What did you learn today that you wish you had known yesterday? If you have an answer for this question (or some variation of it, like “last week,” “last month”), chances are that you missed a good use case for some predictive technology. That use case could be anything like customer churn, a materialized risk, a detected fraud, a surprise spike in cost, an unexpected breakdown of a machine, or a sudden drop in product quality.
Predictive technologies are suited not only for financial forecasts or other numerical metrics. Many use cases are very operational in nature; for example, preventive maintenance of an asset, optimizing a product’s selling price for best market adoption, or scheduling production for high yield and minimal waste. There are hundreds of potential predictive use cases in every organization; they just need to be found and implemented.
Decisions are made to shape the future
It’s been said many times that the area of predictive technologies, machine learning, or data mining (pick your favorite term) is the gold medal of the analytics continuum. Here is why:
- Business intelligence (BI)/reporting: The system tells you what happened.
- Planning: You tell the system what you want to happen.
- Predictive: The system tells you what is going to happen.
Don’t resort to simple reporting or dashboarding and expect your gut feel or visual interpretation of graphs and charts to be a good enough indicator for what’s going to happen. Cutting-edge algorithms combined with the current horsepower of IT machinery and the ability to process vast amounts of information (more than you will ever be!) are generally able to provide you with much better predictions for your question than simple brainpower.
So you don’t have a data scientist? That’s okay. Most organizations cannot afford the luxury to hire one or more data scientists in their IT departments or lines of business. First of all, they are a rare breed, but also expensive, hard to find, and hard to keep. However, this does not mean that you cannot take advantage of innovative technologies such as predictive modeling or machine learning. You just may need to take a different path and leverage augmented analytics from within modern BI platforms, enabling users to benefit from predictive scenarios, such as classification, regression, or time series. With those embedded algorithms not requiring a group of data scientists, you can address a vast area of predictive situations.
Embedding predictive capabilities into information workflows
In the not so distant past, information workers had to regularly switch tools and platforms to jump from reporting to analysis, from planning to predictive. In most cases, data had to be replicated, remapped, or restructured, and there was often something lost in translation, either metadata or definitions, calculations, or formatting.
Today’s sophisticated solutions avoid that dilemma with an architecture that enables users to freely move between the BI, planning, and predictive functionalities without ever leaving the platform. Depending on your individual workflow, for example, you can start with a report, then based on the information you receive, plan for the next period, and compare the plan with the output of a predictive model, continuously moving back and forth among the various processes – all without ever leaving the tool.
If you are still wondering how to take advantage of your data beyond reports and dashboards and let the data tell its story about the future, SAP Analytics Cloud may just be the ticket.
To dive in deeper on the topic of modernizing and optimizing enterprise reporting, read the full research study “The Future of Reporting” by BARC, an independent market analysis firm.
This article originally appeared on the SAP Analytics blog and is republished by permission.