By Geoff Thomas, SVP, APAC & Japan, Qlik
Over the years, organisations have faced unexpected turbulence, prompting strategic shifts in thinking. Many recognise the value of data, analytics, and artificial intelligence (AI) to their business performance in response to this turbulence. Data-driven enterprises in India, especially those who relied on real-time decisions based on high-quality data, were best positioned to tackle these challenges. India’s data analytics industry recorded a substantial increase of 34.6% year-on-year in 2022, with the market value reaching USD 61.1 billion.
Similarly, demand for tech-driven solutions, advanced AI reporting and business intelligence, and remote working models supporting this growth has surged. These figures underscore the widespread adoption of digital technologies, making data a vital driver of connectivity and progress in India. Insights collected from trillions of data in use help organisations predict and plan. But equally essential is ensuring our data is analytics-ready.
While having data and the right technologies in place is essential, the ability to turn data into insights and insights into action is where businesses see value begin to materialise.
Having the data is one thing, but integrating it is just as crucial. Businesses need to calibrate this integration to achieve connected governance – which means having the ability to access, combine and oversee distributed data sets to ensure certainty at all times. Leveraging data integration can equip businesses to with the necessary tools for navigating a fast-evolving landscape.
Consolidation for newer opportunities
The consolidation of previously siloed systems in data integration and management, analytics/AI, visualisation, data science, and automation has gained pace. We’re moving beyond singular function – today, leaders gain market dominance by providing comprehensive, end-to-end platforms. Combining these functions opens opportunities that weren’t possible before, making it easier for data producers and consumers to collaborate, starting with the product, outcomes, or decisions they have in mind and working backward to build agile data pipelines around their business goals. A successful data structure needs components to exchange information and work together seamlessly.
Data integration for governance
Accumulating data is just the first step in data integration. Having a proper data warehouse/lake and infrastructure is necessary for building accountability. The warehouse is the Holy Grail or the single source of truth for the organisation. From the warehouse, the data can be used by different verticals in an organisation.
This also helps with data governance, as all the data is based in one place with catalogued profiles and documents of every data source and defines who in an organisation can take which actions on which data. These policies and standards allow users to find, prepare, use, and share trusted datasets on their own, without relying on IT.
Building a smarter pipeline
More than ever, businesses are tuned in to the potential of AI and how they can leverage this technology. The convergence of analytics, automation, and AI is becoming increasingly evident, with these domains overlapping and cross-pollinating to unlock novel insights that were previously beyond reach. But we must consider how these components can move deeper into the data pipeline before an application or dashboard is even built. This approach promises substantial benefits for organisations, paving the way for enhanced data-driven decision-making and driving innovation to new heights.
Using AI in data management would shift the perennial 80/20 distribution (between preparing the data and analysing it) by automating more of the rote tasks in data engineering. It could, for example, automate anomaly detection and reporting, take advantage of self-healing, use just-in- time deployment, and find risky attributes such as PII data. This would also provide access to algorithms to ‘crawl’ the data and surface insights outside the defined hypothesis, and automated annotations and tagging would drive better engagement with less skilled integrators.
While AI has a role in the data pipeline, it doesn’t eliminate human involvement. Humans excel at tackling intricate problems with many different elements. However, AI automates mundane data preparation tasks, allowing data engineers and scientists to concentrate on more impactful endeavours.
To leverage and bank on the benefits of real-time, meaningful analytics, enterprises need to make sure that they have a successful framework in place – which is the reason they need to incorporate data integration.
With changing business environments, it’s important to be future-ready. Data will help companies navigate these times and also be prepared for the future to mitigate all the risks external or internal.