Many people still seem to believe the value of big data analytics lies in a group of highly trained data scientists, sitting in a back room. Where they conjure up magic answers to business problems from vast amounts of big and dark data.
In 2014, the apparent “big data skills shortage” has been heralded as the biggest challenge to making big data a success. But let’s be real here, the skills shortage is more perceived than real; there is indeed a shortage of data scientists but they just aren’t fundamental to big data analytics.
The question should be do you really need a data scientist to define your business problem?
Data scientists may have contributed to the development of big data analytics but as organisations increasingly turn to analytics, a more strategic, long-term, cost-effective, accessible, and sustainable approach is needed. Successful initiatives and solutions depend on more than a single person’s talent and skillset.
The power of analytics must be made available to everyone in a format they can understand. This is achieved, as it always has been, with applications – in this case, big data applications.
Organisations should utilise their industry knowledge, trust their team and their history of delivery – but use the applications, rather than the data, to turn the information they hold into knowledge.
Furthermore, operational analytics needs to be shared with not just the techies who can simply look at a row of numbers and see what events happened in 24 hours – on say a telecommunications network – but with any member of the organisation who could benefit from the knowledge or offer routes to improvement. When people talk about the democratisation of data, they should look to put the power of analytics into the hands of 10s or even 100s of people.
As Fourth Source recently stated in its ‘Big Technology Trends for 2014’, “…organisations will start focusing more on how to enable decision makers to make informed decisions quickly from large data sets, so as to create economic value across their workflows and function…”
This is where visualisation comes into play. Visualisation within applications enables the users to understand their data. Let’s be clear, a user cannot interpret 20 billion records a day, but their brain will understand anomalies shown in a picture. Visualisation means simplicity and we all know we should KISS (keep it simple, stupid).
Simplicity and visualisation is why we’ve involved companies such as Tableau in our real-time big data analytics solution suite, Zen, to enable the automation and comprehension of data.
Many of our customers were spending hours, and days, creating management reports in standard office tools, accessing many data sources to derive the detailed information they needed. They now create the same knowledge automatically, in real-time, and make it available to everyone who needs it through the internet.
While having that mystical capability of interpreting the reports is a talent indeed, unless that same person is calling all the shots, it doesn’t benefit the overall organisation. For data analytics to really transform the way a business works, its story needs to be told simply and quickly to every decision-making group.
The real-time analysis of data is no longer the domain of a few elite experts – with analytical applications solutions data has finally become fast, simple and democratised.