The stakes are high: Digital transformation
According to the Boston Consulting Group (BCG)1, digital leaders achieve earnings 1.8 times those of digital laggards and are better placed to accelerate their digital adoption, increasing the gap between the leaders and the rest.
In 2017 McKinsey2 showed that, in aggregate, digitization shifted revenue growth and profitability away from firms and increased value to their customers. Only those firms with bold digital transformation approaches, ones that changed corporate strategy, were predicted to be able to reverse the negative impact and grow revenues and profits more than the losses.
On the stock market front, Bryan Berman3 of Courageous, reports that 52% of the 2000 Fortune 500 companies are now extinct. Many of the companies that have replaced them are digitally focused companies such as Amazon, Alphabet (Google), Facebook, Tencent, and Adobe.
In other words, the stakes are high—firms that are not prepared for a data-driven, agile, and proactively planned future will struggle to compete and survive. Much has been invested in digital transformation, especially during the pandemic, yet BCG’s recent research1 shows 70% of digital transformations fail often due to too much focus on technology as the answer and not enough focus on the other elements required to implement change—management, commitment, strategy and measurement, education, skills and resources, agility and cultural change, data quality, and governance.
Digital transformation goes beyond technology. Do you have an agile data infrastructure to quickly adapt to changing market needs? Do you have the right skills to effectively manage data across the organization? It’s important to have the right processes and metrics in place to ensure the data used for AI/ML models are accurate and complete—so your decision-making is on point.
Enter: AI and ML
AI and ML are already being used extensively to improve efficiency and performance4.
For example, retailers are using advanced techniques to facilitate self-service while reducing fraud, improving customer service using chatbots, identifying consumer buying habits and make purchase recommendations, improving in-store layouts and assortments, supporting easier online searches by recognizing voice commands and images, tracking customer sentiment, and predicting behaviors, simplifying apparel purchases with virtual fitting rooms, and in many other ways.
Each tool that is used produces even more data.
Think about it: With more data (high-quality data, of course), how can you use those insights to stay ahead of the competition, bring a new trend to market, start virtual fitting rooms, or streamline operations? Data opens the door to opportunities.
AI and ML: It's more than just technology
The effects of any innovation can be hard to predict—especially when the market is changing so rapidly. Organizations need to be prepared to not only extrapolate the impact of the past but model, predict, and influence the future.
Organizations that were able to quickly understand the impact of COVID-19 on consumers were better able to weather the storm or capitalize on it. This required good data management, accurate data, the right skills in place to do the analytics, and the ability to act on the results.
The time is now: The AI and ML revolution
Digital transformation is happening now. To keep up with the accelerated changes and successfully transform, organizations are seeing the next wave of innovation rolling in, which is driven by AI and ML.
The move toward innovation is already underway across industries—and most businesses are not ready. Organizations have implemented AI/ML capabilities and are already seeing results. The data that is being collected can disrupt how we all do business—no matter what industry you are in or what customers you have. Let’s dive into a few examples where we are seeing this today:
Retailers: 200,000 points of data from the virtual fitting room technology
One vendor of virtual fitting rooms scans customers’ images and measures 200,000 points of data. This data has the potential to go beyond the store and impact new trends, business models, competitive insight, manufacturers, supply chains, inventory management, logistics requirements, and more. For instance, you could even think about how this data could innovate the way we design brick-and-mortar stores.
This is about getting the most out of your data—you can collect it in one place but use the insights for various initiatives in your business and across industries. But, to do this, you need to go beyond the technology. We’ll talk about this more at the end, but data maturity becomes a key component as you try to become an innovator in the market.
Bankers: Personal account management is a win-win, data-first solution
In financial services, new mobile banking, account management, payments, investment management, loan, and insurance services are growing rapidly5. And despite growing privacy concerns, consumers seem more than willing to provide access to their account information to startups or otherwise previously unknown companies in exchange for a range of benefits. This data can go a long way in shaping new products and services coming to market, consumer buying behaviors, and competitive responses.
For example, with the vast array of information available from their clients, firms can better personalize offerings than other traditional banks and credit unions. This could also be an opportunity to position just-in-time loan services based on income and payment data analysis. Or firms can improve risk management by gauging creditworthiness without recourse to traditional credit bureau services. Traditional institutions must look to their own capabilities and decide how to best protect and grow their businesses.
