The recent time’s data is revolutionizing how we do business analytics as witnessed through exploding data in becoming big. The cloud comes along to store big data and to offer timely unlimited access as well as data retrieval. There is the need for predictive analytics through the use of spreadsheets to visualize data, give insightful data for interactive action plans for the business. Large to medium and small business are no longer asking if they require predictive analytics, but they’re asking what are the effective predictive analytics solutions for their specific business models are.
So here we dive into predictive analysis describing its benefit to a business organization. Businesses are eager to answer questions such as, what will most likely happen in the coming future? What criteria should we adapt to make this prediction happen? Are we well set to handle this changes expected in the future?
This blog post explains more on predictive analytics as a practical technique of data extraction whose primary use is forecasting future probabilities and possibilities of different business trends. Predictive analytics by itself is an estimation of the future using g data from the past and the present as it is hence should provide an error margin. It further describes the future happening with a consideration of how reliable it will be, gives alternative solutions and conducts a risk assessment as well.
How it works
In a business-related environment predictive works by first analyzing the current data and combining it with the historical facts in this case; this is an excellent tool for key stakeholders to get a detailed customer behavior understanding, product marketability, and how their trading partners are influencing performance. With such data, further analysis to pinpoint potential risks and the potential opportunities provide vital information in making informed decisions.
So what is the line between predictive analytics and data science?
This blog post explains how data professional dealing with predictive analytics prefer using data science powerful tool and skills. The most used statistical tools include R and Python as the open source. As technology advances, so are the analytics tools being updated to accommodate the latest technology, hence need to keep one’s skills more relevant to the marketplace.
This blog post explains the top two predictive analytics methods that have over time attracted much public affinity.
- Artificial Neural Networks (ANN)
ANN is a technology whereby data input into a mathematical neuron, processing takes place, and the output is the predicted results. It means that the mathematical formula gets repeated multiple times. Neural networks are very powerful; they connect sets of neurons into layers to form a multidimensional system altogether. Criteria followed is that the input to the second layer comes from the first layer’s output and the process repeats to execute the data. With ANN it is possible to pinpoint regular patterns, get to capture data associations in diverse data sets.
- Autoregressive Integrated Moving Average (ARIMA)
ARIMA applies for the time series analysis from the past to create a model for present data and then make predictions on the future. The autocorrelators undergo inspection by making a comparison of how data values depend on past values regarding the chosen step in the past. The autoregressive part (AR) estimates the current value by considering the previous data value. The moving average (MA) is tasked to use any difference resulting from predicted data and the real data value. ARIMA provides a powerful tool to assist in identifying any time series behavior, predict new anomalies and discover underlying data patterns.
With a well-rewarded team of predictive analytics professionals, a business is bound to make a difference through translation of analysis into insights thus enjoy high returns on investments. Predictive analytics will move you from the back room to the board’s room!