Information analytics used to be called ‘data mining’ in many circles. Although the term itself has almost been lost in the mists of the last bewildering decade or so of big data, we used to think of analytics as a far more static entity when we compare it to modern notions of data exploration.
As we have built up our contemporary post-millennial methodologies for data analytics, we have brought forward real time interactive analytics practices to the fore.
Real time speed
Although so-called ‘real time’ is never really real (i.e. it is only as fast as computer processing and information communications pipes will travel), it is fast enough to give us the opportunity to act upon data as it is created, in situ.
In areas such as retail (to pick a very good example, although there are many others), real time interactive analytics gives us a chance to act upon data as it is created and make more informed decisions. Even if we only have SOME of the data, real time interactive analytics allows us to know MORE than we did when we knew nothing about the situation being played out… and this is crucial.
This is the interactive element — and this is the intelligence element.
If we can start working with data as it is being produced, then we can start to deliver new cognitive insights. Today we know that analytics can be applied to monitor customer behavior and deliver wider business value across a number of domains. From physical retail stores, to online banking services, to the way we interact with electronic healthcare devices that sit within the ‘wearables’ category of the Internet of Things.
Data visualization applications can provide businesspeople with a far more dynamic, faster and more accurate means of understanding what data trends mean — and therefore a far more rapid means of being able to change business decisions, market strategy or alter customer policies to react to the trends being evidenced. In simple terms, we have gone from a world of endless Excel spreadsheets and other database columns (although these still exist at the back end) to a new upper layer of analytics that can almost immediately applied to business decisions.
Using our healthcare example, users are starting to adopt the new breed of wristband wearables that ship with enough intelligence to monitor skin temperature, heart rate and overall energy expenditure. As we start to aggregate this information and know more about the lifestyle of the users who willingly sign up to share this data, pharmaceutical companies can a) start to develop new products based on human lifestyles b) target us with healthcare supplements to tune our bodies more accurately and c) re-channel their own supply chains to get the right products to the right place for right customers.
We could re-purpose this example in finance, legal services, transport, logistics & shipping, media & entertainment or farming livestock control — you get the point i.e. the use of data analytics at a fast, personal and practical level is not restricted to any one business vertical. We are now on a learning curve to find out exactly how analytics fits onto every business shell, but we can be sure that a fit will be found for every use case.
What steps do we need to take for closer integration with data? We need to get to a point where Human-Computer Interaction (HCI) allows us to interact with data on the fly so that human intelligence can be truly augmented with Business Intelligence (BI). The interactivity will mean that we can start to get the right kinds of insights out of the data that we process. To be clear, we were getting lots of insights beforehand too… but they often came too late or weren’t in the shape, format and form that we had anticipated, so it was hard to exploit any insight that they have been harboring.
Domain specificity, visualization simplicity
As we become more interactive with our data analytics we can also become more finely tuned and finer grained. We can start to look at more domain specific information sources and apply our analytics processes at the point where they will most directly be able to benefit the Line Of Business functions they should be supporting.
As we start to put this information in the hands of the people who can make better strategic business decisions when using it, we must consider the option to start using data visualization platforms and tools. Taking a spreadsheet of information and converting it into a graphical form that can be presented on a tablet computer and interacted with via touch commands is, indeed, interactive analytics.
Actionable trumps interesting
If we can start to apply the correct level of interactive analytics for the right strategic business reasons then we can get to a place where we produce valuable actionable insight. It’s worth remembering (when it comes to data analytics), actionable trumps interesting every time — or at least it always should.
When we get even better at doing analytics, we start to understand how analytics works and we start to know what questions we want to ask to get the business to where it needs to be. Finally we get to understand how to connect analytics to the corporate mission … and how to develop the muscle to execute what will now be far more data-driven decisions.
Powering up a more interactive style of data analytics is not done overnight or with the flick or any switch, it is also about embracing analytics as part of the business culture. This is the part we can’t tell you how to do i.e. you have to want to change and you have to want to do it for your own business success. But in truth, it’s not painful, it’s only gainful — so who wouldn’t want that change for the better?
SAS is a leader in business analytics software and services and the largest independent vendor in the business intelligence market.