Big data: Connecting the dots…

Our new data world is a place where chaos runs rampant. This world is free, it is loose, it is not standard, it is us.

Data is ultimately about insights. How are people connecting the data dots to gain new insights? How are they completing the context? Or obtaining a more complete context? We are creating a new "sense of things" from all the information we are collecting, processing and reordering.

This Data-driven business demands a shift from collecting to connecting. Because data on its own is meaningless, data must be imbued with context to give it meaning, to make it unambiguous, to make it exciting. Without the relationships between data - the context - we are left with a fragmented picture.

Meaning and insights can only be derived as the various data sets are brought together relating the who, what, when, where, how and why of the story. Time, location and situational awareness are three important elements of context, addressing the above elements. From national intelligence to healthcare to commerce, organizations need guidance on how to connect the dots. Connecting the dots is one thing, but putting them to use is quite another. The data leads the way to insights, but it is up to us to take action.

Situational awareness brings together many dimensions of context, from time and location to terrain, weather, objects and other elements. The essence of situational awareness is situational intelligence:

a unified, consistent, up-to-date picture of all relevant information for a given situation.

 

Situational intelligence correlates massive amounts of disparate data to facilitate immediate and informed decision-

making. Using situational intelligence, people can easily recognize abnormal conditions and take prompt remedial action using defined rules and processes.

Today the company's technology aggregates temporal information from the Web (e.g., articles, blogs, financial databases, comments) and behind the corporate firewall. It uses input from past trends and current activities to forecast behavior and issue "Futures," email alerts about predicted events.

Recorded Future factors in sentiment analysis (is what people are saying good or bad) and source credibility (e.g., a document from a government agency has greater weight than a blog post). Organizations can use Recorded Future for brand monitoring, competitive intelligence, equities research, research on a public figure or event, and other activities.

Advances with location data are taking logistics in the enterprise to new levels. CSC's Logistics Center of Excellence (COE) calls it "enterprise visibility." Enterprise visibility integrates location, distance and status data with business information to create actionable intelligence. Improving enterprise visibility accelerates workflow, lowers costs and improves the speed and accuracy of delivery.

The COE's signature tracking technology, OmniLocation®, is being used in a variety of ways including: connecting

doctors to at-home patients for after-hours visits, monitoring the location and physical condition of jet engines being delivered, guiding the workflow of field technicians managing marine traffic

tracking cyclists.

Better location data also means smarter marketing. now that common devices such as smartphones can capture location-based information via GPS or other means, essentially every person with a smartphone has turned into a sensor. Organizations can use these new sensors as a source of valuable information about customers, which can then be used to provide advanced location-based services back to customers. The data riding on these networks "knows," but teaching the network to make decisions on that data is where the future of network analytics lies.

Mobility has made network analytics even more valuable due to the decoupling of producers and consumers of enterprise data from the network fabric between them.

Companies can figure out how to understand social patterns and what people are saying, good or bad - i.e., sentiment analysis. Facebook, Twitter and others recognize the opportunity, creating APIs so enterprises can tap into the social mother lode. It is another way of examining customer satisfaction, and, in a parallel with the intelligence world, it includes looking for nefarious activities, such as someone trouncing the brand.

Companies can use sentiment analysis to help them understand why customers buy. Even financial traders are tapping sentiment analysis, using software to parse news reports, blogs and Twitter posts in an effort to keep a pulse on changing market moods. This auto-analysis of the news includes future innovations like monitoring and digesting broadcast TV and public statements by business executives.

Today, your own data is not enough. For example, it's not enough for financial services to live with its own transactional data; it needs social data to help define and quantify the transactional data further. Thus, the idea of Data as a Service - and ultimately Analytics as a Service - starts to make sense because the data needs to be available to multiple parties, who need to come together and collaborate on the data.

Organizations need to answer questions about yesterday, make decisions about today, and perhaps most importantly, know how to plan for tomorrow. A world of data-backed predictions is opening up, driven by a combination of better and more data plus the desire to make predictions with increasing accuracy and speed.

The harder it is to connect the dots, the harder it is to make predictions. These challenges, include legal, ethical, technical and economic issues. In addition to these issues, which apply to both the public and private sector, there is the human element. Better predictions will improve agility, risk management and performance in the enterprise.

Great discoveries come from having a broad spectrum of knowledge. The Data Revolution opens up a world of exciting opportunities for establishing new insights, products, services, partnerships and roles.

With data a key factor of production, data's growth and leverage will be a basis for business innovation for years to come. The move reflects the natural progression from Software as a Service to Data as a Service to, now, Analytics as a Service.

(Paul Gustafson is director of Leading Edge Forum-Technology Programs CSC)