Data-Driven Work Cultures: EY’s Traci Gusher On How To Effectively Leverage Data To Take Your Company To The Next Level

An Interview With Pierre Brunelle

Make it trusted: This means constructing a robust data governance program that puts people, processes, and technology in place to effectively manage data. It also means ensuring there is a data function in the organization that is looked upon as being an imperative for the finance function. All too often we hear clients tell us that they are not getting value form their data because of poor quality. What that really means is they can’t trust it.

As part of our series about “How To Effectively Leverage Data To Take Your Company To The Next Level”, I had the pleasure of interviewing Traci Gusher.

Traci Gusher, EY Americas Data and Analytics Leader, has deep experience in leading programs focused on artificial intelligence, advanced analytics, and emerging technologies. She is responsible for leading data and analytics teams in the Americas across Consulting and Strategy and Transactions, and she is focused on building innovative approaches and platforms to enable digital transformation journeys.

Thank you so much for joining us in this interview series. Before we dive in, our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?

I started my career in the consulting industry over 20 years ago in an internal strategy and operations role. My passion for driving insights to improve decision making drove me into data and analytics nearly 15 years ago, and I have been focusing on helping my clients use data to drive analytics and train AI. Throughout my experience, I have learned the value in connecting an organization’s strategic business imperatives to its AI strategy.

Additionally, having grown up in a small town with relatively limited career options, I have a personal passion to drive awareness about consulting as a career path, especially due to the increasing digitization across the globe and the immense opportunity it provides.

Can you share a story about the funniest mistake you made when you were first starting? Can you tell us what lessons or ‘take aways’ you learned from that?

I didn’t travel a lot when I first started my career. On one of my first business trips, I was taking a train from Washington, D.C. to New York City, and I heard the call to get off at (what I thought was New York). Despite it being significantly earlier than I thought I would be getting in, I packed up my stuff and got off the train, only to find out I was in Newark, New Jersey — not New York. If I took one thing away from this mistake, it was that listening closely is an important and underrated skill.

Is there a particular book, podcast, or film that made a significant impact on you? Can you share a story or explain why it resonated with you so much?

There have been many over the years, but most recently I have been obsessed with the book, “Think Again” by Adam Grant. I love the idea that we need to be more comfortable with what “we don’t know” over what “we know.” The book also delves into how better thinking and resolution tactics enables constructive and respectful debates. It truly is an eye-opening topic, both in business and personally.

Are you working on any new, exciting projects now? How do you think that might help people?

There are lots of exciting projects in motion today — many of which use artificial intelligence (AI). For instance, we are leveraging AI to improve demand, inventory, and financial forecasting, and to enhance key parts of M&A projects and data monetization. We also have programs underway that focus on treating data as a product to help clients gain the most value from it. The world of data, analytics and AI right now is pretty amazing, and we are just getting started.

Let’s now turn to the main focus of our discussion about empowering organizations to be more “data-driven.” My work centers on the value of data visualization and data collaboration at all levels of an organization, so I’m particularly passionate about this topic. For the benefit of our readers, can you help explain what exactly it means to be data-driven? On a practical level, what does it look like to use data to make decisions?

A data-driven organization comes in varying levels of maturity, with lower-level sophistication coming through just as better basic access to Critical Data Elements (CDEs), standard key performance indicators (KPIs) and key risk indicators (KRIs). These are easier said than done and still require robust data governance (processes, policies and operating models) and technology enablement.

More advanced data-driven organizations are utilizing a data fabric or data mesh that enables disparate data in modern architectures and leverages virtualization instead of movement to enhance data access. These more mature organizations also use exogenous data from outside their four walls as enablers in predictive modeling and machine learning driven insights to not just look at the past but also effectively make informed decisions about the now and the future.

At the heart of being data driven though, is a culture that demands data as a key enabler of decisions across the front, middle and back office.

Which companies can most benefit from tools that empower data collaboration?

All companies can benefit from tools that empower data collaboration. There are no sectors, sizes or geographies that do not generate great benefits from leveraging data.

We’d love to hear about your experiences using data to drive decisions. In your experience, how has data analytics and data collaboration helped improve operations, processes, and customer experiences? We’d love to hear some stories if possible.

The examples here are endless. They include improving customer experiences with artificial intelligence; using external data and machine learning to better forecast inventory for extensive working capital improvement; incorporating Internet of Things (IoT) data to improve manufacturing productivity; and leveraging extended data assets during merger and acquisition (M&A) transactions to get to more accurate valuations and performance predictions.

