predicatable game changers.
Many of us may recall how a Business Intelligence (BI) dashboard looked was pretty important for both the adoption of data analysis, and its ROI. Some even cited user uptake projects ‘the war on attention’ with larger BI investments treating their overall user experiences on the same level as a rather large web project, with research and analysis dictating their dashboards’ ultimate look and feel. But is visualised analytics now a bit old school?
In God we trust. All others must bring dataW. Edwards Deming
Traditional BI encompassed self-service reporting and data discovery and as these capabilities developed, tools such as Qlik, Tableau and Power B developed also as they rather adeptly not only provided the visuals but also the predictive analysis too. These tools did the job just dandy as they allowed business heads to understand, summarise and visualise what has happened, in the past and fascinatingly, what may happen in the future, through robust predictive analytical capabilities.
It is fair to say that predictive and prescriptive analytics have the potential to be huge. They are game changers, because they can help improve and adjust business decisions based on what may happen in the future and impact outcomes in ‘real time’ too, as tools like Kafka and Spark are now enabling. Wow.
And if organisations haven’t budgeted to add predictive or prescriptive analytics into their data analytics armoury as yet, a recent Gartner Predicts 2016; Analytics Strategy paper suggests that they are running a bit behind their competitors, and need to catch up, fast, as the data giant predicts big data alone will drive enterprise IT spending to $242 billion over the next few years.
Global market predictions echo this, with recent research suggesting that the world-wide predictive analytics segment will increase from $2.74 billion in 2015 to $9.20 billion by 2020, with the cognitive analytics space estimated by IDC to become a $60 billion market by 2025.
facts for the future.
You have to know the past to understand the presentCarl Sagan
In 2012, Forbes magazine asked if big data was the new oil, but by 2016 with the rise of deep learning, Fortune had become a bit more certain that “data is the new oil.” Then Amazon’s Neil Lawrence suggested that “data, is coal.” Not the coal of today, however, but coal in the early days of the 18th century, when Thomas Newcomen invented the steam engine.
The problems with Newcomen’s steam engine and start-ups in the world of deep learning are eerily similar; whilst what they have created is revolutionary, a vast amount of raw material is needed for them to actually provide any advantage. Arguably, the key to unlocking the potency of deep learning is to strengthen its efficiencies.
Intriguingly, much like the coal analogy, it is clear that raw data is being ‘mined’. We see it daily as consumers as we go about our daily lives, browsing here and there. Big data is clearly at play when we are decisively recommended anything from playlists to listen to, books to read or films to see. But this is not big data as we all think we think we know it, this is knowledge through advanced analytics, this is machine learning through a big data stream, taking rich raw material through machine learning to discover insights and generate predictive models to take advantage of all the data to hand.
I keep saying that the sexy job in the next 10 years will be statisticians. And I’m not kiddingHal Varian
And whilst enterprise organisations’ data reserves grow and grow; this abundance of information isn’t offered up in perfectly packaged neat bundles, really smart people are needed to help it on its way from raw asset to competitive advantage. Herald the data scientists, the statisticians, the evolutionary CMOs, CDOs and CAOs and the analytics talent. No surprise then that ‘data scientist’ was the hottest listed tech job in the US in a recent Glassdoor survey.
Ultimately, deploying advanced data analytics techs and practices and hiring skilled professionals will mean that instead of looking into the past for generating reports, businesses can predict what will happen in the future. This can be achieved by the creation of accurate models to guide future actions and unearth patterns that simply haven’t been seen before, to gain a competitive edge.
This isn’t just analysing and predicting the future, this is the future.