You may not realize it, but the everyday technologies which we have come to rely on use predictive analytics. For example, your car’s navigation system uses predictive analytics when planning the fastest route to your destination. Your smartphone weather app uses a similar method to predict if it’s going to rain tomorrow or not.
These days data science, and more specifically machine learning methods, dominate prediction systems and methods. Researchers are applying these systems and methods, specifically algorithms, across a wide range of everyday situations. Although the science of predictive analytics is quite new, its popularity is spreading like wildfire. That’s because it has real-world impact on businesses and their bottom lines.
Managing Data
More and more companies are storing and managing their data in digital form. At the same time, the amount of data is getting humongous. New smart devices too are adding data in such a fast pace and increasing volumes. It is becoming beyond human capability due to this massive data burst. All this data has value, trends and can be used to anyway dashboard intelligently on reporting and more than that, use it for prediction. Data driven indicators are more healthy for any organization. Big Data moving into intelligent exercises for any organization is key now for any organization.
Business Intelligence
BI in its terms refers to retrieve information from Data. Once you are absolutely sure of correctness in your data, BI opens a new world for you and deploying it with real time makes it all the more sensible for any organization. It is very important to be aware of the methods different BI tools use for predictive analytics and find the best fit for your data and prediction.
Data Analysis
Before going into predictive analytics in more depth, familiarize yourself with the fundamentals of data-analysis concepts.
You need to ask yourself: What do I want to know? What do I need to know? For example, which one of my products generates the highest profit margin? Are our sales seasonal? Which employees work the most and who creates the most value? Furthermore you should determine what you want to observe and how you want to measure it. Once these fundamental steps are covered, in comes data analysis, the real ‘hunting ground’ of business intelligence systems. Every BI system visualizes data in order for you to perform a thorough analysis. You can also analyse data in an explanatory fashion. This is when data analysis tools let you dig deep down into the data and discover connections. For example, a ‘drill down’ feature or a pivot table.
Perhaps the most interesting data analysis method is descriptive statistics. This method is intended to describe the characteristics or features of the studied data. Examples of characteristics are whether or not the data is increasing or decreasing, homogeneity or diversity, or revenue. Descriptive statistics are well-known measures of central tendency or deviation like average, standard deviation, median, and variance. These measures can be found both visually and numerically. These statistical features calculated from the observed data can be generalized or applied to the entire mass of data. This means it can also be applied to the data collected in the future. This method leads us to predictive analytics.
Predictive Analytics
Predictive analytics is born from descriptive analytics. Descriptive analysis is capable of showing us whether a time series is characterized by an increasing or decreasing trend. If the measured data increased every day for the past two years then we can almost certainly say that next Monday it will still be increasing. Predictive analysis is much more complicated than this in most cases because the descriptive analysis cannot always find principles in the studied data sets. Despite this, predictions still need to be made. In these cases algorithms are used. Algorithms involve a multi-step process that results in a desired result.
The general method used in predictive analysis is separating a significant amount of data, usually 80%, and teaching the algorithm to use this set. The remaining data, usually 20%, is used to test out the efficiency of the algorithm. Since these algorithms learn the features of the dataset during the process, we call them learning algorithms or in the IT world, machine learning. This is what people most commonly refer to as artificial intelligence or AI.
Now Machine Learning (ML) is about creating and training models and AI is about using those models to infer conclusions under certain conditions. Traditional BI tools have strong analytical features, but they utilize machine learning techniques poorly. Insights provided by machine learning and classical analytics are different. Learning algorithms need much more data, they need training on a part of data, and they are improving over time using their learning ability. There are a lot of New Gen BI and Analytics tools that one needs to look at over the usual legacy names in the market. Please reach out for MORE.
