Why data modernisation matters

Some businesses might think they already extract great value from data. However, for most, this isn’t true. Data should be instrumental in how every business operates. Being a modern enterprise is about becoming data-native, monetising data assets, and looking ahead with intelligence guiding every step. If this isn’t the case, there is room for businesses to get more from their data. Start by creating an environment where data can thrive. Data modernisation builds an information architecture that breaks down traditional siloes, allowing data to flow to all corners of the business and forming the kind of data-driven environment that empowers the C-Suite with actionable intelligence.

Can AI be used to unlock powerful data insights?

 We’re amid a data explosion. Unaided, it’s almost impossible for us to draw valuable insights. Fortunately, AI can do the hard work for us. Algorithms can segment customers, understand their behaviour, and offer personalised experiences. Industry use cases for pairing AI and data are especially convincing. In FinTech, AI can detect fraud. In Healthcare, it can support disease management. There’s no doubt AI is the future, but to be useful, it needs data, or more specifically, a data environment that permits it to thrive.

What is the relationship between AI and Big Data?

To generate insights, we need quality data. Previously, we used data in structured formats. But in the last decade, we’ve begun tapping into giant unstructured volumes – known as Big Data. It’s a whole new world of insight. As businesses realise the potential of these giant volumes, they’re processing them with sophisticated AI algorithms for sentiment analysis, image processing, and text mining.

Are there barriers to AI?

When it comes to digital endeavours, the Middle East punches above its weight. However, a lack of digital maturity in some foundational areas, including data management and modernisation, holds businesses back. Disparate systems hinder them from maximising their AI. This results in a gap between the enthusiasm around AI and its adoption, where despite AI being mostly high on the C-Suite agenda, initiatives never go beyond the concept stage.

How can digital endeavour be accelerated?

Given the ongoing crisis, efficiency and resilience must be balanced to keep operations ticking. Here, digital strategies are critical. A strong data strategy acts as the foundation for digital maturity, enabling the business to modernise systems, empower the workforce, and improve the customer experience, all while achieving a strong ROI. This way, AI can work with data across the entire enterprise.

Are there common data modernisation challenges?

Some businesses lack a clear strategy or a roadmap. Often, multiple siloed departments all want their say in data modernisation, but the lack of a clear top-down strategy impedes progress. Other businesses lack strategy around the handling of Big Data. The absence of a proper data governance framework further affects overall data quality.

What’s the key to an effective data modernisation strategy?

It’s critical to consider AI throughout the process. After all, data modernisation, more than data itself, is about ensuring the IT environment is ready to accelerate AI transformation. There are four real steps to consider: Ideation and prioritisation of business outcomes; modernisation and democratisation of data; decision-making, including insight generation, prediction, and prescription; and activating decisions and monetising data assets.

What factors should users consider before choosing a data analytics platform?

Data analytics is an investment that’ll pay if the correct product is selected. It is advisable first to identify which product has the required functionality. Next, consider compatibility with existing applications – what is required is a product that fits seamlessly into the technology stack. Lastly, explore the product’s performance with Big Data. Without this capability, the value of data modernisation will forever be restricted.