You Have the Data ... Why is No One Using It?!
There’s no doubt that as consumer goods companies try to keep pace with increasing demands, the plethora of data at their fingertips can be insurmountable — and collecting it is only half the battle. Finding value is dependent on the ability to drive decisive action, so learn how and why brands are investing in AI and big data to unlock insight-driven decision making at scale.
As part of this, dig into what brands like PepsiCo are doing right, as well as what we can all learn from Google about bringing analytics into the business world.
Lisa Johnston: Hello, everyone. Welcome to today’s webinar “You've Got the Data ...Why is No One Using It?” My name is Lisa Johnston. I'm a senior editor at CGT and I'm excited to welcome you here today. I'm looking forward to diving into today's topic, which is top of mind for the consumer industry.
There's certainly no shortage of insights available to CGs today, but talk to executives and it's not the amount of data available or the level of granularity that’s the issue, but rather making this accessible and actionable across the enterprise that’s proving to be among the most challenging tasks.
I’d like to welcome Kate DuBois and Megan Edwards of Skai Market Intelligence to dig in deep on the ways that brands are investing in AI and predictive analytics in order to overcome data democratization challenges.
Kate is general manager at Skai with nearly 15 years of experience in performance media, digital marketing, and communications, including leadership roles at Omnicom's global performance marketing agency, Resolution Media, and global communications firm, Edelman. At Resolution Media, Kate oversaw the rapid growth of Omnicom's digital media and performance marketing capabilities, leading Midwest client services and operations. Most recently, she led Edelman's full-service digital practice, including creative and performance marketing along with launching Edelman's first e-commerce offering.
Megan Edwards is VP client growth at Skai. A strategic leader with experience in software and digital media across sales, account management, and operations, Megan has a proven track record of building strong, high-performing sales teams focused on growing revenue and developing true client partnerships. She's worked with some of the biggest CPG brands in the industry, including Pepsi, Mars, and Reckitt.
Today, Kate will start us off by reviewing some of the challenges the industry is facing and providing an insightful perspective on what they're hearing from CPG clients on the subject. Then we're going to get into the weeds a little bit more with the Q&A with both Megan and Kate.
Kate DuBois: Thanks, Lisa, and thanks everyone for joining us today. We are going to unpack this question: You've got the data, why is no one using it? We have more data and access than ever before with 92% of brands saying they want to increase investment in external data.
Well, 54% are already investing in this data as well as in the analytics to make sense of all the data. With all this data and all of the investment, we should see the results fall in line as well, right? Well, actually, that's wrong.
We're seeing that success isn't necessarily following. According to Forrester, only 7% of businesses and organizations that are investing in data are actually using it for insights and decision-making. This is resulting in product launches that are failing, we're seeing that M&A activity is also failing, and nearly half of media investment is being wasted — there's a slower time to insight overall.
Why is this? We really wanted to unpack this. Over the summer sky, we launched a series of round tables with a lot of the leading consumer brands in the industry in order to better understand the investment in data, and why the focus on data isn’t translating into access and business results that are expected.
We're going to share some of those insights with you today.
Three key themes were bubbled up in terms of the challenges, access, and data democratization. The first was technology. It's not about the data, so at least everyone has access to the set. There's more and more data than ever, but how do you get the data into the hands of those that need to use it to make these decisions? That's driven largely by technology and there's a lot of questions that come with this. Is technology enabling the right sources of data? Do those that are enabling the technology across the organization have the bandwidth to do so? They're structurally spread too thin and they're not able to deliver this into the business units that make sense.
One of the guests at the round table series said, "Look, if I could eliminate the human element, I would be able to make decisions much faster." They want to use their own data. We see that many business units are leveraging their own data in order to make decisions that impact just their business, but decision-making across these organizations has fragmented and it's leading to a lot of additional challenges.
