As organisations continue to expand upon their digital offerings, AI is becoming a popular solution to ensure clean and accurate data.
Within a global business context, AI tools are able to process large volumes of data much faster than humans. As a result, enterprises can gain greater and more accurate insights, making it easier for them to make and act on decisions quickly. AI can enable them to improve their data quality, encompassing practices such as data validation, cleansing and quality monitoring.
AI also enables organisations to gain meaningful insights from data, as well as completing repetitive tasks faster than a human employee could, ultimately saving time and resources.
With this in mind, AI Magazine considers some of the leading tools in AI that seek to analyse data, therefore presenting myriad benefits to global business operations.
10. RapidMiner
Company: Altair Engineering (Acquired in 2022)
CEO: Peter Lee
RapidMiner is a data science platform that analyses the collective impact of a company’s data. With more than one million users around the world, the program operates an enterprise-ready data science platform designed to amplify the collective impact of people, expertise and data within a business.
Written in the Java programming language, RapidMiner provides a graphical user interface (GUI) to design and execute analytical workflows.
Additionally, the platform provides data mining and machine learning procedures including: data loading and transformation (ETL), data preprocessing and visualisation, predictive analytics and statistical modelling, evaluation and deployment.
9. Tableau
Company: Tableau Software (Parent company: Salesforce Inc.)
CEO: Mark Nelson
Tableau Software is an American interactive data visualisation software company focused on business intelligence. It was founded in 2003 in California and is currently headquartered in Seattle, Washington. The company was acquired in 2019 by Salesforce for US$15.7bn.
As an AI tool, Tableau business science is a new class of AI-powered analytics that is designed to bring data science capabilities to business domain experts. It harnesses AI, machine learning and other statistical methods to solve enterprise challenges.
8. Qlik
Company: Qlik
CEO: Michael L Capone
Qlik offers a business analytics platform, including main products Qlik Replicate and Qlik Sense - both software for business intelligence and data integration.
The company offers data analysis tools that turn data into AI-driven insights and action by integrating AI and machine learning tools to auto-generate insights and predictions. It is a cloud platform that turns data into automated, AI-driven actions all in one platform.
The company’s Active Intelligence Platform allows open-ended, curiosity-driven exploration, offering everyone at any skill level the ability to make real, AI-led discoveries that lead to transformative change.
7. Polymer
Company: Polymer
CEO: Yasir Ali
Polymer is an agentless data security platform that uses advanced machine learning techniques to inspect data. Its intuitive business intelligence tool empowers users to create data visualisations, comprehensive dashboards and embed data into presentations without the need to write any code.
Its policy engine also contextualises data so the user can easily identify threats and mitigate risks.
Its feature, PolyAI, is a built-in conversational AI assistant that analyses data and instantly generates visualisations based on instructions.
6. Databricks Unified Data Analytics Platform
Company: Databricks, Inc.
CEO: Ali Ghodsi
Databricks is a unified open analytics platform designed to build, deploy, share and maintain enterprise-grade data, analytics and AI solutions at scale. Founded by the original creators of Apache Spark, the company as a whole provides a cloud-based platform to help businesses build and govern data and AI, including generative AI (Gen AI) and other machine learning models.
The Databricks Unified Data Analytics Platform enables technology and software companies to harness the potential of data and machine learning to develop new technologies and applications.
5. Sisense
Company: Sisense
CEO: Amir Orad
Sisense is a data analytics platform that enables both analysts and developers to sort through and visualise their data. It embeds AI-powered analytics with pro-code, low-code and no-code capabilities.
The AI-driven analytics cloud platform provides intelligence for more than 2,000 global companies to innovate and drive meaningful change worldwide. It also helps to create intuitive data products, thereby driving engagement and increasing solution value.
In particular, Sisense Fusion offers the AI and analytics foundation to create product differentiation and sustain data-hungry user growth.
4. The KNIME Analytics Platform
Company: KNIME
CEO: Michael R. Berthold
KNIME is an open-source, low-code analytics platform that supports over 300 data connectors. It includes all the essential tools for blended databases, data transformation, data analysis and reporting.
KNIME integrates various components for machine learning and data mining through its modular data pipelining "Building Blocks of Analytics" concept. Today, it has a strong community of more than 300,000 users across all industries and in over 60 countries. The KNIME Analytics Platform has an intuitive interface that allows anyone from spreadsheet users to seasoned data scientists to analyse their data.
Additionally, KNIME Business Hub allows for large scale collaboration and dissemination of insights across an enterprise.
3. IBM Watson Analytics
Company: IBM
CEO: Arvind Krishna
IBM Watson Content Analytics collects and analyses structured and unstructured content in documents, email, databases, websites and other enterprise repositories.
By providing a platform for importing and analysing content, in addition to creating a searchable index, Watson Content Analytics helps users perform text analytics across all enterprise data. It also makes the data available for analysis and search.
Business analysts can also interactively explore through a provided content mining interface. This can enable them to discover relationships and anomalies between values.
Additionally, enterprise users can use a search interface to quickly find and retrieve relevant documents from a ranked list of results.
2. Google Cloud Smart Analytics
Company: Google Cloud
CEO: Thomas Kurian
Google Cloud offers a broad range of AI tools for businesses to improve their analytics.
In particular, Google Cloud Smart Analytics is a flexible, open and secure data analytics platform that provides an easy path to becoming an intelligence-driven organisation. It works to build on decades of Google’s innovation in AI and building internet-scale services and is based on the same proven and reliable technology principles that power Google’s services (e.g. Search, Gmail, Maps, YouTube).
Organisations ultimately choose Google Cloud to build their data cloud because of its ability to fuel data-driven transformation. As a wide range of industries are using data and AI as a strategic asset, the platform delivers a wide range of analytics services including business, data science and marketing, to name a few.
1. Microsoft Azure Machine Learning
Company: Microsoft
CEO: Satya Nadella
Azure Machine Learning facilitates the building of business-critical machine learning models at scale.
The tool empowers data scientists and developers to build, deploy and manage high-quality models faster and with greater confidence. It also accelerates time-to-value with industry-leading machine learning operations (MLOps), open-source interoperability and integrated tools. Likewise, it is designed for responsible AI applications in machine learning.
Harnessing Gen AI to streamline workflows, Azure Machine Learning customers are also able to only pay for what they need with a range of pricing options. Some of its leading customers include the United Kingdom’s National Health Service (NHS), Axon and Seven Bank.
Also offered by the platform is the Azure Machine Learning studio which offers a centralised place for data scientists and developers to build, train and deploy machine learning models.
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