How Analytics Can Role Over To Improve Performance
Historically, planning, budgeting, forecasting, and financial consolidation applications have primarily served the finance department. But companies are realizing that these applications, which have helped finance teams improve performance for many years, should be extended to managers throughout the company. Sales, marketing, human resources, production, inventory, and procurement workers can all benefit from better forecasting, budgeting, and reporting. They can also benefit from having financial-data analysis that is readily available and delivered within the context of the business issues that they live and breathe every day.
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Some analytic applications are now able to provide this type of functionality, to enable different views of data depending on a person's role within the organization. Such role-based software encourages individual decision-making that aligns with corporate strategy, while optimizing resources at the same time.
Here's an example of how role-based analytics would work: Susan Jones is a senior production manager for a publicly traded electronics manufacturer. Her job is to make sure that her manufacturing plant, one of five located around the world, meets its quarterly sales commitments. To do that, Susan must ensure that 98 percent of sales orders are turned within five days. She also must meet this objective while keeping costs under control.
To achieve her target, Susan has to stay on top of where her plant stands at all times, both financially and in terms of key operational indicators. She needs to monitor, measure, and manage inventory, backlog, lead times, orders shipped, orders on hold, and any temporary head count. That means she must pull data from a variety of source systems: sales forecasts from a forecasting application, bookings from an ERP system, inventory reports from a data warehouse, product lead times from an internally developed database, task completion times from another custom database, and budgeted head-count expenditures from a budgeting application. A role-based performance analytics application could make her job much easier by sitting atop all those disparate data sources and managing the data.
When departments across an organization have integrated views of both operational and financial drivers and results, companywide collaboration becomes reality. Everyone is finally working off the same data, and role-based planning and forecasting applications tend also to include functionality for sharing information. The entire organization becomes able to make more effective decisions, faster. The office of finance also benefits when others in the organization use role-based analytics because doing so enables them to provide a broader and deeper view into the financial health of the organization.
When Everyone Has the Same Data -- at the Same Time
Delivering the right information, on demand, to many different types of business users presents significant challenges. Some companies may worry about the accessibility of their data. Most store a vast amount of information in data sources including ERP systems, data warehouses, datamarts, OLAP cubes, and individual spreadsheets, but if they can't properly extract and leverage that information, it is useless. Data sources must be integrated in a way that is transparent to business users, and the analysis of performance data must be presented in a way that supports decision-making but has minimal impact on the company's existing technology investments.
Data consistency is another issue most companies face. Have you ever shown up to a meeting where everyone truly believed they all had the right numbers, yet everyone had different answers to the same question? Role-based analytics can lessen the impact of this problem. As exhibit 1 demonstrates, different departments may sit atop the same process, but if they all use role-based analytics systems, they will be pulling data from the same source systems to answer their different questions.
A third key issue with financial and operational analytics is timing. Does anyone actually believe anymore that the person with the most data will win? What is done with the data is what makes the difference. And frequently, the winner is the company that can most quickly analyze its data and deliver that analysis to the right people. Companies that gain an advantage over their competition generally have good processes in place to quickly move critical information along a path and put business-specific analysis in the hands of the decision-makers.
Susan Jones, the senior production manager mentioned previously, currently works in an environment with inefficient performance management practices. She spends much of her time working with database administrators and IT developers trying to get to the data she needs to do her job. Accessing the data is half the battle. Once she has the right information, she has to pull it into an Excel spreadsheet, make assumptions about which data points she should use, and then write calculations to find the metrics she needs. From there, she reviews all her calculations to see if anything looks out of line. She does a bit more data cleansing, then creates a dashboard with charts, graphs, and gauges to present to executive management. This manual process takes her one full day to complete, and she is able to access data only on a weekly basis. If her analysis reveals anomalies or areas that need adjustment, she has little time to deal with those problems, as she has already spent so much time trying to answer her basic questions. In the time it takes to do the analysis, Susan could be facing rework and scrap on orders that have shipped already.
