Today's CXOs are aware of the advantages of leveraging insights delivered by data analytics, but they also realize that the growing size and variety of data complicates analysis. Data preparation is becoming more critical, as is the use of machine learning-based predictive analytics, in terms of using algorithms to harness the full power of big data and generate automated insights. For CXOs in charge of making business decisions, these insights crunched by machine learning algorithms promise not only speed but also nuance.
The logic of automated insights is straightforward. Algorithms powered by machine learning methods can be programmed to parse through massive datasets not only to answer complex questions but also to examine other possibilities suggested by the data. This is done at a speed beyond the analytical capabilities of humans. The only hurdle is the data itself - greater granularity, volume, and variety ensure that algorithms can be more accurately and reliably directed. While human sensibilities are required to examine the final insights and determine which are actionable, most of the legwork in arriving at those insights is done by machines.
Nonlinear thinking, nonlinear insights
Businesses usually look at the value that data analytics is likely to bring to their processes before investing in such insight generating capabilities. However, the power of automated insights is such that the value is often tangential; adopting them usually requires thinking in the same nonlinear fashion as the machines that run the machine learning algorithms. Common business questions to answer which analytics are deployed include those of asset utilization, demand-supply mapping, human resource allocation, etc. The insights delivered by an artificial intelligence-driven analytics engine can offer multidimensional answers.
Linearity is prevalent not only in the framing of business questions but also in the collection of data and the programming of algorithms. The power of machine learning combined with the scope of big data demands a radical rethink – a paradigm shift, so to speak – which may result in a complete overhaul of the business processes under examination. The abstractness of automated insights may seem like science fiction. Still, today’s machine learning algorithms are entirely capable of automatically framing the ultimate business question, if not about life, the universe, and everything else – besides also offering more concrete answers rather than random numbers.
No question of redundancy
It is useful to remember that the first transformation engendered by the growing power of automated insights was widening the field of stakeholders to include not only data scientists but business leaders and others with an interest in working with machine learning. Programming algorithms require a degree of skill that was restricted before now to academic circles. While today's artificial intelligence engines are themselves capable of writing new algorithms, scientists focus their efforts on fine-tuning the algorithms for specific purposes.
CXOs worrying that such processes make the entire idea of human management redundant can relax. Research already suggests that supervised or semi-supervised machine learning algorithms, which operate with some level of human guidance, are more powerful, while still delivering automated insights at speed. Humans are also more capable of translating insights into actions, at least for now.
If we look at micro-segmentation as an example, a machine looking for patterns across various data variables might throw up arbitrary correlations that do not hold up against human experience. Determining whether or not this necessitates a genuine change in business strategy will require human intervention.
Data-driven analytics is already a specialized enterprise; businesses now prefer to outsource the task to external vendors who undertake the arduous task of preprocessing the data before programming the algorithms, which will churn out the automated insights. The extent to which businesses can leverage predictive analytics capabilities is proving to be a significant differentiator. The decision to switch to business models that successfully tap into automated insights is the more significant challenge for CXOs today.
One of the critiques of using machine learning to power automated insights is that decision making happens in a "black box"; the algorithm cannot explain the process that led to the insight. While this is undoubtedly a concern with the deep learning branch of machine learning, the key to unlocking the puzzle is the data itself. As long as the data is credible and not "spurious", there is a higher probability of the insights being logical. Again, advances in machine learning are complex enough to render such explanations of processes barely legible to anyone other than a technical specialist.
The widespread use of algorithmic decision making has also raised questions about privacy and ethical values. Compliance with data privacy regulations is a significant business concern, but algorithms are fast becoming trade secrets protected under local laws almost universally. The data collection and preprocessing phase is arguably the point at which privacy concerns can be addressed. This necessitates greater scrutiny for businesses dealing with data mining vendors and has to be made part of the business decision when transitioning to the use of automated insights.
Depending on the sector in which the firm operates, CXOs will already have gauged the general trend regarding the seemingly inevitable adoption of algorithm-based processes and using automated insights for streamlining business processes. For most business leaders, the question is no longer why or if but when. As businesses grapple with global concerns regarding ethical sourcing, environmental degradation, and fair employment policies, they stand to benefit by taking advantage of automated insights to ease the constant pressure of decision making via arming themselves with more detailed information.