Both business and technology executives overwhelmingly believe that data is one of their most critical strategic assets. And yet, according to the same report by Capgemini, only 20% of executives trust their data.
Poor data quality was the primary driver behind this lack of trust, with Capgemini finding that less than a third (27%) of executives were happy with their data quality.
Data can be the engine of a business, powering everything from decision-making to innovation. In turn, poor-quality data can have significant negative impacts, leading to inaccurate insights and denting your business’s revenue. When this is left unchecked, it could even lead to hefty fines and reputational damage.
That’s why more and more businesses are looking for smarter ways to manage their data, seeking platforms that will offer them an accurate and unified overview of all their data pipelines throughout their organisation. This harmonised view of data is often referred to as a golden source.
What is a golden source of data?
A golden source of data is the single, authoritative source of data within an organisation. It represents the most accurate and complete version of a firm’s data. There are many different terms that encapsulate this concept, including a single source of truth, a golden record or a master dataset.
Creating a golden source within an organisation involves consolidating data from multiple sources, resolving discrepancies, eliminating duplicates and then maintaining data quality best practice moving forward.
A golden source of data is seen as the pinnacle of data quality for many across different industries.
What would a golden source look like?
So, what would a golden source of data look like? According to the UK Data Management Association, there are six core pillars of data quality: accuracy, consistency, completeness, timeliness, uniqueness, and validity.
Let’s take a closer look at what these would mean in practice:
Accurate: Accuracy refers to data which properly represents real-world values or events. High data accuracy allows you to produce analytics than can be trusted and leads to correct reporting and confident decision-making.
Consistent: Consistency refers to the extent to which datasets are coherent and compatible across different systems or other sets of data. Examples of consistency would be standardised naming conventions, formats or units of measurement across a dataset. Consistency improves the ability to link data from multiple sources.
Complete: This describes to whether a dataset contains all the necessary information, without gaps or missing values. The more complete a dataset, the more comprehensive its subsequent analysis is and the better the decisions made from it are.
Timely: this refers to whether a dataset is up to date and available when needed. This doesn’t necessarily mean the data has to be live to the very moment, but rather that the time lag between collection and availability is appropriate for its intended use.
Unique: This refers to the absence of duplicate records from a dataset, meaning each piece of data is different from the rest. Duplicate data can lead to distorted analysis and inaccurate reporting.
Valid: This refers to data that conforms to an expected format, type, or range. An obvious example of this would be dates or postcodes. Having validated data helps the smooth running of automated processes and allows data to be used with other sources.
What are the benefits?
A golden source of data helps organisations make informed decisions, improve operational efficiency and maintain a high-quality customer experience. This is because by having an accurate and unified view of data, businesses can better ensure consistency and accuracy across each of their systems.
When an organisation is consistent and accurate across their pipelines, this enables them to reduce the likelihood of errors and discrepancies. As a result, they are empowered to uncover better, data-driven insights which can improve their decision-making and even unlock new revenue streams.
A golden source of data also helps businesses enhance their data governance and compliance efforts by providing a clear audit trail across data pipelines. This is because organisations can more easily track the lineage of their data, helping to ensure whether their data is being used appropriately and in accordance with any external regulation or internal policies.
Is it achievable?
Establishing a golden source of data is not an easy task. Doing so requires a lot of work – organisations must work to eliminate data silos across their systems, identify and resolve any inconsistencies and redundancies within their data, and find a way to consolidate all this data.
It can be a time-consuming and resource-intensive process. And that’s only the beginning. Once a golden source has been established within an organisation, the business must work even hard to maintain it.
One study, conducted by Harvard Business Review, found that, on average, 47% of newly created data records within businesses have at least one work-impacting error. This is a concerning statistic when we consider that, on average, data volumes are growing by around 63% a month within organisation, according to data professionals.
How Raw Knowledge could help
At Raw Knowledge, we are currently deploying an innovative new platform that will change the way data is acquired, verified and processed.
Using large language models and cutting-edge data architecture, our Managed Smart Data Platform creates a unified view of disparate data sources so businesses can streamline their operations, make better data-driven decisions, and uncover new revenue streams.
It offers complete traceability, right down to an individual data element, thanks to its time-travel capabilities. This core functionality helps firms preserve their data accuracy, maintain a clear audit trail, and meet regulatory reporting requirements.
