The future of AI is doomed without verified golden source data, because it offers a beacon of light as a single, trusted source of information within an organisation. It’s a safe space where data is thoroughly checked and validated to ensure accuracy and consistency, acting as the ‘single version of truth’, where all users can rely on it as the most reliable source for decision making. In short, it's a data set that has undergone rigorous quality checks and is considered the definitive record for every piece of key information which an organisation holds.
And we can’t ignore the fact that it’s being taken seriously by the government, its response being a new smart data scheme. This refers to a proposed system where consumers can securely share their personal data with authorised third-party providers, upon their explicit consent, to access personalised products and services across various sectors. This essentially gives individuals more control over their data while enabling innovative services based on their needs.
In a world where automated CRMs - at one time - were considered the gold standard for accuracy, they can be difficult to keep up-to-date, meaning the data within them can be rendered unreliable. Yes, they are an extremely valuable resource for recruitment businesses and are jam packed with critical client and candidate data. The challenge is that performing regular housekeeping on a CRM can be time consuming, leading to inflated spend and leads failing to be nurtured.
This is why - while AI is evolving at an incredibly rapid rate - it’s vital to harness golden source data as a way to keep this complex technology in check and ensure your internal systems and processes are up to the mark.
It’s even an ethos now shared by mainstream media, after every UK national newspaper recently printed an identical front page for the first time in history. This was part of a plea from journalists and the music industry to Labour to abandon plans to water down copyright laws for AI firms, which would otherwise result in source data becoming contaminated and therefore compromised.
Here, we delve into the critical importance of verified golden source data in today's data-driven marketing world, especially for businesses navigating the complexities of AI and automation.
The Problem with ‘Dirty’ Data: Dirty data - referring to inaccurate, incomplete, inconsistent or duplicated data - creates a significant problem because it leads to flawed analysis, wasted resources, poor decision-making and strategic choices and inefficient operations. It ultimately has the power to damage a company's reputation by providing unreliable insights based on faulty information and data sources. After all, who hasn’t been privy to the generic ‘Dear <name>’ when the code is flawed in an important email campaign?
It’s important to remember that the AI tools and platforms you’ve invested in are only as good as the data they’re fed. Millions of companies are now kicking themselves as they've been tech magpies over the years, investing in all sorts of tools and platforms with little or no ownership. Use adoption is low and often there is no structure or process in place to ensure only the very best data sits within your CRM.
This is why it’s vital to prioritise data standardisation at the point of entry, implement data validation rules, regularly audit your database for inconsistencies and duplicates, establish clear data governance guidelines, automate data cleaning processes where possible and educate all users on proper data entry practices.
The Benefits of Golden Source Data: A golden source of data results in significant organisational benefits like improved decision-making, increased operational efficiency, reduced data redundancy, enhanced collaboration, more accurate customer insights, better data governance and improved regulatory compliance. It achieves this by streamlining operations to ensure that everyone is working with the same accurate and up-to-date information across different systems and departments. This can be the difference between making informed strategic choices and avoiding costly mistakes, all while driving business growth.
How to Achieve a Golden Source and Who Should Own It: To achieve a golden source of data, it’s important to establish a single, authoritative source of information by identifying the most reliable data origin point, implementing robust data governance practices, consistently cleansing and standardising data across all systems and utilising Master Data Management tools to maintain data accuracy and consistency over time.
In an organisation, a Chief Data Officer (CDO) is typically responsible for overseeing and managing the golden source data, as they are primarily in charge of data governance and ensuring data quality across the entire enterprise. This includes defining and maintaining the single, authoritative source of truth (golden source) for critical data points. However, we believe that marketing, IT and sales functions across a business should all embrace it to create a harmonious framework from which to operate from.
Important considerations include:
Identify the Primary Data Source: It’s important to determine which system or process generates the most accurate and reliable data for each data element, while considering its origin and lineage.
Data Governance Framework: Establish clear data governance policies, including data ownership, GDPR, quality standards and data validation procedures to ensure data quality at the source.
Data Cleansing and Consolidation: Regularly clean and standardise data by removing duplicates, correcting inconsistencies and applying consistent formats across different systems.
Data Validation at Entry Points: Implement validation rules at data entry points to catch errors early and prevent inaccurate data from entering the system.
Master Data Management (MDM): Use MDM tools to consolidate and manage master data across multiple systems, ensuring a single, unified view of critical data elements.
Data Quality Monitoring and Improvement: Continuously monitor data quality through regular audits and reporting to identify and address issues proactively.
Data Lineage Tracking: Maintain detailed information about the origin and flow of data to understand how it is transformed and used throughout the system.
