Most Influential Trends Affecting Cloud Analytics Platform Selection
Cloud analytics tools are complex systems, and there is a range of factors that impact their selection. These are a few trends influencing how buyers choose cloud business analytics tools:
Cloud BI tools capable of being implemented using agile methodology will continue to attain results the fastest. Cloud platforms that are capable of being implemented incrementally while taking into account internal and external stakeholder requirements (such as those from customers, IT, marketing, sales, service and senior management) get the best results.
Agile development methodologies dominate enterprise software development today. Forward-thinking cloud platform vendors are giving their customers the flexibility of taking an agile based approach to implementing their analytics platforms as well.
The majority of enterprises acquiring cloud analytics platform solutions are opting for short-term contracts with 12 to 24 months being most common. The factors driving short-term commitments for cloud platforms include budgeting and spending constraints within business units, the opportunity to negotiate better pricing at renewal, and greater influence on product and service roadmaps in the short-term. Given how fast new algorithms, apps and platform extensions are happening today, shorter contracts are freeing up enterprises to get out in front of the innovation curve and make it work to their advantage.
The algorithm economy has arrived, and competitors are moving fast to reorder industries using cloud platforms as the catalyst to deliver greater insights corporate-wide. The advanced analytics capabilities being developed and tested today will change the competitive landscape of manufacturing and service industries within the next three years or less, according to Louis Columbus.
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Leading companies, including AstraZeneca, Ingram, General Electric, FedEx, UPS and others, are all defining business models today based on algorithms that deliver insights and intelligence not possible before. Cloud based analytics platforms including IBM, Microsoft, SAS, Salesforce and others are supporting advanced algorithms capable of supporting entirely new business models. The need for data scientists with algorithm expertise is skyrocketing as a result.
Users should benchmark cloud analytics platforms on how quickly they are adding advanced prescriptive and cognitive analytics apps to the workflow level. The challenge for analytics platforms providers is the majority of work being done today is reactive, or at best, anticipating future events. Analytics vendors pushing forward with a greater focus on prescriptive and cognitive-based analytics apps are ahead of the market. They realize that machine learning and advanced algorithms will be the new normal in three years or less and are planning for that today.
Look to scale beyond descriptive and predictive analytics apps by finding analytics platform providers capable of propelling your company to the upper levels of the Intelligent Cloud Maturity Model. The majority of companies today are locked in the lower layers of the Intelligent Cloud Maturity Model, as shown in the graphic below. Using analytics apps that only deliver descriptive analytics is like trying to drive forward by staring in the rearview mirror.
Enterprises need to push analytics platform providers to develop and launch machine learning, advanced prescriptive analytics and cognitive analytics. When this happens, companies will be able to see how the timing of a decision during a given financial period makes a major difference in outcomes.
Best of all, there will be less guessing and more knowing why a given strategy or business model is succeeding or not. By using the Intelligent Cloud Maturity Model as one of many frameworks to evaluate analytics platform providers, companies can make the best possible decision when it comes to the analytics platform they choose.
The innovations happening in data discovery will transform cloud BI tools quickly, integrating search and visual-based data discovery with automated data preparation and natural language support. Imagine being able to have Tableau running on top of a cloud analytics platform that capitalizes on the latent semantic index (LSI) algorithms used for capturing insights from unstructured data. Use cases like this and others are in development today. Data discovery will be significantly different on these platforms in the next three years as a result.