As mountains of data continue to accumulate within organisations, businesses are faced with a growing challenge: reversing a situation where a wealth of data is typified by poverty of insights. Business users have entered an era where the appetite for timely decision-facilitating insights is at an all-time high, driving the need for more efficient, automated data analysis and AI.
According to an Alteryx-commissioned survey by IDC released in February 2022, 62% of practitioners and 75% of mid-to-upper management are now expected to use data to make agile and scalable data-driven decisions.
While data-driven decision making produces continuously more beneficial results than gut feeling alone, many businesses still struggle to make data-led decisions a reality… a goal that could be pushed further out of reach by the sheer – ever-increasing – volume of data generated around the globe daily.
Highlighting the wealth of data available for analysis, the daily amount of data created is predicted to grow to over a staggering 180 zettabytes by 2025. Despite businesses already struggling with the rising tide posed by this increase, just 2% of that data is even stored.
In short, if the only data captured is specifically chosen for a narrowly defined purpose, this raises significant questions about the unconscious bias of the person choosing that data and presents a significant barrier to quality insights. Just 2% of a strategy is simply not sufficient.
Ethical data insights through AI
Organisations that used to spend days and weeks manually repeating tedious and time-consuming manual processes are turning to AI-powered algorithms to shrink the time and skills barriers needed to make critical decisions. From early cancer detection to checking legal documents, credit applications and insurance claims, AI can be a great help to humans in all parts of our daily lives. But while AI can quickly analyse large volumes of disparate data sources to empower domain experts to make decisions, AI cannot replace human judgment.
Delivering ethical data insights is a decades-long challenge, with hugely biased datasets – codified by non-representative samples and inaccurate conclusions – still referred to and referenced today. There have been numerous instances of biased recruitment algorithms against women and minorities, for example, facial recognition software that has failed to recognise people of colour.
Ethical AI insights are our opportunity to break that chain today and build a strong foundation of quality information and insights for the future… but as the prevalence of these decisions scales up, the pressing need for leadership in AI ethics, and the delivery of an ethical data culture, becomes ever more apparent.
Does your company need a Chief Ethics Officer?
Organisations need to be accountable for AI decisions and demonstrate an audit trail regarding data lineage. In short, if you’re thinking about adopting AI for decision making, it is crucial to keep humans and ethics at the centre of this innovation. Acknowledging the need for proactive governance and ethics planning and ensuring steps to identify and mitigate potential bias within any related training data will help keep responsible and ethical AI at the forefront of business decisions.
According to a report by Gartner, a key factor for any successful AI deployment is a strong level of data management and analytical maturity due to a high dependency on reliable, high-quality data. However, Alteryx commissioned research into data literacy in the UK found that, shockingly, 42% of employees responsible for data work saw data ethics as “irrelevant” to their role – casting a shadow over future AI-based projects. It is crucial that any AI ethics strategy has a human connection baked in and a data culture that reduces bias risks.
The human factor: combining AI efficiency with human intuition
Every enterprise faces unknown market variables, high data volumes and an increased need for speed. What’s changed is the pace at which these variables change and the pressure these decisions are made under.
With data production at one of the highest levels in history, businesses cannot afford for analysis and insights to be slowed down by manual, mundane, and repetitive processes. Data science and AI play a powerful role in gaining an edge and getting ahead of the competition, but ethical AI provides long term benefits and a foundational level of insight accuracy that will – given the right leadership and strategy – pay off both the short and long term.
How do you safeguard models and alleviate any concerns around bias and prejudice? By ensuring domain experts are trained in data literacy to provide more insight into the data gathering and analysis. AI-driven insights have increasingly helped businesses put the responsibility for generating insights in the hands of the people closest to the challenge. We refer to this as the democratisation of insights. It is a key enabler of bias reduction by using those with direct subject knowledge to help develop decisions around that area.
This, combined with establishing a data governance framework, delivers model transparency, accountability and integrity within the data gathering and collection. All are easily achieved through no-code, low-code assisted AI/ML capabilities – allowing anyone to develop models using semi-guided and fully automated machine learning approaches in the training and development of AI.
Today, data is knowledge, and knowledge is power. We see this trend across a huge range of businesses as data-driven decisions continually deliver results. Accessible, repeatable, and ethical AI-driven insights open up a wealth of possibilities to move beyond the hype of AI and operationalise responsible AI – effectively serving as the backbone for trusted, timely decision intelligence.
Only those able to harness automation and AI to tame the complexity, power the refinement, and analyse the terabytes of raw data for fast and sound decisions will be able to drive high-value ethical business outcomes that deliver distinct advantages.
By Alan Jacobson, Chief Data and Analytic Officer at Alteryx.