Do you “buy and build as you go” with your analytics architecture? Most companies do, and have for decades. The result is a heterogeneous environment for analytics with a variety of hardware, software, databases and analytical applications used in silos. There’s tremendous duplication of data and inconsistency in the analytical process, leading to a lot of wasted time and money.
What can be done to improve your analytics architecture? It’s much like improving a home – you can renovate your existing house (modernize), expand the existing structure (extend) or knock down what you have and rebuild (innovate). Let’s take a closer look at these three scenarios:
Modernization: This is the best choice when you’re reasonably happy with your existing analytics infrastructure. Your users have invested time and effort to build it, but you know you can improve analytics efficiency, productivity and ROI. In this scenario, you keep your core analytics software and infrastructure and launch a modernization project. You update your software using metadata and web technology, review your data governance, move to commodity hardware with grid technology to reduce costs and industrialize the analytical process to do more with less.
Extension: In this case, you’ve already modernized your existing infrastructure, but you want to add more space -- maybe you want an innovation lab that allows your data scientists to experiment more often and faster. Or, if you've standardized your analytics with SAS, you might decide to extend your SAS platform with new technologies like in-memory analytics.
Innovation: Let’s say you have a heterogeneous analytics architecture made up analytics software from multiple vendors. Modernization would be costly and difficult because of the complexity of the existing architecture, process and data. You might decide that the current analytics infrastructure and processes can be decommissioned in the near term, and to build a new modern analytics infrastructure with processes based on the latest technologies like Hadoop, event stream processing, machine learning, in-memory analytics, in-database analytics and enterprise decision management.
As you can imagine, selecting the scenario that's right for you must be determined on a case-by-case basis. Your decision depends on the value of the existing analytical infrastructure and process -- and also the value the business puts on it.
The more value analytics gives to the business, the more budget you’ll get to modernize, extend or innovate your existing analytics infrastructure. SAS has both existing and new technologies to give you the best value, no matter which of these three scenarios you choose.
To better determine the best way to move forward, please have a look at the SAS Business Analytics Assessment. In just five minutes, you can identify your organization's analytical strengths and discover areas that need improvement .