Moving to a hybrid cloud analytics model can help you remove analytics roadblocks, but today’s new data environment can introduce new problems because it is so complex. Being aware of these issues is the first step to overcoming them.
Hybrid Cloud Analytics- As big data gets bigger, more diverse, and more dispersed, enterprises large and small are turning to hybrid cloud analytics — where data from on premises and cloud sources is analyzed seamlessly.
In a recent Webinar, “Achieving Business Value Using Hybrid Analytics,” Fern Halper, vice president and senior director of TDWI Research for advanced analytics, discussed what hybrid cloud analytics is all about (and how it can quickly become complex) and the data and analytics challenges of this kind of environment. By taking into account all these dimensions, your enterprise will be better able to plan for — and reap the benefits of — a hybrid cloud analytics environment.
The Data Dimension
When you think of “hybrid,” you may think first about the type of cloud configuration (public, private, or a combination). However, hybrid can describe the type of data your enterprise is working with — structured, unstructured, and multi-structured data. Hybrid can alsodescribe the data’s location. Data once only on premises is moving to the cloud (or is being created there), or it’s both on premises and in the cloud if it’s replicated.
Sometimes the data is offloaded from the on-premises data warehouse to the cloud, for example, to offload burdened systems. It’s not necessarily a lift and shift but more of a gradual value-based approach. Sometimes data landing and staging is being moved to the cloud, and some organizations are very gutsy and have moved all their data to the cloud. However, more often we see a hybrid mixed model; often organizations are pulling data from one source to another.
Add new storage platforms to this mix, as Halper explains. “In a survey we asked, ‘What infrastructure do you have or plan to have in place for predictive analytics?’ Enterprises have data warehouses, of course, but its use in the public cloud is now being used by over a quarter (28 percent) of respondents. That figure is set to more than double if users stick to their plan.” Just as popular are new data storage platforms, such as the data lake, which can handle a variety of data types. Data lakes — along with Hadoop on the public cloud and analytics platforms on the public cloud — are also set to double in use within a few years.
The analytics performed on this dispersed data is also changing to a hybrid model. Organizations are expanding their analytics tools kits, adopting machine learning, natural language processing (NLP), and AI technologies. In a recent survey conducted by TDWI, Halper asked respondents what they were doing with these technologies Over half were deploying them in operations for use cases as diverse as predictive maintenance and supply chain optimization. Marketing, a top user noted, put predictive analytics and machine learning to use analyzing customer behaviors, in particular, and churn. Text analysis, a sort of offshoot of NLP, is being used to analyze social media data and perform sentiment analysis. IT is also using these technologies to predict machine failures.
“Different types of analytics are being used together and they’re being used separately to gain business value. For example, someone might use — in the same analysis — text analytics to determine the sentiment of customers then integrate that information with other data about the customer to create a big data set for customers in order to create a model for retention. Likewise with geospatial data: you might marry it with customer data to predict risk for an insurance company,” Halper points out.