As Vince Lombardi, the American football player and coach, so succinctly put it:

“It takes months to find a customer.. seconds to lose one”.

Acquiring customers and then holding on to them for longer by keeping them happier can be a daunting task even with a mature platform like The data generated from vast troves of customer touchpoints has often been called the panacea. Whilst customer data is the lifeblood for any organisation, business leaders often assume customer data is the key to gaining an unbeatable competitive edge. However, the power of the insight can often be overestimated and may not always be to your advantage, as was articulated in an article by Harvard Business Review. With the digital transformation frenzy taking over the organisations and data being generated from various sources, driving value from that data is the cornerstone for an organisation’s survival.

So how do you generate value from customer data on Data science provides an answer. 

The goal of data science is to improve decision-making through information extracted from your customer data. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from customer data. It is closely related to the fields of data mining and machine learning, but is broader in scope. Today, data science drives decision-making in nearly all parts of modern society. 

However, it has never been easier to do data science badly. 

With the advent of big data, computing power and optimised machine learning algorithms, modern data science software is easy to use, making data science a cakewalk. In addition, User-friendly data science tools have also lowered the barriers to entry into data science. Many organisations embark on their data science journey without first creating a firm foundation. With 87% of data science projects never making it into production, it means that only one out of every 10 projects sees success.

Fail-fast is a notion that works well as a concept but without the right foundation, this can result in a huge waste of business resources.

Insights generated from customer data via data science are useful only if they are followed by an action. Action highlights the fact that the insight generated needs to have the capacity to be used in some way. Without action, capturing customer data or generating insights is a moot point. As Gartner says, through 2022, only 20% of analytic insights will deliver business outcomes.

This begs the question: how do you do customer data science in the ‘right way’, deriving actionable insights that will help your organisation to stay truly competitive?

The easy answer is that you need a framework that can enable successful data science outcomes throughout the organisation, and that is underpinned by a solid strategic data foundation. This foundation critically consists of setting up data governance and data compliance for your organisation in a short span of time. Acrotrend can help you to unravel this answer further so that you can create your own modern data science framework for that can help build your data science capabilities on top of a robust foundation.

Sounds good? Let’s start the conversation.