By the end of 2019, many enterprise CIOs were busy working on moving to various cloud services, modernizing custom applications and investing in data science initiatives. After all, new-gen IT is all about satisfying the user in order to provide a positive UX (user experience) for anybody who comes into contact with a company’s online assets.
Thus, we have the current phenomenon of digital changeover, or digital transformation, from old-school tech. However, as in any important undertaking, caution should be observed. Monte Zweben, CEO of operational AI data platform Splice Machine, says there are some easy-to-overlook missteps that should be avoided during that journey. Zweben explains what he’s talking about in this eWEEK Data Points article.
Data Point No. 1: Moving to the cloud without a clear plan.
Simply migrating your infrastructure to the cloud won’t make your legacy applications agile, scalable, and intelligent. Once you move to the cloud, the meter is always running. Companies that rushed to the cloud finished their first phase of projects and realized that their operating expenses increased because the savings in human operators were surpassed by the cost of the cloud compute resources for applications that are usually online. Before rushing into it, consider the objective that you are trying to achieve by moving your infrastructure to the cloud.
Data Point No. 2: Rewriting your legacy apps from scratch.
Mission-critical applications are slow because the volume of data that they are required to process has skyrocketed. Most importantly, the volume and pace of online transactions require these apps to be smart and take intelligent action in real-time without human intervention.
But you don’t have to rewrite them from scratch. By keeping the app intact and placing it on a modern foundation, you preserve the interface and business logic that is the secret sauce of your company and prevent embarking on an expensive, lengthy and risky project.
Data Point No. 3: Abandoning SQL applications in favor of the NoSQL database.
Before replacing a back-end SQL database with a NoSQL database to modernize an application, consider that you could incur significant risk and expansion of the project scope. A lack of full SQL support in NoSQL databases necessitates a large amount of application code to be rewritten, and finding developers with NoSQL chops isn’t easy. NoSQL systems also typically excel at short-running operational queries, but their performance on analytical queries can be poor and not up to par to meet the application’s needs.
Data Point No. 4: Relegating data science to a backroom activity.
Data science, data engineering and application development teams simply do not have enough deep knowledge about the business to execute models into production. Data science is a team sport that needs representation from all three teams working side by side with people who have a solid understanding of the business processes to build an intelligent application that will deliver tangible business outcomes.
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