Imagine if your business or organisation could predict the future. You’d have a massive competitive advantage over similar businesses. Now, by understanding the historical data you already have and using Artificial Intelligence, you can make better decisions without bias or predetermination – all by simply utilising your own accumulated data. This is the power of machine learning and predictive analytics.
WHAT IS MACHINE LEARNING?
Machine learning is when an algorithm is ‘trained’ based on historical data. The better the volume and variety of the data, the more effectively the machine can learn what real-life looks like – improving its prediction accuracy. As more data is included, the algorithm learns and becomes more accurate over time as the new data is reworked and assimilated.
WHAT’S THE DIFFERENCE BETWEEN STATISTICAL ANALYSIS AND PREDICTIVE ANALYTICS?
Statistical analysis is the collection, examination, summarization, manipulation, and interpretation of quantitative data to discover its underlying causes, patterns, relationships, and trends. It’s a fixed model that is static so it doesn’t adapt over time. If an update is required, the model or rules need to be recreated manually. Predictive analytics uses historical data to model what the future will look like. Then as new data comes in, it will dynamically use that data in all its complexity to further predict future values. An example of its use is when a new customer requires a tailored product. Using predictive analytics, their future needs can be better understood, helping match the best product or configuration for that specific customer.
WHAT ARE THE TYPES OF PREDICTIVE ANALYTICS?
A common class of predictive analytics is known as regression techniques – involving allocating suitable mathematical algorithms to analyse relationships between variables. For example, models such as ‘Classification Predictions’ and ‘Time Series Analysis’. Classification Predictions inform us on how to classify or segment data, based on mathematical groupings using their accumulated data. Time Series Analysis allow us to predict, for example, demand or outcome over a period of time, based on relevant historical data.
WHAT ARE SOME EXAMPLES OF MACHINE LEARNING AND PREDICTIVE ANALYTICS?
Example 1: A transport company may want to predict the type and amount of demand for the use of their vehicles and services in the future. Using data from GPS data and transport logs, a time series prediction will use this historical activity to inform future logistics.
Example 2: A sales company wanting information about future demand for types and quantities of products, will provide previous sales information for analysis and incorporate customer preferences.
Example 3: An insurance company may want to classify their customers, to inform future types of clients and locations to target based on the current information held in their CRM system.
Example 4: A consulting service can predict what resources and staffing may be required for future projects and for what duration, using their consultant and client data.
FARRAGO BRINGS A NEW SOLUTION IN PREDICTIVE ANALYTICS
Traditionally, even hiring the very best team of experts, going from raw data to one single useful prediction can take months of work because there’s lots of super laborious stuff to do. In fact, data scientists usually spend 80% of their time just on data preparation and engineering. Farrago is the cutting-edge platform that can give your business or organisation invaluable predictions in seven clicks, instead of seven weeks. Simply put, Farrago automates all of the data preparation and data engineering tasks, so you can make sense of your unclean data of any kind or context. The algorithm is then continuously trained based on this cleaned data by way of machine learning.