Analytics and business intelligence (ABI) platforms enable organizations to understand their data. For example, what are the dimensions of their data — such as product, customer, time, and geography? People need to be able to ask questions about their data (e.g., which customers are likely to churn? Which salespeople are not reaching their quotas?). They need to be able to create measures from their data, such as on-time delivery, accidents in the workplace and customer or employee satisfaction. Organizations need to blend modeled and non modeled data to create new data pipelines that can be explored to find anomalies and other insights. ABI platforms make all of this possible.
Gartner defines data integration as the discipline comprising the architectural patterns, methodologies and tools that allow organizations to achieve consistent access and delivery of data across a wide spectrum of data sources and data types to meet the data consumption requirements of business applications and end users. Data integration tools enable organizations to access, integrate, transform, process and move data that spans various endpoints and across any infrastructure to support their data integration use cases. The market for data integration tools includes vendors that offer a stand-alone software product (or products) to enable the construction and implementation of data access and data delivery infrastructure for a variety of data integration use cases.
Gartner defines a data science and machine learning platform as an integrated set of code-based libraries and low-code tooling that support the independent use by, and collaboration between, data scientists and their business and IT counterparts through all stages of the data science life cycle. These stages include business understanding, data access and preparation, experimentation and model creation, and sharing of insights. They also support machine learning engineering workflows including creation of data, feature, deployment and testing pipelines. The platforms are provided via desktop client or browser with supporting compute instances and/or as a fully managed cloud offering.
The market for ESP platforms consists of software subsystems that perform real-time computation on streaming event data. They execute calculations on unbounded input data continuously as it arrives, enabling immediate responses to current situations and/or storing results in files, object stores or other databases for later use. Examples of input data include clickstreams; copies of business transactions or database updates; social media posts; market data feeds; images; and sensor data from physical assets, such as mobile devices, machines and vehicles.
Predictive analytics software uses advanced analytics capabilities to analyze current and historical data to make predictions about future events. This software connects data from different data sources and employs techniques like data mining and statistical analysis to forecast future trends, detect patterns, identify potential risks and opportunities, and plan for the best possible outcome. As a result, organizations can make better business decisions with machine-generated analytics, visualization, and reporting on predictive insights. These can be used in a wide range of industries, including healthcare, finance, marketing, and manufacturing.