Context turns big data into knowledge

It’s really hard to understate the importance of context— especially when it comes to data.

Data is great, but it’s meaningless and even cumbersome without context. Put a sign up saying “Keep Out,” and most will ignore it. Put one up saying “Keep Out, Unexploded Bomb” and all but a foolish few will stay away.

Data in context is what we use to make educated and informed decisions.

By now, we’re used to the idea of finding patterns in data. But by adding context, we can go a step further.

Imagine, instead of just collecting data about diet and exercise habits, users were warned about the likely consequences of an action — or inaction — based on that data. For instance, “If you don’t go for a run today, your blood pressure is likely to rise by 2% within the next week.” That would definitely inspire some of us to go for a jog!

Putting data to better use — that’s the shift happening all around us.

The shift to modern platform architectures and inexpensive computing resources makes it easier for enterprises to tap into new sources of data, and these data have significantly higher volumes, velocities and varieties. But modern platforms don’t just produce and store new sources of data; they help create meaning.

Enterprise Service Integration Management (ESIM) becomes more than a record of IT systems transactions in this environment; it becomes a unique, real-time view of how the business is operating. Even the network becomes a source of business innovation. Network traffic may be used as a real-time indicator of buyer intent or a way to anticipate shifts in a customer’s use of critical-service value chains.

Data is a potential source of insight and leadership, but only if enterprises have the right workforce to uncover it. The next-generation workforce will need to be able to draw actionable insights from data. This means being adept at doing exploratory data analysis. A data-driven workforce needs the agility to experiment in small increments, build complexity as needed and improve hypotheses as more data becomes available.

While the next-generation workforce must be able to draw actionable insights from data, we can’t just transform employees into an army of data scientists. Data has to meet us halfway. The workforce has to become more data literate, and data has to become more workforce friendly.

The shift to modern applications is helping with this, as are APIs. Ubiquitous APIs will emerge as the building blocks of an economy based on richer varieties of data. API-enabled tools will automate connections to the right people and systems and get us answers faster.

Context turns data into knowledge, and knowledge is power.

We are now full-swing into the Age of Context, where contextual data means smarter decisions in everyday situations. It’s time to get much better at putting data to good use. In CSC’s 2016 IT Trends our CTO, Dan Hushon (@DanHushon), describes how contextual data will make its mark next year.

Let’s think bigger when it comes to Big Data. We can go beyond hoarding it to making sense of it – and  discovering insights that answer the all-important question of “So What?”

overton-2015Jerry Overton is head of advanced analytics research in CSC’s ResearchNetwork and founder of CSC’s FutureTense competency, which includes the Predictive Modeling Research Group, Advanced Analytics Lab and Predictive Modeling School. Connect with him on Twitter.


  1. This is precisely the transition that is happening. Context is the catalyst adding newer dimensions and greater meanings to the tons of data we already have. Classical data mining methods have always been singular and generic. The idea of context takes it 10 steps further.

    • Agreed. I’m especially excited about the possibility that greater context lets us do more with simple algorithms. I, personally, believe that the future of data and analytics is in the direction of data with greater variety and context, with simpler, more straightforward algorithms layered on top. I look forward to the day when people will brag about spending 80% of their time munging and enriching data rather than looking for ways to get around it.

      • sksanjesh says:

        That day is clearly discernible in the burgeoning fray of the Internet of Things. It won’t be long when the current data-driven model of possibilities evolves to that of a context-driven one. Understanding contexts and theorizing and/or standardizing it’s operational frameworks is the challenge we face today. Context can be seen as adapting to incessant circumstantial changes, like how us humans have evolved, and we’re someway away from putting this into definitive perspective. Once this challenge is surmounted, a whole new era of technology will emerge. This inevitability excites me beyond anything.


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