As data becomes increasingly intrinsic to the workings of corporate and everyday life, further integration is needed in the analytic community to drive more powerful data simulation and analysis, to link theory and experimentation, and to better extend the reach and results of big data.
The potential power of big data is arguably not currently being achieved but, by building a community it’s possible to gain the skill set of a broad group and boost the range of applications for corporate data.
Today, everything from production and distribution to customer services can be and is analysed, monitored and developed for a more personalised customer experience and to optimise business operations. Businesses that don’t engage with digital transformation risk becoming lost in the market while those that embrace big data through communities – and constantly learn and iterate will become innovators.
Top five business analytics intelligence trends for 2019
From explainable AI to natural language humanising data analytics, James Eiloart from Tableau gives his take on the top trends in business analytics intelligence as we head into 2019. Read here
Because of the demand explosion and the volume and variety of data being drawn upon, the data science field has become interdisciplinary, integrating approaches from data mining, predictive analytics and data management. A data talent gap has restricted full use of this mass of data and a report by the European Commission forecast that there would be a 160% increase in demand for big data specialists between 2013 and 2020. To combat this data skills shortage that we are in, it is vital that we build a culture of analytics whereby data knowledge and insight is shared, and employees are encouraged to become data savvy.
A data scientist inside all of us
While further education investment is needed to support the training of more data scientists (and engineers, architects and governance officers), there is a more immediate solution so that businesses aren’t left behind in the skills stakes – the emergence of citizen data scientists. Individuals are now able to interact with data in a professional setting thanks to the development of self-service analytics platforms which provide relevant tools and platforms without the learning curve of the old BI tools. These platforms allow the user to either develop technical proficiency in the data field through using code-friendly interfaces or to opt for the drag-and-drop option which is entirely code-free and the way the majority of consumers approach computing. The significance of these self-service platforms is that they expands the reach of the data science field, supporting the growing role of data in all spheres and the entire data community – opening access to data for all.
A 2017 survey found that out of the 85% of companies trying to be data-driven, to date only 37% have been successful in their initiatives. This shows a clear lack of clarity among executives regarding the organisational changes required to embrace big data. The technology is not an issue – but the management and organisational knowhow surrounding it can be. The expansion of the data analytics community is integral to cultural alignment. In the same survey, a majority of firms (55.9%) reported having appointed a Chief Data Officer (CDO) with 48.3% believing that the primary role of the CDO should be to drive innovation and establish a data culture, and 41.4% indicated that the role should be to manage and leverage data as an enterprise business asset. Both figures demonstrate a clear agenda to create a data culture movement, with an expectation that CDOs will lead the data innovation charge.
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The chaos of information can be made less daunting if individuals were to identify what is relevant to them and to take ownership of upskilling themselves. By sharing information across a community, individuals will have a better understanding of and greater control over the data that matters to them, and still benefit from insights from the entire data community. The CDO can set the tone and the framework – but trying to micromanage the actions of each employee can’t keep them as agile as the data ecosystem demands they be. By growing a thriving data community within, and between businesses, the enterprise can rapidly share best practices, rank and share good data safely and in accordance with regulatory guidelines. The key is using an agile and robust data platform so that all parties involved in the data ecosystem can track and trust the data in play.
Analytics needs to become inclusive (but compliantly!), and by creating data communities in a careful and considered way, on the right platform, a CDO or a citizen data scientist with no deep statistical skill can each learn and use the tools and skills needed for their part of the big data puzzle.