Your Guide to Software Selection

Selecting The Best Cloud Analytics Platform: Trends To Watch In 2016

The bottom line is that the scale, speed of deployment, agility and ability to rapidly prototype analytics workflows on cloud analytics platforms is winning out over more costly and time-consuming alternatives that require IT’s time and attention.

Cloud analytics and business intelligence (BI) platforms that can responsively scale to support a company’s evolving business models are starting to replace on-premise reporting systems. Legacy systems lack the easily customized user interfaces, self-service and prototyping tools cloud platforms provide. Companies of all sizes today are challenged to scale their new business models and initiatives while keeping existing operations running as efficiently and responsively as possible. As a result, cloud-platform analytics platforms are closing the gap between what legacy systems provide and what enterprises need to compete and grow. The goal of this article is to provide insights into the top trends most influencing cloud analytics selection decisions today. Each of the trends is analyzed below.

Trends Most Influencing Cloud Analytics Platform Selection In 2016

The following trends are having the greatest impact on Cloud Analytics Platforms in 2016:

  • Cloud analytics platforms capable of being implemented using an 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 including customers, IT, marketing, sales, service and senior management are attaining the best results today. 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 platforms are opting for short-term contracts with twelve to twenty-four 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. 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 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.
  • 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 shown 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 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 less guessing and more knowing about 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.
  • Cloud Maturity ModelSupport for Hadoop, R, Python and Spark matures to provide more process flexibility and advanced workflow integration with cloud analytics platforms. IBM is a leader in this area, with their Python and Spark support in the IBM SPSS Predictive Analytics Gallery. One of the weaknesses of cloud analytics platform is the lack of end-to-end application support. The goal of many vendors in this market is to provide greater extensibility through integration to development environments. Expect to see open source platforms and platform integration flourish in the 2016/2017 timeframe as cloud platform providers look to deliver end-to-end process and workflow solutions for enterprises.
  • The innovations happening in data discovery will transform cloud analytics platforms 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. Uses cases like this and others with comparable agility in managing structured and unstructured content are in development today. Data discovery will be significantly different on these platforms in the next three years as a result.
Louis ColumbusSelecting The Best Cloud Analytics Platform: Trends To Watch In 2016

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