Recent Insight into Analytics in the Cloud

Featured article by John L. Myers, Research Director of Business Intelligence, EMA

As we enter the middle of the second decade of the 21^st century, many organizations are recognizing and utilizing cloud infrastructure as a way to implement their analytical and business intelligence requirements. In the past, cloud-based implementations of operational applications such as sales operations and customer relationship management were relatively easy. Standardized data models and processes made it possible for organizations to engage with platforms such as SugarCRM and Salesforce.com. Analytical challenges were more difficult because of customized data model requirements and the individualized process and compute requirements of different companies. One organization may want multi-dimensional analysis on one set of data, while another may desire predictive analytics on a different data set. Now developments in configuration and processing make it possible for cloud-based platforms to follow similar deployment models as Salesforce.com.

This utilization can take the form of implementing a cloud-based data integration to knit together disparate platforms both on-premises and in the cloud. Data management platforms can be used toward the goal of information storage or improving data quality. Analytical platforms can also be used to perform advanced or modern analytics using cloud-based computer platforms. The implementation can be used for data visualization packages for standard reporting, static dashboards, or ad-hoc visual data analysis; it can also be a combination of one or more of these concepts pulled together to build a hybrid ecosystem of data management, integration, processing, and presentation. In any event, organizations of all sizes are using cloud-based implementations to implement and/or augment their analytics and business intelligence requirements.

In recently-released end-user research from leading IT analyst firm Enterprise Management Associates (EMA), organizations overwhelmingly indicated that cloud-based offerings for data integration, management, quality, analytical processing, presentation, and collaboration were on a par with, or superior to, on-premises options available for their technical environments. Some of the highlights from this new research include:

– As part of the 2014 EMA cloud-based analytics and business intelligence study, survey panelists were asked to identify the depth of their strategies on cloud-based strategies for analytics and business intelligence. More than 31% of respondents indicated that they had adopted cloud-based strategies and those strategies were an important part (Currently Adopted and Important) of their business. Another 24% of respondents indicated those strategies were Currently Adopted and Essential to their businesses, placing 56% of the EMA panel into an extensive cloud-based strategy.

– Approximately 18% of EMA panel respondents indicated that either one or two projects were associated with their cloud-based analytical initiatives. Over 41% of respondents said they had three or four projects within their organization. The remaining 40% indicated their organizations had over five projects associated with their cloud-based analytics strategies.

– Security is the most important component of a cloud-based solution. When information and data processing leaves the confines of an on-premises data center, the importance of security becomes acute. Nearly 55% of EMA panelists indicated that security was Extremely Critical to their cloud-based analytical implementations.

– The backing of analytical applications—Developer Support and End-User Support—is also important. Because cloud-based implementations are marked by constantly evolving feature/function sets, it is important to provide the developers, who are creating the analytical applications, and the business stakeholders, who are using and often-times doing their own configuration, with the information that they need to effectively utilize a cloud-based analytical platform.

– Organizations indicated that they are much more interested in the financial stability and cost certainty associated with annual subscriptions or multi-year agreements than they are in the potential tactical savings associated with monthly or utility-based pricing. Due to this focus on yearly or multi-year agreements, the budgetary approval for cloud-based implementations stays at a relatively high level.

Looking at the attributes of today’s cloud-based analytics and business intelligence strategies and environments, you can see a set of best practices emerging. Organizations continue to mature both their implementations with, and their confidence in, cloud-based components for their analytical requirements. As organizations continue to develop a cloud-based analytics hybrid data ecosystem for their modern analytical needs, opportunities evolve to mix and match the best options for data integration, data management, analytical processing, visualization, and collaboration. This ecosystem will not be focused on the technical constraints of current platforms. Instead, this modern analytics ecosystem will branch out to find the right technical tools for the business objectives no matter the implementation avenue: cloud-based vs on-premises; data structure—structured/relational vs multi-structured; or analytical model—dimensional vs predictive/advanced. In 2015 and beyond, we will see a continued blending of on-premises with cloud-based options for analytics to create and refine a hybrid data ecosystem for modern cloud-based analytics.

About EMA

Founded in 1996, EMA is a leading industry analyst firm that specializes in providing deep insight across the full spectrum of IT and data management technologies. EMA analysts leverage a unique combination of practical experience, insight into industry best practices, and in-depth knowledge of current and planned vendor solutions to help clients achieve their goals. Learn more about EMA research, analysis, and consulting services for enterprise line of business users, IT professionals and IT vendors at http://www.enterprisemanagement.com or blogs.enterprisemanagement.com. You can also follow EMA on LinkedIn, Twitter, and Facebook.

John Myers joined Enterprise Management Associates in 2011 as senior analyst of the
business intelligence (BI) practice area. In this role, John delivers comprehensive coverage
of the business intelligence and data warehouse industry with a focus on database
management, data integration, data visualization, and process management solutions.

John has years of experience working in areas related to business analytics in professional
services consulting and product development roles, as well as helping organizations solve
their business analytics problems, whether they relate to operational platforms, such as
customer care or billing, or applied analytical applications, such as revenue assurance or
fraud management.