Large companies and organizations are already using big data for some specialized applications, but the majority of big data is still structured data (transactions, clicks, etc.). In 2017 we will continue to see a large uptick in big data usage – but with a greater focus on leveraging unstructured data. Digital companies whose businesses rely on data have been leading the pack in how to gain beneficial insights from unstructured data. This ability is expanding to enterprises that aren’t as data-reliant, as more data becomes available along with the tools to manage it without huge investments and long development cycles. Historically, the focus of data has been on making sense of numbers (structured data), analytics and business intelligence. That focus will continue, but in 2017 more organizations will be tapping into their vast stores of disparate, unstructured data by finding ways to leverage it along with their structured data assets.
This will be a fundamental shift. Numbers are typically about revenue performance, operational metrics, etc., but unstructured text holds the critical information about how business actually gets done. A company’s institutional knowledge (or secret sauce), discoveries, internal processes and competitive edge are often contained in a vast array of written text. It takes natural language processing, unified information access and cognitive search capabilities to extract information and share it in a useful way with those who need it. This will allow organizations to understand what the text is saying and use that to drive innovation and efficiency and to improve operational effectiveness.
Old Persistent Challenges Are Best Tackled Through the Use of Search Technology
Two challenges have plagued organizations when trying to tackle unstructured data: infrastructure and unification. They’re starting to solve the infrastructure problem with data lakes or the cloud, but unification remains tricky. Of the three Vs of big data (volume, variety and velocity), the one that poses the greatest challenge is variety, which makes unification absolutely critical. Data lakes, while providing more flexibility than traditional data warehouses, don’t solve the variety conundrum; they just postpone the need to solve the problem – to the point of data usage rather than the point of ingestion. In 2017, the use of search technology to create a logical data warehouse will gain significant traction as an alternative and more flexible solution to the data unification problem. It’s key to bringing together disparate data from data stores and silos across the organization – without the need to build a data lake. Adoption of unified insight engines will be on the rise so that companies can access and quickly gain insights from the content buried in their vast unstructured data stores. Creating a logical data warehouse with the technology of today’s insight engines takes only weeks and supports both structured and unstructured data, for a faster and more comprehensive solution.
How Unstructured Data Will Help the Healthcare Industry
Healthcare is an industry that holds tremendous promise for improved big data analytics. The human body is immensely complex with high variances and the industry has amassed huge repositories of data we don’t fully understand, but we’re starting to. More plentiful and higher quality data holds tremendous potential to unpack meaning alone, but the real treasure trove is understanding relationships and interactions within and between the documents and data. Leveraging unstructured in addition to structured data will not only help bring better drugs to market faster but accelerate cures, reduce healthcare costs and improve quality of life through things like personalized medicine. This won’t hit the mainstream in 2017 but the pace is accelerating.
The biggest hurdle here again is the unification of all kinds of information regardless of source, silo or type, and in particular the ability to analyze structured and semi-structured data in context. The human genome, for instance, generates structured data but analysis on that kind of data isn’t the same as analysis on revenue figures or last quarter’s sales results. Context is crucial, and that context is made usable only with the same semantic analytics techniques that are required for extracting meaning from text, or unstructured data.
Big data use cases and analysis technologies will take tremendous strides in 2017. By embracing natural language processing, unified information access and the advanced capabilities of today’s cognitive insight engines, organizations of all sizes from various sectors will start to leverage their unstructured data, and better understand the context of their structured data. By doing so, they will add meaning, nuance and sentiment to their big data analyses, leading to efficiencies, strategies and discoveries that will change the way they do business, and in some cases, the world.