Big Data Integration Mistakes to Avoid
by Daniel Newman | December 27, 2016
Listen to this article now

Integration is complex for most organizations. Often, leadership starts to collect data, but they aren’t sure how to use it or what to do with it. Data by itself is relatively one-dimensional and not at all useful—it doesn’t mean much without insight as to how best to use it. To master big data integration, companies must look at their past mistakes and work backward to find a better way to integrate.

I’ve seen many of the same mistakes pop up across industries, and I’ve listed a few below. Is your company guilty of these? If so, learn from the experience and improve your processes so you accomplish more the next time around. If you have yet to encounter any of these issues, consider yourself fortunate and keep this information in mind whenever integration appears on the horizon.

Don’t misunderstand the data you are collecting

Collected data is usually unstructured at first. It requires careful analysis to establish value. When you don’t understand what data your company is collecting, it becomes even more difficult to apply what you gather into actionable improvements for your business.

Data collection should be approached with a clear strategy. Determine which data to collect and how you will collect it. Once your basic data needs are outlined, you can always revisit your initial strategy later down the road and adjust for new developments.

Understand the type of data you need

Avoiding this mistake relies heavily on your human investment. You need data scientists and analysts who know what type of data you’re collecting and how to collate it into meaningful and valuable information for your organization. Managing big data is a massive undertaking that requires a great deal of manpower and labor, and integrating big data into your company’s processes will be even more labor-intensive.

Determine what types of data exist in your source and target systems and develop a method for extracting the valuable bits. Investing in machine learning algorithms is crucial to navigating massive data stores. As use of these systems increases, they can identify valuable data strands more quickly and with greater accuracy.

Don’t neglect security

In today’s world, data security should never be an afterthought—it should be a critical element of every data integration process from the planning phase and beyond. Approaching big data integration entails reviewing your organization’s encryption capabilities for data in motion, access management, protection against malicious code and programs, and compliance.

Consider implementing a robust multilevel security system. Restrict access to data as well as the ability to manipulate, move, or otherwise change it. Access control should span multiple levels for the best security. If everyone in the organization has the same level of access, anyone can potentially be the source of a breach in the future.

Understand the multiple levels of integration

Integration is a multifaceted, complex process. Big data is collected from multiple sources. When data is unstructured, it can be exceedingly difficult to recognize the valuable bits or make insightful correlations between data points. This makes integration even more complex. To successfully integrate, you need a vast amount of data that is organized in a way that it can be used.

Invest in the right tech to successfully integrate big data in a meaningful way for your organization. As companies move toward digitalization, due diligence becomes more important for business leaders who want to make sound investments. Carefully explore your options when shopping for an integration solution. You need to be able to collect and organize data securely from multiple sources, and, ideally, interpret and extract the valuable and relevant data in a usable format.

Don’t make assumptions about the data

Don’t assume that just because you find a correlation in the data that it’s useful. You’ll be collecting large swaths of observational data that may or may not intersect at times. Sometimes correlations appear that seem logical but wind up misleading managers into erroneously assuming a best course of action. You must analyze correlations on a deeper level and uncover causal patterns. Those patterns—and not merely the appearance of a pattern—should form the foundation of major strategic decisions,

Virtual machines will be a wonderful asset when it comes to extracting valuable data from the vast sea of available sources, but these systems are not error-proof. I believe successful big data integration will rely heavily on technology to find the useful bits of information that drive companies forward, but it will be up to people to use that data in meaningful ways.

Photo Credit: Christoph Scholz Flickr via Compfight cc

About the Author

Daniel Newman is the Principal Analyst of Futurum Research and the CEO of Broadsuite Media Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise. Read Full Bio