How industries are using data analytics to accelerate the digital transformation
The secret sauce for survival relies on extracting the value of data analytics.
The secret sauce for survival relies on extracting the value of data analytics.
Businesses are undergoing a digital transformation due to an explosion of data. Data is generated with every credit card swipe, social media post, and customer-support call, and through the myriad devices that are connected to the Internet of Things (IoT). Organizations can use this data to gain critical information and generate insights that drive business decisions.
Yet, while more people, processes, and things collect data, the rapid growth of information and connected devices also poses a risk to existing business models. Senior officials often cite the quick pace of technological change as one of the biggest threats to their business. Many organizations simply cannot keep up with technological growth.
As a result, only 12% of Fortune 500 companies from 1955 still exist. Only 50% of today’s Fortune 500 companies are expected to be in business 10 years from now. This is especially concerning, as today’s Fortune 500 companies represent more than two-thirds of the U.S. GDP.
As today’s digital transformation continues, businesses are starting to understand that more must be done with data that goes well beyond receiving and storing this information. Raw data alone does not generate insights to drive business growth. Rather, it’s the analytics derived from data that creates true value.
“Historically, we see quite clearly that if enterprises do not adapt to changing technology, they fall behind their competitors and fail,” said Raghunath Nambiar, CTO of Cisco Unified Computing Systems Group. “Change is occurring faster than ever before, resulting in widespread digital transformation. The IoT offers new data sources, and the technology is evolving to collect, process, and store this data. Analytics on the IoT data, particularly when combined with other business data, provides insights into the business, helping organizations to better understand their customers’ wants and needs, ultimately differentiating themselves from their competitors.”
By the year 2020, it is predicted there will be 50 zettabytes of stored data. To put this in perspective, that’s equivalent to 50 billion one-terabyte disk drives. By the year 2030, this number is expected to increase by an order of magnitude. Additionally, 90% of this data is unstructured, coming from sources such as social media, digital channels, and IoT devices.
In order to effectively leverage the value that can be gained from data analytics, a cultural change must be made in how organizations approach analytics. This cultural change can be described as the three “I’s” of big data:
“Raw data is like crude oil, the datacenter is the refinery, and analytics is the refinement process—all are critical. The three I’s refer to a cultural change in how to approach analysis in the era of digital transformation,” said Raghunath. “We no longer look to the data for what we already know (or think we know). Instead, we explore the data and turn it into actionable insight in a continuous cycle. This improvisation leads to innovation, which in turn leads to optimizations and new opportunities. This, of course, requires new investments, in people and technology, and it’s this investment that heralds the new focus: creating an agile, adaptable, and resilient enterprise through the application of data analytics.”
To put the three “I’s” of big data into perspective, it’s important to understand how businesses today are “investing, innovating, and improvising” with data to drive business value.
Here are some examples of how three mainstream industries are applying data analytics to gain business value and create new opportunities for growth:
Retailers are becoming more competitive with online sellers through “omni-channel” sales, which allow consumers to purchase goods in-store or online. Merchandise can also be purchased through a combination of the two. Data being generated from digitally connected channels, as well as in-store, is being used to generate insights to help drive sales and promote growth, allowing brick-and-mortar stores to survive and compete alongside major online retailers.
One way retailers are using data analytics to leverage insights is through RFID tagging. RFID lets retailers identify individual items, cases, or pallets the same way bar codes do, but wirelessly, with richer data and without need for a line of sight.
For instance, there are certain stores that are equipped with in-aisle video displays that play videos based on the product customers have in their hands as they walk by. The video aims to sell other products related to the item that a customer has already picked up. Retailers capture data from RFID tags placed on merchandise to determine other products purchased by customers who have previously bought the same items current customers are interested in.
RFID tags generate data through a simple chip and antenna that are embedded into product pricing labels, such as those that hang from apparel. They are powered by the radio signal emitted from readers, which can be hand-held or fixed in place. Analyzing this data can result in a number of business opportunities and growth for retailers.
Real-time data from sensors are being applied to improve the agriculture sector by monitoring crops, controlling irrigation, and much more. Data analysis is crucial, as the agriculture industry will become more important than ever in the coming years. According to the UN Food and Agriculture Organization, the world will need to produce 70% more food in 2050 than it did in 2006 in order to feed earth’s growing population.
To meet this demand, farmers and agricultural companies are turning to connected devices for analytics and greater production capabilities. For example, farmers are now placing sensors in fields to obtain detailed maps of topography and resources in certain areas. These sensors also provide farmers with important information such as the temperature or acidity of the soil.
These sensors work with smartphones, which allow farmers to remotely monitor their equipment, crops, and livestock, as well as obtain stats on their livestock feeding and production. This technology can even be applied for statistical predictions for crops and livestock.
Industrial facilities are using real-time data from sensors to identify maintenance issues before they become serious. While the majority of factories today are still running on decade-old machinery, manufacturers have started to install sensors on factory equipment and connect them to computer networks in order to monitor machine health and ensure well-maintained machinery.
Sensors connected to computer networks enable factory managers to track metrics such as temperature and vibration to ensure machinery operates at peak efficiency. Sensors will send off signals and alarms, such as vibrations, to alert managers to problems that could slow production or even shut down entire factories. The real-time data being generated from these machines also powers analytics systems that can predict problems before they even occur. Of course, while sensors make equipment more complicated, the complexity also leads to a massive opportunity.
Today’s digital transformation, brought on by the explosion of data and connected devices, should be seen as a world of opportunity for businesses, rather than as a threat. The secret sauce for survival, however, relies on a cultural change that focuses on the value of data analytics.
Businesses that consider the critical importance of analytics will gain the most benefit from their data—now and in the future. Organizations that invest in collecting and analyzing data will avoid extinction in the digital era. Innovating and coming up with new ideas on how to use these insights to create new products and better customer experiences is the next step in the process. Finally, improvising and exploring data to find new meaning will result in a number of insights that fuel the cycle of continuous data.
This post is a collaboration between O’Reilly and Cisco. See our statement of editorial independence.