The second bit we uncovered is that people are an adoption issue. This is where you have many different users with different data analytics skill sets or even comfort in using data. The question is, what do we do with it? Now that we have the access we're not sure how to make sense of it, how to make decisions. There's also the question on how to roll out adoption. There's a lot of different models that have been evaluated across these organizations, whether it's a top-down approach, a bottom-up approach, or a hybrid approach.
This is all challenging for organizations in terms of determining the best fit, who's in charge, and how to make sure that there's accountability across these groups.
There's also a resistance to change in technology. I relate this to the mid-2000s when we were working and all of a sudden we had a different email application. Even though you may be working on a technology tool that makes sense for you, it may not be perfect, and people really are resistant to change. This is where we need that adoption to be stewarded across these organizations in terms of leveraging new technology and the new data to bring in.
We also have a trust issue — this is a big theme that came up in many of the round tables. How do you understand if this data is accurate? How do you know that the data being brought in is applicable to your business? Does it make sense?
With that, we also understand that there's the ability to actually interpret the results, again, that skill set level. There's a different acuity of data across the organization and ability to leverage the technology.
The third challenge is processes, again, relating to the ability. As business users and consumers, we want to leverage technology in a way similar to how we use consumer technology. One of the themes that came up at the round tables is interfacing with the technology and tools in the same way you would with a chat box or easy to use — it's very intuitive. It was a big theme that these heavy analytic applications are hard to use, to the point that it doesn't necessarily make sense for the business leaders and decision makers.
At Skai, we have access to over 13,000 different data sets, but that doesn't mean that it's applicable to your business unit or the decisions that you need to make. How do you slice and dice this to give access and filter what makes sense for each of these groups?
With these centralized models there may be data, analytics, and insights teams that are interpreting the data and delivering it into the business groups. They may not necessarily be as close to the decision-making and understand the goals of these groups, which creates tension and frustration.
Where there's a large demand for new data, technology, or processes, it may be driven by one group, but then it's filtered out to the whole organization. You have this challenge of, is this relevant to me? Why do I need this if it's only applicable to one group?
With that, we're seeing it's no surprise that data democratization is a top priority. Then in this survey with over 500 executives, we see that 90% were saying prioritization of bringing the data across the organization is one of the top priorities.
This is a challenge that isn't new. We've seen this before with the inception of the internet. If you think back to the early days in the 1960s when the internet was developed, it was specific for a group of government agencies and heavy computer science users. We knew that there was technology here, but it wasn't accessible to the large mass consumers.
It took nearly 25 years to get into the homes of consumers. In the mid-1980s dial-up became more accessible and allowed internet access into consumer homes, but it was still a small fraction of the group, they didn't have the technology to access it or enable it. It also was expensive, which created a limited amount of application.
Fast forward a few years, we saw an evolution of texting in the 1990s with the inception of the World Wide Web, HTML, HTTP, and sites like Amazon and eBay burst into the scene. However, there still was a challenge with adoption of internet speeds not quite picking up to the pace that it needed to be, and challenges with access. I don't know who here used AOL, but if you remember you had to log in that took over the phone lines. Many people needed additional phone lines to access this and it became an overall challenge.
In the early 2000s we saw this completely change when Google came onto the scene and changed the democratization of the internet for everyone. Now we see that more than 93% of homes in the U.S. have access to the internet and the World Wide Web.
What can we learn from Google as data and analytics and bringing this into the business world? Well, there's three key things. First, Google fully understood the demand. For each of us in our business environments we know that there is a demand, there's a demand not only from the business leaders across the organization, but in terms of users. They're willing to try new technology and adopt new processes.
There's also a need to understand all these different data sets that are coming in, to make sense of it, and to make better decisions — the demand couldn't be greater than now. However, there are two other key elements that are important in terms of democratizing data and making this accessible. It's the technology, the innovation, and the usability.
Again, thinking of ourselves as business users, how can we access the technology and the tools that make sense for our specific business challenges? Where do we go from here? What do we do as business leaders to bring this democratization of data across our organizations?
