Opinion Data contextualization continues to evolve in 2018

Published
  • January 11 2018, 6:59am EST

The concept of big data has become more widely understood in the past few years, as companies experienced first-hand how collecting data and turning it into information enables timelier, smarter decisions.

In 2018, the ways in which big data is incorporated into all levels of organizations will continue to evolve as users explore new opportunities created by the industrial Internet of Things (IoT).

A recent Forrester report, Predictions 2018: The Honeymoon For AI Is Over, indicates that 2018 will be the year that enterprises move beyond the initial hype of exciting new technology and recognize that big data requires hard work to plan, deploy, and govern. While organizations are seeing the benefit of analyzing data to determine where to make business improvements, they also need to be looking for where improvements can be made to the data collection process itself.

Data overload can lessen the effectiveness of an organization’s powerful new IoT tools. Users might lack insights into data’s purpose or patterns, struggle with getting the right users access to data, and still be creating manual workarounds for compiling data-based insights.

It’s vital that in addition to data connectivity, organizations create data contextualization—so users get the right data, right when they need it. Organizations must evolve to think about information as a collection of business-critical data, rather than individual pieces.

For example, factory visibility depends on collecting from multiple data sources at the same time to obtain the most accurate information and implement the correct improvements.

Many organizations could contextualize their data much earlier, and closer to the data source, in order to obtain the most accurate, actionable information. By collecting and analyzing insights earlier in big data cycles, businesses gain greater visibility into asset and resource allocation, and can take earlier actions to improve, if needed. This also helps differentiate good data from bad data—a goal that will become increasingly important in the coming years, as data is further integrated to boost efficiency across an organization.

The challenge now is taking raw data points from these solutions and determining their meaning earlier in the process—and making this data readily available to stakeholders across the enterprise.

The role of big data has evolved significantly in recent years and will continue to do so in 2018. As organizations get increasingly smarter about how their data is used, they will begin to recognize the importance of optimizing their data collection. As more and more IoT platforms are developed, more future-forward tools will be incorporated to improve the collection and analysis of big data—especially as augmented reality, virtual reality, and other powerful technologies start to redefine the ever-evolving IoT realm.

Today’s IoT platforms are the buildings blocks for a more connected future, and it’s important that organizations choose solutions that fit their needs for multi-layered insights—and that are ready to adapt towards collecting on the promise of big data into 2018, and beyond.

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