Data science focuses on answering questions about the future. It can tackle a big question that affects the world and also help you determine in advance whether you will like a particular brand of wine. At its heart, data science looks to tackle the three Vs. They are velocity, variety, and volume.
This information is used to create models and algorithms. Data scientists are focused on AI and machine learning. In this process, they are creating models that can improve themselves because they learn from past mistakes.
Cloud storage has completely changed data science. It has given data scientists the ability to store information, share information, and analyze information easier.
Industries That Have Been Impacted by Data Science
There is almost no industry that has not been affected by data science. For example, algorithms are helping the medical community predict patient side effects from medications or therapies. In sports, data science has changed how coaches and managers predict a player’s athletic potential. If it seems like you are spending less time in traffic, it is likely because data science has been used to create optimized routes for traffic and traffic lights to minimize the effect of rush-hour traffic.
How Data Scientists Teach Machines to Learn
Data scientists can teach machines to learn by breaking the learning process down into four categories. There is loading the data, training the model, understanding the results, and applying the model.
SAP Analytics Cloud Smart Predict is used by many data scientists to teach computers to learn and to predict possible outcomes. Computers learn from experience in the same way that humans do. However, computers need to process large amounts of data gathered over long periods of time before they can make accurate predictions.
If you wanted a computer to predict whether or not you would like a particular wine, you would first have to give the computer some attributes of wine, including its taste, acidity, alcohol level, pH, sugar, etc. You would also have to teach the machine what wines you like and place them on a scale of between one to 10. The machine can take all that information and try to identify patterns that could predict how you would respond to a wine that had characteristics that fell within a standard.
At this point, it is good to point out the limitations of what machine learning can do. It is true that SAP Analytics Cloud Smart Predict can be used to help create accurate predictions of many things. However, we are not talking about making computers smart. In this context, computers are being trained to analyze statistics in a way that is narrow and inflexible.
For example, when trying to predict whether you will like a particular wine, machine learning is going to be limited to the attributes of wine that you have inputted and the samples of wines you have categorized. It cannot see things in an abstract way. It lacks the ability to re-mix abstract context and concepts to adapt to alternative solutions. While machine learning might be excellent at determining the wine you may like based on the information that you input, it could not take your taste in wine and extrapolate what your favorite type of pizza might be.
This same process is used for creating better encryption algorithms. Many of today’s most popular public and private key technology utilize multiple datasets to create more robust passwords and management policies. Hash functions are one such aspect.
Using Data Science to Make Accurate Predictions
Going back to our wine example, we would need to create two data sets. One data set would be for white wine and the other for red wine. In each category, we might include hundreds, if not thousands, of wines. We would assign attributes to each wine, such as sugar content, acidity, and alcohol. You would load all of this information into SAC.
Now comes the creation of the training model. SAC is going to look at the attributes that we inputted to describe the wine and your feeling about the wine on the scale of one to 10. It is going to be checking variables, like sugar content, acidity, and alcohol content and try to find correlations between those and the ranking you gave wine on that scale of one to 10. It is going to try to pick a model that best identifies patterns.
For the next step, human interaction is needed again because SAC is going to attempt to evaluate the quality of the model it created. You are going to look at the results and see how accurate they are. If they say that based on all the data gathered you should enjoy drinking wine number 17, 37, and 46 and you do, then you can move forward to the final step.
Here is where you put the model to the test. Give it the attributes of wine that it has never reviewed before. It should be able to predict whether you are going to like the wine based on the information that it was given before.
This is, of course, an oversimplification of what SAP Analytics Cloud does, but it gives a high-level explanation for how this tool can be used with machine learning.
How Predictive Analysis Can Simplify Business
SAC has been designed to carry out the three main predictive techniques that are primarily needed in business.
Classification is where data is mined to derive target categories or to create classes. For example, a company that provides energy and resources might group people in to age groups based on their demand for rooftop solar panels.
Regression is the cause-and-effect relationship that reflects the relationship between certain variables. For example, the question in the retail field is, what causes people to buy? What factors can impact a business’s revenue?
Timeseries is data that is collected over a set period used to predict behavior. For example, a factory might monitor how frequently their machinery needs to be repaired and based on that predict the time when they may need to make repairs in the future.
Predictive analysis can simplify business by giving business owners a data driven way to predict what the future will bring. This allows business owners to make wise decisions that can improve revenue.