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, a sudden drop in product quality. Predictive technologies are suited not only for financial forecasts or other numerical metrics, as 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:

  • 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 current horsepower of IT machinery and its 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 brain power.

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 modelling or machine learning. You just may need to take a different path, for example, leverage the built-in predictive capabilities of Smart Predict in SAP Analytics Cloud, which currently provides three types of predictive scenarios:

  • Classification
  • Regression
  • Time Series

With those algorithms and 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.

It was one of the core architectural design decisions of SAP Analytics Cloud to avoid that dilemma, so users now can freely move between the business intelligence, 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 received, 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.

SAP Analytics Cloud Decision Flows

 

So, 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.