An agile business is characterized by its ability to quickly respond and adapt to the changes happening around the world. Agility is not a tool, framework or methodology, its a culture based on solid principles. To be able to survive the ever growing fierce competition, we have to be agile, but for a company to become truly agile, the change has to happen within its DNA.

Ability to respond and adapt quickly is about empowering companies to think fast. Companies should be able to take faster decisions, implement them quickly, monitor and measure their impact, and change them if needed, as quickly as possible. A good action taken in time is better than the best decision that never gets executed.

One of the biggest constraints of companies to be agile is their inability to use business intelligence systems for effective and fast decision making. Most of these systems are very rigid, and don’t offer the flexibility that is required for the businesses to be competitive. The requirements are quickly changing, the availability of data is changing, but the systems that are used to convert data into useful insights are not able to keep up with the pace. Their needs to be a fundamental shift in how these systems are built and used in large corporations.

Traditionally, it took anywhere between one year to several years to build these enterprise decision support systems. They followed a well defined but time consuming software development life cycle process that started with collecting the requirements, designing the systems, building the system, testing it, deploying it, and managing the ongoing changes as regular maintenance activities. These systems took several years of development efforts and millions of dollars of investment, and were expected to serve for the next few decades until the investments payoff.

This will no longer work in the fast changing business scenario. We have to be able to build several small and flexible intelligence systems quickly, and start using them in time, and be able to throw them away as soon as they become irrelevant to the business. The process of building new systems and throwing away the old ones should be a continuous process, and hence, companies need to adopt cheaper methods of building these systems.

The architecture principles should change fundamentally to adopt this new culture. Open source technologies should be leveraged to reduce the costs of building the systems. Proprietary systems should not be encouraged. Instead of building specific purpose products, companies should invest in building platforms that can host re-usable products. Pay-as-you-go analytical products should be used, such that they can be scrapped off when they are no longer relevant.

Instead of trying to confine the data into a single well managed repository, companies should acknowledge the fact that data, just like knowledge, cannot be confined. It is ever changing, ever growing, its usage is ever changing, its meaning or context to the business is ever changing. Companies should instead invest in data management methods like building dictionary, data trace-ability, quality monitoring methods and integrity monitoring methods. Companies have to invest in understanding data more than simply managing the data.

With data science emerging as a new field, data should be considered as an asset of the business, and not an overhead of Information technology department. Businesses should take control of gathering, managing, and exploring data of their concern, without having to battle through the barriers of IT vs business. They should have the flexibility to outsource data analytics to dedicated vendors, to become more agile and competitive.

Data is the oxygen for businesses to survive and scale, and being able to continuously and effectively leverage data for taking decisions is what distinguishes an agile business from the rest of them.

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