Nikola Tesla, with his equipment
Nikola Tesla, with his equipment (source: Wellcome Images on Wikimedia Commons)

Contrary to what many believe, insights are difficult to identify and effectively apply. As the difficulty of insight generation becomes apparent, we are starting to see companies that offer insight generation as a service.

Data storage, management and analytics are maturing into commoditized services, and the companies that provide these services are well-positioned to provide insight on the basis not just of data, but data access and other metadata patterns.

Companies like DataHero and Host Analytics [full disclosure: Host Analytics is one of my portfolio companies] are paving the way in the insight-as-a-service space. Host Analytics' initial product offering was a cloud-based Enterprise Performance Management (EPM) Suite, but far more important is what they are now enabling for the enterprise: they have moved from being an EPM company to being an insight generation company.  In this post, I will discuss a few of the trends that have enabled insight as a service (IaaS) and discuss the general case of using a software-as-a-service (SaaS) EPM solution to corral data and deliver insight as a service as the next level of product.

Insight generation is the identification of novel, interesting, plausible and understandable relations among elements of a data set that a) lead to the formation of an action plan and b) result in an improvement as measured by a set of KPIs. The evaluation of the set of identified relations to establish an insight, and the creation of an action plan associated with a particular insight or insights, needs to be done within a particular context and necessitates the use of domain knowledge.

Insight as a service refers to action-oriented, analytic-driven, cloud-based solutions that generate insights and associated action plans. Insight as a service is a distinct layer of the cloud stack (I've discussed IaaS in earlier posts here and here). In the case of Host Analytics, its EPM solution integrates a customer's financial planning data with actuals from their Enterprise Resource Planning (ERP) applications (e.g., SAP or NetSuite, and relevant syndicated and open source data), creating an insight-as-a-service offering that complements their existing solution. EPM, in other words, is not just a matter of streamlining data provisions within the enterprise; it's an opportunity to provide a true insight generation solution.

EPM has evolved as a category much like the rest of the data industry: from in-house solutions for enterprises to off-the-shelf but hard-to-maintain software to SaaS and cloud-based storage and access. Throughout this evolution, improving the financial planning, forecasting, close, and reporting processes continue to be a priority for corporations. EPM started, as many applications do, in Excel but gave way to automated solutions starting about 20 years ago with the rise of vendors like Hyperion Solutions. Hyperion's Essbase was the first to use OLAP technology to perform both traditional financial analysis as well as line-of-business analysis. Like many other strategic enterprise applications, EPM started moving to the cloud a few years ago. As such, a corporation's financial data is now available to easily combine with other data sources, open source and proprietary, and deliver insight-generating solutions.

The rise of big data — and the access and management of such data by SaaS applications, in particular — are enabling the business user to access internal and external data, including public data. As a result, it has become possible to access the data that companies really care about, everything from the internal financial numbers and sales pipelines to external benchmarking data as well as data about best practices. Analyzing this data to derive insights is critical for corporations for two reasons. First, great companies require agility, and want to use all the data that's available to them. Second, company leadership and corporate boards are now requiring more detailed analysis.

Legacy EPM applications historically have been centralized in the finance department. This led to several different operational "data hubs" existing within each corporation. Because such EPM solutions didn't effectively reach all departments, critical corporate information was "siloed," with critical information like CRM data housed separately from the corporate financial plan. This has left the departments to analyze and report and deliver their data to corporate using manually integrated Excel spreadsheets that are incredibly inefficient to manage and usually require significant time to understand the data's source and how they were calculated rather than what to do to drive better performance.

In most corporations, this data remains disconnected. Understanding the ramifications of this barrier to achieving true enterprise performance management, IaaS applications are now stretching EPM to incorporate operational functions like marketing, sales, and services into the planning process. IaaS applications are beginning to integrate data sets from those departments to produce a more comprehensive corporate financial plan, improving the planning process and helping companies better realize the benefits of insight as a service. In this way the CFO, VP of sales, CMO, and VP of services can clearly see the actions that will improve performance in their departments and, by extension, elevate the performance of the entire corporation.

Article image: Nikola Tesla, with his equipment (source: Wellcome Images on Wikimedia Commons).