Making Sense of Data - Why Knowing 'Where' Matters

This article is an abridged form of a presentation I delivered recently to members of Sales Institute of Ireland.

We are all acutely aware that world we live and work in today is a world that is highly connected. As a result, and whether we like it or not, we now actively participate in the digital realities of the Internet of Things, Big Data and Data Analytics. 

It is said, that by the year 2020, there will be:

• 4 Billion Connected People
• $4 Trillion Dollars in Revenue Opportunity
• Over 25 Million Apps
• Over 25 Billion Embedded and Interconnected System

And all of this ‘connectedness’ will generate 50 Trillion Gb of data! So with this volume of data, how do we even begin to create meaningful information from it. The answer to that question lies in the discipline of ‘Data Analytics’ and its one of the reasons why Data Analytics is becoming more and more relevant to businesses today.

Better Decision Making

Ultimately, and quite simply, ‘Data Analytics’ is about Decision Making! Everything we do in life, is based on us having to make decisions. And we make decisions based on the data we have available to us at a particular point in time. In our personal lives, most of the decisions we make, we do so instinctively, and somewhat unbeknown to ourselves. However, in our business lives, it is a little bit more deliberate and formal.

So the actual concept of analysing data to make better decisions is nothing new at all. We have been doing that for hundreds of years! So why then do we seem to see the world of data analytics as something that is more complicated than it actually is? The answer to that question lies in the Data itself.

If we go back to the connected world that we live in:

• Data comes to us in an ever increasing array of data sources (sources that most of us don’t yet fully understand)
• As a result there are ever increasing volumes of data being generated (volumes that most of us can't even contemplate)
• And then there is the constant evolution & development of new technologies that allow us to access that data to improve our decision making ability (and this, for most of us, can lead to too much choice)

Better Sense Making

Therefore we need to come up with ways in which we can simplify the ‘sense making process’ so that we can maximise our ability to make the right decision for our respective businesses. So how do we make sense of all of this data, now? One of the primary ways we do this is to ‘Visualise’ it.  And there are a number of very common ways that we visualise data, today, most of which we take for granted; Graphs (Line Graphs, PictoGraphs, Scattergrams, Spider Diagrams, Bubble Diagrams), Charts (Column Charts, Barcharts, PieCharts, Sunbursts, Donuts) & more recently the Infographic, as a visual aid.

But there is still one form of ‘visualisation’ that we don’t seem to use very much in business. One that has significant value in terms of helping to make sense of data; one that helps us create information; develop meaning and generate insight; and one that helps to simplify the world of ‘data analytics’. And this form of visualisation is, a Map. 

Quite simply 'maps make sense'! As human beings we are hardwired to process visual information 60000 times faster than the time it takes us to decode something. It only takes us 1/10 of a second to get a sense of a visual scene. The fact that 50% of our brain is involved in visual processing is why a picture 'really' does paint a thousand words.

Better Insight

Translating ‘Data’ into ‘Insight’, is what helps us to make better decisions. This is what the discipline of data analytics has been, what it currently is, and what it will continue to be, for a long time yet. Knowing 'where', as part of your data analytics function will enable businesses to gain even further insight from their data.

Answering questions associated with 'where things are' requires us to understand the impact and influence of location, place and geography in a way that helps us better visualise, analyse and optimise our data.

• Location - Where is it? By using an address or an explicit co-ordinate location (i.e. GPS) we can immediately create information that we can then visualise on a map.
• Place - What is going on around me? By know where 'it' is, we can now ask questions regading its impact and influence on the surrounding area. Or, just as impotantly, what's going on in the surrounding area that has impact and influence on my location. This is where we analyse location based data to develop meaning directly associated with where something is;
• Geography - How do we take this 'meaning' and further generate insight that we can then use to optimise business performance? Can we make better decisions by understanding the bigger picture?

Better Questioning

This combination of Location, Place & Geography to help us Visualise, Analyse & Optimise our data allows us to ask and answer questions like:

• Where are my customers, prospects or assets?
• What is my geographical spread? What is the extent of my service area or sales territory? Is there a pattern or trend; ’a common ‘location based’ thread’ if you like?
• Is the information I have about somewhere, impacted or influenced by its location? For example, is this store performing poorly because of where it is, or where something else is?
• Can I find out where things are based on specific business criteria or conditions (geo-marketing/prospecting, where is my target audience)
• Show me what has changed over time? (population and demographics, infrastructure, landscape, urban development, etc)
• What does it contain or is it contained by? (i.e. is this property in a flood zone or does this flood zone contain any properties)
• Do they overlap? (territory/routes/catchments/jurisdictions) If you have overlapping entities then there is a high risk of inefficiency.
• Are they connected? Physically (i.e. pipes, wires, routes, etc) OR non-physically (i.e. relationship between areas of affluence and consumer spending, flood risk and insurance premiums, property prices and the rural/urban divide)
• Are they situated within a certain distance of one another? (store location, retail optimisation, service areas, travel times)
• What is the best route from one entity to the others? (how do I get there in the most cost effective and efficient way; route optimisation)
• Where are entities with similar attributes located? (Where are the other areas in the country that have similar criteria to those that I am having success with here)
• Are the attributes of one entity influenced by changes in another entity? For example scenario planning, predictive analysis (i.e. if I do this here, what happens there)

The very fact that we can only answer all of these questions through our knowledge and understanding of location, place and geography is why, for me, ‘Knowing Where Matters’.