How companies are investing in data and analytics | EY
Q: Where have companies made strides when it comes to data and analytics throughout the pandemic and what work still needs to be done moving forward?
A: There’s been a lot of advancements in the use of analytics over the pandemic and one of the trends that we’re seeing is companies continuing to invest aggressively. In one EY study, 93% of companies indicated that they plan to continue to increase investments in the area of data and analytics. But through the pandemic, there was a number of different ways that organizations were investing. A lot of organizations realized they had some catching up to do. Investments in just foundational capabilities were germane to how they were going to be driving value.
There was a lot of organizations that took advantage of the pandemic in a way to accelerate other types of advancements in AI. One example is conversational agents. As organizations struggled with a supply and demand issue in their customer service channels, they started leveraging conversational AI as a method to capitalize on new advancements. But we’re not there yet in terms of being perfect or anywhere near it. There’s a lot of missteps still happening. So, organizations continue to place analytics and AI as more of an IT issue when they need to be looking at AI as a business issue and really upping their digital literacy across the organization accordingly.
Q: What do the CIOs of today need to focus on and dedicate resources to as they look into the future?
A: Well, number one is outcomes. As we look at how data and analytics are used within an organization, we need to be laser-focused with a relentless drive to drive value out of analytics. Another is advocating for cultural change. As I mentioned, AI is not just an IT issue, it’s a business issue and driving change through the organization is another area that CIOs should be focused on.
Q: Has the role of artificial intelligence advanced when it comes to analytical decision-making and what are the concerns that come with that?
A: Yes. AI has created a much broader channel to insights that are driving decision making. And we’re seeing real benefits within organizations. As one example, an organization that leveraged AI, specifically looking at cross-selling and upselling in their B2B channel, resulted in over an 8% increase in their B2B revenue. The advantages and ability to capitalize on insights are there, but we also have an issue with trust in AI. This is something that we’re seeing emerging globally on the regulatory landscape as well as within organizations.
We have to ask questions. Are we properly managing the data that is feeding into AI models? Are we ensuring that we are managing bias to the best of our abilities to be able to trust AI models? And furthermore, we need to have governance over the models, the data – the full life cycle of the production of AI. This is something that on a region-by-region basis has the attention of regulators. We’re seeing new regulations emerge in Europe and now in the US.
Q: How can data play a role in understanding how an organization’s operations have an impact on the environment?
A: As we look to measure our carbon footprint and hit targets related to how we will reduce our overall carbon footprints, one of the foundational areas that enable this is the data – data coming off sensor devices that are measuring carbon, methane and other things. Going back to that trust in data and trust in AI, we have to be able to trust those data sources. Putting methods in place that enable us to not only capture that information, but then use it to measure and inform future decisions as it relates to how we can impact our carbon footprint is absolutely imperative.
