Standardising data structures

Most businesses recognise that data holds strategic value, but few use it to its full potential. Despite heavy investment in digital systems and tools, many are still struggling to extract reliable, actionable insights. Poor data quality wipes out around 20% of potential revenue and consumes up to 30% of operational budgets, reducing efficiency, delaying decision-making, and constraining innovation. And McKinsey notes that data-driven companies consistently outperform their rivals.

 

In contrast, organisations weighed down by unstructured, siloed data face persistent challenges, such as operational drag and digital friction. In response, many companies have prioritised data visibility, oversight, and governance. However, these measures don’t go far enough. To drive real value from data, businesses must have a strong, comprehensive strategy in place that drives structure and the standardisation of data sources.

 

 

From data disorder to competitive clarity

When data is fragmented, inconsistently or poorly structured, it becomes impossible to gain a clear picture of overall business performance. For example, mismatched product codes across systems can delay forecasting cycles, forcing teams to rely on manual reconciliation.

 

Whilst, inconsistent customer records can hinder service teams, creating delays and reducing confidence in daily reporting. Companies can find themselves unable to answer even simple questions, such as how many units were sold last week or forecast demand with confidence. The cumulative effect is a gradual loss of strategic clarity, making it harder for leaders to steer the business confidently.

 

Whilst many firms are rightly prioritising data observability, oversight and governance, these measures might improve control, but don’t tackle the structural fragmentation of data that restricts meaningful analysis.

 

Organisations need to adopt a more strategic approach to data that delivers tangible results by standardising, structuring, and enriching across data sources. These steps ensure different functions work from the same definitions, reducing duplication and improving coordination across teams, creating a strong foundation for smarter strategic planning and decision making. But achieving this clarity of data must take place in phases.

 

 

The power of structure and standardisation

Businesses should start by cleansing their data – removing redundant information and ensuring it sits in a logical, usable format. Once data is cleansed, standardisation provides the foundation for meaningful structure. Organisations can use ontologies to organise their data. Defining key concepts, data attributes and the relationships between different sources in this way creates a consistent semantic framework.

 

Essentially, ontologies keep processes aligned, making it easier to find and reuse data, creating a shared understanding of data for both humans and machines. This structured approach enables automated reasoning and more advanced analysis as datasets grow.

 

The next step is to tap into unstructured data for true ROI. IDC reports that 90% of all enterprise data is unstructured. These sources – including text or audio files, meeting notes, transcripts, emails and system logs – hold a treasure trove of value, but can be hard to access or challenging for machines to read.

 

Technical limitations, rather than intent, is preventing teams from unlocking the value buried in these sources. Processes such as tagging, metadata generation and entity linking can help turn unstructured data into value, making it easier to access, filter and analyse for use in a business context. By conducting semantic enrichment, organisations can then build context and make unstructured data discoverable.

 

This process also creates a shared understanding of how key entities such as customers, products and different business units relate to each other. The result is a clearer, more connected view of the organisation’s operations, customers and products. Knowledge discovery is much faster, as this enriched data is far easier to search, navigate and reuse.

 

 

Turning data discipline into business benefits

Whilst every organisation will approach data management in a different way, the essentials of a sound strategy don’t change. Data must be easy to locate, cleansed and consistently structured. Having established these principles, data shifts from being a scattered collection of records, into an asset that can inform decisions across the business.

 

When any one of these foundations is missing, the strategic advantages of a data-driven operation remain out of reach. With them, businesses can move with much greater speed and certainty. For instance, they can bring new products to market more efficiently, give teams access to technologies like AI to enhance day-to-day work, and strengthen their ability to plan for the future. A robust, holistic data management strategy becomes a core enabler of performance, ensuring the company is equipped to adapt, grow and compete in today’s volatile world.

 

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Nick Hill is Practice Lead at TXP

 

Main image courtesy of iStockPhoto.com and anyaberkut