In a world increasingly saturated with data and information, visualizations are a potent way to break through the clutter, tell your story, and persuade people to action. Raw statistics by themselves are fine. But showing in context, whether with a simple chart or more creatively in an interactive form, is the future of sharing information, and needs to be embedded in the thinking of all communications professionals. Combining data – which can be dry – with real creativity – which isn’t something humans, even creative ones, can simply turn on and off – can be challenging. Yet we live in a stream-powered mobile world that is increasingly visual, inspiring demands from media to achieve equal parts style and substance for news. This explains why unique and truly compelling visualizations are an underused, yet devastatingly effective tactic. They are equal parts rare and in demand. They beg to be shared. They are a catalyst for conversation, awareness, and action.
The need for data is not new for businesses.
They have been attempting to accumulate data for many years to help them know how to increase revenue, improve profits or better appeal to customers. New technologies have made it easier to collect that data and now to use it different ways.
Blending data and using visualization is providing more information for businesses to be more competitive.
Data Blending is the Key to Analysis Data blending and visualization work together to give companies a better look at their options and opportunities. It can be used in many different ways to determine how best to proceed. For example, an organization can determine how the results will differ with a completed project if it were to be attempted with new data. Instead of just plugging in projected numbers and guessing what difference they would make, data blending allows companies to see the differences in the processes and get the details for why the results are different.
Can a relatively mature technology help content publishers and marketers make website visitors more sticky and allow them to retain digital subscribers while also raising prices? The answer is yes.
The science behind what is making the aforementioned possible–predictive analytics–has been around for quite awhile. In its former life, it was known as data mining. Add in Big Data and the rapidly maturing technology looks as if it’s ready for its close-up.
PREDICTIVE ANALYTICS 101
Predictive analytics (PA) “describes a range of analytical and statistical techniques used for developing models that may be used to predict future events or behaviors,” as defined by Techopedia. It’s been successfully employed in many industries, including banking, telecomm, and healthcare.
With all the talk about big data, one thing is very clear: the vast majority of us have very little insight into how to actually find insight in it. We have neither strategies nor experience, mostly because the use of data at scale is relatively new. Sure, humans have been using data for thousands of years to tell stories, pass along ideas, record history and, in more modern times, produce ROI and eliminate functions with high spend and low return.
But, big data is unlike data of the past. Not necessarily in its use. Data certainly still tells stories, passes along ideas, records history, produces ROI and saves money. But its sheer size makes it completely different than any data set humanity has managed to date.
We are no longer working with a Rosetta Stone size of information (neither the actual stone nor the modern day disks), or even spreadsheets of data that can in turn be put into a semi-useful pivot chart. We are dealing with massive, and I repeat massive, scales of data. Umbel’s Digital Genome alone collects, analyzes and visualizes 18,446,744,073,709,600,000 data pointsper person in less than one second.