Will your next colleague be a data-driven AI (or maybe even a data-loving CEO!)?
Ever wondered what the future might hold? It’s easy to guess, but harder to predict correctly. Nonetheless, in the world of data there are some big clues to help make some kind of analysis. Here are some of the questions that may be making analysts of all types, business and technical, IT and users, get excited in 2017.
How will the democratisation of data will come into its own?
Democratisation of data - that is, self-service by the casual user - will undoubtedly come into its own and create the need for greater data governance. As a consequence, analytics and metadata management will eventually converge into single platform. Only eight per cent of employees are advanced spreadsheet users and they spend 1.3 billion hours on repeatable work, compromising the integrity of data. The question for companies is how they harness the growing enthusiasm for colleagues to want to “play” with their own data. What will the cultural impact look like in an organisation?
Will enterprises trust self-learning AI to pull business insights and data?
IBM’s Watson may have been the first of its kind, but with intelligent AI such as Amazon’s Alexa, making its way into the mainstream consumer market, I would expect to see businesses embracing algorithmic business information. While I wouldn’t go so far as to say they could function independently within the next year, we should begin to see innovative organisations using them for services such as forecasting business growth and financial information that can be interpreted by the financial team and senior management. At some point, self-learning models will build analytics very fast - by themselves. Maybe before too long, analysts and AI will be working very closely together…
Can even traditional “last-frontier” sectors begin using data analytics?
As the benefits of big data become increasingly obvious, I believe we will start seeing greater adoption of big data in sectors such as human resources and education that have historically proven reticent. Thanks to a broadening analytics culture and self-service analytics systems that enable everybody, regardless of technical knowledge, to understand and decipher big data, I would expect to see even the most qualitative of businesses look to embrace the revolution.
In education, pupil data can be combined with data from society to help make better decisions to improve institutional and individual student activity. For the HR department, there’s a wealth of analytical techniques to turn on, from capability analytics (measuring the organisation’s talent), to competency acquisition analytics (assessing the acquisition of skills), capacity analytics (personnel efficiency), and, of course, employee churn analytics.
Going deeper, there are corporate culture analytics, recruitment channel analytics, leadership analytics, and, of course, employee performance analytics - which is where analytics might start, but needn’t stop. It’s a compelling thought and, to my mind, the question is, how fast will it arrive once the ball starts rolling?
When will Man and machine enter a truly symbiotic relationship?
Humanity has bought into a machine symbiosis and the trend is for more convenient, smarter technology that is easy to pick up and run with. This empowerment trend is part of the democratisation of data. The new simplicity means more users come online, using more applications and creating an ever-better man/machine relationship, as well as the continued upward curve of the data and metadata explosion.
So when a tool like IBM Watson can go through medical papers, research and journals, to present the best range of clinical decisions, the final stage is for the trained doctor to make the final decision for a patient, with the context and humanity of a real human being.
And in step with the rise of new, or more well-known tools and technologies, we will see the workforce reskilling through education courses like nanodegrees to further simplify interactions with data. I’m sure that there would be few to argue that this is the shape of the future.
Can it be long until the bastions of traditional industry fall to the data and analytics hordes? Some industries have been historically qualitative, or have only embraced analytics at the division/department level. This will change, all will become more quantitative. The broadening analytics will bring this new culture across all departments and will give organisations a truly 360-degree view of the business. From HR, educational institutions to non-profits, there are swathes of industry underserved by quality data practices.
Could anyone argue that any industry could possibly remain, for long, untouched by the benefits of deeper data insights from a sound analytical footing?
Might we finally bust the ‘gut’? Will chief executives really start to trust their data in 2017?
The numbers are revealing. Managing directors still prefer their personal experiences - or hunches - over neutral, industry data. A 2016 study by KPMG of 400 CEOs revealed that only a third had a high-level of trust in the accuracy of their organisation’s data and analytics. In fact, 29% had limited trust or outright active distrust.
Why should this be? Well, to many up to now, data has been seen as a black box. The skills to manipulate it (play with it, really) were specialised and the technology was expensive, time-consuming and required further skills or knowledge to utilise properly, from IT to software coding abilities.
Yet now, organisations increasingly have a curator of data. Some few pioneers have chief data or chief analytics officers helping to create a data-driven culture across the enterprise. Many more are changing from a bottom-up approach.
For the C-suite, better workflow visualisation has increased understanding of data at top levels. Yet 2017 should now see a broadening of reporting outputs to serve a wider variety of executives, and indeed, data users at all levels. People consume data in different ways. For some, visualisation has been the missing link that unlocks their data passion. For others, self-service solutions that remove the barriers of advanced statistical knowledge or coding skills.
But, when it comes down to it, if organisations are really looking to become truly insights-driven, they must eventually assign data responsibilities. It might be to the CIO, CMO and even the CEO. It’s more about the type of person who drives it, not the role. The personality with the will to unlock a data culture is the natural fit to drive fast business activity based on data-driven insights and to share and ignite that passion for the organisation.
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