Enterprises that are not curious about the dynamics of their customer acquisitions, business growth and prevention of failures – will overlook the flood of incoming data and will most likely be forever reactive in the nature of their strategic operations. Other enterprises that are more inclined to observe feedback from their business processes and analyse the dynamics of operations are more likely to emerge as proactive and opportunistic in their business development activities.
The implementation of machine learning to power business intelligence for trading organisations can prove to be of significant value especially for large enterprises. Machine learning algorithms can help decision makers grasp the likelihood of the occurrence of a particular phenomenon based on converging internal and external environmental factors – and decide on strategic approaches that take timely advantages of arising situations. Consider the example:
A large scale enterprise – a retail chain – that is scattered across a country, caters to the hardware and building needs of households and businesses. The organisation currently has over 1,500 outlets that stock building supplies and requires adequate warehousing and staffing at each of its locations to ensure smooth generation of daily sales. Without a machine learning powered business intelligence solution, at most, the product teams can only review product performances every quarter, and plan ahead for only two quarters at a time.
With a machine learning powered business intelligence solution in place, product teams at the same organisation are able to revise product decisions in real time – making adjustments to stocks and product placements everyday. Machine learning algorithms can also be tuned to deliver other business insights as well.
Such other business enhancing insights can be in the likes of:
- Social listening to decide which products are likely to trend and for how long the trend will continue
- Recommendations such as up-selling and cross-selling related products to customers based on the buying habits of similar customers
- Dynamic product promotions such as flash sales based on geographical locations of customers
- Sifting through datastreams from external factors to discover hidden opportunities, as well as recognise the most suitable approach to optimise benefits from such opportunities.
For a machine to be able to report insights and deliver business foresight, the machine requires access to a wealth of relevant data. Data lakes are a newer technology concept that allows enterprises to store and accumulate vast amounts of business data – that can then be made accessible to machines. All connected devices and systems offload data into such a data storage facility for the purpose of processing and extracting insights.
Next — Part 3 (to be published soon and is being written as you read this)
Data lakes are conceptually similar to the real world term ‘lakes’ in the manner that they are capable of holding large reserves of valuable resources that are continuously accumulating. Data lakes refer to virtual cloud data storage facilities that are inherently scalable. With the availability of scalable data lakes, enterprises are able to implement machine learning applications to sift through the large volumes of data and identify trends, patterns, and behaviours.
- Introduction to Praedictio – machine learning and prediction model
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