First, it starts with the decisions, not the data. We need to understand the key business questions that need answers with the data that’s being sourced. What do we need to learn and why? That will allow a better understanding of the data that we bring in and the technology to enable that. What we recommend is to create a robust test and learning agenda. This provides theories and hypotheses in order to answer key business questions before rolling out a massive data integration or new technology across organizations.
Second, in terms of enablement, successful leaders are building robust, accurate, data foundations. Master data management was noted as a key priority for brands that participated in the round tables over the summer. They need to either build their own centralized data platforms or work with third parties to enhance them.
This not only connects internal data with external data, but helps bring new context and new texture into decision making.
One of our customers found that when they changed from using one data source — which for a lot of brands is typically sales data — to using one data source to make decisions and leverage predictive analytics, there was a 26% decision-making accuracy.
When they increased that across four to five data sources, they were able to increase the accuracy to 97%. This is nothing to sneeze over, this is a huge impact that can be made across the business. We're looking at data sets and being able to connect them in an efficient and effective way.
The third area where we see success is in data literacy or actionability. How do you take the data and make it actionable across the organization? This starts with a data common denominator, which we call data literacy, but it's making the data speak into the language of the organization.
PepsiCo took their data platform and overlaid the benefit zones or consumer demand spaces on top of that. Taking this data and translating it into the business language, the company was able to reduce time to insight from two to three months to two to three weeks. This is amazing success and speed, just taking the data in a language that makes sense to the business.
Another area where there’s the translation to actionability is understanding the why behind it. We know accuracy is super important. You can't understand the why unless you have accuracy, but making decisions off predictive analytics or data insights, there needs to be an understanding of what's underpinning what's behind it.
Another example here is a pet food company that was looking at ingredient trends to drive consumer demand and consumer sales. The ingredient hemp protein was a key indicator for new sales for the ingredient, but why? They had a key decision to make: are they going to develop this ingredient as a standalone product, or include it in the existing formulation and highlight that as a key benefit for pets to pet owners?
Without the reason, the company could either make the wrong decision by not highlighting this ingredient, or as a champion based on what the data says.
Fourth, the data needs to be accessible for everyone and quickly. This is the money slide — that democratization is decentralizing ownership. Companies like Estée Lauder have created citizen analysts that are embedded into the teams and organizations, allowing for quick insight to key business decision-makers by making sure that this practice is across the organization.
Other leading companies, such as Reckitt, have created product councils where they bring data analytics, business decision-makers, and the insights teams into product councils to make product development decisions and go-to market strategies. It's not just about democratizing the impact of the data, but thinking about how to do this not only on a local scale for your own business unit or market, but expanding it globally, and making sure that all teams have the power to act autonomously.
This is critical. Taking a page from Google, there should be insights on-demand, providing access quickly to the business user.
Last week, Forester said that NLP (natural language processing) is coming into the data and analytics world. We love to hear this because this is where we've been developing our own products. Ask MI, which we rolled out last week, is bringing in these insights to business users that don't have the time, ability, or the skill sets to go deep into the analytics, but to get these key insights for the products in their marketplace very quickly.
One last example from a client that had success bringing data and actionability into their business. This is a vitamins and supplements company. It was seeing sales declines looking at IRI data and Amazon sales for the apple cider vinegar supplement. This was interesting for them because this was right at the peak of the pandemic where immunity and health was a key theme for consumers. Looking at not just sales data, but external data sets like social media, allowed insight into the cause of the decline in apple cider vinegar supplements. They learned the current format of the taste and digestibility had negative consumer sentiment.
However, when they looked at the consumer sentiment on apple cider vinegar as health and immunity benefits it was positive. There was a challenge and a decision to make. The decision was to change the form of the supplement into gummies and focus on pushing the immunity and health benefits to consumers. From not only looking at the prediction of sales growth data, but also real life results, it became a bestseller on 2021 Prime Day.
