Blockchain Data Analytics For Dummies. Michael G. Solomon
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Название: Blockchain Data Analytics For Dummies

Автор: Michael G. Solomon

Издательство: John Wiley & Sons Limited

Жанр: Базы данных

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isbn: 9781119651789

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      Predicting behavior

      Although the capability to classify entities to identify groups of similarity can be valuable, analytics can also make predictions. Past behavior is a strong indication of future behavior. Humans tend to repeat actions and decisions, so you can use models that identify patterns to predict future actions. The capability to predict future actions can have tremendous value to organizations. If an organization can determine items that tend to be purchased together frequently, it can use that information to make additional purchase suggestions.

      One of the common analytics models you learn about in Chapter 7 and build in Chapter 11 is regression. Don’t worry about the name right now (or the math). Regression is kind of like calculating the slope of a line on steroids. A regression model basically examines your data and figures out a line (or a curve) that matches the data you’ve seen. After you can graph your data, you can use that graph to guess what will happen based on new input data.

Screenshot depicting a linear regression model built on audition data and resulting score data.

      FIGURE 1-2: Linear regression model using hours practiced and audition scores data.

      Regression models can help to accurately predict future actions. Using data to know what’s next can be worth its weight in gold when making business decisions. (Yeah, I know data doesn’t have weight, but you get the point.)

      Making decisions based on models

      Analytics models can help organizations make astounding decisions and gain lots of money. They can also lead organizations to make dumb decisions and lose lots of money. The trick is in knowing how good your models are.

      This book is about building analytics models using blockchain data. You learn about blockchain technology and data in Chapters 2 and 3, but don’t forget that although the quality of your data is important, building the right model is crucial to getting quality output. Never rely on your first choice of a model or on a single model. Always compare model types and configurations to find the right combination to return the highest quality results.

      

If you take only one thing away from this book, I hope that it is to demand measurable verification from every model you build. You should be able to provide metrics for each model indicating its accuracy and that it actually works. Never release a model to your business unit without exhaustive verification. Your organization will use your models to make big decisions. Do your best to give it good tools.

      Classifying your customers or building models to predict what comes next can help your organization be more responsive to needs. You can use analytics to help plan better and be ready for whatever comes next. But with some additional work, you can do far more with analytics results. Instead of just getting ready for what might happen next, you can use analytics results to alter today’s activities and affect future outcome.

      Defining the desired outcome

      In the preceding section, you learned about using analytics models to make predictions of future outcomes. There can be tremendous value in prediction, but you can use analytics also to set the outcome and tell you how to get there. Think about it. It's one thing to predict next week’s sales, but wouldn’t it be cool to set your next week’s sales goals and let your analytics models tell you how to get there? With good analytics models, it's possible.

      Predictive analytics basically gives you an equation: y = mx + b (yes, that’s a simple one and the same as the point-slope form of a line). Your model provides values for m and b. Your data provides a value for x and you solve for y. Simple algebra.

      Prescriptive analytics is a little different. Prescriptive analytics ask the question: “If I choose a value of y, what value of x will get me there?” In other words, you choose a value of y (maybe your goal for next week’s sales), and then solve for x. After you know x (perhaps x represents the number of prospect calls you need to make), you know what it will take to reach y (your sales goal). At its core, it's still simple algebra.

      Even though the algebra is simple, putting prescriptive analytics into practice can be tricky. In algebra, equality is reflexive, which means you can read left-to-right or right-to-left. Technically, models should work the same way, but they don’t always work that simply. Prescriptive analytics can provide some guidance on reaching goals, but you always have to take that guidance with a grain of salt. Try your model’s recommendations, and then evaluate the results. Fine-tune your changes, and then try it again. The best use of prescriptive analytics is as a good suggestion, not a surefire approach to reaching goals.

      Building models for simulation

      One of the challenges in prescriptive analytics is the iterative and flexible nature of using models this way. Predictive analytics is pretty straightforward. You can determine future outcomes within a known range of error. When turning that model around and using it for prescriptive purposes, you can never be sure that your model is taking into account all the influences that affect outcome. The outcomes your predictive model measures may include unsampled features (characteristics) that happen even though you don’t measure them. If this is the СКАЧАТЬ