Maximize business value with data-driven strategies
Every organization is now collecting data, but few are truly data driven. Here are five ways data can transform your business.
Every company is collecting data, whether it’s consumer buying habits, demographic data from third-party sources or insights from weather patterns. That’s good news—it wasn’t long ago that this kind of critical information was mostly ignored. But it’s not enough: companies must now start using that data to run every part of their business.
There’s more progress to be made: just 34% of executives in a recent PwC U.S. Cloud Business Survey say they’re achieving their target business outcome when it comes to improved decision-making through better data analytics. And a mere 16% say they’re realizing substantial value from their data.
What’s holding companies back? They’ve built up technical debt—investments in legacy systems that they’re reluctant to abandon; they can’t keep up with new technology innovations coming to market; and they’re concerned that their business can’t cope with change. There’s also a general lack of data literacy within companies, with many having trouble figuring out how to make data-based decisions and how to truly activate insights.
Five ways data can transform organizations
To successfully modernize, companies must invest in technology and embrace change, especially around data. The payoff? Your company can become more productive, efficient, and responsive. Here are five ways data-driven organizations can realize greater value.
1. Creating personalized customer experiences in physical and virtual worlds
Most companies want to provide personalized experiences. The only way to do that is to use significant amounts of customer data—whether it’s first-party information gleaned from consumers themselves or third-party data gathered by other organizations or data consortiums. While some of the largest retailers are putting products they know their customers will want in front of them, whether it’s through ads or the front page of their online stores, data-enabled personalization is only getting started.
Virtual environments like the metaverse are going to be the next emerging area that could offer a higher level of personalized customer experience. Unlike in the real world, where retail stores carry products for everyone, the companies that know their customers best can create hyper-personalized stores in a virtual environment that only showcase what a particular persona would be interested in. Customers could browse clothing options that only include their style and color preferences. The ultimate goal is to give people a personalized experience and increase brand affinity.
2. Generating new revenue streams through data monetization
Many suggest data is the new oil. We see that becoming true, as a number of our customers have started generating revenue from the information they collect. While data monetization within the enterprise is a given, external monetization of information is a rapidly expanding business.
To do this right, companies need to improve their data collection methods with better data quality and adherence to privacy regulations, and they must generate unique insights. With data sharing becoming more common, technology platform companies are working across industries to create data sets that provide a 360-degree customer view they wouldn’t obtain on their own.
For instance, consider a large bank and retailer working together to see how financial transactions influence purchasing habits. This data is valuable for retailers, but they can then sell that information to health-care providers, who can then leverage this data to track food habits and influence health and well-being.
3. Empowering sustainable decision-making
Environmental, social, and governance (ESG) issues are making companies rethink the way they do business. Whether it’s planning decisions around building locations, future supply chain routes, or the amount of insurance to buy, almost every aspect of business operations is impacted by ESG. Artificial intelligence tools, which can ingest and analyze all kinds of information—such as climate patterns, optimal delivery routes, and population growth trends—are helping companies make better ESG decisions.
Many companies, for example, are using data to see whether they should build warehouses in a certain area or if climate change will eventually impact those operations. Others are using data to reduce their carbon footprints. For instance, a large detergent company wanted to lower its emissions by reducing its packaging size, but at the same time increase detergent concentration so consumers could wash the same number of loads. Its retailer said that even with the same efficiency, a smaller size might not sell, as consumers think bigger packages are a better deal. Rather than stick with the larger size, the retailer got every detergent manufacturer to reduce their packaging by showing them how they can maintain the same number of loads in a smaller size container, while becoming more sustainable. This proved the power of analytics—one company influenced the entire sector to reduce their carbon emissions because of timely data-based decisions.
4. Enhancing productivity
The digital age is all about hyper-precision. By consolidating, analyzing, and leveraging the right quality data at the right time to assess, predict, and prescribe decisions, companies can significantly enhance productivity and the value of their resources.
For instance, global automotive supplier ZF wanted to compare efficiencies between its various plants. It created a digital manufacturing program, built on Azure cloud with PwC’s Factory Intelligence, to analyze performance data between each location. Using advanced analytics, visualizations, and automated workflows, the company has reduced conversion costs, improved overall performance, and increased workforce efficiency and effectiveness across its more than 200 plants.
5. Boosting product or service innovation
When it comes to creating new products and services, data is a game changer. The more you know about a customer, the better idea you’ll have about the kinds of products they might want. However, companies need to go beyond just big data and start looking at what’s called “thick data” to effectively influence product and service usage through human-centric design.
While big data is about capturing what people spent their money on, when they bought an item, and how much they paid, thick data is focused on human behavior and digs deeper into people’s motivations for buying something and the ways they use a product. For example, a credit company typically identifies fraud by looking at unusual transaction patterns. But gathering thick data around customers impacted by fraud and the behavior of fraudsters can bring in a new level of sophistication. By interviewing people who have committed fraud and identifying their motivations and behavior patterns, those insights can be incorporated into the more traditional fraud-tracking analytics, the combination of which allows companies to track when a fraud might occur before it happens. This ultimately leads to better fraud solutions.
Bring data expertise and tech together
Achieving high-value outcomes will take new solutions and a different approach to data. You now have to think about what actions your data can inform.
Working together, PwC and Microsoft have seen firsthand how challenging it is for businesses to understand what “data driven” really looks like. Many businesses believe that simply collecting information and running numbers through a data visualization tool is enough. While basic analysis can help you get information on something that’s already happened, this type of information, when paired with real action and outcomes, can help you assess what can happen in the future and tell you what you can do about an issue before it occurs.
Explore how PwC and Microsoft are using data and the latest Azure cloud, AI and mixed reality technology to transform experiences, from the football field to your industry.
This content was produced by PwC. It was not written by MIT Technology Review’s editorial staff.
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