Johnston: Thank you so much, Kate. It's a great insight and perspective you provided. When you mentioned AOL I had an immediate flashback to that screeching sound that you used to hear when you connected. I may be showing my age, but talking about how far we've come with Google and NLP, it's pretty amazing. We're going to dig in a little deeper with this as we welcome Megan.
I noticed in the presentation that data democratization is a priority for 90% of executives. That's pretty high. I have to ask, what's the difference you see in how brands are prioritizing it across your clients?
Megan Edwards: It's a great question, Lisa. Everyone's at different stages, this still seems to be a new priority for a lot of executives. Some clients are really far along this journey and some are just starting out. We're working with a lot of brands in those various stages. Most of them have reached a point where there's a top-down strategic initiative to build out an internal data lake to power different areas of the organization. We're not just seeing this for consumer insights teams or marketing teams, but truly for everything from sales forecasting and finance, all the way through all elements of the organization.
Ultimately, this process is a journey. We've got people who are saying, "We're six months into this, we're two years into this." It takes a lot of collaboration, a lot of executive support to gain momentum, and get the support and resources needed in order to do something like this.
Johnston: I imagine the first challenge in undergoing a large data transformation is getting the right talent on board. How are brands approaching this from hiring, especially given today's serious labor challenges? We know from our CGT coverage that this is a really important topic of conversation and it's nearly universal across the industry though playing out in different ways.
DuBois: We're seeing that consumer goods companies need to act more like tech companies, but they're not going to attract the talent without bringing them in through different innovation and different purposes. If you're trying to attract engineers or data analysts that Google is going after as well, you need to give them something else to be excited about for joining your company. There’s a lot of recruitment and PR efforts around the impact that these consumer goods companies can make on things outside of just business sustainability, brand purpose, and paying that off in terms of investment and employee development.
This is paying off in the long-term as companies that produce and manufacture goods are also translating that innovation into doing right for the world and bringing new career opportunities that you may not find being in a typical tech company. Again, it's attracting the right talent, giving them a reason to invest themselves into an organization that's driving innovation and maybe different than the typical telcos, and then aligning the brand purpose to how you operate and invest back into your employees to keep them.
Johnston: I would absolutely agree with that in terms of what we're seeing in our coverage, especially the point of investing back in your employees to keep them competitive. This was a point of view that wasn't widely held pre-pandemic. Companies are more afraid of investing in employees only for them to leave, but we know that this mindset is absolutely changing. With that said, given the shortage of talent, how are you seeing brands manage the balance between in-house versus outsourced data analytics?
DuBois: It is this balancing act. To your point it's investing in your existing talent, so it's upskilling them — that's a key theme. You may see that you need to invest in new training, new resources and education, but also understanding the gaps that these organizations have and that's where outsourcing or working with third parties, or understanding that there's an ebb and flow in your own development investment in data analytics and technology.
It's not necessarily one-size-fits-all. It is a balance for a lot of organizations because it's a big investment and you want to train the employees that are loyal and have been with you a long time. You want to give them the skills and opportunities to upskill, but also keep them invested because this talent pool, it's a crazy world right now. Having that experience in technology, especially in data analytics as it relates to the growth in these key industries, is a hot commodity. It’s important for the businesses to give something back to the employees and make them feel invested in the growth of the business, which is key and critical.
Johnston: We keep seeing Levi's as being pointed to as an especially strong example. They established a new data science program for employees with AI literacy courses and they even have a machine learning bootcamp for upskilling. It's being painted as a gold standard, but you wonder how many companies can do something like this. Obviously, the challenges are going to differ depending on the size of the business. Data democratization across large organizations in particular presents its own set of unique challenges. How are CPGs in these environments organizing teams to help bring data in the hands of the organization?
