Becoming a data-driven company involves more than a shift from one IT system to another. Customers, employees, processes, products, and ultimately the entire organization are transformed into data streams to be analyzed and understood. Some processes will become obsolete, while critical thinking and analytical skills, more in demand.
Those analytical skills include: developing business intelligence, identifying hidden patterns of information to help make forecasts, and the strategic use of complex data sets. Moreover, artificial intelligence, machine learning and data science can help automate data analysis through algorithms with self-learning capabilities. These data streams can then improve productivity and create new products and services.
To truly benefit, the data-driven company must re-organise in order to master new technologies, complex decision making and creative problem solving. Rather than deciding simply what goals are possible, it should determine what skills and knowledge are needed to achieve them.
Consider UK-based B&K Boiler Services. The firm wanted to reduce fuel costs. So it deployed big data, GPS and analytics to track its vehicle fleet to get a better sense of prevailing driving patterns such as speeding, incorrect routes and the misuse of corporate vehicles. The same technologies has been used by the NGO Save the Elephants to monitor these animals in real time and detect anomalous movements that would suggest poaching and other threats.
Non-tech companies in engineering, energy, finance or research can even supply data-driven solutions. The Formula 1 team McLaren, for example, has developed scenario-simulation capacities; real-time data processing and analysis; and the creation of big data systems that manufacturers lack. A dedicated business unit even helped the British skeleton team win gold at the Sochi Winter Olympics by redesigning a bobsleigh that could better handle local terrain and clothing that minimized friction.
In fact, entire industries are being redefined. Apeel Sciences, an organic agriculture company, wanted to create a new mechanism to extend the life of perishable products, such as strawberries, using the properties of more resistant fruits rather than relying on the cold chain. Deploying a range of technologies and techniques such as materials science, Arduino-based big data, time-lapse video and analytics, the company’s data-driven product can reduce pesticide use, save energy and extend the shelf life of fruit.
To truly benefit, the data-driven company must re-organise in order to master new technologies, complex decision making and creative problem solving.
How to create a data-driven company
Becoming a data-driven company is a long-term process. But the following stages can help guide a successful transformation.
- Identify the business need: Ask, what problem you are trying to solve, and then identify and prioritize the most important needs of your business.
- Make the business case: Develop a viable project with a clear return on investment, and determine which competencies, datasets and algorithms will be relevant.
- Define the strategy: Align the competencies and data with the business and technology strategies. Determine an appropriate technological approach, and decide which competencies should be developed internally and which can be outsourced.
- Experiment: Conduct pilot tests to validate your hypotheses and be flexible in adjusting the models and the business.
- Launch: Launch production. Some companies fail at this stage because of the project’s complexity, so learn to scale technology flexibly using an approach known as DevOps.
- Repeat: After your first project proves a success, embark on the next, one department at a time, and involve everyone from the CEO to the receptionist.