By Tony Zambito
Illustration by Aneeque Ahmed
In the past few years, the data analytics revolution has continued unabated in the worlds of B2B and B2C. The rise of data analytics is resulting in increased budgets and staff to account for the volume of data that can now be computed and accessed. This growth in the volume of data has spawned numerous types of data analytics services provided for marketing and sales management to deal with the ever-increasing volume of data.
Insular We Have Become
While organizations may have decreed that the purpose for all this data crunching is for customer-centricity, something else may be happening unintentionally. That is, the actual outcome is a business by data-centricity versus business by customer-centricity.
Through natural laws of inertia, companies can become insular because of data. Creating a data island unto itself that is consumed by data and exists through data only. With ever-increasing demands for access to data to support initiatives.
An outcome of insularity is organizations can develop an over-reliance on quantitative data in all aspects of a business. Creating an addictive dependency on data analytics, which in turn attempts to reduce all decisions down to an arbitrary quantitative equation. There is a degree of irony to the growing influence of data analytics. It goes something like this:
After decades of senior management making decisions on quantitative data alone, a movement began to incorporate more “voice of the customer” into decision-making at the advent of a new century. The rapid rise of data analytics is causing the opposite effect. Which is, a reinforcement of data and a doubling down on making decisions based on quantitative data alone.
This may be hard to see happening. After all, the data is supposed to be about customers. And, does that not make us customer-centric? The answer lies in whether an organization is consumed by the quantity of data as opposed to a focus on obtaining critical business, as well as, buyer insights. If an organization has become insular about data, the danger they may face is that they may focus disproportionately on the quantity of data versus the quality of business and buyer insights.
Human-Learning Versus Machine Learning
Hot buzzwords steamrolling through 2017 are Machine-Learning, Natural Language Processing (NLP), and Artificial Intelligence. Although all are not the same, the common goal is to make data “smart”. Machines with access to data can learn and this resulting learning contributes towards artificial intelligence that can carry out “smart” tasks. In B2C, this has significant value. For example, in banking, artificial intelligence can recognize patterns of consumer transaction activities and begin to anticipate whether consumers are wanting to perform a deposit, make a payment, or conduct a wire transfer.
Such an ability to learn patterns that lead to artificial intelligence can result in vastly improved customer experiences. This use of artificial intelligence can be harder to achieve in certain B2B markets. However, the impact of machine-learning in industrial settings can be significant.
What executives must think about is what gets stripped away. An over-reliance on data and even in recent Go to the full article.
Source:: Business 2 Community