Calibrating for crisis: data integration in uncertain times

The data we generate, store and share is growing exponentially, as the world inexorably digitises.

With the global data sphere expected to double in size by 2026, as organisations and consumers increasingly go online, automate and digitise processes, the right tools are required to mine this massive and ever-widening trove of valuable data.

Today, businesses recognise the value of data and the potential it holds. The competitive edge gained by rapidly converting complex data into business insights is a crucial growth driver. But how can we ensure our data is analytics-ready?

Having the data is one thing, but integrating it is just as crucial. Businesses need to calibrate this integration. The goal should be connected governance — the ability to access, combine and oversee distributed data sets – to ensure certainty during times of crisis.

Surveys indicate that data integration, analytics, automation, API management and AI are all top technologies that Chief Experience Officers (CXOs) rely on for crisis management.

In a world where crisis has become a constant, calibrating for crisis becomes a core competency — so we can react in the moment and anticipate what’s coming next. Leveraging data integration is a key competency to equip businesses to do this.

Convergence and consolidation open new opportunities

In an increasingly fragmented world, we are seeing market trend in the opposite direction: convergence. We’re seeing the consolidation of previously siloed systems, not only in data integration, but also in management, analytics/AI, visualisation, data science and automation. 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. It makes 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 goal. Interoperability is key — a successful data structure needs components that can exchange information and work together seamlessly.

Building a smarter pipeline

More than ever, businesses are tuned in to the potential of AI, and how they can leverage this in their operations. Analytics, automation and AI are converging — they’re increasingly overlapping with each other. In the process, they’re cross-pollinating, generating new insights that weren’t possible before.

But what about moving those components deeper into the data pipeline, before an application or dashboard has even been built? There are several ways this could benefit organisations.

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.

Algorithms would be able to ‘crawl’ the data and surface insights outside your hypothesis. And finally, automated annotations and tagging would drive better engagement with less skilled integrators.

More AI in the data pipeline doesn’t mean that humans won’t be involved. After all, humans are exceptionally good at synthesising complex problems with multiple component parts. But AI will automate some of the more menial data preparation tasks, so data engineers and scientists can focus on more impactful work.

If organisations want to reap the benefits of real-time, meaningful analytics, they need to ensure they have a successful framework in place first — which is why they need to leverage data integration.

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