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Three technology triggers that impact the EPM space

January 10, 2017

According to a December 2016 McKinsey & Company report, four in ten CFOs reported that they spent the majority of their time on roles besides traditional finance over the past 12 months. The remit of the CFO has expanded to include strategic leadership, organizational transformation, performance management, and capital allocation among other responsibilities.

The McKinsey & Company report indicated that two in three CFOs say their companies do not yet have the capabilities for agile decision making, scenario planning, or decentralized decision making that are required to remain competitive in the coming years.

IT providers have sought to address these requirements by systematizing the budgeting, planning, and consolidation functions and by offering in-memory computing to drive data insights. McKinsey & Company recommended that, “CFOs should increasingly use such tools to lead complex enterprise-resource planning efforts, among other challenges that they are being tasked with managing.”

CFOs are heeding this call to action. In fact, beginning in 2016, Gartner split the Magic Quadrant to address two discrete subject areas – Financial Performance Management (FPM) and Enterprise Performance Management (EPM).

FPM addresses the application of performance management to Finance — Financial Budgeting and Planning, Financial Consolidation, Reporting, Disclosure Management, and Profitability Management. EPM more broadly applies the tenets of performance management across the entire business enterprise to include Sales, Marketing, Services, Manufacturing, Supply Chain, and other business operations. The strategic focus has moved from Finance process automation to driving enterprise-wide strategic value.

According to Gartner Research Director Chris Ierovolino, the market shift towards EPM solutions is attributed to “three technology triggers that impact the EPM space – cloud, in-memory computing, and analytics.”

1. The Cloud (SaaS) Model

While Finance has been slower than other departments to adopt cloud-based solutions – market penetration is around 13 percent according to Forrester Research – analysts suggest that the tide is turning.

In the Gartner Magic Quadrant Report for CPM Suites, 2015, Ierovolino suggests that the software market is “shifting toward cloud-based solutions that deliver a shorter time to value and improved ease of use.” Indeed, under the SaaS model, the vendor is responsible for all infrastructure, maintenance, and upgrades, and thereby speed of delivery is often cut in half relative to on-premises applications.

In addition to a shorter time-to-value and improved ease of use, the SaaS model also allows for more frequent software updates. On-premises applications typically have release cycles of 12-18 months whereas SaaS updates are pushed out to customers as often as quarterly.

SaaS solutions are arguably more secure than on-premises solutions as well. The SaaS model places the onus for data security squarely on the vendor. Vendors have increasingly adopted standards to reinforce best practices in data security, including ISO 27001, a security certification for firms establishing, implementing, maintaining, and improving upon Information Security Management Systems (ISMS) within an organization.

2. In-Memory Computing

In-memory computing refers to the storage of information in the main RAM of dedicated servers rather than in complicated relational databases. The productivity advantages of in-memory computing enable users to cache countless amounts of data constantly and ensure faster response times for searches.

According to Ierovolino, “speed is important, but performance is really only part of it. Performing existing processes faster is useful, but the more significant transformational benefits are in creating new capabilities.”

The second way that in-memory computing is impacting the EPM market relates to what Gartner calls hybrid transactional-analytic processing (HTAP). This refers to the ability to store both transactional and higher-level analytics data in the same database.

Whereas transactional databases are optimized for speed and analytics databases (or OLAP databases) are configured for repeated query, in-memory computing bridges the gap to provide real-time analytic capability in a matter of seconds instead of long hours. This analytic functionality helps business customers, including retailers, banks, and utilities, to quickly detect patterns, analyze massive data volumes on the fly, and perform analyses quickly.

3. Analytics

The third EPM market trend has to do with analytics. In the Gartner Magic Quadrant Report for Strategic CPM Suites, 2016, the authors state that “The evolution and sophistication of the self-service data preparation and data discovery capabilities in the market have shifted the focus of buyers in the BI and analytics market — toward easy-to-use tools that support a full range of analytic workflow capabilities and do not require significant involvement from IT to predefine data models up front as a prerequisite to analysis.”

This shift has been driven by new data discovery tools fueled by in-memory and data visualization capabilities. By embedding analytics within in-memory EPM tools and empowering non-Finance users with easy-to-use tools, operators are now able to interact with their data and drive new business insights.

Technology continues to drive EPM innovation and is making data more accessible to business operators. The existence of more detailed data is also providing new capabilities in terms of predictive analytics, being able to understand causality between key performance indicators and other non-financial metrics to enable firms to drive actionable insights earlier in the process.
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