Edwards: This has been some of the more interesting conversations that we've had more recently. Everyone's doing it a little bit differently. Kate talked about the concept of citizen analysts within organizations, and I really like this. You have teams where you need to vet data, make sure you have the right data, you ingest data, and there's technical skills involved with that.
Then, you also need to be building out and have the skill sets to interpret the data, tie it to the business challenges that you have, and gain insights to make faster business decisions. How do you connect those two together in a way where they're speaking the same language? The citizen analyst is really interesting because it's essentially taking an individual with the data analytics skills and getting them closer to those business challenges, those business users, and putting them within their teams.
We're also seeing almost all operations planning-type of meetings where data teams are presenting tech and solutions to business stakeholders saying, "Okay, I've vetted all this for you. Here are all the best solutions, we know you don't have the time to vet all of these pieces." Having individuals with that specific responsibility and saying, "Okay, here's what we think we can do in here, this is what we think the ROI will be for us." It's really complicated and you have companies with thousands and thousands of people.
Often, we see tech solutions or data solutions that are purchased by one person in an office over here, but are not known by an individual across the world. Building data marketplaces, building tools to help centralize is important. I have a number of clients who have tech tool repositories that help people understand what their options are and do a lot of training and evangelizing for the tools out there that are helping make business decisions.
Innovation is the area where we're seeing the most growth and the biggest change within a number of these companies. People are creating innovation teams specifically focused on these challenges. A client yesterday said, "We have a new CEO. We're reorganizing. Actually, we don't have innovation teams now, but I wouldn't be surprised if I call you back in a couple of weeks and I think that that's a priority for us." Every organizational structure is going through modifications right now in order to create these appropriate dedicated teams where everyone collaborates the best way they can and leverages each person's skill sets.
Johnston: That leads well into my next question, which is thinking about trust and understanding of data. How can companies foster these environments that promote and build trust and the understanding of data among those who are especially new to data analytics?
Edwards: It’s interesting. Starting out, these consumer insights teams, these innovation teams are really the ones that need to be the champions and analysts for getting the data in the hands of business users all over and evangelizing their own work too. We've got a couple of organizations that are competitive internally with this.
It's no surprise to anyone who knows PepsiCo and the organization well that they have a lot of internal competitions, they do a ton of PR, they champion innovation very well and it is absolutely a strategy. Being a voice internally within the organization says, "Hey, I've got something good and I'm doing good work with it," It goes a long way. Trust in the data is gained through insights and proof points. Ultimately, you've got to have people top-down who are setting the charter and making people make better decisions with data, asking them why. Asking those questions and engaging their business units across the organization.
With any partners that you're working with, learn from other companies that they're partnering with as well. Learn what's working, help bring that into your organization, and ultimately dig deep into that data in order to really, truly understand the power of it and use it for decisions. The trust is gained over time, so it's always going to take a little bit of time there.
DuBois: I would also add to that, the transparency factor, which is so important. That it's data, understanding where it came from, how it's being used. Then you get in that gamification of organizations that are bringing that out across their groups and having competitions around how to use it to drive business results, understanding where it comes from, and why it's important to the business growth. Everything that Megan said, but I always think of data as such a loaded word. It can mean so many things and understanding where it came from, how it's being used, and how it's impacting business. Again, those results are critical for trust.
Johnston: Arguably, one of the most important questions, what does success look like? How do you know when you've gotten there?
Edwards: That’s a great question, and obviously it's going to look very different based on who you are with the organization — what success will look like for you. There's a top beauty company that I'm working with frequently, they have data and the consumer voice in the forefront of every conversation. It is presented regularly. It is the central point of which they make a lot of business decisions. To me that's success. It's not making business decisions based upon an idea or hypothesis overheard, or someone's opinion about something over here, but it's truly putting the customer first. Having that understanding, those data sources, and then bringing them to the front of your organization and your business, that's what we all want to get to.
