Leveraging Smart Predict Functionality In SAP Analytics Cloud
SAP analytics cloud is an excellent use of modern technology. The Analytics Cloud gives everyday users the chance to experience machine learning technology that we can use. Predictive analytics remains one of the most powerful of all the things that machine learning can offer to the average user. Imagine figuring out if something was going to happen before it actually did. While machine learning remains an expert topic of discussion, SAP Analytics Cloud gives us a taste of this topic and sees its use in the real world. Predictive analytics is by no means foolproof, but it does offer a basis based on historical precedent. Predictive analytics models use historical data to derive a projection for the future. Predictive analytics could turn all of us into citizen data scientists.
The Beginner Data Scientist
Experts in a field rely on communicators to disperse what they learn to non-specialists. There’s a great chasm of knowledge between scientists and average citizens. Data scientists, therefore, have a hard time communicating what they find to business users and vice versa. What if we could give business users the power to do their own data science in accordance with the goals they want? That’s what SAP Analytics Cloud hopes to do. Power business users can use models to develop data layouts that suit their questions. The positive side effect of business users doing the modeling themselves is that they have far more faith in the model results. By removing the complexities of data science and simplifying it, SAP Analytics Cloud creates a more viable environment for the average business case user to leverage data science directly.
Smart Predict and Creating More Scientists
Instead of training for years in data science and the statistical theories behind it, Smart Predict gives a more human approach to data scientists. Augmented analytics refers to how Smart Predict does this. These augmented analytics are assigned to a subset of AI and ML functions that iterate through complex tasks. This automation allows the beginner data scientist to perform far more complex analytics than they would be able to, given their familiarity with the system. Through Smart Predict, any business user can develop complex algorithms through a few clicks and set it to the task of learning. It humanizes the data science problem and moves the question away from algorithms and towards questions that a person can ask and reliably answer.
Simplified Process Flow for Smart Predict
The first step in developing a Smart Predict query is choosing the right algorithm. The model algorithm determines the type of model that you’ll be building and can come in one of three types:
- Time Series
Your choice of algorithm will depend on the business question you’re trying to answer. For example, a business that provides affiliate ad tracking might wonder whether a particular client will spend money on a particular product class. Given the data, the algorithm chosen will allow the company to answer the question reliably. Once you select an algorithm, an interface shows up, prompting you to:
- Select the data source for the engine to use as its basis
- Choose the variables within the data that will be most useful for the algorithm
- Select the roles those variables have (what are they linked to, in context to your question)
Smart Predict iterates through the data set, learning as it goes. Using the underlying data, it can develop a predictive model that can then be used to answer the question asked in the previous section. At the end of this training, it will offer users a training report, which states the performance indicators it used to draw its conclusion.
It’s important to remember that Smart Predict is a powerful tool, but it’s only as good as the data used to generate the model. Practice can help a business user figure out which algorithms are best suited to what questions.