The Data Natives

Businesses today, be it from the old economy or of the digital era, gather and store massive amounts of data. It is an established fact that when used effectively, data can improve and transform businesses. There are ample opportunities to obtain critical insights from data in an organization and to deliver impactful business value.

Organizations gather different types of data (structured, unstructured, semi-structured), across different data states (at rest or in-motion), from different sources (internal or external), in different forms (paper based or digital), sitting across on-premise, private and public cloud infrastructure.

While organizations aspire to be data-driven, the thought and conviction to treat data as a corporate asset has not seeped in many of them. Ditto with separation of data ownership from business system owners and technical solution owners. Organizations also carry the burden of technical debt in the form of legacy IT infrastructure, legacy business applications (EOL and EOSL), energy guzzling infra and applications, suspect data quality and more - all of which impede the agility and ability to right leverage the data assets and to make timely, data-driven business decisions. Such tech debt goes against the theme of sustainability and go-green that many organizations commit themselves to. It is another matter that tech debt is an open invitation to business disruptions. Strange that many organizations remain ignorant or turn a blind eye to this real problem.

In addition to increasing data volumes and data complexity, organizations face the challenge of a plethora of solution options to choose from, all claiming and promising to make complete sense of an organization's data sets. Amidst this chaos, businesses can't ignore screeches of the new power couple - "AI and Gen AI".

Organizations seem to be experiencing a surge in a fragmented approach with AI adoption, merely address department specific use cases. In such cases, the right checks and balances to ascertain the applicability / suitability of the solution gets a miss. While one could argue this as a passing "experimentation phase", the real danger is such temporary solutions tending to stay permanently temporary in the org ecosystem, and adding to the already precarious technical debt. All of these result in the mushrooming of not-so-fit-for-purpose AI/Gen AI solutions, duplicity in organizational spend, a sub-optimal approach, a non-standardized approach etc. Without a doubt, they hinder the ability to optimally utilize data and in adopting AI at scale.

Organizations are also increasingly getting exposed to issues linked to explainability and trustworthiness of AI algorithms, responsible AI model development, and concerns related to data privacy, regulatory, legal, and ethical dilemmas. In the rush to tick the AI and Gen AI adoption checkbox, not suitably addressing these aspects is a huge risk to an organization. Simply hoarding data or investing in technologies and tools without a clear strategy and an operating plan is likely to waste time, money, and other such resources. Developing such a strategy is an in-depth process. Existence of a solid Data and AI strategy - a live document - means an organization has a thorough knowledge of what data it has, where it resides, where it flows, who uses it, and how. The last thing an organization wants to do is to get this wrong.

A well nurtured data culture in an organization means data literacy is widespread, and data-driven insights are the norm rather than the exception. Data is seamlessly woven into the operations, mindset, and identity of such an organization - the Data Native organization.