Currently, more data is being created in one day than that which was created by all of humanity before the year 2000, notes Andreas Weigend, a former executive at Amazon.
A similar volume of data is used in predictive analytics to predict limitations or future circumstances and thus be able to plan ahead for decision-making and actions that can produce the best result.
When it comes to undertaking a predictive analysis, we are faced with various methods depending on the result we wish to obtain. Some of the most important methods in predictive analytics are:
Data Mining is a process that collects and makes use of large quantities of data. The objective is to find repetitive or relationship patterns. The data is structured in a way that makes its use comprehensible. That is to say, to discover useful trends in order to later harness that information in future situations.
Artificial intelligence is the result of combining previously defined algorithms with the aim of programming the same capabilities that a human has. A simulation of various processes is employed, which includes a specific acquisition of data in order to develop an independent learning and some rules based on a standardized reasoning.
Within the discipline of artificial intelligence, a type of independent learning based on data can be found that doesn’t require program rules or algorithms in order to determine a conclusion. Through an algorithm, Machine Learning identifies patterns within millions of data units, and can predict behaviors and make decisions without hardly any human intervention.
Deep Learning performs a learning process. It is made up of an artificial neural network with several hierarchical levels. The information learned in each level is carried over into the following. This continues until all of the information is combined. The first levels recognize specific details that, added in each level, produce a complete learning as a final result.
There are several areas of business where predictive analytics has been expanded:
- Finance: predicting operational risk, loans management, unusual fluctuations in the market, which can avoid future financial problems.
- Sales: Providing future sales, inventory management or an income forecast. Anticipating offers and cross-selling according to the needs of each customer.
- Operations: Quantifying and analyzing, in terms of quality, the chains of production in order to plan ahead, thus avoiding future failures in the manufacturing process.
- Marketing: Implementing through data mining processes a predictive marketing that can predict purchase and consumption patterns, anticipate customer needs and obtain a loyal e-commerce customer base.
Solutions and trends in predictive analytics in 2019
The “The Forrester Wave,” report conducted by the prestigious market research company Forrester carried out one of the most internationally-important web analytics comparisons.
Forrester defines and describes the business PAML (providers of predictive multimodal analytics and machine learning) as a tool for analyzing data, constructing predictive models with statistical algorithms, and for machine learning; for implementing and managing results (within its compatibility) with engineers and developers of artificial intelligence applications.
Multimodal, one of the features recognized in the report, provides the widest and most advanced range of work applications, such as user interfaces, configuration assistants, automation and coding environments.
Based on a total of 24 criteria, evaluated at three levels for each application, Forrester evaluated the strengths and weaknesses of the main providers of PAML multimodal solutions:
- Current offering: The strength of its current offering based on criteria such as model architecture, operations, algorithms and business solutions.
- Strategy: The strength of the provider strategies, such as their implementation capacity, implementation support, acquisition prices and partners.
- Market presence: Customer acceptance of each solution, quantified revenue and the familiarity of the solution within the market.
Even though three solutions providers are mentioned, all of those included in the “Forrester Wave” have unique features that satisfy (in terms of data) business needs.
In the report for the final trimester of 2018 regarding multimodal predictive analytics and Machine Learning solutions, the SAS, IBM and RapidMiner applications are described as market leaders.
We’re going to do a comparison of these two leading solutions for 2019:
SAS PAML Solution: The new platform Visual Data Mining and SAS Machine Learning is the fastest to date, and offers integrated automated functions, including Deep Learning, in a unique landscape. It is also highly visual. The Model Studio function makes algorithm programming easier without requiring an advanced knowledge of SAS code. Diverse neural networks can be found at the second tier for discovering the optimal, efficient and effective solution in SAS softwares.
The second leading application among SAS providers, Enterprise Miner, is described as being the most sophisticated application for the preparation and exploration of data. It has an advanced predictive modeling for big projects, with process codes in batches and in time series. It includes open source integration with R as well as the option of running SAS Viya codes within the process and eventually deploying them within the cloud.
IBM Watson Studio: Within the product range for the IBM Watson, one can find the PAML IBM Watson Studio solution. It is a solution for Data Intelligence teams that desire a more visual Deep Learning tool. The SPSS Modeler’s drag and drop system has a new, more-intuitive interface. Open-source access through notebook applications can be combined with its programming parameters until the ideal model is found.
RapidMiner Platform: Within the same software, it consolidates data preparation, Machine Learning and the analysis of the predictive model. It is described as being of the applications with a visual landscape and as having one of the simplest and fastest drag & drop methods in Machine Learning. The model has guides and suggested actions that are ranked according to different ability and transparency levels. It has the ability to run in Hadoopo through Spark for Big Data projects, and recently added the ability to work in real time.
Choosing a predictive analytics tool - it's not an easy task
Your predictive analysis application must satisfy the predefined objectives, adapting itself to the company while maintaining the best possible quality, and without having to migrate data from other external applications. This is addition to other factors, such as usability, the information source, how we are going to process the data and future reporting. All of that with the most competitive depreciation cost.
In conclusion, when it comes to emerging technologies that continuously generate data, the important thing is to not only possess it, but to analyze it. As Peter Sondergaard was saying “Information is the oil of the 21st century, and analytics is the combustion engine.” It is for this reason that business intelligence teams can plan for the future and decision-making, one of the most important qualities for achieving success in the business world.