Amazon is renowned for its lightning quick delivery, even for obscure product requirements. This is not usually because of the delivery system alone, but also due to the concept of demand sensing. Top digital firms such as Amazon are noted for the enormous quantities of data warehousing done, which enables them to predict demand. The obscure product gets delivered so quick because based on the user’s historical data, the company may have simply predicted it being ordered, so had shipped the item long before the actual order was placed. This eases the pressure on the supply chain, reducing market volatility. New data sources are now available that can be tapped to get such vital customer insights. This is even happening in low-volume industries such as wind power equipment or aerospace. The former for example is being worked out by The Swedish-Danish duo of Vattenfall and Orsted leveraging existing neural networks. Several business analytics tools are available to glean this information out of platforms such as social media, connected devices, wearables, geo-location devices and digital personal assistants.


Uploaded Date:16 March 2018

Digital trust has already emerged as one of the key areas of thrust in the so far young year of 2018. The General Data Protection Regulation (GDPR) of the European Union (EU) has already states its aim to better regulate personal data, its protection and the subsequent corporate trust-building. Facebook and Google have similarly voiced their concerns at data protection norms. The former is de-prioritizing content from third party publishers, with the latter making the country origin of content mandatory. Even China’s till-date opaque systems will no longer get a free run at data warehousing on sensitive, personal matters. A study was conducted across forty-two countries, by researchers from the Harvard Business Review to understand the major dimensions on which digital trust is captured. The first of the four captured is Behavior, which in particular talks about the responses by digital players post any friction in the environment or during such digital experiences. The second of these is how users view the attitudes of the players within the digital trust environment. Market research firm Ipsos played a big part in capturing insights among people on data privacy. Then there is the overall Digital Environment that tracks the robustness in implementation of regulator norms towards control mechanisms. And finally, there is the experience of the digital users. While some countries are on the trust-surplus side, majority are on what is the trust-deficit. So, China, Malaysia, Turkey and Saudi Arabia for example are high on trust, Pakistan and Australia are on the other side, with Germany and Japan right towards the middle.


Uploaded Date:03 March 2018

Most people think of business innovation as a single ‘aha’ moment that changed the course of history. However, a closer introspection suggests that instead of being moments of inspiration, innovation arose due to a series of coherent steps being taken on a step-by-step basis. If one studies the most innovative companies around today, a common theme will emerge. The names of the companies would be Google, Facebook, Tesla, Amazon and IBM. The theme common to theme all is the enormous data warehousing capabilities they all possess and making use of to glean effective business insights. The most innovative companies are thus not the ones, with the best minds, but simply as a result of possessing the largest treasure trove of data. This trend should alarm policy makers who would want a competitive business scenario, because these data giants are leveraging the same to grow further.


Uploaded Date:17 February 2018

A huge litany of business literature now exists on the scope and role of Machine Learning (ML) both for the present and future. While some doomsayers predict ML will take away all human jobs, others say machines and robots will simply supplement human work to help us achieve tasks more efficiently. However, more than being game changers, ML algorithms will have to steer clear of becoming the new PowerPoint presentations. No one intends to have boring presentations, yet many end up doing so. These end up sucking the life out of otherwise engaging meetings or discussions. Similarly, inadvertently, a lot of programmers will end up developing algorithms which will draw up false conclusions. This will be true for both Active and Passive MLs. In the former, a human programmer is constantly working to improvise on the results, while the latter is self-sustaining. To avoid this happening en masse, certain steps have been suggested by experts. The organization concerned must start off by writing a Declaration of Machine Intelligence which will outline the tasks the management expects the algorithms to perform. Proper transparency measures must be set up with grounds for reviews, validation and verification. As large corporations perform data warehousing tasks with these huge chunks of data, a lot of it will become junk. Thus, they need to be periodically reviewed by a panel. Finally, a trade-off road map must be curated so that when the algorithm fails, there is enough scope to improvise.


Uploaded Date:06 February 2018

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