Whether it’s infographics, maps, flow charts, or other design-driven diagrams, data visualization is now seen as the preferred way to interact with data. In fact, infographics and other visualizations have been some of the most shared images in social media history. Why? They’re easy to understand, quick, and beautiful. They engage.
These visuals are important, but the ability for businesses to go the next step and make actionable decisions based on predictive visual analytics allows them to stay ahead of the competition. With the amount of data being collected all around the world, new and interesting pieces of data are being uncovered all the time, demanding the need for analytic sophistication. Companies need to go beyond what is just there visually and interact with predictive models in order to get true value out of their decisions.
Data as Art
In a world where many of us scroll through dry spreadsheets and presentations filled with stale clip art, beautiful data is a sight for sore eyes. Data visualization has been a powerful tool for marketers and journalists with infographic designers charging between $1,000-$10,000 for an engaging, sharable piece of data art. But while companies are investing in designers to drive engagement externally, how many companies are using our appetite for data visualization to drive change with its tough audience – its internal one? [Read more...]
If your company purports to want to innovate and become a market leader, why do they seem satisfied with an average data solution? What’s even worse, businesses unwillingly aspire to be just average while spouting off to employees and customers they are anything but. Companies with average IT solutions will always produce average results and never go from average or good to great.
Recognizing North Korea and Kim Jong-un’s recent actions as probable bluster has parallels to assessing a rogue computer process or questionable user activity on a network. When a process goes wrong in a system, log monitoring software gives off a real-time alert as a warning. With a less-than-enterprise class solution, this alert might be all that happens, which forces systems administrators to decide on an action based on isolated, incomplete information. With lives at stake rather than system and network resources, the result could be tragic.
People do not often think of data points as having dire consequences, but it could mean the life and death of a business. In every realm of business and sector of life from politics, economics, society, the environment, to technology, there are big data complications. Without the blend of analytics and integration solutions, there are growing risks of security threats, need for efficiency and cost reductions, and a desire for more collaboration. Big data is not just big because companies and organizations collect large volumes of it, but also because it has real and lasting implications on everyone.
I frequently discuss big data with executives from some of the largest Fortune 500 companies in the US. I continually act as a sounding board for their frustration as they try to extract value from historical big data. There is a prevalent misbelief among them that there is a great deal of value in years and countless rows of data, but they struggle to monetize this hidden value, and for good reason.
Pinterest, the social image-sharing board, has been incredibly successful at creating a loyal community of users who love to organize and share ideas with other users. I’m a pretty active user, and if you haven’t started using it, I encourage you to try it out (though I warn you, it’s incredibly addictive). Pinterest does an excellent job of communicating with users in a way that is both engaging and informative. Of course, they always have a little nugget about themselves (new features, a new partner, etc.), but it always includes something for me and something about me.
Now, well into 2013, the concept of Big Data is already becoming an outdated non sequitur. As data increases rapidly, storing huge amounts of data in uncorrelated, separated silos (in database or data warehouse storage) that need to be constantly queried can’t drive any new, intelligent change in a business. In fact, this approach creates even greater challenges. Big Data by itself can’t drive change because it is just a more efficient, more technological way of doing business as usual. Databases that store transaction history are a practice as old as a shop keeper maintaining a ledger of purchases and sales. How is simply scaling that same idea into the millions of entries going to drive any real change in business? That old approach is Big Data 1.0 and it can’t compete with correlated, referential Big Data. Integrating varied information in an individual context, in the moment of customer’s engagement is fundamental to move business forward in any way and has to be the foundation of any conception of Big Data 2.0.