Has the shift towards becoming more data-driven been challenging for some teams or organizations from your vantage point? What are the challenges? How can organizations solve these challenges?

Absolutely. First, many executives have decades of experience, and that drives great success for their companies. However, companies have started to rely more on data and insights rather than their “gut.” This is a personal behavioral shift from how they have traditionally operated.

The key here is to not lose your gut instincts but to leverage them to question data carefully so that you can trust it and make decisions based on data that you believe will be successful.

Also, there is a huge gap in data literacy within organizations. Much of the “upskilling” related to data has been within the IT organization and focused on technology versus data management, analytics techniques and data storytelling. For organizations to be truly data driven, they must increase the overall data literacy of the entire organization, not just in IT or dedicated analytics teams.

Here is the primary question of our discussion. Based on your experience and success, what are “Five Ways a Company Can Effectively Leverage Data to Take It To The Next Level”? Please share a story or an example for each.

Make it trusted: This means constructing a robust data governance program that puts people, processes, and technology in place to effectively manage data. It also means ensuring there is a data function in the organization that is looked upon as being an imperative for the finance function. All too often we hear clients tell us that they are not getting value form their data because of poor quality. What that really means is they can’t trust it.

Treat it as an asset (product): Organizing data into products and aligning value with each data product is essential for success. Additionally, with each data product created, there should be an understanding of who needs it, why they need it and how it will realize value from it. For instance, we have helped a client identify the 20 or so most impactful insights needed to drive their business and used those as a map to create data products that could deliver high quality, easily accessible data upon which they could rely. Key to this was having data product owners who are responsible for caring and maintaining their data products — just as if they were tangible objects.

Utilize new datasets: Many organizations are only focused on internal and structured data. Instead, organizations should also ensure their overall data strategy includes external data sources that can be leveraged in conjunction with their internal sources. They also need to consider semi-structured (i.e., Internet of Things, IoT data) and unstructured data (i.e., text, voice and images) as sources of new information that can be very impactful and even create a competitive advantage. The recent pandemic and war in the Ukraine have shown the need for external data in a dramatic fashion, as companies very quickly have seen, that their internal data alone is not enough to help plan and adjust for such black swan events. Companies that were able to utilize external data more easily, were better able to adjust their customer channels and supply chains for overall less disruption and better performance.

Monetize data: This includes the ability to understand how all the data within an organization drives value. It also includes the commercialization of data, which can include selling raw data, joining a data ecosystem to share and sell data and creating models or insights from their data to sell. As data is commercialized, organizations should consider whether a separate new business should be established to solely focus on data, as many companies today are opting to create new entities that can extract value from their data, without impacting the core business.

Challenge the usage of dashboards: Focus on insights that inform decisions. Dashboards are great for visualizing data, but they are rarely built with the analytics needed to help guide decision making. They also tend to be merely informative — driving people back to their spreadsheets to get to what they need. As an example, instead of your human capital dashboard just tracking an “attrition” statistic, have it report the employees that are at risk for attrition with the key attributes of why they are at risk. Simply knowing that you have high attrition in a given group or area isn’t information that can be used to make a decision, but names with indicators of why they are at risk is something that can be acted upon. For every repeatable analytic created, ask the question: “What action or decision can be made using this insight?”

The name of this series is “Data-Driven Work Cultures.” Changing a culture is hard. What would you suggest is needed to change a work culture to become more Data Driven?

Better access to trusted data and increased literacy are keys to changing culture. Additionally, as we leverage more automation to remove repeatable processes, we should use the increased capacity to spend more time with data — accessing, analyzing, and activating creativity around what can be done with it.

The future of work has recently become very fluid. Based on your experience, how do you think the needs for data will evolve and change over the next five years?

As has been occurring over the last couple of decades, data will increasingly become more abundant and important. As our work lives become more digital, more types of data will be created that we can use to make our work lives better.

Does your organization have any exciting goals for the near future? What challenges will you need to tackle to reach them? How do you think data analytics can best help you to achieve these goals?

EY has a lot of exciting goals for our growth and our people. One that I think is of the highest importance is our commitment to sustainability. EY is already carbon negative and has an ambitious goal to further this and be net zero by 2025. As with all organizations, charging down this path requires us to leverage data and analytics (including measuring, scenario modeling, etc.) effectively and successfully to support ongoing efforts to reduce our carbon footprint.

How can our readers further follow your work?

Feel free to follow me on LinkedIn where I frequently post my own perspectives, as well as share other perspectives that I think are inspiring.

Thank you so much for sharing these important insights. We wish you continued success and good health!