Data Integration: Implement robust data integration processes to seamlessly combine data from disparate sources into the golden source.
Data auditing and cleansing: This comprises a comprehensive assessment of all aspects of data gathering, storage and usage, including internal data such as financial records and external data like customer and market trend information.
Further aspects to keep in mind:
Business Requirements: Clearly define the critical data elements and their required level of accuracy based on business needs.
Data Ownership: Assign clear data ownership to specific individuals or teams to ensure accountability for data quality.
Change Management: Communicate changes related to data governance and the golden source effectively to all stakeholders.
Customer control:
Current government focus:
The Importance of the Open Data Institute: The Open Data Institute (ODI) is crucial because it actively works to build a trustworthy and open data ecosystem where data is used responsibly by businesses, governments and civil society to enable better decision-making. It tackles complex challenges by providing research, training, consultancy services and tools to improve data practices across various organisations, ultimately aiming to create a world where data works for everyone.
More recently, it developed an AI Data Transparency Index which shone a light around how poor data practices are threatening rights and livelihoods, how little openness there is in certain AI models and how this needs to change moving forwards.
Instrumental in this are Emma Thwaites, a former journalist and editor, a member of the ODI's Executive Leadership team and who advises the board, and Charlotte Crosswell OBE, who is Chair for the Centre for Finance, Innovation and Technology.
Best Use Examples of Golden Source Data: There are a few inspiring real-world examples of how businesses have successfully implemented and benefited from a verified golden source.
A prime example of a tech reliant business that heavily utilises golden source data is a large financial institution like an investment bank or asset management firm. They often use data management platforms like GoldenSource to manage and distribute accurate, consistent reference data across their systems for functions like trading, risk management and client reporting, ensuring all departments are pulling from a single, trusted source of information.
We like to think that Marmalade Marketing's clients that are either just embarking on their journey to achieving golden source data or those that have already nailed it will thrive this year because AI - without the right boundaries in place - is akin to joyriding without a destination. The talent and tech clients we're working with have had this on their radar for a while now as a business critical objective to achieve. It's not an overnight process or a silver bullet to marketing success, but it does mean that anything you do has the very best chance of being triumphant. The beauty of golden source data is that it can be applied to so many emerging or growing sectors, such as the geo spatial market and AI roles within pharma and biomedical sciences. The possibilities are endless.
The Future of Golden Source Data: The future of golden source data - and what’s on the horizon when it comes to keeping it squeaky clean - is likely to be heavily focused on cloud-based, AI-powered data management platforms that provide a single, centralised view of critical information across an organisation. This will help to enable better decision-making through enhanced data quality, accessibility and real-time insights, with particular emphasis on integrating complex data sets like ESG factors and leveraging advanced analytics capabilities to navigate evolving regulatory landscapes.
Advanced AI Integration: Using machine learning and AI algorithms to automate data cleansing, validation and lineage tracking, improving data accuracy and reducing manual intervention.
Cloud-Native Architecture: Moving towards fully cloud-based platforms to scale data storage and processing capabilities, providing greater flexibility and cost-efficiency.
Enhanced Data Governance: Implementing robust data governance frameworks to ensure data quality, compliance with regulations, and proper data access controls.
Real-time Insights: Enabling near-instantaneous data analysis and reporting to facilitate timely decision-making across different business functions.
Cross-functional Data Integration: Breaking down data silos by connecting disparate data sources from different systems within an organisation, providing a holistic view of information.
ESG Data Focus: A growing emphasis on integrating ESG data into the golden source to meet evolving regulatory requirements and investor demands.
Data Virtualisation: Embracing a data management technique that allows users to access data from multiple sources as if it were in a single location.
Blockchain: Creating a record that can't be altered and is encrypted end-to-end, the blockchain helps to prevent fraud and unauthorised activity.
AI-Powered Data Quality Tools: Harnessing AI algorithms to automatically identify, analyse and rectify data inconsistencies, duplicates, missing values and other errors within large datasets.
Overall, the future of Golden Source Data is about providing a comprehensive, intelligent data platform that leverages the power of cloud computing and AI to deliver reliable, actionable insights across an organisation. This is especially prevalent in professional services sectors like recruitment, where data accuracy and regulatory compliance are critical.
It might seem daunting at first, but it’s vital to take steps to establish or boost your own golden source data, primarily via data governance and quality management, which are great places to embark on this journey. After all, there’s nothing worse than being held back by the dirty data! Bypassing this bottleneck will unlock significant benefits for businesses in the tech and talent markets.