Why an intelligent data cloud is key to digital transformation
Published on 21 Jun 2022
In the current age of digital disruption, firms in every industry confront competition from alternatives that provide superior technology, business models, operational value chains, and consumer experiences. Digital transformation is not a new phenomenon, but it has accelerated across retail, healthcare, financial services, transportation, automotive, media and entertainment, and manufacturing during the last several years.
Digital innovators are disrupting the business models of incumbents by inventing at a far quicker rate. These digital pioneers comprehend the potential of integrating data throughout their whole business to generate true change and value generation. They reap the benefits of durable, mission-critical databases, analytics, and machine learning systems that can dependably operate the company 24 hours a day, seven days a week, and drive innovation. Digital innovators are constructing their data clouds using a platform that’s open, intelligent and trustworthy, and they are doing it now.
Customers whose digital transformations are powered by intelligent data clouds
- The Home Depot (THD) makes over 400k associates smarter across more than 50,000 goods stored at over 2,000 locations by providing them with insight into the items each client requires. In addition to using Cloud SQL, Spanner, and Bigtable for operational use cases, THD employs AI to assist with the location of items using their mobile applications for in-store navigation.
- Using ML and computers, the American Cancer Society identified unique patterns in digital pathology pictures to possibly enhance patient outcomes and analyzed breast cancer images 12 times quicker. Even with a staff of devoted pathologists, it would have taken years as opposed to three months to assess 1,700 tissue samples without machine learning.
- UPS saves up to $400 million annually by lowering fuel usage by 10 million gallons annually by using BigQuery and Spanner to deliver more goods at a cheaper cost and serve its clients in a more intelligent and agile manner.
- The online store Zulily utilizes the most up-to-date capabilities in artificial intelligence, machine learning, and cloud computing to develop and serve its consumers in a meaningful way. To provide online buyers with an accurate representation of how a garment would appear when worn, Zulily trains machine learning models to analyze product photos and extract information. They develop such solutions using cloud-based technologies such as AutoML Vision.
- Using BigQuery, Cloud SQL, and Spanner, ANZ Bank processes transactions up to 250 times quicker than previously.
- With the support of ML, BigQuery, and Google Kubernetes Engine, Priceline is able to make data-driven choices much more swiftly, enabling them to adapt more quickly to changing client demands.
Modern data methods locked in archaic data systems
Google Cloud client The Home Depot (THD) has created a reputation for itself by going large—large locations, a large product assortment, and, most importantly, large customer happiness. However, as time passed, THD discovered it had an issue, which was, of course, big data. THD sought a method to update its strategy, despite its success over the years being entirely data-driven. They wanted to combine the intricacies of their linked companies, such as equipment rental and home services, more effectively. In addition, they desired to better equip their data analysis teams and store associates with mobile computing devices, as well as leverage ecommerce and new modern tools such as artificial intelligence (AI) to meet the needs of their customers.
Their current on-premises data warehouse became unable to address modern challenges, being overburdened by the continual need for analytics and failing to manage their data analysts' increasingly sophisticated use cases.
This not only resulted in the tremendous expansion of the data warehouse, but also posed difficulties in controlling priorities, performance, and expenses.
Adding capacity to the environment required extensive planning, design, and testing on the part of THD. In one instance, expanding capacity on-premises required six months of preparation and a three-day service interruption. However, the benefits were short-lived; within a year, capacity shortages had returned, hurting performance and the ability to execute all essential reporting and analytics workloads. The Home Depot was also required to upgrade its operational databases in order to speed up the deployment of apps for their teams and eliminate the requirement to manage resources.
THD lacked real-time access to the sales, product, and shipping analytics required to enhance the customer experience, product SKUs, and more, thus hindering their ability to compete in an industry where the customer experience is key.
Does this sound familiar? These difficulties are now widespread across the organization. Operating historical technology while attempting to implement a contemporary data strategy is no longer feasible for the vast majority of organizations, including THD.
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