Data is your MOST important asset – how to use it to drive AI-powered business intelligence

When we talk to clients, our first questions are always about their data; where is it held, how do they access it, who has access, how many different systems are there?

Why do we always start here? Because the quality and location of your data is fundamental to the success of your AI strategy.

Our AI-driven business analytics platform, QEA (pronounced ‘key’), is never just thrown into a business. Instead, we know from over a decade of experience that the first thing we always have to do is sort out your data. 

Once we’ve done that, you’ll have a centralised, standardised, real time and reliable data source for every operation across your business. 

To find out more about why data is so important to successful AI-powered business intelligence, we chatted to our Product Director, Chand Mohammad. 

Chand’s team works closely with our clients to pull their data into a common model, giving our QEA platform and its bespoke AI-driven solutions a guaranteed basis for delivery. 

If you have any questions about your own data, and what you might need to do to get it AI-ready for your business,  book a call today.

ID: Thanks for talking to us, Chand. You have wide experience in data and BI solutions. Can you tell us why clean, well-organised data is considered to be the foundation for successful AI analytics?

Chand: What we know from years of best practice in this industry is that AI is only as good as the data it learns from. 

Clean, structured, and unified data ensures AI models detect business-critical patterns rather than noise. 

For example, when Netflix built its recommendation engine, the key was not the algorithm itself but its meticulous user data structuring – watch times, preferences, and browsing behaviour were cleaned and tagged consistently. Without that, the algorithm would have been ineffective.  

That’s the core principle with our bespoke data exchange engine, DeX, where we consolidate and standardise our client’s data from multiple sources, building a foundation of trust before AI insights are applied.

ID: With that in mind, what risks do businesses face if they try to apply AI to messy or incomplete data?

Chand: Applying AI-driven business analytics tools to poor-quality data leads to flawed predictions, wasted investment, and loss of confidence among stakeholders. 

For example, if sales and inventory data are mismatched, AI could recommend wrong replenishment strategies, causing either shortages or overstocking. A good strategy is required to prevent these risks by validating, reconciling, and enriching data during ingestion so that AI models are trained only on business-ready inputs.

ID: How does poor data quality impact the accuracy and trustworthiness of AI insights?

Chand: Trusting your data is fundamental to embedding AI-driven systems into your business. If data is inconsistent or incomplete, AI outputs become unreliable, leading to poor adoption. 

When decision-makers see dashboards or forecasts that don’t reflect reality, their trust in AI-driven business intelligence evaporates quickly. 

Errors like duplicated records, missing values, or outdated entries compound into unreliable insights. Our QEA platform’s data lineage and validation framework ensures users can trace every metric back to its source, making insights transparent and trustworthy. 

ID: Many businesses bring AI business intelligence into their organisation to fix a specific problem. Can you explain how data consistency across systems affects the future scalability of AI solutions?

Chand: AI thrives on repeatability and standardisation. If every system in an organisation defines core concepts differently – for example, one CRM defining a ‘customer’ as a billing entity, while the ERP defines it as a shipping location – then AI models built on these sources won’t align. 

What happens in this case, is that each AI initiative becomes a siloed experiment: one works in Finance, another in Supply Chain, but they can’t be integrated, compared, or scaled across the enterprise. 

Without consistent definitions, AI models may work in one division but fail to scale across others. 

This is why our DeX module, which brings clients’ data together and cleans it, is so important. Even if we’re focusing on just one business challenge, we make sure that we start by harmonising schemas and creating a single source of truth so models can scale enterprise wide.

ID: What role does metadata and proper labelling play in preparing data for AI?

Chand: Metadata answers critical questions like who owns the data, when was it updated, and how it should be used. Proper labelling gives AI clarity about what it’s learning from. Without them, models risk misinterpretation or hallucination.

ID: How does data governance, such as ownership, security, and compliance, influence the success of AI adoption?

Chand: Data governance is the backbone of sustainable AI-driven analytics and insights. 

Without clear ownership, business units argue over accountability. Without security, sensitive information risks exposure. Without compliance, AI initiatives face regulatory pushback.

Governance is not about slowing innovation – it’s about ensuring AI adoption can survive scrutiny and scale safely. 

Here at Insight Delivered, we embed governance rules into DeX at the data integration layer, so ownership is transparent, access rights are role-based, and compliance requirements (like GDPR or SOX) are baked in from the start. This gives enterprises the confidence to deploy AI at scale without fearing regulatory or reputational fallout.

ID: Why is it often said that 80% of the work in AI projects is data preparation, not modelling?

Chand: It might seem an odd way round, but the hardest part of AI is not the maths, it’s the plumbing. 

Preparing data means cleaning, merging, transforming, deduplicating, reconciling, and validating across systems. Every inconsistency multiplies effort. 

By the time data scientists reach the modelling stage, most of the time has already been spent on prep. At Insight Delivered we reduce this 80% burden drastically by automating ingestion, harmonisation, and transformation through DeX, and embedding data quality checks in QEA. 

This shifts effort back to insights, scenario planning, and business impact, instead of wrangling spreadsheets and SQL.

ID: How can companies balance the need for speed in adopting AI with the slower, more rigorous process of cleaning and structuring data?

Chand: This is one of the biggest tensions in AI adoption. 

On one hand, leadership teams want quick wins to justify investment. On the other hand, rushing ahead without clean and structured data almost always leads to poor results, eroded trust, and stalled projects.

The way to balance these two forces is through modular adoption. 

Instead of waiting years to build a massive, enterprise-wide data foundation, companies should begin with a single high-impact domain, for example, financial forecasting, customer insights, or supply chain planning. By focusing narrowly, teams can clean and structure the data in that area, deliver measurable AI-driven results, and build organisational confidence.

ID: If a business could only do one thing to get their data ‘AI-ready’, what would you recommend as the top priority?

Chand: The single most important step is establishing a centralised, trusted data exchange layer: a ‘single source of truth’.

Without it, every AI model is built on fragmented or inconsistent sources, leading to conflicting outputs. But with it, governance, quality control, and lineage tracking become systematic rather than ad-hoc.

Good data prep transforms AI from isolated pilots into enterprise-wide capability. 

This is exactly why our DeX module is the foundation of Insight Delivered’s ecosystem. It acts as the backbone, connecting legacy and modern systems, cleaning and validating data at ingestion, embedding metadata and governance, and harmonising definitions across business functions. 

If you would like to find out more, then book a call now. Let’s talk.