Immense amounts of data are flowing into and out of today’s businesses, but it's often difficult to know how to turn this data into actionable insights. Data science has incredible potential for businesses of all types to create models that find patterns in this data and use them as the basis for transformative software. From location sensor data and customer loyalty programs to predictive analytics that improve the customer experience, employee engagement, and operational efficiency, a world of possibility awaits organizations that can crack the data science code.
In the current hypercompetitive business environment, it’s not enough to automate processes and increase efficiency. To succeed, companies need to differentiate themselves from their competitors. But with the growth of digitally savvy customers who expect more from every transaction, it’s becoming increasingly difficult to differentiate on product alone. Customers are demanding a more personal, service-oriented approach from the companies with which they do business, and the bar continues to be reset at higher and higher levels. To meet this demand, and stay competitive, companies need to move from a transaction-based model to more value-based interactions. This means putting the experience first.
Digital transformation is on the tip of many tongues in the technology industry of late; but like many potentially seismic shifts, this concept’s meaning and the impact it will have on how day-to-day business gets done are taking some time to develop. CIO defines digital transformation as “the acceleration of business activities, processes, competencies, and models to fully leverage the changes and opportunities of digital technologies and their impact in a strategic and prioritized way.” But more than just acceleration, digital transformation is about the need for businesses to outpace digital disruption and stay competitive in a rapidly evolving business environment.
Digital disruption is the new normal. And, as part of their digitalization journey, manufacturers now have a whole spectrum of modern tools to embrace. Greater agility is often touted as one of the important benefits of these digital tactics. Greater speed is a coveted prize, sought after by manufacturers since the era of Henry Ford’s assembly line.
Now, though, responding to change with lightning fast reflexes is not enough. Manufacturers must anticipate future trends and strive to predict customer demands before the customer even has acknowledged the need. Being the first in a market pace is often the key to owning it.
So, having a view of tomorrow is now more important than ever. Today we call it predictive algorithms and data science. We strive to speed product releases and adopt new features as quickly possible. Accelerating speed in one department drives the need for acceleration in other departments. The continuous rush of change, when not controlled, can start to resemble a hamster spinning in its wheel—but getting nowhere. Without meaningful objectives, speed for the sake of speed starts to become fruitless.
As manufacturers undertake digital initiatives, they should pause and consider the ongoing quest for speed and understand its true value. It’s important to be cautious about blind, over-emphasis. Speed has its risks, from higher levels of errors, quality issues, and a workforce that isn’t trained on new policies or processes. There is a fine line between efficient decision-making and rash, knee-jerk responses which can take a company down circuitous routes, far from the prime objectives. In fact, some would contend that reactionary measures lacking cost analysis and thorough financial impact study are simply reflexes, with a 50-50 chance of being right or wrong.
How did we get here?