By Kevin Stolarski - Marketing at Answerthink
May 12, 2020
C-level executives are always seeking faster ways to answer their toughest business questions. However, most small and midsized companies feel they are only scratching the surface of what they need from an analytics perspective to compete effectively. But what if the data that organizations collect could be captured, reorganized and used to accurately predict the buying behaviors of a company’s most loyal customers or improve business processes? Predictive analytics has the power to make tasks like these possible.
Predictive analytics is the practice of using historical data to predict future events. Businesses often use predictive analytics as a form of advanced analytics that uses both new and historical data to forecast business activities by utilizing a wide variety of statistical techniques and technologies. This information allows organizations to become proactive, forward thinking, and anticipate outcomes and behaviors based upon data and no one assumption. This new wave of analytical innovation will accelerate and automate many aspects in your short- and long-term business decision-making through machine learning, artificial intelligence, and blockchain. Such capabilities can give small and midsized companies the opportunity to compete like a large enterprise with the speed of a startup environment.
Historical analytics requires a certain amount of time to pass in order to derive meaningful conclusions, so it takes longer to see which strategies are beneficial or failing. Predictive analytics, in contrast, encourages constant testing and iteration because it doesn’t require as much observation in order to come to a robust conclusion. It gives companies the agility to try new and different methods and quickly determine how effective they will be going forward. The statistical methods used in predictive analytics are built to calculate the probability of their accuracy. This allows decision-makers to be informed of the risks of relying on a given prediction, as opposed to historical analytics, where it is more of a subjective call as to how accurate a projection will be. Predictive analytics can be most instructive when there is insufficient historical data – or when the past is not representative of the future. For example, if a retailer is introducing a new product category, it will not be able to rely on a historical baseline.
Organizations today use predictive analytics in a virtually endless number of ways. By looking into the future with more accurate and reliable data tools, adopters can find ways to save money, grow their businesses, and get an in-depth and precise picture of who their customers are and what they really want. Business applications for predictive analytics may include analyzing customer behaviors to determine buying patterns, flagging fraudulent financial transactions, identifying patients in a clinical trial study or reducing supply chain disruption. Predictive analytics can streamline the process of customer acquisition by predicting the future risk behavior of a customer using application-level data.
SAP Analytics Cloud
SAP Analytics Cloud (SAC) helps you supply all analytics for all users. Everything your intelligent enterprise needs for reporting, planning and predicting is at hand in one high-performance, in-memory system, with a single-user experience. A live connection to your on-premise applications eliminates the need to upload the data itself to the cloud. You enjoy hybrid analytics, in which your existing local ecosystem and cloud applications work smoothly together. You can discover new insights and deliver them to decision-makers in context, with minimal new investment and risk. Preconfigured content based on best practices and line of business gives you a jump-start on developing your analytics system. And the open innovation technology behind SAC lets you tailor analytical applications with content from our partners. Comprehensive data management and analytics help ensure that a consistent data model and trusted information underpin analysis and planning. Machine learning embedded into your applications enriches and accelerates decision-making within daily enterprise resource planning functions.
With SAC, predictive forecasting gives you an unbiased understanding of your key business influencers and allows you to dive deeper into your data – whether it’s from an SAP application or a third-party data source. You can then use these findings to influence business decisions and aid in future planning. This predictive forecasting feature is just one of the many key benefits to the SAC solution. To learn more about SAC capabilities powered by the in-memory technology of SAP HANA, and how it can be extended to any device, click here.