Data Ecosystem
When we look at the ever-growing data ecosystem, companies are continuing to expand their data estates by adding new analytics databases, data sources, data platforms, data warehouses, data lakes, and applications. Data warehouse modernization provides a progressive master plan for efficiently modernizing and managing all data-related platforms and data automation is the heart for doing so.
Reason 2: Wholly Unified Data-Driven Decision Making
With this expansion of the data ecosystem, we’ve seen some companies still housing their various data systems in disparate locations. This decentralization and the lack of having all your data systems unified can cause problems. Data automation helps correct these issues by integrating, consolidating, centralizing, managing, and simplifying the enterprise data architecture, resulting in a significant improvement to the quality of decision making that leads to «one version of the truth.»
Reason 3: Addressing Limits of Data Integration
Another justification for data automation is that it picks up where data integration falls short. Data integration integrates all of your data, and cleans and prepares it. However, doing so takes significant time. Using data automation technology can substantially reduce the time needed to perform data integration tasks.
Reason 4: The Rise of DataOps
Data automation can be the custodian that oversees the emergence of DevOps into corporate data. In a research study, analyst firm Enterprise Management Associates examined the impact DevOps is having on corporate data programs. The report indicated that «to orchestrate, operate, and optimize hybrid data ecosystems made up of data warehouses and data lakes, mature organizations are now instituting DataOps.»
The study went on to state that, according to research participants surveyed, «automation is a high priority for DataOps. Thirty-five percent of participants (those surveyed) indicated that ‘automation’ is an important aspect of operating hybrid data ecosystems.» The research also pointed out that quality control was one of the most pressing needs because «data leaders are tired of the inordinate amount of time their teams are spending on break-and-fix incidents,» and that automation was one of the surest methods for strengthening quality control.
Reason 5: Out with the Old, In with the New
When it’s time to retire an existing data warehouse and transition to a modern data management estate, data automation plays a keen role. With modernization as the vision, you can accomplish more with your data management platform than previously possible. Along with realizing all the benefits as outlined above, you can achieve real-time decision making, increase computing power, and maximize all your data (big data, social data, analytics data, and so on). For companies that want to move their analytics data to the cloud, data automation comes to the rescue yet again by rapidly accelerating and simplifying deployment. For either objective, data automation can be the backbone to drastically improving speed, quality, performance, and costs controls. This article explains how to transform a traditional data warehouse to a modern data estate.
Bottom-Line Improvement from Data Automation
For any organization that has data residing across its infrastructure and stored in databases, data warehouses, or data lakes, this is the time to consider modernization with data automation. The total cost of ownership (TCO) is significantly improved and scalability and readiness for the future is greatly enhanced. TCO is improved even more for companies that opt for cloud computing for their corporate data program.
Data automation can empower business users to make better quality decisions by providing instant access to pertinent data — literally delivering the data right to their fingertips. Without data automation, users can wait days for data, only to be looking at reports that are inconsistent or faulty because data may be missing, outdated, or hard to locate.
Furthermore, with data automation, companies can reduce their need for long-term consultancy projects. Let’s not forget that with data automation, IT can be liberated from spending significant time on mundane tasks, allowing them to focus on more strategic, game-changing breakthroughs for the enterprise, thereby helping it move forward to achieve greater prosperity.
Fuente: By Heine Krog Iversen