Flip through the pages of a newspaper or thumb through a magazine, we cannot escape writing on digitization or automation in today’s world. We come across terms like Machine Learning, Artificial Intelligence, Big Data, Internet of Things, Cloud, BlockChain, etc. every single day. But what is it exactly that we should know about these technologies? As an expert in Procurement, Supply Chain or Marketing, do we really need to understand the details of the technology or will it suffice to understand the vast scope that these technologies open up for us and change the way we live and work? Here, I attempt to paint a big picture, the way I see it.
There are three broad aspects that need to be understood to make a successful foray into Digitization – Consumer Behavior, Data and Change Management.
Thanks to mobile and low-cost data, the world is more connected and flatter than ever before. As a result, today’s consumer is an informed lot. Consumers have become more global and their preferences are also seeing a shift. ‘Convenience’, ‘accumulating experiences’ and ‘living the moment’ are redefining tastes of people. People are looking for personalization in everything that they spend money on.
With personalization becoming so vital, how can companies even address the needs of consumers? Here comes the role of data!
We need to have a rough knowledge of technology in order to use it effectively. And therefore to understand digitization and emerging technologies, we need to first understand Data. ‘Data is the new oil’ is a popular refrain these days. That is how the importance of data in today’s context is being portrayed. Gone are the days when it used to be a behemoth task to collect responses from our customers. Today, customer data is pouring in from various sources – from direct customer feedback, from comments and responses in social media, from scanning devices in supermarkets or from sensors that control our home temperature.
How effectively we make data work for us will determine how the world of tomorrow will be structured. The traditional way to derive inference from data is by running a linear regression on it. Traditionally, linear regression has been a popular model to establish co-relation between various data points and parameters. But the curve to fit the data in linear regression is always a straight line. However, most phenomena in the world are non-linear. This is where learning algorithms or Machine Learning can be useful as they open up a world of non-linear models.
Marketers today are trying to move away from ‘mass marketing’ to ‘mass customization’. This shift has been triggered by changing consumer behavior inclined towards personalization. And this is possible when the power of available data is harnessed. Data will increasingly play an important role in reaching out to customers and in taking decisions.
Driven by data, the focus area of every function within an organization and how they operate will undergo an overhaul too. Real-time demand generation would make companies respond better and faster to market changes. Real-time inventory monitoring will improve customer response time as well as positively impact the working capital of companies. Robotics will play a significant role in automating warehouses and this trend is already being noticed in some industries.
Data and learning algorithms will make it simpler to take sourcing decisions – at which point to buy and from which source to buy from. Data will help us track the various tiers of suppliers who directly or indirectly impact the supply chain of a company. These will make buying decisions less subjective.
We are on the verge of seeing huge changes in the way we live and work due to changing consumer behaviors as well as emerging technologies. This calls for a sustained Change Management agenda built into Organizational Strategies.
Today, there are discussions and debates on diverse aspects relating to this topic in various forums. People are apprehensive about what the future available jobs will be like. And will upskilling and reskilling the current workforce suffice?
Then there is the other big question on data privacy. Who is the owner of all the data that is being collected, both with or without the knowledge of the subject? Governments are yet to come up with laws that will effectively handle such cases.
There are no clear answers at the moment to many such questions. Data aided technologies are just evolving and as they evolve, more questions will arise. It will then be the responsibility of each one of us to engage in discussions and find the best way out.
It will be to the benefit of humans to make the best use of the power of technology. And it is all about managing the change well. The starting point for all this is to understand technology. All of us need not learn how to write algorithms. But we definitely must understand the power of data, and how, with data, self-learning algorithms become increasingly powerful.
To new beginnings!
- ‘The Master Algorithm’ by Pedro Domingos
- ‘Marketing Whitebook 2017-18’ from BW Books