5 dimensions of making smart data-driven business decisions
6 min. read

5 dimensions of making smart data-driven business decisions

Better decisions for enterprises rests on 5 dimensions. With digital transformation taking a lead, businesses must use data to drive decisions. Read to find how to make data driven business decisions.

No one will disagree that digital transformation has become an imperative for every business, big or small. What most people still can’t agree on is a definition for it. While every day we are served with an alphabet soup of latest tech buzz words like AI, Industry 4.0, Big Data, IOT et al, what executives want to know is what it all means for their business. Simply put, Digital Transformation is a data-driven way of running a smart business. It helps leaders make quick decisions confidently, improve customer engagement, eliminate inefficiencies, and while doing all of that, stay relevant.

Using data to drive executive decisions

Businesses today operate in an increasingly complex and dynamic environment. Executives need to take critical decisions every day and for that they not only need constant streams of real-world information but also the means to extract timely and actionable insights from a sea of data. Let’s consider our work with Mumbai’s Municipal Corporation in managing the COVID-19 outbreak. It’s an apt example of data-led dynamic decision-making where problem statements kept changing frequently. In the early days of the pandemic, within days our goal post shifted form tracking city arrivals to managing emerging hotspots and creating containments zones. This was soon followed by a new challenge of modelling healthcare capacity for critical supplies like ventilators and PPE suits. The next issue we encountered was how and which industries to reopen first. The problem statement kept changing and we needed to continuously evolve to enable decision makers, doctors, and administrators. While this was a public health crisis thrust upon us, we have been providing similar insights to big companies like P&G where we developed an executive dashboard using Crux Intelligence with a smartphone interface that allowed them to interact with data in a personalized way and take important decisions in minutes.

Improve consumer engagement

Data analytics can help you understand your customer and their changing needs better than ever before. This can enable your business to make custom-made offers at scale through hyper-personalization. A large telecom player found this out the hard way. They noticed that during the iPhone launch, out of 25 million customers visiting their online store every month, only 1.5% ended up making a transaction. Our role was to understand the remaining 98% and plug the holes. Using machine learning we were able to find errors and fix them in real time to ensure a clear path to engaging customers. This led a 26% improvement in the conversion rate translating into $100 million additional revenue per month. Similarly, our Customer Genomics solution helped HDFC Bank apply Next Big Action by understanding customer behavior based on their transaction history, credit card purchases, and bank balance data. The bank is now able to offer financial products & services their customers need rather than pushing the ones that they think would be most profitable.

Eliminate inefficiencies, increase productivity

One of the biggest ways machine learning & data analytics helps businesses is by enabling them to do their best while minimizing waste from forecasting inaccuracies. For example, 50% of the inventory produced in the food sector is lost because of supply chain inefficiencies or consumers buying more than they consume. We were able to help a leading tea manufacturer avoid this by improving their inventory forecast accuracy by 8-10% using deep learning models. This ensured they were never out of stock, or conversely, in an excess stock situation. Another case in point was a consumer goods player paying $25 million in annual fines because of a leading retail client’s just-in-time delivery policy. Using Bayesian learning and machine learning models we figured out exactly what kind of shipment services to use, the ideal timing for dispatch, and the right decisions to make in transit to eliminate logistical inefficiencies. This led to an improvement in the overall on-time shipment performance from 82% to 94% resulting in a $16-17 million in savings for the company.

Building better products faster

Despite the lure of finding new ways to create value, the failure rate of innovation remains high. It is common knowledge that 90-95% of new products go belly up soon after launch. You want to build better products and reduce their overall failure rate, but you also want to increase the speed of innovation so that your business is not left behind. So how do you balance both? Another learning from this pandemic is how new use cases can be developed quickly for products based on deep insights. Last year, we realized that one of the major uses for the Qure.ai powered chest X-ray technology could be COVID detection. Soon the solution was being used by hospitals in India and around the world. It didn’t stop there. Within a matter of weeks into the lockdown, we were working with a hospital in Italy to identify patients who were likely to go into ICU based on daily changes in their scans. While humans are not very good at finding small changes, machines are phenomenally good at it.

Fighting disruption

For every successful business model there are a thousand trying to hack and disrupt it. What keeps executives awake at night is the fear that someone with a smarter solution will soon put them out of business. One would think that legacy financial institutions and investment funds are too big to worry about such disruption. The fact is that they are increasingly wary of competition using alternate sources of data to beat them at their game. From satellite images of retail store parking lots to gauge business volume to analyzing a CEO voice patterns in earnings calls, investors are using fundamentally new sources of data to make decisions.

That all traditional sectors are already in the midst of digital reinvention is evident from the success of Amazon’s touchless retail stores relying solely on customer data, and the way in which the global beauty industry is embracing Virtual Reality (VR) & Augmented Reality (AR) technologies to improve consumer experience. Whether you want to be a disruptor or want to fight disruption, you need to make sure you’re tapping into all available sources of data to inform your every next move.

Transforming a business across these 5 dimensions requires scaled problem-solving using a combination of AI, Engineering, & Design. To know more watch this video.