How data can drive Agile decision making

Gut-based decision-making processes have now died an unlamented death in enterprises. Most organizations can collect huge amounts of data with ease without hurting their pockets. This has brought in the phenomenon of data-driven decision making at pace, i.e. Agile decision-making.

Decisions taken after analyzing the data collected is impacting every sphere of business operation. This can help organizations predict how the market may change in the future. It is driving the digital strategiesof companies and helping them make product searches for their customers easier. From route optimization in supply chains to room pricing for Airbnb, everything is driven by data-driven decision-making. Data is driving sales, increasing revenues, and improving operations. The major driver has been the ease with which data can be collected, managed, and analyzed.

But what is Agile decision making? This is best understood with an example. Imagine a banking customer-facing app. The landing page of the App has a multitude of modules to build in. The modules are categorized based on the Moscow principles (Must have, should have, could have but won’t get now) and the most appropriate modules are developed on an Agile basis. Subsequent modules getpipelined as a part of later releases. How to prioritize the modules or features, which features will be on the landing page for a specific customer, which for all the customers, etc. are decisions best taken after analyzing copious amounts of customer data and A/B testing to validate the planned layout. This is decision-making on the fly, driven by changing parameters defined by rapid inputs from the situation on the ground. It’s instant. It’s nimble. It’s Agile.

Data influences the Agiledecision-making process of an organization and defines the strategy. Here are three use-cases to show how it can drive business.

To improve customer engagement and retention

Recommendation engines are a great testingground to engage customers and understand what they want. With relevant products, offerings or content being shown at the landing page of each customer, engagement levels increase. On the flip side, data can help identify which customer’s engagement level is dipping. Customized products or bundled offerings can be provided to her, tailored to her liking to prevent the impending churn. Identifying the right customer preferences, to bundling the products -it’s all driven by scientific data-driven Agiledecision making.

Enhance operational efficiency

Analytics is being leveraged massively to make operational processes leaner and reduce overheads. Fleet management ensures the transport logistics are being streamlined, using GPS data to the last mile, to make it efficient. IoT data is being studied to ensure that critical equipment is in perfect health.This, in turn, reduces downtime. Businesses can identify the parts which might fail in the future and predictive maintenance can be brought into play. Businesses are also realizing opportunities for new revenue generation in real-time. A great example of this is renting out idle equipment during production lulls. The key to such decisions is being able to make them in real-time, i.e. with agility.

Increased capacity and insights without extra investments

As we can see from the earlier examples,by leveraging analytics and taking quick decisions,businesses can gain tremendously. Among the other benefits, automation can be embedded into processes to make ops faster, more accurate, and less person dependent. This suggests that resources could be more optimally utilized for greater bang for the investment buck. These freed-up resources can open up new sources of revenue and move up the value chain.

One key facet is that data-driven decision making negates the adverse effects of hidden biases inherent in human decision-making. Data can unearth insights hidden in plain sight and help organizations set clear goals. By collecting the right data and by measuring the right KPIs organizations can understand the nuances driving decisions. Today, organizations have to beAgile. With increased competition and ever-changing customer preferences, they have no choice but to reduce turnaround times and accelerate their market response. Agile decision-making becomes imperative in such a scenario as adaptability is baked into the process. Organizations have the opportunity to quickly learn what is working and what is not. Agile decision making can help incorporate these learnings into their business strategies, ring in the changes instantly, and quickly cater to customer demand. And as we have seen, it’s data that must be at the core of an Agile decision-making strategy.