Predictive and prescriptive analytics help companies use the increasing amounts of data to improve their business and financial performance.
Predictive analytics use statistical analysis, data modelling, real-time scoring, and machine learning to detect trends for forecasting. Prescriptive analytics rank the trade-offs of different courses of action companies may take to reach certain objectives, for example through scenario modelling.
“Traditional analytics such as profit, cash flow, and return on assets don’t really tell the full story,” said Chris Ortega, an artificial intelligence expert and senior finance manager at Emarsys, a global software-as-a-service marketing cloud company.
Advanced analytics can increase learning and knowledge throughout the business, produce repeatable analytics to measure success or failure, and hold the business accountable to results, Ortega suggested.
While the potential benefits are compelling, most companies face challenges in implementing advanced analytics. They revolve around the classic pillars of any company – people, processes, and technologies. “Some organisations don’t have the right processes driving the data, or the people in place to identify and understand the analytics, or some don’t have the technology in place to make sense of advanced analytics,” Ortega said.
The biggest challenges of implementing analytics, according to the Financial Executives Research Foundation, include:
- Getting quality data out of multiple legacy IT systems that don’t share information, in companies with processes that aren’t standardised, or in companies that rely on spreadsheets.
- Overcoming cultural resistance to change.
- Finding qualified data scientists who can work well with IT, understand databases, and know how to explain and meet business needs.
Best analytics practices
The increasing flow of data, generated rapidly inside and outside the company, can be overwhelming, but advanced analytics can turn the data into a competitive advantage for a company. The Financial Executives Research Foundation found that companies prefer to use advanced analytics for these four purposes:
Gain deeper insights. Analytics can help companies anticipate the effects of variables such as marketing campaigns, market conditions in a specific region, or price discounts to project sales and plan production. Analytics also provide insights into supply-chain metrics such as inventory turnover, stock values, procurement trends, warehouse performance, quality control, and compliance.
Identify root causes. To figure out what causes, for example, higher reports of damaged items among custom-ordered products, a company can use analytics to determine that improper packing and shipping of delicate materials was not the problem. Instead, the company may find that sales and return data point towards incorrect orders. To resolve the issue, the company can then retrain its sales team.
Assess market competition. Companies can use analytics to identify patterns that suggest which customers are more likely to leave, and quantify the likely effects on revenue and profit. This information is valuable in determining which investments are most likely to increase customer retention and growth.
Identify and manage risk. Analytics can help companies analyse accounts receivable to create profiles that rank customers based on the likelihood that they will pay invoices promptly. This, in turn, allows a company to customise its credit terms and communications plans to increase the efficiency of its collection efforts.
August 15 2017 By Sabine Vollmer - CGMA Magazine senior editor
online im Internet:
http://www.cgma.org/magazine/2017/aug/how-to-use-advanced-analytics-201717253.html
