Turbocharging digital transformations with honest data

By Jean-Michel Franco
What is it that makes data trustworthy – and how can business leaders ensure that their data is fit for purpose...

Data enables confident, insightful and timely decision-making – at least it should, when it is trusted. As businesses pivot in the midst of a pandemic, the ability to make real-time business critical decisions, based on reliable data is a necessity. So, why do CEOs waste so much time debating the credibility of their data insights?

Since the dawn of the digital era an exponential growth in data has been prophesied. Now, as emerging technologies converge with increasing consumer demand and as digital transformations take root, that deluge is truly being felt. The data landscape has become chaotic, as data volumes grow and businesses struggle to effectively manage it. The true value of data is lost when mismanaged – and its reliability is called into question. This is when we see digital transformations fail.

For business leaders, trusted data is crucial for making responsible corporate decisions, bringing much needed agility to their organisations as they adapt or react to key moments. This ability makes businesses competitive, with valuable time spent on making those critical decisions rather than debating the quality of data that should be driving those decisions. Time is money, and the businesses that can pivot with pace will stay ahead of the curve. Trusted data drives confident decision making leading to revenue boosts, faster innovation and reduced risk.

But what is it that makes data trustworthy – and how can business leaders ensure that their data is fit for purpose? Healthy data sets share a few common hallmarks, the ‘Five Ts of Trust’. When this is applied, it means that data must be thorough, transparent, timely, traceable, and tested to be considered reliable. It needs to tick all five boxes in order to be trustworthy – and the truth is, the majority of businesses’ data does not pass these criteria.

Getting to grips with the five T’s of trusted data

Developing clean, trustworthy data means a set of data that is thorough, the data isn’t burdened by duplications and includes data from across the organisation that can paint a holistic picture. For example, the Covid-19 mortality rate based on only the number of cases per country does not provide a trustworthy view as it negates other complementary datasets that provide essential context – or the full picture. It’s not thorough.

Meanwhile, data needs to be timely to be trusted. The time between pulling the data and making that critical business decision needs to be taken into account. If a local council is forming a social policy based on data that is a week or month old is untrustworthy, as it is no longer up-to-date, and circumstances may have changed. In these uncertain times, where situations can change at pace and employee or citizen health is at risk, timeliness is more important than ever. Time gaps can impact relationships by delaying decisions that bring business and customer closer – and it works the other way, too. Real-time decisions count.

Knowing the source of data is critical, particularly in today’s world – it must be traceable to be trustworthy. Humans are inherently biased and awareness of where data has come from, how it has been moulded and who signed it off will help to inform decision makers of the data’s integrity. It is a factor that is not always considered but is paramount for data to be trusted. 

Meanwhile, the quest for transparent data becomes increasingly pertinent as the application of innovative technologies like Machine Learning and AI evolve. It is essential that humans understand when and how these technologies are being applied, so that key decisions based on this process are fully understood and explainable. The black box approach diminishes trust. 

In the same vein, testing data sets to ensure they are streamlined, free of error – for example with aligned language throughout – and fully explainable is key. The development of self-driving cars provides a prime example of when it is absolutely crucial to understand the root cause of a decision. In the event of an accident, being able to trace the decision back to the trigger will help to understand and therefore prevent the situation occurring multiple times. As data drives decisions in self-driving cars having total traceability is critical, with serious implications for road safety.

New reality for trusted data

On the surface of this issue, achieving trusted data looks to be complicated. Yet, as business leaders debate at length the insights put together by their data counterparts, it’s clear a single standardised measure of trust is needed.

Today, as more industries rely on data to drive business growth many strive to be data driven, but, in reality, they have unreliable data health. To achieve the data integrity needed to confidently make those business-critical decisions, businesses need to look for a solution that effectively measures data health to bring clarity, reliability and accuracy to business data. 

The good news is that technologies have emerged to address these issues in an automated and systematic way. For example, Cloud to increase data reach and accessibility, search and indexing technologies to document the data, AI to decipher the content and potential quality or compliance issues and social networks to turn everyone into a contributor to rate and improve data. The result is a complete picture of data health at a glance, before using it to approach business decisions. When calculating data trust with the five T’s a full understanding of how the data is measured and how it ranks under each dimension should be clear, making everyone a data citizen. Finally, the best tools make good use of advanced AI and human expertise to build context around data and once again, give a complete view of data health. 

Waste management company Covanta is an example of a business prioritising data trust. Covanta had over 1000 business terms, which was making it hard for different stakeholders to pull data and get consistent insights. By consolidating these teams into 300, it greatly simplified reporting as well as helping to improve trust in the reporting so that it could become actionable. Examples like this provide a small window into the future, where trust is quantified and tangible. That’s thanks to automated calculations that can be applied to any dataset, understood by everyone and empower active data citizens with actionable insights. 

Looking ahead, amid the chaotic data landscape there is now an alluring calm on the horizon, with businesses able to integrate this game-changing tool, bringing clarity to a once complex process. Trust can be delivered automatically to any data set in a way that is understandable to everyone. Real-time business critical decisions can be made at scale and consistently, enabling the competitive edge promised by digital transformation. Business leaders win time back, risk is minimised, and innovation takes flight. 

By Jean-Michel Franco, Senior Director Data Governance & Protection, Talend


Featured Articles

Building Cyber Resilience into ‘OT in Manufacturing’ webinar

Join Acronis' webinar, Building Cyber Resilience into ‘OT in Manufacturing’, 21st September 2023

Google at 25: From a Search pioneer to AI breakthroughs

Technology Magazine explores how the tech giant went from being based in a California garage to a pioneer in technologies from AI to quantum computing

McKinsey: Nine actions for CIOs and CTOs to embrace gen AI

McKinsey identifies nine actions to help CIOs and CTOs create value, orchestrate technology and data, scale solutions, and manage risk for generative AI

OpenAI ChatGPT Enterprise tier drives digital transformation

AI & Machine Learning

Sustainability LIVE: A must-attend for technology leaders

Digital Transformation

VMware and NVIDIA to unlock generative AI for enterprises

AI & Machine Learning