It's also having the right structures for collaboration within your organization. We've talked about a couple of different options there, and ultimately getting data that you can use and leads you to insight. It's not just that one data source, like Kate was mentioning, it's multiple data sources and joining them together. It's understanding white space opportunities. It's understanding and getting to the place where you're not only analyzing what happened, but you're able to predict trends, predict what's going to happen in the industry.
Johnston: Here’s a question on retail growth. How can the strategies that you've discussed be used to propel retail growth?
DuBois: I can take that. You might've seen recently PepsiCo just announced their new Pepsi, I think it's called Pepviz. It's going into retail, and I'm no expert on this. It just came out, but there's more of the relationship and the partnership between consumer goods and retail partners to share data. To leverage that data to understand what's driving the consumer behavior and how to facilitate that growth with the key customers that these consumer brands work with.
When you look at retail holistically, we have the traditional brick and mortar, you have your e-commerce platforms, and this movement into hybrid. It's about what the consumer wants, when they want it, how they want to access the goods, and where they're shopping. For brands it does add this challenge to how you work with retail partners and how you meet the consumer needs based on what's available. Retail growth is definitely a key factor in terms of data availability, but also data back in to meet that consumer to drive growth for the business.
Johnston: Here we’re asking about the need for enhanced standardization of data across organizations. One issue we see is companies can ingest data, but not actually be able to interpret it. I'm curious to get your thoughts on that subject.
Edwards: We hear that a lot. One of the biggest challenges is combining data sources, bringing them together. It takes advanced analytics teams to do that. One of the things that we've tried to do over at Skai is to take that out of the equation, bring 13,000 data sources together, route it in a taxonomy that makes sense for your vertical, and then allow you to leverage that data in different views.
It's not an easy thing to do, it takes years of development in order to be able to do that. Explore partners out there who have some of that technology built out so you can get there faster. A number of clients are doing it on their own as well. But there is a need for data standardization and it's a really big challenge.
Johnston: Can you elaborate on the omnichannel performance KPIs, digitally influenced sales between channels, and the accuracy of them?
DuBois: Again, this comes down to the business objectives and goals. A key challenge that we're addressing at Skai — I didn't talk much about our background — we were formally a company called Kenshoo. We acquired a business called Signals Analytics, which is the data and analytics division of Market Intelligence. It brings together data advertising performance into the analytics world, that’s how brands are going to need to navigate how to understand and calibrate all the different KPIs. Whether it's driving sales through retail partners, offline, or the online experience and looking at the consumer demand and growth — it's not through the traditional methods.
For us, it's overall sales lift, category growth, understanding brand growth, brand sentiment, all of these key factors that lead to that relationship, that love you have with your consumer to partner with these retail companies. It's a critical question, and it's understanding how you are driving that growth as a business. Is it new products in existing markets? Is it new consumers for existing products? Understanding all those different facets, but the retail partner and the retail relationship is important to that in addition to understanding if there is a D2C element. D2C is a whole other area to unpack in terms of data collection and the consumer relationship.
Johnston: Okay, one more, here. I'm working on a democratization of data and organization, but struggle with how other departments identify the "insights." I run the strategic insights and innovation group and worry that some may be choosing specific metrics to support the story they want to tell versus a holistic point of view. That's an interesting topic, is that something common that you're seeing across other businesses?
Edwards: This one resonates a ton. It's a big challenge. Where I've seen this done well is when the initiatives that people are focusing on, the business challenges that they're focusing on are driven top-down. When people are not just making the decisions or reading favored positive comments about the things that they care about within the business, but saying, "Okay, great. We need to create a new brand that associates. We need to revamp the ingredients." Giving a challenge that they need to surface works better than allowing people to make silo decisions, taking them down the path that they want to take them as well. A little bit of challenge there and a little bit of just executive accountability is also important.
