Businesses now operate in a data-dependent universe, and the ones that can harness data and employ it will be the most successful this year and in years to come. A 2016 Gartner survey reported that companies’ big data investments reached a possible peak last year with nearly half (48 percent) of companies investing in big data (up only three percent from 2015). Gartner’s findings indicate that investments are contracting, and based on the fact that organizations delivered significant big data insights in 2016, and in the year ahead will aim to operationalize them.
This is made possible thanks to the rise of hybrid cloud environments, which will enable companies to operationalize big data sets to increase efficiency in business operations. Foundationally, companies will need to orchestrate the movement and access of big data from the ground (on-premises) to cloud sources and vice versa.
Ground to Cloud
In 2016 customer management cloud apps were used to begin exploring the benefits of integrating big data insights from companies’ on-prem platforms, separated by functional areas and disconnected technology stacks. 2017 promises to be the year that these merge, with functional areas collaborating further with IT teams, and better connectivity within the big data ecosystem to expand upon the use of existing big data platforms via increased access to data.
Take, for example, company data that is critical to customer experiences. Data lakes are a valuable source to store all facets of customer data from a variety of data streams (the different systems used by internal functional departments like CRM, web analytics, survey platforms, webinar data and more). This then creates one repository of data insights, building the base for new, advanced analytics techniques using data sets with value that has yet to be derived.
The predicted movement of this data will be driven from customer management cloud applications to access the detailed “big data” data on-demand in the flexible spirit of the data lake. In contrast, some of the more aggregated data will get transported to the cloud for more repeatable use cases.
Cloud to Ground
A great deal of analytics and reporting platforms will continue to run on-prem on either private cloud or grid infrastructures in the enterprise. But as big data volumes continue to expand in the cloud, it’s not feasible to crunch those big data sets in on-premises data centers.
This is exactly where cloud big data platforms like Amazon EMR, IBM BigInsights on Cloud, Microsoft Azure HDInsight or SAP Altiscale come in. They are often more scalable and cost effective to crunch and transform big data sets into digestible business insights. In 2017, businesses will start to integrate these insights – now more manageable in terms of size – by moving them to on-prem databases and analytics platforms to drive core business operations.