Over the past few decades, computers have turned from filing cabinets for data into technological crystal balls that promise to predict the future by analyzing data. The tools that do this fall under the term predictive analytics and essentially fulfill two functions:
the analysis of databases or stocks in order to derive recommendations for action for the future
as well as the preparation of the analysis data, which rarely have the required consistency.
The latter function includes both uncomplicated tasks such as the standardization of formatting and the often time-consuming elimination of errors. A real challenge is often to maintain data integrity. Sophisticated predictive analytics tools master both requirements from the FF. We have compiled 15 of the most popular predictive analytics tools (ets) for you.
In recent years, Alteryx has focused on equipping its reporting and workflow management platform with predictive algorithms. The tool has a broad library and numerous interfaces for data import and supports a variety of common and less common data sources.
The Alteryx tool is versatile and designed for managers with data know-how rather than for developers who want to delve deeper into predictive analytics and want to link it with reporting and business intelligence on a broad level. Alteryx also offers specific solutions for specialist departments, for example for the marketing or research area.
The AWS toolset to examine data streams for signals or patterns is growing steadily. The offers are traditionally separated according to product lines. For example, Amazon Forecast focuses on expanding economic time series to predict what sales are expected for the next quarter and how many resources will be needed to meet demand. Amazon Code Guru, on the other hand, looks for uncertainties within source code to improve the processes.
Some AWS tools such as Fraud Detector or Personalize primarily support Amazon’s business itself – but are now also being resold to other companies that want to create their own e-commerce realm.
Companies that want to continue to use dashboards in the future to summarize data trends should take a closer look at Board’s offer. The tool enables a multitude of data silos (ERP, SQL, etc.) to be tapped, the information stored there to be analyzed and the results to be output in the form of a report that provides information about both the business past and the future (predictive).
The focus is on summarizing data from as many sources as possible and “pressing” it into a standardized form, which in turn can then flow directly into the visualization or predictive analytics.
The Dash toolset is available in a free, open-source version and an enterprise version and enables cloud-based management of predictive analytics models that are either already in use or are currently being developed.
The open source version comes with Python libraries for data analysis and visualization, the enterprise version comes with additional tools, for example for Kubernetes, authentication or the integration of GPUs in deployments for large user groups. The paid version also offers users more low-code extensions to create dashboards and other interfaces.
The Databricks toolset builds on the four major open source frameworks Apache Spark, Delta Lake, TensorFlow and ML Flow and is suitable for companies with large databases. In order to integrate predictive analytics as well as possible in workflows, the package also includes collaborative notebooks and data processing pipelines. Databricks has also built integrated versions of its tool set for AWS and Azure.
Companies that value the option of accommodating their predictive analytics models in local hardware, the cloud or a hybrid solution can manage their data and models with DataRobot. The tools combine automated machine learning with a number of routines focused on specific industries.
IBM’s Predictive Analytics Toolset stems from two different branches: SPSS was founded in the 1960s and has become the basis for many companies that wanted to use statistics to optimize their production lines. The tool has long since left the punch card era behind: In the meantime, non-programmers can also use drag & drop to transfer data to a graphical user interface in order to generate detailed reports. IBM acquired SPSS in the summer of 2019 for around $ 1.2 billion.
Under the roof of the Watson brand, IBM gathers another analytics toolset that is constantly being expanded. The Watson tools for predictive analytics are largely based on iterative machine learning algorithms that can both train data and train data models. The tools are able to process numbers, pictures or unstructured text.
- Lars Schwabe (Associate Director at Lufthansa Industry Solutions
“The success rate of predictive analytics projects has increased since the companies have finally done the necessary preparatory work, for example the creation of modern data architectures. In addition, both the staff have become more knowledgeable and the tools have improved. “
- Daniel Eiduzzis (Solution Architect Analytics at Datavard)
“Technically, companies have to open up and should not slavishly commit to a manufacturer. Today, it is much more a matter of identifying the ideal instrument, depending on the respective use case, with which the questions are best served. Therefore, a best-of-breed approach can make sense here. “
- Jan Henrik Fischer (Head of Business Intelligence & Big Data at Seven Principles)
“With methods of predictive analytics and the increasing digitization in parallel, we will understand processes better. Without exception, this will affect all areas of a company. The greatest potential lies in the optimization of customer processes. With a deeper understanding of their needs, we will be able to serve customers more efficiently and better, and increase their loyalty. ”
- Vladislav Malicevic (Vice President Development & Support at Jedox)
“Many companies have been experimenting with predictive analytics for a long time. So far, there has often been a lack of specific applications with a clear added value, the so-called business case. But the next phase in the technology lifecycle has already begun, and companies are no longer just conducting innovation-driven experiments. They increasingly link predictive analytics and AI projects with a clearly defined added value for certain specialist areas or business processes, including the expected results and the possible effects on previous processes. “
The Information Builders data platform enables Data Architects to set up a visual pipeline that collects, cleans, and then “throws” data into the Analytics Engine. If information is processed that should not be visible to everyone, there is the option of “Full Data Governance Models”. In addition, there are also specific templates available for individual sectors such as industry, which should give users particularly qui
ck insights into data secrets .
With its MATLAB solution, MathWorks originally wanted to support scientists with large amounts of data. In the meantime, however, MATLAB has mastered much more than just numerical analysis of data: The product line now focuses on optimizing statistical analyzes, while the SIMULINK product group is used for simulation and modeling purposes. The company also offers special toolboxes for many individual markets, such as autonomous mobility or image processing.
Python is now one of the most popular programming languages - but also one of the most popular languages for data analysis in the field of science. Many research institutions use Python code to analyze their data. Data scientists have meanwhile bundled the data and the analytical code in the Jupyter Notebook app. Python tools such as PyCharm, Spyder or IDLE bring new, innovative approaches into play, which, however, often still require some fine-tuning and are therefore primarily suitable for data scientists and software developers.
Technically speaking, R is just an open source programming language for data analysis, which is largely from the academic community. The integrated tools R Studio, Radiant or Visual Studio are of good quality, but rather something for hardcore data scientists and programmers. If you are looking for current community ideas to experiment with, you will definitely find it here. Many of the tools listed in this article allow the integration of R code in the form of modules.
Rapid Miner is designed in such a way that predictive data models can be created automatically without assistance in the shortest possible time. The developers also offer Jupyter notebooks with “automated selection” and “guided data preparation”. The available models are based on principles such as classic machine learning, Bayesch statistics or various forms of clustering. Explanations of the individual models provide information on how exactly the models derive their results.
Many companies rely on SAP to manage their supply chains. So it’s a good thing that Walldorf’s reporting tools now also support predictive analytics. For example, predictions can be made about machine learning models based on “old” data. The software also comes with AI capabilities, which can either run locally on-premises or in the cloud. Specific user interfaces with interdisciplinary consistency and the distinct possibilities on mobile devices round off the predictive analytics package from SAP.
The Predictive Analytics Toolset from SAS bundles almost two dozen different packages on one platform, which converts data into insights as well as predictions. The focus of the SAS toolset is on the analysis of unstructured texts.
Tableau has made a name for itself with its almost artistic preparation of reporting information and was bought up by Salesforce last year. With the help of an embedded analytics model, dashboards can now be used at Tableau to be informed interactively about the results of the data analysis. (fm)
This article is based on an article from our US sister publication cio.com.