Renowned futurist and computer scientist Ray Kurzweil once observed that the rate of technological progress is doubling every decade. “We won’t experience 100 years of progress in the 21st century—it will be more like 20,000 years of progress (at today’s rate),” Kurzweil wrote in an in-depth study on the topic.
The accuracy of Kurzweil’s forecast remains to be seen, but there’s no denying that technology is evolving faster than ever. Businesses today must pivot quickly, adjusting not only to rapid changes in tech but also to evolving customer needs. Cloud solutions may be the key to success.
Big data analytics can help companies stay competitive in a global market. Data sources that didn’t exist a few years ago—consider the fast rise of social platforms like Facebook, Twitter, and Yelp—can play a pivotal role in business strategy. Furthermore, an emerging class of Internet of Things (IoT) devices—featuring everything from machine sensors to smart-home gadgets—now delivers data streams that promise actionable insights to companies capable of analyzing them.
Big data is notoriously unwieldy and complex to manage, however, particularly if your on-premise IT platform isn’t up to the task. That’s why businesses are increasingly turning to cloud solutions. The cloud has many advantages over legacy systems, including increased efficiency and agility, the ability to capture, store, analyze, and act on data that originates in the cloud, and resources to make better business decisions that generate revenue and value.
The synergies between the cloud and big data aren’t lost on IT professionals. Fifty-three percent of respondents to AtScale’s Big Data Maturity Survey said they have already deployed big data in the cloud, and 72 percent said they plan to use the cloud for future big data deployment, CIO reports.
So, what does it look like when the cloud and big data are put to good use?
Cloud technology makes it easier to review data evidence on the real-world performance of your products—a boon for proactive, predictive maintenance that stops breakdowns before they occur. In addition to reducing the cost and overhead of keeping products working, predictive maintenance can improve customer satisfaction too.
Big data and cloud tools also enable companies of all sizes to future-proof manufacturing operations, examine results quickly, and fine-tune steps along the way. Data analytics can assess key performance indicators and other metrics for customers, processes, and machinery in your operation. And data-driven insights from production and distribution processes can help bring new products to market faster and more profitably.
The connected car is a great example of how cloud and big data are working together, yielding insights that automakers can use to understand how consumers drive their vehicles. Data from sensors on IoT-connected cars help research and development teams test and validate new capabilities—and then make updates to meet the changing needs of their customers.
Online shopping is forecasted to account for between 8 and 12 percent of U.S. retail sales by 2020, as consumers increasingly use smartphones and tablets to make purchases. Cloud and big data tools can enable retailers to understand customer behavior more accurately than ever before.
Consumer activity generates terabytes of data every day about shoppers’ actions, intentions, and preferences. This information comes in many forms—everything from in-store purchases and account histories to security camera videos and weather patterns.
This motley mix of user information is a treasure trove to retailers, who can use the data to form and test hypotheses that impact operations. Lowe’s, for instance, used big data analysis to help design the Manhattan store it opened in 2015. Using data-driven insights, the home improvement retailer chose to stock small appliances and other apartment-oriented products in the new store rather than its usual, broader mix of inventory—a move that’s benefiting both Lowe’s and the community.
Financial institutions must guard against new forms of fraud that appear daily. A data-driven cloud solution enables banks to protect themselves against fraudsters, potentially saving millions of dollars in the process.
A bank can collect, store, and analyze big data streams in the cloud, enabling regional managers to quickly identify problems at a local level. Managers can then apply predictive analytics to limit financial risks in their branches. This real-time decision-making greatly reduces fraud and improves the customer experience.
The takeaway here? The cloud helps drive business operations and efficiency. It helps organizations transform big data into actionable intelligence, unlock new insights, increase efficiency, and reduce costs.
Discover ways to make the most of cloud computing as it pertains to your big data goals.