As a CIO, you are a key driver of the business. You advance service and product innovation. You drive the bottom line. However, we talk to a lot of CIOs, and we noticed an unfortunate trend. It seems that we are collectively failing to fix our data problems. And that is directly impacting business innovation.
Reliable and accurate data is the bedrock of any good digital user experience, such as eCommerce sites, learning platforms, and interfaces designed for complex workflows. As UX professionals, we have consistently found that the issues customers complain about most cannot be fixed by changing the design of an interface. These problems can only be fixed by addressing fundamental issues with underlying data accuracy and reliability.
We work with many companies running advanced innovation. In one example, we were asked to assist with innovating on an eCommerce site. When researching the pain points associated with the current design of checking order status, we found that the biggest pain point for their end users was that the delivery dates were often inaccurate and unreliable.
Partnering with the ordering and development teams, the eCommerce company found that the problem was caused by data issues. The way the data was structured and stored resulted in inaccurate estimates. They did a rough estimate of the effort needed to fix the back-end data issues to improve the accuracy of delivery dates. The estimate indicated a substantial investment and a multi-year timeline. There were no improvements or updates to the design of the pages that would fix this pain point. The design does not even matter until the data can be trusted by end users. The eCommerce company was left with a choice: fix the data or have disgruntled customers.
Can you guess what the eCommerce company chose?
In the end, leadership decided not to fix the data. They did not prioritize the investment, and the experience remains the same.
As experience researchers, we see this a lot. In general, there is a tendency for leaders to prioritize “quick wins” over projects that might take many resources and a considerable amount of time to complete, even if it is not in the best interests of the customers they serve. Why might this be the case?
Future discounting, temporal discounting, delay discounting, and hyperbolic discounting are all terms used to describe the tendency of individuals to select smaller rewards in the present over larger rewards in the future. For example, people are more likely to take a $100 dollar payout today over a $120 payout in 3 months. This cognitive bias affects our behavior in a variety of domains such as saving for retirement, overcoming addiction, and making healthy choices. It is also evident in decision-making in corporations when leaders prioritize quick wins that will demonstrate what they can accomplish as a leader in brief time frames rather than focusing on longer-term goals and investments that will address the root causes of poor customer experiences.
Leaders are also often incentivized to show what they can do with few resources in a short amount of time. If, however, companies want to truly improve the experiences of their users, they must make an investment and plan for tackling their data issues. Improving data accuracy, reliability, and hygiene is the only way to move toward modern, personalized experiences built on automation and machine learning. Below, we provide ideas for helping leaders move beyond the bias for quick wins to make impactful improvements in their digital customer experiences.
How can CIOs start to prioritize and approach these long-term data projects? Consider these three steps:
- Drive problem and cost transparency,
- Counteract future discounting, and
- Create measurable momentum.
Drive problem and cost transparency.
Most of you already know if you have a data problem. This is often evidenced in the solutions, products, or innovation vectors at your business. Like most companies, your team is probably challenged by the availability, accuracy, volume, or reliability of data.
If you have not already documented the data issues, creating a list of the issues, and generating a list of the impact of those issues is a critical step. Using our example from above, it is not enough to state that the delivery time is inaccurate. We must draw the line between the shipping inaccuracy and the customer net promoter score. The less accurate the dates, the less happy the customer was with their purchase. In our example, the company failed to take the next step – they need to then tie the low net promoter score back to missed revenue.
If you look at the shipping data and see that the data problem is leading to a revenue loss, the investment in fixing the issue can be assessed with the urgency it is due.
The goal of this step is to flip the mindset from ‘the challenge is too large to solve’ to ‘the challenge is too large NOT to solve’.
Counteract future discounting.
If your company rewards short-term wins over long-term value, even the biggest revenue impact numbers are going to be met with future discounting when making project tradeoffs. To counteract this thinking, consider the following tactics:
- Create relative value metrics for the data project. Can you show the relative value of the short-term cost vs long term gains?
- Review your existing incentives. Are you adding to the problem by rewarding short term thinking?
- Create new incentives to invest in projects that move your data goals forward. Improving data can increase innovation velocity as your company moves toward using data as an asset.
Create measurable momentum.
Consider why you are not already working on the issue. Has the company determined that fixing the issue is too difficult, costly, or time consuming? If the answer is yes, you are not alone. One of the barriers to solving data problems is that the challenge is perceived to be too large to solve.
With the team on board to the severity of the issue, and incentivized to prioritize data, it is critical to take a page from the Agile book and break the projects down into bite-size objectives that show progress on a regular basis. While each project is unique to the organization we are working with, we find that simply setting up sprints with clear short-term measurable objectives and holding transparent readouts goes a long way in ensuring the momentum is kept up in complicated progress.
We know fixing your data problems may not be the sexy, quick wins for which teams are typically rewarded. However, we know as researchers that data is the only way to future-proof your company for automated individualized experiences. With just a little transparency, counteraction of discounting, and momentum creation, you can set your organization up for the long-term win.