Data integration is the process of combining data from many disparate sources, so that it can be analyzed and converted into reports and visualizations that provide useful, meaningful and valuable insights to users throughout your organization.
Organizations of all types and sizes collect, hold and generate a growing volume of data, which is often stored in different applications by different teams and functions. For example, an organization’s marketing, finance, sales, HR, operations, production, distribution and logistics teams may all use separate software to manage their individual processes. Frequently, these sources can be incompatible, but the information they hold may add value to other teams if it could be easily and efficiently shared. The aim of big data integration or enterprise data integration is to overcome this problem and provide a unified view of the combined data, so that you can query and manipulate it from a single interface in order to expedite analysis and visualization.
Similarly, when organizations or functions merge, it’s necessary to consolidate applications and bring together systems so that your new, combined organization provides the best, most comprehensive data to all users, irrespective of the data sources. The challenges, as outlined above, are similar, although often on a much larger and more complicated scale.
Here’s an overview of the benefits of big data integration and data integration methods to help you decide what’s the best solution for your organization.
Why data integration is essential for your organization
Data integration is critical for ensuring that you get the most comprehensive, precise and valuable results from your BI and analytics. With the right data integration architecture in place, you can be confident that the decisions you make for your organization are derived from insights based on the best and most thorough data available. The benefits are as follows:
As our world has become increasingly data-driven, so the ways of integrating data have become more sophisticated. The best methods provide you with the most flexibility and capacity to handle Big Data from the greatest variety of sources. As your organization grows, so does the need for increasingly versatile data integration. It’s therefore important to consider the main methods listed below, from the least to the most sophisticated, which of them are most suited to your current and future requirements, and which BI and analytics platform can provide you with the data integration tools you need.
In reality, this isn’t really data integration because it involves little or no assistance with data integration tools. It leaves all the work up to you and involves simply locating the information in each data source and comparing or cross-referencing them yourself in order to get the insight(s) you need. There is no unified view of data from the different sources that you’re accessing. This is only a viable method when handling a minimal number of sources and a small volume of data. Anything larger will be unmanageable.
You can use these specialized programs to locate, retrieve and integrate the information you seek. Applications will do the work of manipulating and integrating data so that information from different sources is made compatible. This process can be problematic and complex when handling large volumes of data and large numbers of sources. It is suitable for handling only a limited amount of data from a limited number of sources and applications at any time.
Middleware is software that links separate applications; for example, establishing a connection between a Web server and a database system. Users can request data from the database via forms on the web browser and the web server. Then the web server returns dynamic web pages that correspond with the user’s request. Some examples of this method of data integration take place between cloud services, cloud-based applications (SaaS), private clouds and other cloud resources; between customer, provider and partner interfaces of different data sources and company-managed applications. Although the middleware does the integration, not the applications, it’s still necessary for applications to take part in the data integration to some extent.
In data warehouses, the data from all the sources you are integrating are extracted, transformed and loaded (ETL). The data warehouse pulls data from all of the various data sources, converts it into a common format to make all the data compatible and then loads it into its own database. When a query is submitted, the data warehouse finds the relevant data, retrieves it and presents it to you in an integrated view. With data warehousing, you can easily and efficiently manage your data history of data (versioning), combine data from disparate sources, and store them in a central repository.
Primarily used in enterprise organizations, data lakes could be described as as an approach to Big Data storage and architecture rather than a particular product or service. An approach that can be summarized as “store now, analyze later.” Data lakes store vast amounts of raw, unstructured data in its original form until they’re needed, in a single repository that serves multiple analytic use cases or services. Only then is it pulled and organized. In comparison, data warehouses structure and format data when it is put into the warehouse. Data lakes use a flat architecture for storage, unlike the data warehouse, which stores data in a database or hierarchical file system. Data lakes are typically used to store data that is generated from high-velocity, high-volume sources in a constant stream – such as IoT, product logs or web interactions – and when the organization needs a high-level of flexibility in terms of how the data will be used. Data lakes can offer great value and highly scalable flexibility.
While organizations share many features and requirements, each has its own specific characteristics and needs. So, when you’re considering which BI and analytics platform has a data integration layer that best meets your requirements, bear in mind the following considerations:
The amount of data you need to manage, and the number of connected devices generating data, is rapidly growing, so it’s vital that the data integration in your BI and analytics platform can handle your current data and a massive increase in data that you can expect in the future.
As your organization grows, you should be sure that your data integration capability can grow with you. It’s important that you have the power and technology for scaling up users, data, or complex analysis as required, without extra infrastructure or consultants.
Your data integration architecture should also be adept at quickly meeting changing demands. From granular customization of functionality to adapting to new requirements and driving shorter times-to-insights, it should ensure performance regardless of the challenges ahead.
Ideally, your choice should precisely meet your requirements, so consider how well your choice of platform architecture can be customized. With the right platform in place, customization is simple, and even the most in-depth changes can be handled with ease. So, make sure that the platform you choose is purpose-built for developers so they can reduce development time; and is able to embed analytics into your products and services. The data integration layer in Sisense’s technology can be used for terabyte scale data and analytics, and to serve multiple users concurrently, enabling a single commodity server to deliver the same data processing power as much larger clusters.
See how the leading vendors of Data Integration tools stack up in the most respected industry analyst reports:
Value delivered, early innovation, and customer success are what make us the leading Visionary in the Magic Quadrant.
Sisense earned top rankings from customers for ease of use, ease of doing business with and quality of support.
“Overall Leader” for customer experience and credibility with high scores for value & integrity put us consistently above the rest.
There’s a lot to consider when choosing a BI tool, but a lot to be gained by making the right decision. So, in addition to the overview you’ve seen so far, look at these comparisons between Sisense and the other leading BI tools, to see why you should choose Sisense:
Developers, cloud data teams, and analysts from some of the world’s leading companies and global enterprises use the power of Sisense to effortlessly combine complex data from a variety of sources and build analytics apps that deliver insights to everyone in the organization.
These power users, plus business users from a wide range of functions, are what we identify as the builders of your strategy. Sisense gives them the power to identify, analyze, and visualize the data that influence the course of your organization, with powerful decision-making capabilities that are potentially game-changing.
The Sisense BI and analytics platform dramatically accelerates the time it takes to build, embed, and deploy intelligent analytics apps that unleash user creativity and engagement. Whether it’s interactive dashboards, self-service analytics, or white-labeled BI apps, Sisense delivers the industry’s lowest TCO at scale, all on a hybrid-cloud platform designed to leverage all your data together, no matter where it is.
Sisense empowers your users to make decisions and challenge assumptions by equipping them with the insights they need, when and where they need them. We help everyone in your organization drive change and build the business using innovations in AI and ML that deliver BI with more impact than you ever thought possible.
It’s not just what we do, it’s how we do it. It takes more than patented technology to make your business successful. You need the product to work in your network, with your requirements, under your constraints. And that’s when our customer-centric culture kicks in. We work with you on every installation, every upgrade, and every project to make sure you feel the value of your BI platform in your business. Sisense scores consistently above the overall sample and is the leader in terms of customer experience and vendor credibility.