Innovation is an important agenda for us, at all levels, across the organization. Being part of an organization that was born during the dotcom boom at the beginning of the century and being among the few who survived and thrived, IT is in our DNA.
But then there is a lot that can be done today that was not possible a couple of years ago. A lot of our focus now is on exploiting the huge heap of available data and continually develop solutions that can collate, store, integrate, analyze and build business cases. The data comes from multiple sources and in multiple forms and formats, which need to be standardized and made interoperable.
Organizations that can successfully build a data DNA or data as a way of life will surely emerge as winners in the times to come.
This has four important imperatives:
1. Data modelling and analysis is an extremely evolutionary process, with a sense of completeness eluding most of the time. New data streams, new possibilities to model them, and new use cases that can be supported by it are all continually emerging and getting redefined. As part of the engineering team, we also ensure that the solution must evolve in tandem with the data and algorithm. Innovative mindset, strong sense of perseverance, openness to experimentation, and staying open to newer possibilities are some traits that people must possess to continually work with data and related use cases.
2. Data storage, modelling, access, and retrieval must be supported by apt databases, data warehousing, data structuring and data processing tools. Though AI and ML are still in nascent stage, their relevance will certainly grow over time. Contemporary technologies like DevOps will also prominently become part of the environment for embracing continuous upgradations and mainstreaming small-sized innovations.
3. The underlying IT infrastructure and applications architecture also need to evolve in order to support the data focus for making innovation a holistic initiative. It’s very common to see piecemeal initiatives in many organizations that lack a common thread to weave them together for an organization level impact. Physical in-premise infrastructure has its own limitations, it cannot grow elastically with the need. It fails to cope with the demands of a data-centric world. Hence a balanced approach that balances out in premise and in cloud infrastructure must be adopted. In short, a hybrid approach is the way forward. One must continuously do cost benefit analysis to be able to strike the right balance.
4. Building predictability into IT management is going to be a critical aspect of embracing a data-centric approach. The server, network and application logs itself produces so much data that can be harnessed to predict the systemic behavior, analyze the impact of any change, and initiate change across the spectrum with minimal efforts.
In short, to support innovation, a dual focus must be put by organizations. First, on continuously crunching new algorithms, reimagining new models of data, and creating new solutions that serves the emerging needs of business users. How fast innovative use cases are built and scaled up are critical questions that may define newer competitive advantage for the businesses. Second, on building a high-performance IT infrastructure that is capable of successfully coping with the demands of the business in terms of elasticity, predictability, efficiency, and security.