Healthcare: AI/ML techniques are in full swing since COVID-19
In the healthcare industry, we see increased public willingness to have trust in data and emergency use to prevent illness and death. In the case of COVID-19, think about all of the parties involved—from the general public getting tested, healthcare providers sharing general statistics, the news stations broadcasting the findings, and how that translates across the public and organizational use of data to make decisions and further innovate industry strategies.
Think about this scenario: The data collected above could be the catalyst to further investment in the research and development of new technology for diseases like Type 2 Diabetes. Another idea is that this data could drive further innovation and investment into the diet and exercise fields—having accessible health data could bring personalization to nutrition and fitness based on your lifestyle.
Automakers: Climate change was a catalyst for electric cars and now we get even more data
In the auto industry, climate change and the consumer’s desire to be eco-friendly have shifted automaker’s strategy to focus on alternative energy sources. Automakers such as Ford6, have committed to massive investments in electric vehicle production and are equipping vehicles with increasingly sophisticated capabilities as they progress towards the development of completely autonomous vehicles. Not only do these new cars come with high-tech features but the data that can be collected can have incredible effects for the automakers and across other industries. Now, what can we do with this data to be more innovative?
The data captured from these high-tech cars go beyond the auto industry and are likely to impact logistics, transportation, and delivery services, insurance, food, travel and entertainment, retail, public safety, and more. For example, driving behavior including speed, braking, steering, and distancing habits can inform auto insurance premiums.
Furthermore, location data can better advise map apps that show the fastest and safest routes, popular destinations can drive more advertising for hospitality organizations, electric consumption data can show the best places for charging stations and electric supply needed. And lastly, this data could give us the most efficient routes for delivery drivers which will give more businesses the opportunity to offer home delivery.
The auto industry is a prime example of the power of AI/ML and how data can impact automakers and other industries. Now, let’s talk about how you can go innovate your organization beyond technology.
Get your data ready
We’re on the verge of the next industrial revolution driven by AI/ML and most firms are not ready. Organizations understand that technology is a key part of innovation, however, that is only one piece of the puzzle. The other pieces make up your data maturity.
Organizations are now prioritizing data skills and strategies to grow their data maturity. Ultimately, this will help an organization become more data-driven and make data fit for a purpose. If there is a lack of data skills, data governance, data quality, and analytics, then there is no way to accelerate digital transformation and support AI/ML techniques. The examples above have five themes in common, and they are all backed by data:
- Organizations need a lot of data and AI/ML to implement digital-first initiatives
- Data quality control and governance is key, or poor data could result in disaster
- Customer centricity is vital if organizations what to continue to collect and use consumer data
- Adopting new business models requires more than just new technology.
- Data sharing and collaboration across organizations is essential
Technology is a powerful enabler and catalyst for change. However, for data-driven organizations, it’s critical to build an agile infrastructure to leverage insights and act quickly on new strategies. A robust data management program becomes vital to ensuring trust in the data and results. Data and technology are foundations and with the right tools, you can enable an analysis of the future to identify and stay ahead of disruptive changes.
Learn how Experian can get you ready for the future
Sources: 1Forth, Patrick et al. (2020, October 18) Flipping the Odds of Digital Transformation Success.BCG https://www.bcg.com/publications/2020/increasing-odds-of-success-in-digital-transformation
2McKinsey Quarterly (2017, February 9) The case for digital reinvention: McKinsey Digital https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/the-case-for-digital-reinvention
3Bryan Berman Business Apocalypse: Fifty-two Percent of Fortune 500 Companies from the Year 2000 Are Extinct: Courageous https://ryanberman.com/glossary/business-apocalypse/
4Chuprina, Roman. (2021, January 20) Artificial Intelligence for Retail in 2021: 12 Real-World Use Cases: SPD Group https://spd.group/artificial-intelligence/ai-for-retail/
5The state of finance app marketing: 2020 global and US trends: AppsFlyer https://www.appsflyer.com/resources/reports/finance-app-marketing-us/
6Dearborn (2021, May 19) The Ford Electric Vehicle Strategy: What You Need to Know: Ford https://media.ford.com/content/fordmedia/fna/us/en/news/2021/05/19/the-ford-electric-vehicle-strategy--what-you-need-to-know.html
