When a services firm lays out the costs that it incurs over the financial year, the one cost that dominates and overshadows the rest of them, are the compensation costs for its workforce. Compensation is a rather crafty eel to handle. The firm can try to simplify a lot of the calculations by using average salary figures, across grades and then calculating the final cost based on the planned headcount. While this might suffice for an up and coming business, with a limited range of operations and the headcount that goes with it; it most definitely will not work for a larger firm trying to navigate the treacherous waters of adequate staffing for prospective projects and spiralling costs.
For a services firm, revenue planning is far from a simple, straightforward affair of arriving at a figure that “appears” to be just right. While the absolute figure might not reflect the complex planning that goes into it, understanding the finer points of the factors that are taken into consideration while planning for revenue items will demonstrate not only the advantage that a EPM tool brings to the table, but also how Adaptive Planning makes the entire exercise a cinch.
Any business based on services, has a multitude of factors to monitor in order to ensure that the business remains profitable and performs as expected. It goes without saying, that the need to build a plan for the year is imperative to be able to contain the costs, and this task by itself is as difficult as it is complicated, given the intangible nature of the revenue and cost generating items. All of these factors taken into consideration, mandate a robust system of data management which needs to be able to store the required data points and the attributes tagged to them; fiendishly complicated enough to give FP&A professionals nightmares during the annual budgeting exercise!
In a fast moving business world today Business Analytics has gained prime significance. Almost all organizations invest heavily in building Data warehouses which can give them an edge to stay attuned with their customer and business needs. However if we take a look at the Gartner’s report more than 70 to 80% of the Data warehouse project fail or never achieve the intended results. Despite having investments in hoards of MPP platforms, reporting and modeling tools, the failure rates are quite alarming. Few important reasons for these failure are lack of understanding of data by IT developing the solutions, complex requirements, Inadequate knowledge or understanding of the technologies used but one that stands out the most is the time taken to deliver an Analytic solution. This solution development still follow the traditional models since the resources available to the solution creators are developed that way. Simple aggregation, drills across various dimensions and star schema creation takes months of development efforts before business can finally see something which they had desired. If this whole lifecycle of solution development is reduced and business can validate the solution much earlier surely investment in analytics will create the desired ROI.