Data intelligence: extracting true business value from data

By Andy Baillie, VP, UK&I at Semarchy.

  • 1 month ago Posted in

For a long time, data management has been all about handling, governing, and complying with regulations. However, these foundational elements – while essential – are just the bare minimum in the current era of data-driven enterprises. To drive innovation, stay ahead in the market, and tap into the full potential of their data, businesses must transform their mindset and methodologies. This means embracing data intelligence.

Data intelligence transcends the entire scope of data management. It involves enhancing and optimising every phase of the data lifecycle, from the way data is collected, merged, and structured to how it’s analysed, distributed, and utilised. Companies can gain accurate, reliable, and actionable insights by adopting a data intelligence approach. These insights empower them to uncover fresh opportunities, make more informed and faster decisions, and steer their overall business strategy and operations.

Nonetheless, a stark reality is that, according to our recent survey, only a quarter of business decisions are based on solid data. A primary obstacle is that many businesses lack the tools, processes, and mindset to truly leverage their data assets throughout the organisation.

Three crucial imperatives compel organisations to adopt data intelligence rapidly:

1. Enabling a data-driven culture

To innovate and stay agile, an organisation must cultivate a culture where data drives decisions across all levels and departments.  Data intelligence encourages this by fostering data literacy and granting everyone access to insights for independent decision-making. Data intelligence platforms with self-service analytics tools enable employees across the business to tap into and analyse data without depending on data specialists. 

Picture a high-speed manufacturing setting where data intelligence tools can give production line workers immediate real-time insights into key performance indicators, such as equipment efficiency, output quality, and wastage rates. They can quickly spot problems, fine-tune processes, and drive continuous improvement from the factory floor without waiting on central analytics teams. This could mean the difference between staying ahead of the competition and falling behind in an increasingly demanding market, or missing out on critical opportunities to optimise production, reduce costs, and improve product quality.

2. Building trustworthy AI models

The widespread application of artificial intelligence (AI) across various sectors highlights the need for developing AI models that are not only accurate but also transparent and explainable. Data intelligence provides the high-quality, comprehensive data needed to build AI systems that can be trusted.

Clean, cohesive, and well-governed data is essential to effectively train AI models and ensure their outputs are reliable and free from bias. Otherwise, AI could yield flawed or biased outcomes, leading to poor decisions and diminishing trust in AI capabilities.

It's also important because AI models need to be able to explain their reasoning and decision-making processes, especially in high-stakes environments like healthcare, finance, or law. Without transparency and explainability, AI runs the risk of being a "black box" that makes decisions without any understandable logic or justification, which can be unacceptable in many contexts. Data intelligence helps provide the high-quality data and data governance needed to build trustworthy, explainable AI that businesses and wider society can have confidence in.

For example, data intelligence can merge and clean diverse datasets like electronic medical records and clinical trial data in healthcare. This clean, unified data fuels AI models that develop personalised treatment options and accurate diagnoses, explaining their recommendations in a way that earns trust from patients and healthcare professionals alike.

Another example could involve using data intelligence to integrate and govern data from various sources in the financial services industry, such as transaction records, market data, and customer information. This comprehensive and well-governed data can then train AI models to detect fraudulent activities, assess credit risks, or provide personalised investment advice, while being able to explain the rationale behind their predictions or recommendations. 

3. Delivering hyper-personalised experiences 

In an economy where experiences reign supreme, customers expect highly personalised products, services and interactions tailored to their needs. Businesses need data intelligence to meet these expectations efficiently and consistently throughout the customer journey. 

Without the ability to capture, integrate, and analyse customer data from various interaction points, companies lack the insights to tailor their approach to individual customers. Data intelligence provides a holistic perspective, enabling businesses to create detailed customer profiles and adapt their offerings, messaging, and experiences in real-time as needs and behaviours shift.

Retailers, for example, can examine comprehensive customer data, including demographics, past purchases, and real-time browsing activity. The insights gathered help form distinct customer profiles that can power AI-driven recommendation engines, dynamic pricing, tailored marketing, and personalised experiences, ensuring customer engagement and enduring loyalty.

Facilitating an end-to-end approach

Businesses need robust technology solutions and strategic planning to transform into truly data-intelligent organisations. Modern data intelligence tools offer automated functionalities for data discovery and structuring, providing easier access to an organisation’s distributed data assets. They also ensure data reliability, comprehensive data lineage, and quality monitoring to safeguard trustworthiness and pinpoint problems. 

These tools also facilitate self-serve data preparation and analysis, empowering teams throughout the business. Essentially, they provide centralised master data management (MDM) to establish a unified "golden record", incorporating data governance and security controls to maintain regulatory compliance. Additionally, they include collaborative elements to share, discuss and enrich data assets.

The strength of these data intelligence solutions lies in their ability to refine the entire data management process, from collection and consolidation to self-serve analysis and insights generation. This creates a virtuous cycle where data becomes more credible, teams become more data literate, and the entire organisation becomes more data-driven in its strategic, decision-making, and operational processes.

As data accumulates at an unprecedented pace, it’s no longer viable for organisations to ignore the wealth of unused data trapped in siloed databases and spreadsheets. By adopting the right tools and a data intelligence mindset, businesses can dismantle internal barriers, create a culture centred around data, and secure a competitive edge by deploying smarter, insight-driven business strategies.

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