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To Achieve Advanced Analytics, Start with Big Data Integration

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The promise of big data is virtually unlimited. Employing advanced analytics throughout an enterprise can deliver huge competitive advantages through reduced costs, maximized production efficiencies, reduced risk, and other gains for the business.

But in order to start paving the road to insights, enterprises must have the tools needed to create the road in the first place. Big data requires new forms of processing and thus, innovative technology to support and create enhanced decision-making and greater insights, and it’s not an easy task given the scale at which we’re doing business today.

So before there’s any talk of obtaining valuable advanced business analytics, there needs to be a solid integration and management platform powering the infrastructure and connecting the B2B, application, people, and cloud technology running to and from the insights-creating infrastructure.

Needed is a platform that easily scales and interoperates with numerous big data tools, data sources, data pipelines, and data storage mechanisms without disruption. Only then can an organization deploy the business intelligence tools. But the question is, where do you start?

Massive amounts of data to integrate

The sheer volume of information – and its various formats – is what makes data “big data.” Trying to manage big data across business units, continents, and data centers with traditional, unscalable tools is a major underestimation of modern needs.

The analytics or business intelligence tools, however, are just one piece of the puzzle. Even today’s smallest companies have dozens of applications, systems of record, ERPs, and other technology from a variety of vendors – deployed via cloud and on-premise networks – producing data, and it all must be connected for a comprehensive, accurate, and real-time view of the business to create insights for decision-making.

Without a proper managed file transfer and integration platform, your IT team will be in for a lot of labor-intensive, manual coding to get these systems to communicate with each other.

Ultimately, big data integration is ingesting, preparing, and delivering data, no matter what the source. This includes leveraging every type of data in an enterprise, including the complex, often-unstructured machine-generated kind, and often requires a more converged enterprise infrastructure that connects this data architecture.

So one of the initial steps – and arguably the most important step – is to deploy a platform to pipe all of these data sources into your data lakes. Here’s what you need to know to take that first step to power your data lake – and all of its promised riches – off the ground.

An eye on the prize

The goal of any big data project is to gain better outcomes – including real-time insights and long-term perspectives based on recurring patterns – but your business must overcome the early integration challenges to get there. Some important questions to ask early on in the process:

  • Identify all your critical data sources
  • Determine whether the current infrastructure can support all your required data pipelines
  • Maximize and optimize the value of your data through investments in technology and infrastructure

In a nutshell, you’ll determine that the means to get there is by prioritizing efficient integration architecture. It may not be as sexy as some of the outcomes you can achieve through advanced big data analytics, but it’s a key component that will enable that raw data to flow through your business, data lakes, and analytics applications.
The most successful big data integration projects, then, feature:

  • Support for any type of data across any endpoint, integrating with any big data application
  • Consolidation of disparate point solutions onto a single integration platform
  • Certified connectors for high-speed Hadoop ingestion and other big data connectivity, as well as deep protocol support
  • Rapid and secure data extraction, ingestion, and integration
  • Carrier-grade scalability to meet the volume, variety, and velocity of even the most demanding big data initiative

A secure, agile integration platform that focuses on securely mobilizing the actual flow of data into and out of the enterprise data lake ensures reliable information exchange within increasingly complex workplace ecosystems, and that drives the promise of advanced business analytics.

A successful big data project ultimately depends on an organization’s ability to capture its data, and rapid ingestion and processing of big data requires a reliable integration infrastructure that can easily scale to accommodate massive data volumes, drive real-time access, and deliver data for every analysis query.

Leveraging information to gain a competitive edge for your organization sounds great, but that only comes after building a usable data lake that reliably and accurately integrates all of your data sources. But only when companies support their big data investment with a reliable integration platform underneath will they reap the big data rewards – the business analytics – leading enterprises seek.

(About the author: John Thielens is the chief technology officer and data scientist at Cleo, a maker of enterprise data integration, managed file transfer and big data gateway solutions. He can be reached at jthielens@cleo.com).

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