Organizations are purchasing all kinds of data and analytics solutions, but they still may not be meeting their business goals. If this describes your company, see how insight-driven (vs. data-driven) practices are helping other firms address the problem.
Vendors and professional services firms are always looking for better and faster ways to help organizations drive more business value from data. Some of them are now offering what they call "Insights-as-a-Service," the definition and implementation of which can vary from company to company.
As a baseline (and not a hard rule), Insights-as-a-Service -- by virtue of its name -- is cloud-based, although in some cases professional services are part of the offering. It may be implemented as a distinct technology layer or not.
In addition, Insights-as-a-Service offerings tend to connect to multiple data sources, including enterprise data. They may also provide users with publicly available data, anonymized data, competitive data, and comparative data that the enterprise lacks. And, because "insights" is part of the name, the insights tend to be directly actionable.
In October 2015, Forrester Research published a brief explaining why organizations should adopt "insight-driven" practices, as opposed to data-driven practices. In the report, Forrester argued that "the data-driven mentality and big data talk focus on closing the gap between more data and business insight. Neither addresses how to test insight for value or make insight-driven action a part of everyday business."
Insights-as-a-Service providers are quick to mention their ability to improve business outcomes because that's the entire point of insights. For example, Capgemini provides Data-as-a-Service, Analytics-as-a-Service, and Insights-as-a-Service options. Data-as-a-Service provides raw data upon which analytical applications are built, Analytics-as-a-Service provides outputs of analyses, and Insights-as-a-Service is linked to tangible outcomes such as revenue increase or cost savings.
"I consider them a progression in terms of sophistication and value, and fundamentally what the '-as-a-Service' unit of measure is," said Goutham Beliappa, a leader in the Business Information Management Data Integration and Reporting Practice for Capgemini North America, in an interview.
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Insights-as-a-Service providers are certainly not uniform, however. Their approaches to Insights-as-a-Service, and the language they use to describe it, vary. Some have been categorizing their solutions as Insights-as-a-Service for months or years, while others aren't using the term at all. There are some common characteristics though: use of the cloud, the ability to provide or link to a variety of different data sources, and a focus on insights and action that lead to business value. At Capgemini, Insights-as-a-Service also includes outcome-based economic models.
"Solicitation of an [Insights-as-a-Service] vendor should be predicated on the need for real decisions with meaningful consequences for business growth. [After all] an insight is only meaningful if it results in action that drives value," said Derik Timmerman, co-founder and CEO of Excel problem-solver Spreadsheet Sherpa, and former member of the McKinsey & Company Data Analytics & Performance Transformation team, in an interview.
Despite the variances in implementation and presentation, expect to hear more about insights in 2016. After all, data has little value without analysis. Analysis has little value if it doesn't provide meaningful insight. Insight has little value if it doesn't trigger action. And, action has little value unless it's tied to desirable business outcomes. Insights-as-a-Service reflects that logic and, as a result, its purpose is to enable and expedite top-line and bottom-line impacts.
After you've reviewed the ways enterprises can drive more value with Insights-as-a-Service, tell us what you think in the comments section below. Is Insights-as-a-Service something you're considering? Has your organization already integrated it into your analytics portfolio?
Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include ... View Full Bio
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