At the recent Forrester Data Strategy & Insights 2020 virtual conference, analysts discussed applying better data, analytics, AI, and automation to redefine the future of work and how the employee experience requires more embeddedness, responsiveness, and innovation, especially as firms build resiliency.
AI has been restricted by data, cost, and trust challenges, but a host of new and emerging technologies in data management are enabling organisations to support the next generation of insights, real-time analytics, customer intelligence, and other emerging user cases. Here are some of the other highlights from the event:
Address and Improve AI Biases
Data science is not founded on empathy, said Brandon Purcell, Forrester principal analyst. Artificial intelligence takes in information and is widely exposed to the biases around it. These factors are what Purcell calls algorithmic and human bias: training data not representative of an entire population and historical inequalities captured in data. “AI is a moral mirror,” he said. “It takes the morality it finds in data and codifies that into a model.”
Purcell emphasised that fairness and bias prevention become a necessity for responsible AI adoption. Purcell gave 4 tips to help to develop algorithms ethically:
- Define what is fair:For every unique user case clearly articulate how it should be deployed, data scientists can often miss the context.
- Deploy diversity:Listen and act on the diverse perspectives at all levels of the organisation. This goes from the data scientist to executive level and beyond.
- Listen where the action is:Deploy voice of the customer technology to hear feedback in real-time.
- Ask for help:Third parties can be utilised as an outside perspective to vet your algorithmic process for biases.
Homogeneity makes for blind spots, which creates a business imperative to solicit a diverse array of viewpoints. This is incredibly important for what Purcell identified as value-based consumers who only do business with organisations who match their values.
Level up your data game
The pandemic has brought on disruption. Digital initiatives once believed to be years ahead are happening in the span of months. In this environment business leaders are sitting on mountains of data. Forrester Vice President and Principal Analyst Michele Goetz recommended firms take a moment to quantify and define the purpose of information to thoughtfully deploy it. This is essential not only for business leaders, but also politicians, doctors, and people of influence who use data to manage uncertainty. Data needs to be a “vaccine to chaos,” said Goetz.
Leaders need to consider how data can be used holistically to define the bigger picture and make interactions better each time. Goetz defined 5 mindsets needed for organisations to improve their “Data Game:”
- Skill up:Data is not always logical. Leaders need to reconsider how data paints a representation of their business in a more abstract sense.
- Design up:Experiences need to come first, architecture can come second. Data needs to be configured with all the experiences customers and employees have with a brand.
- Adapt up:A competitive advantage in the data space is finding areas to be innovative with emerging technology. Do not become stagnant in an evolving world.
- Tinker up:Leaders need to look holistically at what their data encompasses. Consider how data affects every programme and solution in an organisation. Do not get siloed in.
- Hire robots:AI needs to rationalise data. AI can help businesses discover opportunities hidden within the information they gather.
Goetz defined this as a ‘delete and repeat’ process that fits into a continuously evolving and expanding business world. In short, it is about understanding that one person or department alone is going to invent a meaningful data strategy. It takes a whole village to make data come alive.
AI and data strategy will reshape the future of work
Data and AI are constantly reshaping what every industry defines as “work,” said J.P. Gownder, Forrester vice president and principal analyst. In some cases, they replace age-old roles, but often they serve as a catalyst to strengthen how people operate. In this age of change brought on by COVID-19, Gownder underlined 4 “Shock Factors” that can both be brought on and solved by AI:
- Systemic risk:Factors from the outside of a business, like COVID-19, that bring on change fast and hard. Organisations need to adapt quickly or perish.
- Robotics and automation:If organisations are not using AI in relevant ways, they risk becoming irrelevant to competitors.
- Employee data:Businesses are drowning in data and if businesses misuse it, they cannot paint a clear picture of their employees and create poor experiences.
- Employee power:People are empowered to deploy social to voice their opinions, good and bad, to large audiences.
According to Gownder, addressing these shocks means augmenting employees with AI, data, and automation to make them more adaptive to changes. For decades computers have filled up manual tasks that took people away from jobs that needed the human touch. Instead of painting a picture where AI replaces human jobs, business leaders should look for a future where AI complements their work to become better versions of themselves.