What is Data Integration?

What is Data Integration?

Data integration is a general industry term referring to the requirement to combine data from multiple sources for a single unified view to process it further into insights. In separate words, it is a practice to consolidate data from multiple sources into a single dataset or unified view with the aim of providing users with consistent access across all data types. 

Note: The unified view is stored in a centralized data repository known as a data warehouse or data lake.

The process is a significant part of the overall data management process, employed with increasing frequency as big data integration.

To make things more clear, the customer data integration process comprises the extraction of data or information of every individual customer from different systems like Sales, Accounts, Marketing, and more. Then all the info is combined into a single database and then used for more effective customer service.

Competently, today’s organizational structures boast of big storage devices, and what’s the real challenge?

Storing or collecting data is an issue in this tech era. But to integrate it with the proper tools remains a challenge in many situations.

Here the ability of Data integration architects comes into play. They develop data integration tools and platforms to automate the process of connecting data from multiple sources to target systems. Of course, they employ many techniques and data integration tools to achieve it. Here are some:

  • Extract, Transform and Load (ETL approach):- Multiple datasets from disparate data sources are gathered together, channelized, and loaded into a destined data warehouse. 
  • Extract, Load, and Transform (ELT approach):- The whole data from disparate data sources are loaded into the system and transformed for the particular analytical use as per the purpose. 
  • Change Data Capture:- Identifies any data tweaks in the database in real-time and applies them to all the data repositories
  • Data Replication:- Data present in one database is replicated to the other data repositories to keep the information for use and backup
  • Data Virtualization:- Data from disparate sources is combined virtually to create a combined view rather than loading data in a dedicated repository.
  • Streaming Data Integration:- This is a real-time data integration method in which different data streams are continuously integrated and used for analytical purposes.

Now how have you got your answer on what are the best tools for data integration.

Read our comprehensive write-up on Top 10 Data analytics tools to look for in 2021.

What are the challenges in Data integration?

Here are the challenges and Data integration criteria:

There are three major data integration criteria to build a data warehouse are:

  • Freshness:– The process of data integration architecture is executed at certain periods. In this, data freshness refers to the delay when data is tweaked on a source system to when the change is executed in the data warehouse.
  • Granularity:- Data from the source system is channelized or aggregated in the very process of data integration. Here the data granularity refers to the level of detail with data stored in the data warehouse.
  • Matching: In the data integration process, data comes from disparate sources and needs to be specified in a specific category. Here matching defines how data from multiple sources should be matched can be complex. For instance, is “Wayne Hamilton” the same individual as “Robert Hamilton”?

These mentioned criteria strongly affect the performance and data storage capability of a data warehouse, and the overall capital required.

Technology Framework for Data Acquisition, Aggregation, and Transformation

  • Provides real-time, unrestricted, and scheduled optimized analysis of the organizational data.
  • Uninterrupted and streamlined integration with internal data assets of the organization in real-time.
  • Custom-tailored data sets for personal, public, and organization.
  • Provides an analytics platform for pattern detection during data extraction.
  • Supports unstructured data extraction & analysis from external sources.

Now what we offer from data integration to transform it into insights

  • Deliver you the insights from the Data, the way you need them. 
  • Semantic information based on rules and patterns is the prime concept of our analytics services. 
  • The cognitive science of analytics empowers the data to reveal the facts through actionable strategies that simply drive business prosperity.

Mobcoder Data Integration Service encompasses industry-specific services as well as solution accelerators

Use cases of data integration and services make data science more distinctive and a capable business tool to manage big data. It embroils perfect data analytic models to meet the insights and foresight to identify new entrepreneurial opportunities.

Our big data integration approach clearly reflects the synchronization of the latest analytics models. Operating through an ideal collaborative approach of – Data Patterns, Statistical skills, Domain expertise, and real-time information, we simultaneously quantify the structured, unstructured, and semi-structured data.

Our data integration platform is intricately devised to align Data & Technology with business. It aims to channel the consultative approach, which is purely segmented on the industry-specific concerns.

Read here: how big data is transforming app development process 

Tailored work process for Data Science

Our in-house data scientists operate through cutting-edge methodologies from – Organizational use cases, cognitive science, business intelligence, and particular industry behavior to deliver ROI, not just insights. Deep collaboration of deep analytics expertise and business problems, allow our data science to give awareness, direction, and lucidity to those handling the data. 

Banking on the quantitative acumen and the diverse experience of our data scientists in manifold streams – Finance, Mathematics, Research, and operations. Our engagement rightly starts with the particular assessment of an analytics query – build a proof of concept – to the deployment of the most scalable data model to answer it.  

Transforming the way the decisions have been made through a fusion of advanced statistical analysis, data visualization, machine learning, complex data integration, data mining, and specific industry research. Here you will also witness, what are the benefits of Data integration.

Performing assessments in data strategy for companies in Healthcare, Manufacturing & Logistics, Telecom & Digital Media, BFSI, Retail, E-Commerce, and Real Estate. Our Data Scientists incorporate some cornerstones to serve these industries –

  • Benchmarking data science and analytics to establish a mutual understanding across a business 
  • Gap analysis; covering data assets, technical competence, and infrastructure to achieve primary objectives of – Data Science and Business intelligence 
  • A detailed review of current, relevant, and internal data sets 
  • Finding the most scalable solution for each use case
  • Harnessing the undermined organizational data infrastructure

Conclusion 

From any business processes to analytics, warehouses, and anything related to data processing, data integration is one of the most significant parts of it. So whether it is the real-time capability or harnessing insights through data warehouses, organizations must be ready to adopt them in their business model. 

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