Big data has quickly become a key ingredient in the success of many modern businesses. Companies large and small are using structured and unstructured data to glean insights they can apply to boost productivity, cut costs, improve marketing efforts, and more.
As such, big data has become a foundational piece of many digital transformation efforts. By exploiting the power of big data, firms can more effectively target the digital transformation projects that will have the biggest impact on their bottom line.
SEE: How to build a successful data scientist career (free PDF) (TechRepublic)
Here are five organizations that have used big data to power digital transformation.
1. The Chicago Cubs
When Andrew McIntyre, vice-president of technology for the Chicago Cubs, joined the organization in 2011, he quickly saw opportunities for IT infrastructure investments that could make a big impact.
"We knew that in order to become the best, both on and off the field, investing time and resources into data to help us make better decisions was a must," McIntyre said.
A top challenge for McIntyre and the Cubs was preserving the "magic" of the legendary Wrigley Field, while still improving the fan experience. They started by focusing on the data pouring into the organization from the three million fans that visit Wrigley every year -- particularly the data that affected internal operations and revenue.
"For most sports organizations, that comes down to five different areas: Ticketing, merchandise, retail, sponsorship, and broadcast rights," McIntyre said.
Using this data, the organization was able to make sure its products were properly distributed and marketed in the correct channels, McIntyre said. This allowed the Cubs to free up capital that could be reinvested in the team itself and other development projects.
The Cubs are halfway through their digital transformation effort, thus far focusing only on operational data. However, McIntyre said, they have begun to expand into fan data, and will use that to inform the rollout of their new fan-facing wi-fi network in 2018.
In terms of the tools used, McIntyre said Informatica's data management platform is essential to the team's data analytics efforts, and the company has worked with the Cubs to build out an enterprise data warehouse.
For other organizations that may be considering a big data play, McIntyre cautioned that it may seem like an uphill battle at first, as some departments may be slow to embrace data integration.
"It's important to place a distinct focus on making sure each business department understands how data and technology can help them reach their goals -- helping them to build trust in the platform," McIntyre said.
Roughly two years ago, online retailer Zappos made the decision to migrate its data warehouse to the cloud. Saul Dave, the company's director of enterprise systems, said that Zappos started with moving its SAP Business Warehouse (BW) environment onto HANA, hosted by Amazon Web Services (AWS).
After the success of that initial move and the data insights that came with it, Zappos decided to move some other SAP systems to HANA on AWS as well. The firm also worked with both SAP and AWS to build out a cloud-based proof-of-concept that gave them the ability to play with real-time data management, said Dave.
"Together they co-innovated and tested a migration methodology that allows for the fast migration of SAP ERP Central Component [SAP ECC] from an on-premises instance running Oracle Database to a Memory Optimized AWS X1 instance running SAP ECC on HANA -- all while relying on the same proven SAP applications for core business processes," Dave said.
By moving their systems to the cloud, Zappos saw a dramatic bump in system performance, Dave said. Additionally, this helped validate the company's investments in big data and the cloud.
"End users can now be reassured that their queries will return results promptly, no matter their complexity and the number of user concurrently accessing the system," Dave said. "With performance improvements across critical business processes -- ranging from 3 to 240 times faster -- and no timeout queries, employees become more productive."
For companies looking to run big data in the cloud, Dave recommended looking to similar organizations that have made the move, and choosing an experienced vendor that offers the hardware architecture needed for their projects.
3. The City of Las Vegas
Big data can also be extremely valuable to big cities. The City of Las Vegas recently began collecting data on road usage, including whether streets are being used by vehicles, pedestrians, or bicycles, according to the city's CIO Michael Sherwood.
Previously, Las Vegas performed those counts manually every year, or as needed. However, the city now uses technology from MotionLoft to conduct the counts, eliminating the need for ad hoc human counts, Sherwood said. By using this technology, the city can perform the counts on an ongoing basis, providing better data about how the roads are used and any needed repairs or improvements.
That also had implications for Las Vegas businesses, given that the city is now collecting up-to-the-minute data. "The data is helpful beyond just traffic planning and safety: the data collected can also be used for economic benefits, such as knowing the number of potential customers who might drive by a business during certain times of the day," Sherwood said.
Before committing to a specific big data project, Sherwood recommended that an organization start small, testing different potential solutions to the biggest problems and gauging the results.
4. Miami University
Many higher education institutions feel great pressure to prove the value of a pricy four-year degree, and those that work at these schools face increasingly high expectations for performance, according to Michael Kabbaz, senior vice president for enrolment management and student success at Miami University.
However, big data is helping to eliminate a lot of that pressure. And, schools like Miami University are collecting a lot of it.
"Now, higher education measures just about everything across the entire student lifecycle, from the spectrum of students we recruit in high school, how we can better support them through the use of predictive analytics during their time on our campuses, and their graduation success and beyond," Kabbaz said.
Predictive analytics is one tool that has helped Miami University break down silos and get a better view of its student body. Combining back office data with data from student-facing offices has given a much clearer picture of the lifecycle of a student, Kabbaz said.
"Predictive analytics can identify students who are having trouble paying their bills, and track students who have grade drops in certain areas or who are missing courses they need to graduate on time," Kabbaz said. "This information provides the institution an ability to better support students and do it sooner, prior to the student leaving the institution."
Improving student success, retention, and graduation rates are the key goals of Miami University's big data and digital transformation effort. Students have a lot of choices for where they will attend school, and Miami University is using data to ensure that it's providing the best education possible and remaining competitive in the market, Kabbaz said.
The value of data must be demonstrated clearly to any skeptics in the organization, said Kabbaz. For example, if university faculty members fear the data may harm the students, Kabbaz needs to show its potential for improving the way students are taught, and their overall experience at the school.
5. NewYork-Presbyterian Hospital
At NewYork-Presbyterian Hospital, big data has helped accomplish two very important goals: Improving patient care and remaining compliant. NewYork-Presbyterian senior vice president and CIO Daniel Barchi said that digital transformation of patient care took off with the hospital's Clinical Operations Center (CLOC) building, where nurses monitor the real-time physiological data of patients currently admitted in the Weill Cornell Medical Center Emergency Department.
"In an eight-hour CLOC shift, nurses monitor hundreds of clinical data points, note alarms and clinical issues, and communicate via an in-house communications network with the clinical team at the bedside," Barchi said. "In that way, we provide a high-level safety net using clinical experience to note and respond to potential issues that could otherwise be missed by a busy bedside nurse."
Once this system was put in place, the hospital was able to more clearly identify patients who needed intervention and improve their care.
In the CLOC, temperature data is also constantly streaming in from more than 1,600 refrigerators and freezers across six hospitals, Barchi said. These devices store fluids like blood, antibiotics, and breast milk. In the past, staff would have to manually check the temperature of these devices twice a day.
However, as part of its digital transformation effort, the hospital installed networked monitor probes in each refrigerator and freezer, which send data back to a reporting and monitoring system at the CLOC.
With the monitor probes, the organization is now able to easily monitor the devices every hour, 24 hours a day, seven days a week. While they expected to see some fluctuations with the new data, they found that some refrigerators were drifting above and below the acceptable narrow temperature band -- similar to a sine wave going in and out of compliance, Barchi said.
"Simply having the real-time data allowed us to identify more than 900 refrigerators that required replacement and upgrade, and improve our overall blood and drug safety compliance," Barchi said.
For other organizations considering a big data approach to digital transformation, Barchi recommended considering any opportunity for automated monitoring, which has allowed NewYork-Presbyterian to improve operations, centralize tasks, and leverage clear, actionable insights.
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