Data Science Informs Successful Business Models
Investments in big data analytics can have a significant impact on your company’s bottom line. The McKinsey Global Institute estimated that companies that inject big data and analytics into their operation show productivity and profitability rates that are 5 to 6 percent higher than those of their peers. Retailers in particular stand to increase their operating margins by more than 60 percent, while the United States healthcare sector could reduce costs by 8 percent through data analytics efficiency and quality improvements (McKinsey Global Institute, 2015). The consensus among healthcare executives is that intelligent machines will provide future industry services on an equal par with qualified healthcare staff and data scientists. Companies that are successful at analyzing data to generate revenue will use data analytics to identify valuable business opportunities and marketing strategies, develop targeted products and services, and effectively deliver those products and services to their customers.
Nine Ways Big Data Analytics can be applied to Increase Your ROI:
- Set Competitive Rates
Consumer demand and market rates can be analyzed using data science to identify prices that will yield the maximum profit in real time and determine when to activate the customer base through promotions. On average, a 1 percent price increase translates into an 8.7 percent increase in operating profits (assuming no loss of volume) (McKinsey Global Institute, 2015). An expert in optimizing prices is Marriott International that has established its Total Hotel Optimization program. Property revenue managers and hotel owners are provided with tools to optimize offerings to frequent customers when they are likely to defect to competitors. The company has also created a revenue opportunity model, which computes actual revenues as a percentage of the optimal rates that could have been charged. That figure has grown from 83 percent to 91 percent as Marriott’s revenue-management analytics has been applied throughout the enterprise (Davenport, 2006).
- Effective Product/Service Delivery
Healthcare centres have integrated triage algorithms to manage staffing, patient transfers, and patient room assignments. Clinical decisions are accelerated by the availability of each patient’s history of medical tests, laboratory reports, and prescribed medications on one electronic dashboard. Some intensive care units are using analytics to evaluate multiple data streams from patient monitors to predict whether a patient’s condition is likely to worsen.
A well-known example of a company using data science to reduce costs in service is the United Parcel Service (UPS). In 2008, UPS launched routing software called Orion to calculate the most efficient route for each truck. Using Orion to analyze 250 million address points a day and perform 30,000 route optimizations per minute, UPS confirmed that routes mapped to maximize right versus left-hand turns saves the company $300 to $400 million annually in fuel, wages, and vehicle running costs as well as reduces emissions equivalent to over 20,000 passenger cars (Prisco, 2017).
- Reduce Employee Costs
Some of the largest company costs are employee salaries. Data science initiatives have the potential to reduce man-hours needed to manually enter and review data. The efficient use of staff resources can be tracked by identifying periods of peak business and staffing demands such as inventory arrivals. In addition, individual employee costs can be compared to their contribution.
- Manage Inventory
Analytics can provide data on equipment use rate, maintenance, and life cycle to predict future inventory needs and the value of repair versus replacement.
- Conduct Client Loyalty Analyses
Companies can review customer satisfaction data as well as measures of purchase frequency by target consumer populations. Likewise, medical centres report a higher ROI from initiatives related to their customer relationship management (CRM).
- Predict and Prevent Readmissions
The United States Hospital Readmission Reduction Program (HRRP) financially penalizes hospitals with relatively high rates of Medicare readmissions for patients with heart attacks, heart failure, and pneumonia. Mount Sinai Hospital in New York recognized that reducing readmissions requires an understanding of the unique drivers of readmission for each patient, such as language, literacy, mental health, and access to social support systems, insurance, housing, and transportation. The Hospital successfully implemented a predictive model called the Preventable Admissions Care Team (PACT) into its electronic medical record system that triggers interventions and enhanced care coordination for high-risk patients after discharge (HIMSS, 2012).
- Detect Fraud and Abuse
Analytics can track fraudulent and incorrect payments.
- Better Error Tracking
Data science allows outlier data to be identified and examined to help develop processes to avoid future mistakes.
- Meet Reporting Requirements Efficiently
When auditors want to review specific clinical or operational data, the collection of multiple streams of data allows targeted, easy-to-read data reports to be quickly generated with minimal work from staff.
For the Most Economical Incorporation of Data Science into Your Business Model, Use an Insights-as-Service Company
John Snow Labs provides data analytics services with our own team of skilled data scientists that help you retrieve relevant datasets and insights to grow and support your business. We have the software, tools, and access to external data sources at our fingertips that we manage and update ourselves. By partnering with John Snow Labs you can leverage your existing infrastructure and avoid substantial data science start-up and maintenance efforts.
For Atigeo, a technology company whose flagship big data analytics product xPatterns™ generates knowledge from all available data to deliver insights, predict outcomes and mitigate risks, data analytics is a necessary priority—but not its first. To reduce the time spent finding, cleaning, formatting, updating, and publishing data for analysis, the team of healthcare data analysts at Atigeo partnered with John Snow Labs to save an average of 4,096 data science man-hours per month!
Davenport, T. (2006) Competing on Analytics. Harvard Business Review, [online] Available at: https://hbr.org/2006/01/competing-on-analytics [Accessed 16 March 2018]
Healthcare Information and Management Systems Society (2012) The Mount Sinai Medical Center Selected as 2012 Enterprise HIMSS Davies Award Winner. HIMSS News, [online] Available at: http://www.himss.org/news/ [Accessed 5 March 2018]
McKinsey & Company (2015) Marketing & Sales: Big Data, Analytics, and the Future of Marketing & Sales. [online] Available at: https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/Marketing%20and%20Sales/Our%20Insights/EBook%20Big%20data%20analytics%20and%20the%20future%20of%20marketing%20sales/Big-Data-eBook.ashx [Accessed 15 March 2018]
Prisco, J. (2017) Why UPS Trucks (Almost) Never Turn Left. CNN, [online] Available at: https://www.cnn.com/2017/02/16/world/ups-trucks-no-left-turns/index.html [Accessed 15 March 2018]
Vennaro, N. (2017) Buy (don’t build) healthcare data insights to improve data investment ROI. MedCityNews. [online] Available at: https://medcitynews.com/2017/03/buy-dont-build-healthcare-data-insights-improve-data-investment-roi/ [Accessed 9 March 2018]