Data integration describes the process of consolidating data from different sources for increased availability and easier processing.
Data integration automatically brings data from countless different locations together to create a unified view that businesses can then refine into meaningful insights and actionable comprehension. If managed correctly, data integration can help reduce costs, improve data quality and promote IT innovation without upending existing data structures.
Occasionally, businesses confuse data integration with the concepts of application integration and extract, transform and load (ETL). But while these approaches may share some similarities, there are important distinctions to be aware of.
Application integration is the process of synchronising multiple applications by allowing data to move freely between them. For example, multiple systems that share customer records may rely on application integration to ensure that there are no discrepancies between the records and that changes in one system are immediately reflected in the other.
ETL is a larger umbrella term that encompasses both application integration and data integration. ETL refers to the process of collecting data from a source system, transforming it into another format and depositing it at a new destination.
At its heart, data integration is about data transparency and availability. Rather than siloing data into individual systems and tools, organisations can access all relevant data through an entire company network, sharing information seamlessly across systems and departments.
Data integration also goes further, converting the data into a usable format that users at all levels can more easily digest and put to work.
This provides several advantages for businesses that correctly apply data integration solutions:
Many business systems tend to operate in isolation, failing to integrate effectively to share data between them. Unfortunately, when data becomes siloed, other departments and systems have difficulty accessing it, reducing the effectiveness of every system on the network. Data integration eliminates those barriers, allowing data to flow freely and improving the return on investment (ROI) of individual systems.
When data integration relies on manually locating, converting and moving data, data quality suffers. Automated data integration preserves data quality as it moves between systems, by applying automated data transformation governed by predefined business rules.
With reliable data always at hand, organisations have a solid foundation for making informed, fact-based decisions. Confident in the knowledge that their data is accurate and up-to-date, teams across the organisation can improve their decision-making capabilities.
Manually collecting, transforming and combining data is not only imprecise; it is also extremely time consuming. Data integration takes this responsibility out of employee hands, freeing them up to concentrate on more strategic concerns and provide better value to the company.
Without a completely holistic view of the data, account executives lack a necessary resource for interacting with customers and partners. Data integration empowers these professionals with the information they need to build, secure and improve vital business relationships.
Here we address some of the possible hurdles to successful data integration and how to recognise them:
Many modern businesses rely on a range of systems, applications and tools to manage accounts and guide decisions. Billing, marketing, lead generation and customer service are only a few and each system creates data. In many cases, these different systems are being maintained by different teams without any unified processes for managing that data. When data is not standardised, it runs the risk of duplication, improper formatting and being overlooked.
Information silos are a major threat to data transparency. As different departments gather data and store it in their own systems, other departments may be looking for insights relevant to that information with no way of knowing that the data they require has already been collected. Data must be easily available to any authorised department that needs it, regardless of where it originates.
Many companies take a more-is-better approach to data collection. But too much data may not necessarily be a good thing, potentially making the valuable hidden gems of data more difficult to identify and extract. Without proper data management, collecting data from multiple channels can create data clutter, burying the vital data that could otherwise be turned into actionable business insights.
Unfortunately, not all data integration software is the right fit for every use case. And even with the right software, some companies create problems for themselves by failing to use it correctly. Understanding data-integration solutions and how best to apply them can help ensure that the data is where it needs to be and in the right format.
Because data availability and transparency are so important, it’s no surprise that there are several distinct kinds of data integration tools available. Some of the most widely used types of data integration include the following:
Although each business will have its own specific data integration needs, top data integration tools share certain similarities in their functionality. When choosing a data integration solution, consider the following:
Most modern organisations rely on a range of different systems to facilitate their business processes. Any effective data integration solution must be capable of supporting every data source on the business network, otherwise the organisation risks developing data silos.
Businesses are not static; they grow and change to meet evolving needs, objectives and markets. The right data integration solution must be likewise dynamic, capable of scaling along with the business it supports.
There is a lot at stake whenever an organisation handles sensitive data. Before committing to any data integration solution, it’s vital to take an in-depth look at how it addresses security and compliance. Otherwise, it may be exposing the business and its customers to potential problems down the road.
Having access to data from multiple sources is only beneficial when that data is fully up to date. The data integration tool must be capable of delivering real-time data solutions, so that the decision makers can take action with confidence.
A key aspect of data integration is ensuring that any data collected is presented in the correct format. Simply gathering and moving data is not enough; that data must be standardised so that all authorised users can easily interpret and apply it.
To remain competitive in an increasingly information-centric world, businesses in all industries depend on available, transparent, actionable data. Unfortunately, eliminating data silos and making essential information accessible across systems and departments in real time can be a challenging task. Data integration tools can help optimise the process, but only if they have the right functionality. ServiceNow, the leader in IT management solutions, has the answer: Integration Hub.
ServiceNow Integration Hub is the bridge connecting and empowering ServiceNow products to accelerate data availability and workflow connectivity across any system. Scalable, secure, simple to use, and easy to customise, Integration Hub employs out-of-the-box ‘spokes’ for unmatched time to value without the need for specialised integration resources. The end results? Lower integration costs, improved IT productivity and fewer barriers standing in the way of developers, IT generalists and no-code builders alike.
See Integration Hub in action; watch the demo, and get ready to experience true data transparency, with ServiceNow.
The Now Platform includes core capabilities that enable you to quickly and efficiently digitise workflows and run them at scale.