From a market trend perspective, it is fair to acknowledge that the EPM software market has taken a hit from the hype surrounding the emergence of technology trends in the data management space, such as business analytics and, particularly, big data.
|Figure 1: Searches for big data, compared with those for EPM (Source: Google Trends)|
In the last four years, at least, interest in big data has grown exponentially, making it a huge emerging market in the software industry. The same has happened with other data management related solutions such as analytics.
While this is not that surprising, my initial reaction came with a bit of discomfort. Such a huge difference makes one wonder how many companies have simply jumped onto the big data wagon rather than making a measured and thoughtful decision regarding the best way to deploy their big data initiative to fit within the larger data management infrastructure in place, especially with regards to having the system co-exist and collaborate effectively with EPM and existing analytics solutions.
Now, don’t get me wrong; I’m not against the deployment of big data solutions and all the potential benefits. On the contrary, I think these solutions are changing the data management landscape for good. But I can’t deny that, over the past couple of years, a number of companies, once past the hype and euphoria, have raised valid concerns about the efficiency of their existing big data initiatives and have questioned its value within the overall data management machinery already in place, especially alongside EPM and analytics solutions, which are vital for measuring performance and providing the right tools for strategy and planning.
The Analytics/EPM/Big Data Conundrum
A study published by Iron Mountain and PwC titled How Organizations Can Unlock Value and Insight from the Information they Hold, for which researchers interviewed 1,800 senior business executives in Europe and North America, concluded that:
“Businesses across all sectors are falling short of realizing the information advantage.”
Even more interesting is that, in the same report, when evaluating what they call an Information Value Index, the authors realized that:
“The enterprise sector, scoring 52.6, performs only slightly better than the mid-market (48.8).”
For some, including me, this statement is surprising. One might have imagined that large companies, which commonly have large data management infrastructures, would logically have already mastered, or at least reached an acceptable level of maturity with, their general data management operations. But despite the availability of a greater number of tools and solutions to deal with data, important issues remain as to finding, on one hand, the right way to make existing and new sources of data play a better role within the intrinsic mechanics of the business, and, on the other, how these solutions can play nicely with existing data management solutions such as EPM and business intelligence (BI).
Despite a number of big data success stories—and examples do exist, including Bristol-Myers Squibb, Xerox, and The Weather Company—some information workers, especially those in key areas of the business like finance and other related areas, are:
- somehow not understanding the potential of big data initiatives within their areas of interest and how to use these to their advantage in the operational, tactical, and strategic execution and planning of their organization, rather than using them for in tangential decisions or for relevant yet siloed management tasks.
- oftentimes swamped with day-to-day data requests and the pressure to deliver based on the amount of data already at their disposal. This means they have a hard time deciphering exactly how to integrate these projects effectively with their own data management arsenals.
In addition, it seems that for a number of information workers on the financial business planning and execution side, key processes and operations remain isolated from others that are directly related to their areas of concern.
The Job Still Needs to Be Done
On the flip side, despite the extensive growth of and hype for big data and advanced analytics solutions, for certain business professionals, especially those in areas such as finance and operations, interest in the EPM software market has not waned.
In every organization, key people from these important areas of the business understand that improving operations and performance is an essential organizational goal. Companies still need to reduce the cost of their performance management cycles as well as make them increasingly agile to be able to promptly respond to the organization’s needs. Frequently, this implies relying on traditional practices and software capabilities.
Activities such as financial reporting, performance monitoring, and strategy planning still assume a big role in any organization concerned with improving its performance and operational efficiency (Figure 2).
|Figure 2: Population’s perception of EPM functional area relevance (%)|
(Source: 2016 Enterprise Performance Management Market Landscape Report)
So, as new technologies make their way into the enterprise world, a core fact remains: organizations still have basic business problems to solve, including budget and sales planning, and financial consolidation and reporting.
Not only do many organizations find the basic aspects of EPM relevant to their practices, an increasing number of them are also becoming more conscious of the importance of performing specific tasks with the software. This signals that organizations have a need to continuously improve their operations and business performance and analyze transactional information while also evolving and expanding the analytic power of the organization beyond this limit.
How Can EPM Fit Within the New Data Management Technology Framework?
When confronted with the need for better integration, some companies will find they need to deploy new data technology solutions, while others will need to make existing EPM practices work along with new technologies to increase analytics accuracy and boost business performance.
In both cases, a number of organizations have taken a holistic approach, to balance business needs by taking a series of steps to enable the integration of data management solutions. Some of these steps include:
- taking a realistic business approach towards technology integration. Understanding the business model and its processes is the starting point. But while technical feasibility is vital, it is equally important to take into account a practical business approach to understand how a company generates value through the use of data. This usually means taking an inside-out approach to understanding, by taking control of data from internal sources and that which might come from structured information channels and/or tangible assets (production, sales, purchase orders, etc.). Only after this is done should the potential external data points be identified. In many cases these will come in the form of data from intangible assets (branding, customer experiences) that can directly benefit the specific process, both new or already in place.
- identifying how data provided by these new technologies can be exploited. Once you understand the business model and how specific big data points can benefit the existing performance measuring process, it is possible to analyze and understand how these new incoming data sources can be incorporated or integrated into the existing data analysis cycle. This means understanding how it will be collected (period, frequency, level of granularity, etc.) and how it will be prepared, curated, and integrated into the existing process to increase its readiness for the specific business model.
- recognizing how to amplify the value of data. By recognizing and making one or two of these sources effectively relate and improve the existing analytics portfolio, organizations can build a solid data management foundation. Once organizations can identify where these new sources of information can provide extended insights into common business processes, the value of the data can be amplified to help explain customer behavior and needs; to see how branding affects sales increases or decreases; or even to find out which sales regions need improved manufacturing processes.
All this may be easier said than done, and the effort devoted to achieving this is considerable, but if you are thinking in terms of the overall business strategy, it makes sense to take a business-to-technical approach that can have a direct impact on the efficiency, efficacy, and success of the adoption of EPM/big data projects while also improving chances of adoption, understanding, and commitment to these projects.
Companies need to understand how the value of data can be amplified by integrating key big data points with the “traditional” data management cycle so it effectively collaborates with the performance management process, from business financial monitoring to planning and strategy.
While enterprise performance management initiatives are alive and kicking, new big data technologies can be put to work alongside them to expand the EPM software’s capabilities and reach.
The full potential of big data for enterprise performance management will only be realized when enterprises are able to fully leverage all available internal and external data sources towards the same business performance management goal to better understand their knowledge-based capital.
(Originally published on TEC's Blog)