There is more progress to be made: only 34% of executives in a recent PwC U.S. Cloud Business Survey say they are getting their target business result when it comes to a better decision through better data analysis. And only 16% say they have achieved substantial value from their data.
What are the companies left with? They have built up technical-investment debt in legacy systems that are reluctant to give up; they cannot keep up with the innovations of the new technology coming to the market; and they are concerned that their business cannot cope with the change. There is also a general lack of data literacy in companies, with many having difficulty understanding how to make data-driven decisions and how to truly activate insights.
Five Ways Data Can Transform Organizations To modernize successfully, companies need to invest in technology and embrace change, particularly around data. The payment? Your company can become more productive, efficient and responsive. Here are five ways that data-driven organizations can achieve greater value.
1. Creating personalized customer experiences in the physical and virtual world Most companies want to provide personalized experiences. The only way to do this is to use significant amounts of customer data, whether it’s first-party information collected by consumers themselves or third-party data collected by other data organizations or consortia. While some of the largest retailers offer products that they know their customers want in front of them, either through ads or the front page of their online stores, data-enabled customization is just beginning.
Virtual environments like the metaverse will be the next emerging area that could offer a higher level of personalized customer experience. Unlike the real world, where retail stores carry products for everyone, companies that know their customers best can create hyper-customized stores in a virtual environment that shows only what a particular person would be interested in. Customers will be able to search for clothing options that are unique to them. includes their style and color preferences. The ultimate goal is to give people a personalized experience and increase the affinity of the brand.
2. Generating new revenue streams through data monetization Many suggest that data is the new oil. We see that coming true, as a number of our customers have begun to generate revenue from the information they collect. While monetizing data in 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 rules, and must generate unique insights. With data sharing becoming more common, technology platform companies are working in all industries to create data sets that provide a 360-degree customer view that is unattainable for itself.
For example, consider a large bank and a vendor working together to see how financial transactions affect buying habits. This data is valuable to marketers, but they can then sell the information to healthcare providers, who can then use this data to track eating habits and influence health and well-being.
3. Empowering sustainable decision-making Environmental, social and governance (ESG) issues make companies rethink the way they do business. Whether it’s planning decisions around construction sites, future supply chain roads, or the amount of insurance to buy, almost every aspect of the business operation is impacted by the ESG. Artificial intelligence tools, which can ingest and analyze all kinds of information, such as climate patterns, optimal delivery routes, and population growth trends, help companies make better ESG decisions.
Many companies, for example, use data to see if they need to build warehouses in a certain area or if climate change will eventually have an impact on those operations. Others use data to reduce their carbon footprints. For example, a large detergent company wanted to lower its emissions by reducing its packaging size, but at the same time increase the concentration of detergents so that consumers could wash the same number of loads. Its retailer said that even with the same efficiency, a smaller size might not sell, since consumers think larger packages are a better deal. Rather than sticking to the larger size, the retailer has made every detergent manufacturer to reduce its packaging, demonstrating how to keep the same number of loads in a smaller-sized container, making it more sustainable. This has demonstrated the power of analytics – a company has influenced the entire industry to reduce its carbon emissions through timely data-driven decisions.
4. Increasing productivity The digital age is all about hyper-precision. By consolidating, analyzing, and leveraging quality data at the right time to evaluate, predict, and prescribe decisions, companies can significantly increase the productivity and value of their resources.
For example, global car supplier ZF wanted to compare efficiency between its different plants. He has created a digital manufacturing program, built on the Azure cloud with PwC’s Factory Intelligence, to analyze performance data across locations. By using advanced analytics, visualization and automated workflows, the company has reduced conversion costs, improved overall performance and increased the efficiency and effectiveness of its workforce at its more than 200 plants.
5. Drive 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 have about the type of products they might want. However, companies need to go beyond big data and start guarding what is called “big data” to effectively influence the use of products and service 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 pay, big data is focused on human behavior and digs deeper into people’s motivations for buying. anything and the ways they use a product. For example, a credit company typically identifies fraudsters by looking for unusual transaction patterns. But gathering big data around customers affected by fraud and the behavior of fraudsters can lead to a new level of sophistication. By interviewing people who have committed fraud and identifying their motivations and patterns of behavior, those insights can be incorporated into more traditional fraud tracking analytics, the combination of which allows companies to track when a fraudster may first arrive. what is up. This ultimately leads to better fraud solutions.
Gathering data expertise and technology Getting high value results will require new solutions and a different approach to data. You now have to think about what actions your data can inform you about.
Working together, PwC and Microsoft have seen first-hand how challenging it is for companies to understand what “data driven” really looks like. Many companies believe that it is enough to collect information and run numbers through a data visualization tool. While basic analysis can help you get information about something that has already happened, this type of information, when associated with actual action and results, can help you assess what may happen in the future and you says what you can do about a problem before it happens. .
Explore how PwC and Microsoft use 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 the editorial staff of the MIT Technology Review.