Digital transformation is the process of moving operations and tools from a traditional offline environment to a digital one. Digital transformation with data specifically enhances business, delivers value through greater understanding and aligns digital and offline data.
Organizations across diverse industries have recognized the need for digital transformation to compete in the new data economy. In the past two decades, companies such as Uber, Amazon, Airbnb, ad Stripe used digital transformation technology to disrupt transportation, commerce, travel and banking sectors. Today's business environment has become hyper-competitive. However, it is understandable that companies following the traditional method as a business strategy might get sidelined in the near future. Hence, it is the need of the hour to adopt AI with data as its core for the improvement and sustainability of the working system.
Data becomes a strategic priority when information acts as an asset. Companies are at the early adoption phase making data a competitive differentiator for leading organisations as they focus on digital transformation. Data acts as the key accelerant of an organisation's digitization and transformation efforts.
The acceptance of digital transformation is fast-tracked by the Covid-19 pandemic. As businesses and organisations started functioning remotely from every end of the globe, the need to push a technological change that was already in the track. Companies quickly adopted AI and its features to keep up the revenue at pace during the period which soon turned out to be normal. Gartner predicts that by 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency.
However, it is disappointing that 70% of digital transformation initiatives fail to reach their stated goal despite massive digitisation as forecasted by McKinsey. There are various reasons behind the crash. But a key problem is not recognising that the data transformation is an essential ornament of digital transformation.
The first initiative that an organisation needs to take towards digitisation is by investing in wide data fluency skills. Next is the investment in data infrastructure, data tools, improved processes and agile organisational structures. Skipping the initial process and proceeding with other investments will draw a flack in the business routine.
Instead of going after data scientists and analytics team, it is better to equip the existing employee group with knowledge on data fluency. Remarkably, data fluency is an inclusive methodology for answering organisational questions where every solution is backed with data. In a nutshell, it is mandatory to have appropriate digital fluency to leverage success in digital transformation.
Data is the key component that accelerates AI to perform its analysis on the organisation's big data. It further unravels solutions, predictions and much more. Henceforth, organisations have acknowledged the need to address the data fluency skill gaps. McKinsey has conducted a survey on thousand businesses from various industries to keep a track on their improvement towards digital transformation. It has found that 43% of the participants believed that the most pressing skill gap to be addressed is data analytics and stated it as the need of the hour.
Organisations that have planned to adopt digital transformation on a large scale have started pouring in investments to upskill their people in the digital age. Marks & Spencer created a retail data academy to upskill over thousand employees on data fluency. Amazon launched a Machine Learning University to equip its engineers with the skills needed to deploy machine learning at scale in their products and services.
Airbnb developed its own Data University to provide every level of the organisation with the skills to make data-driven decisions.
AT&T embarked on a ten-year-long project to upskill more than half of its 250,000 people workplace with an investment of US$1 billion.
Sensitising employees and investing in improving their proficiency in data fluency can't sort all problems. Data fluency answers business questions that organisations should embrace over time. Adopting AI technology even after addressing data fluency comes with a cost. The challenges are huge and it takes a lot of time to overcome it.
Learning differs from person to person according to their capabilities. It also depends on the level of interaction different individuals may have with data. Henceforth, concluding that giving knowledge about data will improve their efficiency is a vague plot. For example, a marketing analyst who regularly works with Excel may need to learn R or Python to succeed at their job, while a manager or leader may only need to know how to make educated decisions using data.
