over. Instead, you can put a small group of people to work on a task, and they iterate on it rapidly. Having witnessed the implementation of big data analytics at Fortune 500 companies and emerging high-growth companies, I can report that the results are uniformly transformational.
Below are three types of data commonly used in innovation-centric data analytics initiatives, and an overview of projects and opportunities you can explore today by bringing them together.
1. Machine data: find gold in once-unseen patterns
Most business and security analysts today do not have the ability to access large volumes of machine data such as Internet of Things (IoT) endpoint device logs, application telemetry, or sensor data, let alone integrate them. But loading all of this data into Hadoop enables analysts to work with years' worth of historical data and discover new correlations in near real-time.
One of the latest opportunities is to observe how customers are interacting with products including set-top boxes, cars, jet engines, and cloud-based software to make strategic changes rapidly in ways that can affect your company's top and bottom lines.
2. Transactional data: integrate your silos
Most data analytics projects today are conducted on a single silo of data such as a point of sale, supply chain, or customer purchase history. However, with multi-structured analytics, you can extend the traditional data warehousing model by aggregating all of these small silos into a single place to analyze much larger data sets within reasonable budgets and timeframes.
Not only do new revenue opportunities emerge when you build a large data reservoir, but reporting and compliance capabilities are also improved. For example, banks need to incorporate new datasets to meet compliance requirements, understand fraud patterns, or evaluate new business opportunities, and doing this in days versus months is transformative.
3. Customer interaction data: unlock new potential
The status quo for customer interaction data analysis is examining the basic parts of the buyer's journey separately, including website visits, shopping activity, and customer reviews. This effort is usually limited to using what's in your transactional data stores -- and does not enable the Holy Grail of omni-channel marketing and retailing.
With multi-structured analytics, you can make huge strides on the competitive front by adding the social media firehose -- various clickstreams and Web data that let connect the dots in ways that were impossible just two years ago. Think about what you could learn if you could analyze a new customer's first 60 days of interaction with your company across all your digital touchpoints. You could discover service bottlenecks and new revenue opportunities faster than ever.
No doubt, we're still in the beginning phases of determining what's possible when companies make data-driven decisions by using multi-structured data. But to be sure, the early-adopting companies are making their moves now. I predict that when we look back five years from now, 70% of the Fortune 500 will be well beyond today's IT-centric data practices and will be using multi-structured data analysis as a competitive weapon across multiple lines of business.
Just 30% of respondents to our new Big Data and Analytics Survey say their companies are very or extremely effective at identifying critical data and analyzing it to make decisions, down from 42% in 2013. What gives? Get the The Trouble With Big Data issue of InformationWeek Tech Digest today. (Free registration required.)