Keeping up with the endless barrage of technology jargon can be a difficult task. Loosely-defined terms and industry-specific vernacular muddy the waters even further. It may seem like a matter of trivialization, but properly defining enterprise technology solutions and the associated terminology has real-world implications.
That’s where Solutions Review comes in. Our job is to scrub every available inch of relevant information on the web to bring you the leading library of content. It is our hope that these resources help you gain a better understanding of what is becoming an increasingly complex technology environment.
To that end, we’re going to take a deep dive into the world of big data in an attempt to uncover the similarities and differences between Data integration and data management.
Without Data integration, accurate analytics are impossible to achieve. Imagine trying to make a decision based on incomplete data. The less information available, the more likely a decision leads to an undesirable outcome. Now, multiply this challenge – decisions will now involve millions of dollars, hundreds of data sources, and terabytes of data. In order to steer a business correctly, integration tools need to handle a heavy burden.
Data integration is a combination of technical and business processes used to combine different data from disparate sources in order to answer important questions. This process generally supports the analytic processing of data by aligning, combining, and presenting each data store to an end-user. Data integration allows organizations to better understand and retain their customers, support collaboration between departments, reduce project timelines with automated development, and maintain security and compliance.
Cloud connectivity, self-service (ad hoc, citizen), and the encroachment of data management functionality are major disruptors in this market. As data volumes grow, we expect to see a continued push by providers in this space to adopt core capabilities of horizontal technology sectors. Organizations are keen on adopting these changes as well, and continue to allocate resources toward the providers that can not only connect data lakes and Hadoop to their analytic frameworks, but cleanse, prepare, and govern data.
These are three pillars of modern data integration software as outlined in our vendor map:
Enterprise companies run an average of roughly 500 different applications. That number has undoubtedly increased over the last few years, but the salient point is that these applications are not designed to communicate with one another. This is where Application Integration comes into play.
In some EAI approaches, a single solution collects incoming data and pushes it out to relevant applications. This is known as a broker model. For example, if a salesperson closes a sale in the CRM, the EAI will push that information to accounts receivable to generate an invoice, payroll to generate a commission, and budget to bank that closed sale that quarter’s earnings.
The benefit of this approach is an automated workflow. Prior to EAI, the chain of events described above would involve a chain of emails or a sneakernet. At scale, this would translate to significant losses in terms of time and efficiency as workers manually transcribe and upload data. Therefore, an EAI solution can recapture a great deal of productivity.
Say that instead of automating a large series of tasks, a company wishes to analyze a large amount of data. Data analytics isn’t new, but its accessibility is. In the days of ETL, creating complicated analytics and data visualizations would require assistance from IT staff. By contrast, self-service data preparation is essentially what it says on the label—a way for business users to explore their data without needing assistance or specialized training.
This flexibility can sometimes be its own enemy.
