Data integration for analytics is the process of combining data from different applications, in different data formats, from multiple locations, to enable users and systems to more easily identify correlations and gain a fuller view of business or operational performance. Integration begins with the data ingestion process, and includes sequential steps like cleansing, prepping, ETL mapping, and transformation.
For example, customer data integration involves the extraction of information about each individual customer from disparate business systems such as sales, accounting, and marketing, which is then combined into a single view of the customer to be used for customer service, loyalty programs and cross-sell/up-sell opportunities.
As a strategy, data integration is the first step toward transforming raw data into meaningful and valuable information. Data integration allows enterprises to combine data from different applications, in different formats, from different locations to enable analysts and data scientists to more easily identify correlations and insights to gain a better view of business or operational performance.
A data integration platform consists of software that is primarily used and governed by IT professionals. It allows data from different applications, in different formats, and multiple locations to be collected, sorted, and transformed so that it can be applied to various business outcomes. The results can be routed to specific users, business units, partners, applications, or prospective solutions and be viewed in analytical dashboards or reports.
A data flow diagram (DFD) is a way of representing how data flows through a process or system. It includes data inputs and outputs, data stores, and the various subprocesses the data moves through. DFDs are built using standardized symbols and notation to describe various entities and their relationships.
An enterprise data integration for analytics tool comprises of software used to perform data integration processes on data from different applications, in different data formats and from multiple locations. These tools perform cleansing, prepping, transformation, and mapping of data. The most ideal tools should be designed to meet your data integration requirements from edge-to-core-to-multicloud and incorporate a metadata architecture supporting data governance.
Data analytics vendors abound providing businesses with ample solutions to solve their data analysis needs. There are stand-alone data tools, but analytics platforms offer businesses solutions with full capabilities to absorb, organize, discover, and analyze their data.
Some platforms require IT expertise to set up the analytics environment, connect data sources, and prepare data for usage; while others are user-friendly, designed with the non-expert in mind. These user-friendly platforms are known as self-service, and allow data consumers to prepare, model, and transform data as they need to make business decisions.
Data analytics software with the following end-to-end features can be classified as platforms.
Data analytics and big data are terms that often collocate and can be confused to mean the same thing. Data analytics is about finding patterns within data, typically structured data, within significantly smaller sets than Big Data sets. Statistical analysis is a primary tool for data analytics. And the purpose is usually business problem-oriented.
Big Data analytics, however, is characterized by a high variety of structured, semi-structured, and unstructured data, drawn from various sources like social media, mobile, smart devices, text, voice, IoT sensors, and web, and further by the high velocity and high volume at which its data pipelines ingest.
Though there is no official big data size, big data operations can be measured in the terabytes and petabytes for organizations like eBay and Walmart, and in the zettabytes for Google or Amazon. Once collected, data can reside in an unstructured form in data lakes available for processing by data preparers. After processing, the filtered and structured data is maintained in data warehouses to be used by data consumers.