What is Information Integration?
Information integration (II) is the merging of information from heterogeneous sources with differing conceptual, contextual and typographical representations. It is used in data mining and consolidation of data from unstructured or semi-structured resources. Typically, information integration refers to textual representations of knowledge but is sometimes applied to rich-media content. Information fusion, which is a related term, involves the combination of information into a new set of information towards reducing redundancy and uncertainty.
Examples of technologies available to integrate information include deduplication, and string metrics which allow the detection of similar text in different data sources by fuzzy matching. A host of methods for these research areas are available such as those presented in the International Society of Information Fusion. Other methods rely on causal estimates of the outcomes based on a model of the sources.
What is data integration?
Data integration is the practice of consolidating data from disparate sources into a single dataset with the ultimate goal of providing users with consistent access and delivery of data across the spectrum of subjects and structure types, and to meet the information needs of all applications and business processes. The data integration process is one of the main components in the overall data management process, employed with increasing frequency as big data integration and the need to share existing data continues to grow.
Data integration architects develop data integration software programs and data integration platforms that facilitate an automated data integration process for connecting and routing data from source systems to target systems. This can be achieved through a variety of data integration techniques, including:
- Extract, Transform and Load: copies of datasets from disparate sources are gathered together, harmonized, and loaded into a data warehouse or database
- Extract, Load and Transform: data is loaded as is into a big data system and transformed at a later time for particular analytics uses
- Change Data Capture: identifies data changes in databases in real-time and applies them to a data warehouse or other repositories
- Data Replication: data in one database is replicated to other databases to keep the information the information synchronized to operational uses and for backup
- Data Virtualization: data from different systems are virtually combined to create a unified view rather than loading data into a new repository
- Streaming Data Integration: a real time data integration method in which different streams of data are continuously integrated and fed into analytics systems and data stores
How does information integration help businesses?
Even if a company is receiving all the data it needs, that data often resides in a number of separate data sources. For example, for a typical customer 360 view use case, the data that must be combined may include data from their CRM systems, web traffic, marketing operations software, customer-facing applications, sales and customer success systems, and even partner data, just to name a few. Information from all of those different sources often needs to be pulled together for analytical needs or operational actions, and that can be no small task for data engineers or developers to bring them all together.
Improved Customer Experience
When data is siloed, it prevents organizations from forming a complete view of customers, which can impact marketing functions, sales, and ultimately revenue. Only when organizations have access to real-time customer information can customers be targeted with the right message at the right time on the right channel. Information integration tools give organizations the real-time, 360-degree view of the customer they need, enabling them to improve customer experience, loyalty, and increase revenue.
Even when organizations are collecting and analyzing data, if they have to constantly move between many different systems to garner insight, productivity is naturally – and significantly – reduced. When a technology-enabled information integration strategy is enabled, on the other hand, the organization’s data from all its different sources is pooled together into a single view, allowing productivity to be improved.
Streamlined Processes and Operations
Be it product management, manufacturing, supply chains or procurement, enabling company-wide real-time access to key information improves processes, increases production, and lowers costs across departments, including sales, production, distribution, and more.
Improved Decision Making
Information integration technologies present real-time data in an easy-to-digest format, often via customizable data dashboards. This helps departments become more proactive, uncover opportunities for process improvements, identify problems before they occur, and run with up-to-the-minute information to make fast, high-quality decisions.
Better Business Intelligence
Information integration technologies supply the business intelligence tools an organization is already using with the data streams teams need to make strategic decisions, and uncover inefficiencies, gaps in processes, and missed revenue opportunities. In addition, having the ability to combine historical data with current sales pipeline information enables organizations to make informed forecasts and anticipate customer demands.
Improves collaboration and unification of systems
Employees in every department — and sometimes in disparate physical locations — increasingly need access to the company’s data for shared and individual projects. IT needs a secure solution for delivering data via self-service access across all lines of business.
Additionally, employees in almost every department are generating and improving data that the rest of the business needs. Information integration needs to be collaborative and unified in order to improve collaboration and unification across the organization.
Saves time and boosts efficiency
When a company takes measures to integrate its information properly, it cuts down significantly on the time it takes to prepare and analyze that data. The automation of unified views cuts out the need for manually gathering data, and employees no longer need to build connections from scratch whenever they need to run a report or build an application.
Additionally, using the right tools, rather than hand-coding the integration, returns even more time (and resources overall) to the dev team.
All the time saved on these tasks can be put to other, better uses, with more hours earmarked for analysis and execution to make an organization more productive and competitive.
Reduces errors (and rework)
There’s a lot to keep up with when it comes to a company’s data resources. To manually gather data, employees must know every location and account that they might need to explore — and have all necessary software installed before they begin — to ensure their datasets will be complete and accurate. If a data repository is added, and that employee is unaware, they will have an incomplete data set.
Additionally, without a data integration solution that synchronizes data, reporting must be periodically redone to account for any changes. With automated updates, however, reports can be run easily in real time, whenever they’re needed.
Delivers more valuable data
Information integration efforts actually improve the value of a business’ data over time. As data is integrated into a centralized system, quality issues are identified and necessary improvements are implemented, which ultimately results in more accurate data — the foundation for quality analysis.