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Business analytics and the data scientist

I read with interest a great article in KM World featuring a conversation between SAS’ own Jim Davis and Fiona McNeil and it really got me thinking about what defines a ‘data scientist.’

It is possible to discuss the role of a data scientist as series of skills and experiences; an understanding of business, of computer science and of course, of maths/analytics. For me, this is just the starting point because I believe that to be fully effective, the data scientist should represent a pinnacle of knowledge engineering in an organisation and in the next few paragraphs I will try to explain why.

I would agree with the contention that business analytics is the must-have capability for converting raw data (facts) into actionable insights (knowledge) – or, to paraphrase from Bloom’s taxonomy, it lifts levels within the cognitive domain from recalling (the job of business intelligence in an organisation) to understanding and then applying knowledge. Big data analytics gives an organisation the ability to sift through all the data, to find hidden patterns and even predict what might happen in the future if the current trends continue but it is not, of itself, creative.

As Jim pointed out, in a sea of data it is the relevance that is critical, and for me the relevance of data and of the associated insights is defined by the ability to drive impact within a context: just knowing something new and interesting does not make it useful. In other words, something has to change as a result and the organisation should be leader of that change, not its victim.

As organisations struggle with a more complex world and a growing range of technical capabilities this context will itself be defined by a process of examining where the organisation is, where it could go and where it wants to be. It is in analysing those current and desired future states and in evaluating possible ways of achieving the latter that the leadership team takes control of creating it – the higher levels of Blooms taxonomy. For me, this is when an organisation achieves true wisdom; in being able to apply prior learning and experiences to new and unique circumstances, not just copying the ‘best of breed.’

It is in assisting an organisation to move beyond recalling facts, and being able to deliver on the creativity of the leadership team and workforce, where the data scientist can have the greatest impact. Facts and their interpretation form an integral part of the collaborative process and in turn, new learning and wisdom can be encapsulated within the corporate knowledge base.

I believe that there are three spheres in which business analytics help a business:

1)      In optimising existing value-creation processes to improve what currently exists

2)      To innovate these processes by identifying new, better ways of achieving the same/better result

3)      To find new (transformational) value-creation processes (ideally that makes best use of what capabilities an organisation already has or can reasonably acquire).

In summary, big data provides a greater repository of facts (whether structured or unstructured). Using business analytics, a data scientist gains understanding and actionable insights from this data which are then used by the leadership team to analyse the current/future state of the organisation, evaluate possible scenarios and then create a new future which can be quantified for impact. The data scientist should therefore be an integral part of a leadership team, advising and guiding; and not just reporting.

Finally, why my emphasis on business analytics? Well it has a synonym, ‘predictive analytics’ – you change the future by looking forward, not by looking back.

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