Every few years, there’s a paradigm shift in technology patterns and digital levers. Recently, it is the age of digital disruption caused by a fundamental need for organizations to digitally transform to stay in the game. New technology platforms and services such as the Internet of Things (IoT), Artificial Intelligence, Robotics Process Automation, Machine Learning, and Blockchain are already paying dividends, enriching the digital transformation journey, and have created the new rock star: data. Even rew roles such as Chief Data Officer, Chief Digital Officer, and the like, have cropped up to harness the power of data.

Read the original article: Digital Transformation: It All Starts With Data Thinking

Nowadays, every organizational decision around a digital transformation strategy is driven by data. Whether it's to optimize inventory stock levels, reduce lead times from suppliers, or design the pricing and promotions strategy for a customer segment, all decisions require data to understand what can be improved to gain a competitive edge for the organization. As businesses are digitizing, digitalizing, and digitally transforming (yes, there's a difference) at breakneck speed, new businesses and business models evolve, and the lines between business processes and technology blur, one element remains a constant denominator. You guessed it, data.

Just as organizational decision-making has evolved, data has undergone significant shape-shifting as well. It has multiplied, exploded and become pervasive and democratized. In today’s world of digital disruption, data is the most valuable asset to an organization. Businesses can transform themselves through data, and data is transformed through digitalization.

However, how many digital programs start with data? Forbes estimates that 7 out of 8 digital transformation programs fail. To reference a personal experience, a leading retailer was in their fourth year of implementing a critical digital transformation program which would impact every single vital business process, from merchandising to order-fulfillment and beyond. There was significant investment sunk already, including software licensing and support costs, as well as development, testing, and consulting hours burnt, but the program was deemed a failure since it fell short of every ROI metric even before any of the functionalities were turned on. I worked with the newly joined VP of IT (who eventually hired my organization to investigate and re-implement) and we were stunned to discover that a negligible fraction of underlying data needed for the applications to be effective was available, clean, synchronized, consistent, or even valid. The new VP of IT shelved the program and executed a new data cleansing project before re-launching the digital transformation program. I’m happy to say that after three more years, it it has successfully launched and the company is reaping the rewards of a well-designed and seamlessly connected digital retail platform.

Data is the lifeblood that feeds and circulates through an organization’s systems, be it process flows, workflows, ERPs, applications, distribution centers, datacenters, you name it. It starts much before the program or process is initiated. High quality data is the beginning (and ending) of a successful digital transformation. It starts with data thinking.

The official definition for data thinking is the generic mental pattern observed during the processes of picking a subject to start with, identifying its parts or components, organizing and describing them in an informative fashion that is relevant to what motivated and initiated the processes. It’s the prerequisite for digitalization.

In other words, data thinking is a holistic approach to data-driven transformations to create a culture of intelligent harnessing and consumption of data. It is an in-depth understanding of data’s role, raison d’etre, consumption, impact internally and externally of the underlying logic, change, patterns, and inculcating awareness, openness to innovate, and adoption to establish a culture of data thinking.