DuBois: I was laughing, smiling too, because I think this is an area where we actually see data analytics insights are actually used to justify a hunch. This is a very common business challenge where you feel like something is going to grow, what's happening, but you don't actually have the validation. In addition to what Megan said, which is, don't ignore the insights or the datasets that you're seeing that are leading to different business outcomes, but it can also be used to validate. To the question there, validate and reinforce that gut feeling that a lot of key business leaders have in order to make those key decisions.
It comes back to transparency. If you're looking at the full view, not just that myopic narrowed view that's going to validate the decision that you already made or that you're investing in, it's only going to benefit the organization in having that full view and full picture.
Johnston: Another question regarding baselining the use of AI and predictive analytics to get the process of democratization started. Do you have any thoughts surrounding baselining its usage in the beginning of the process?
DuBois: This is interesting because you need to start somewhere. For every business the baseline is going to be at a different point in time or a different level of sophistication. One area we're seeing is the ability to take two different data sets and apply that to some of the key business trends or the market that you're looking at from a business standpoint and manage that over time. Creating a baseline in a moment in time is a way to start.
This is where the investment and the focus on AI is always on. It's going to get the organization more closely tied to the data and decision-making, but comfortable with the predictive analytics and validating that against business decisions that are made. It's an important investment to make, it’s not necessarily easy, and a lot of times companies need partners to work with to stand up and have that starting point to get going.
Johnston: What do you think are some best practices for sourcing and integrating external third-party data?
DuBois: It starts with the business question. Understanding what you are trying to determine. For example R&D groups and consumer goods companies often look towards leading innovation factors like patents filed and research papers. They're looking at different data sets to calibrate against product trends being seen from ratings and reviews.
For someone in a consumer marketing team, those patents and research papers don't necessarily help in determining the right messaging and strategy to go after in terms of promoting existing products into the market, changing the way a key ingredient is positioned, or a key feature of benefits. Again, it comes with understanding that business question and then looking at the universe of data available to then understand what's the prioritization of investment there. Which, there's a lot of data to the point. You don't necessarily need it all, but you do need the data sets that are going to give insight and context to those key decisions.
Johnston: For the organizations who are just getting started to becoming more data-driven, what advice would you give them to avoid common pitfalls when investing in tech and new solutions?
DuBois: This one comes down to the business leaders working together and having alignment. There's a lot of questions on having this citizenship approach, but that's only going to work if an organization has set clear objectives and clear prioritization of what we're trying to accomplish. When you look at multinational CPGs like PepsiCo, for example, different business units, and even within those units, there's different product initiatives to launch, to develop new insights and ideas, but the methodology, focus, and the top-down/bottom-up alignment is critical in terms of that success.
These business leaders, they want to validate their own hunches. When you see a team's going rogue, discounting processes, or the charter that's laid out from the leadership, that's when you see a lot of dysfunction in these organizations. It’s really important to have alignment and have the enablement from a process across these unique business units.
Johnston: The last question for today is: how do the insights that are generated from data analysis get converted into business strategy? How do the insights come into execution?
Edwards: I love this one. We've talked a lot about top-down, but it takes an executive team that's challenging the status quo, that's looking for white spaces, looking for opportunities, that's thinking about and prioritizing innovation. Ultimately, you need to reverse a little bit. It's a little bit of a chicken and an egg. You need to share the insights, share what the consumers are saying, what people are thinking, what the sales data that's backing certain products, and then identify those white space opportunities and present them up.
At that point, you need the buy-in, the strategic focus to say, "Okay, we want to go this way or we're missing here." Innovation teams, strategy teams, people with that mindset and lens are really key to that, and then the executives who are saying, "What's next? How are we going to propel our business growth? What is that going to look like for us in the future?"
Johnston: Great. Well, thank you very much. I'm afraid that's all the time we have for today, I'd like to thank our speakers for giving us their time and subject matter expertise. I'd also like to thank Skai for sponsoring today's webinar. Finally, thank you to our attendees for giving us your time today, we hope you found it worthwhile. Have a great rest of your day.