Business success is a result of smart business decisions. What enables people, right from the C-suite executives to customer support representatives, to take a great decision, consistently? The answer is — data. Of course, not raw data. Your cutting-edge business intelligence and data analytics applications will deliver tremendous business benefits by enabling your organization’s people to make dependable decisions based on reliable data. This, however, isn’t always a cakewalk, because business organizations deal with a vast variety of data formats and data sources every day. The complexity of the data ecosystem of enterprises is only going to increase further. Data integration tools make it easier
Understanding data integration tools better
To prepare datastreams for feeding data into business applications, you need to put together trickling data from different sources and make it all coalesce into a consistent stream. Data integration tools empower your organization to do this.
A data integration tool is your most dependable weapon to win any data integration project. But you must choose it after due deliberation. For starters, you’d need to know the number of data sources and target systems you’re going to include in the project scope. Next, envision the range of data formats you need to manage. Also, understand the level and nature of integration you intend to realize with the project.
New-age data integration tools offer you advanced capabilities such as data profiling, data visualization, structured and unstructured data integration, real-time integration, and extract, transform, and loading operations. Goes without saying, you have all the options you could possibly imagine. How do you choose reliable data integration tools, then? Let’s tell you how.
Preparing for the selection process
Expect being swamped by jargon-filled marketing materials from all data integration tool vendors. Expect their pitches to be ambiguous. You need clarity about your data integration requirements to make the right choice, instead of losing time and patience in consuming an overload of marketing material and canned pitches from vendors.
Prepare a list of the must-have data integration features you need. Software that doesn’t offer all of these features must not be considered. Note — vendors like to flaunt their tools’ custom code capabilities to address all your concerns about missing product features. Undertake deep discussions that help you understand how much time and money you’ll need to put into the custom code building to get your must-haves.
Should-have features deserve your attention and concern, too. These are the features that can make your data integration efforts more productive, scalable, and secure. A year down the line, you don’t want to be bogged down because your data integration software doesn’t have its should-have features.
Apart from the must haves, the nice-to-have features could help you identify a better data integration product from two or more seemingly similar products.
Use your organizational data complexity
The better you understand the true complexity of your business’ data, the more accurately you can specify requirements. A general-purpose data integration tool needs to be able to manage all kinds of data structures. Enterprise data is stored in all kinds of databases — relational, in memory, columnar, and NoSQL databases. Data integration tools should also be able to work across application messaging technologies like JSON and extensible markup.
Apart from these, your data integration tool will have to work with ERP and CRM solutions’ data, web applications, and industry-specific communication protocols. If your organization uses sensors to provide regular datastreams, depends on unstructured data from social media platforms, and uses a large number of proprietary web-based applications, you’ll need a data integration tool that can be integrated with Hadoop, NoSQL, and Spark.
Bird’s eye view of data sources and targets
The whole Big Data movement is centered on capturing datastreams from all sources and making them coalesce to be fed into data warehouses. To make sure your data integration tool gets the job done, you need to have a broad understanding of the different data sources and targets in your enterprise IT ecosystem. This bird’s eye view helps you imagine the kind of integration you’re looking for, and, hence, helps you choose a worthy tool.
If your enterprise uses a large number of cloud-based SaaS applications along with on-premises systems, you need a data integration tool that can bring together both data lakes to ensure that end users are able to access wholesome and accurate data. The DI tool must support a variety of data capture and delivery mechanisms. These include batch acquisition and delivery, bulk import, bulk extract, and the ability to capture change data. Apart from event-based and time-based data ingestion, the DI tool should also support real-time data ingestion and streaming data ingestion.
Evaluate the DI tool on data transformation capabilities
The better the transformation capabilities of your data integration tools, the more flexibility you get in making data from different sources talk! Basic data handling capabilities for any DI tool include data string handling, data type conversions, NULL processing, and arithmetic operations. Then, it must offer you data-mapping capabilities such as merge, join, substitute, aggregate, and workflow support.
The ability to pass variables, execute “if and else” commands, and execute loops also delivers great advantages to your project teams. Remember, you can’t predict which of these data transformation capabilities will become crucial for a business intelligence project. You don’t want to commission third-party extensions or embark on complicated custom coding projects in such situations. So look for a DI tool that brings all these data transformation capabilities together.
Crucial tool in your toolbox
A data integration tool is a crucial element of the complete data management toolkit for an enterprise. This is the tool that will make systems talk among themselves and help you make sense of all datastreams to draw insights. Look for the must-have features discussed in this guide to make sure you choose a data integration product that can stand the test of time, particularly in the context of the evolving nature of data.
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