Data-Driven Innovation (DDI) benefits all sectors of our economy, increases efficiency, saves money and resources, and improves quality of life. From safety and security, to the environment and infrastructure, to health and education, the opportunities for DDI to improve our lives are boundless. In SIIA’s recent whitepaper, Data-Driven Innovation A Guide for Policymakers: Understanding and Enabling the Economic and Social Value of Data, we explored the ways our member companies are leveraging data to provide cutting edge solutions. Here’s one case study, from Intuit.

Running a small business is a lonely job at times. Key business decisions are too often made in a vacuum, without access to pertinent data. In this new era of big data, Intuit is working to give small businesses powerful, data-driven insights once only available to much larger businesses.

The Trends feature in Intuit’s QuickBooksOnline empowers small businesses to benefit from the power of their own data as well as the collective wisdom of fellow Intuit customers. Trends anonymously aggregates customer data, allowing small businesses to see how their income and expenses stack up against similar businesses. For example, a roofer in Philadelphia grossing $250,000 a year can compare results with other roofers in the area or across the country. Is that revenue good or bad? Is five percent growth normal or better than companies in your area like you? With Intuit Trends, small businesses can now answer those questions in seconds.

An Intuit customer in Illinois uses Trends to see how his consulting firm’s expenses compare to others in his industry. The business can easily recognize if it needs to continue to increase its profit margins and reduce costs to stay competitive. Trends make it easy for them to stay aware of what is going on in the industry and make key business decisions.

Data-Driven Innovation (DDI) benefits all sectors of our economy, increases efficiency, saves money and resources, and improves quality of life. From safety and security, to the environment and infrastructure, to health and education, the opportunities for DDI to improve our lives are boundless. In SIIA’s recent whitepaper, Data-Driven Innovation A Guide for Policymakers: Understanding and Enabling the Economic and Social Value of Data, we explored the ways our member companies are leveraging data to provide cutting edge solutions. Here’s one case study, from the University of Ontario Institute of Technology.

The rapid advance of medical monitoring technology has done wonders to improve patient outcomes. Today, patients are routinely connected to equipment that continuously monitors vital signs such as blood pressure, heart rate and temperature. The equipment issues an alert when any vital sign goes out of the normal range, prompting hospital staff to take action immediately, but many life-threatening conditions do not reach critical level right away. Often, signs that something is wrong begin to appear long before the situation becomes serious, and even a skilled and experienced nurse or physician might not be able to spot and interpret these trends in time to avoid serious complications.

Project Artemis, part of IBM’s First-of-a-Kind pro-gram which pairs IBM’s scientists with clients to explore how emerging technologies, can solve real-world business problems. The system captured the data stream from bedside monitors and processed it using algorithms designed to spot the telltale signs of nosocomial infection. The truly significant aspect of the Project Artemis approach is how it brings human knowledge and expertise together with device-generated data to produce a better result. The system’s outputs are based on algorithms developed as collaboration between the clinicians themselves and programmers. The algorithm concept is the essential difference between the Artemis system and the existing alarms built into bedside monitors.

The flexibility of the platform means that in the future, any condition that can be detected through subtle changes in the underlying data streams can be the target of the system’s early-warning capabilities. Also, since it depends only on the availability of a data stream, it holds the potential for use outside the ICU and even outside the hospital. For example, the use of remote sensors and wireless connectivity would allow the system to monitor patients wherever they are, while still pro-viding life-saving alerts in near-real time.

Data-Driven Innovation (DDI) benefits all sectors of our economy, increases efficiency, saves money and resources, and improves quality of life. From safety and security, to the environment and infrastructure, to health and education, the opportunities for DDI to improve our lives are boundless. In SIIA’s recent whitepaper, Data-Driven Innovation A Guide for Policymakers: Understanding and Enabling the Economic and Social Value of Data, we explored the ways our member companies are leveraging data to provide cutting edge solutions. Here’s one case study, from Scripps Health.

Scripps Health, a nonprofit community health system, innovative and patient-focused process that has virtually eliminated wait times and has changed the way the hospital delivers care to patients seeking treatment in the emergency department at multiple campuses. Scripps is changing its culture from one in which quality is measured almost entirely by the performance of physicians, to one in which quality is measured by the performance of the processes, systems and teams that support them. They don’t want physicians to be exclusively responsible for quality, but for quality to be measured by the team.

To inform its approach to these changes, Scripps collected and analyzed variation data, or information about whether a particular process was in control. For example, in anticipation of re-engineering its emergency room procedures, Scripps collected and analyzed massive amounts of data on wait times and cross-referenced the information against the type of injury, tests that were ordered and how long it took to discharge the patient. Then they did extensive simulation of our processes using real-life data, modeling how new and different processes might work.

Scripps found that the triage process added an unnecessary and wasteful step in getting patients from the door to a doctor. It was adding time and cost to the system, and not adding significant value. So the company eliminated it. They reduced the critical door-to-doctor time, add capacity to our emergency rooms and improve the quality of our service. As they build a new hospital, Scripps Health is looking into whether they even need to build a waiting room in the ER.

Data-Driven Innovation (DDI) benefits all sectors of our economy, increases efficiency, saves money and resources, and improves quality of life. From safety and security, to the environment and infrastructure, to health and education, the opportunities for DDI to improve our lives are boundless. In SIIA’s recent whitepaper, Data-Driven Innovation A Guide for Policymakers: Understanding and Enabling the Economic and Social Value of Data, we explored the ways our member companies are leveraging data to provide cutting edge solutions. Here’s one case study, from the Memphis PD.

Blue CRUSH (Criminal Reduction Utilizing Statistical History) is a data analytical initiative that provides the Memphis Police Department (MPD) the ability to gain an advantage through insight and agility. At the heart of it is a predictive model that incorporates fresh crime data from sources that range from the MPD’s records management system to video cameras monitoring events on the street. In the realm of crime-fighting analytics, there’s a fine line between the “interesting” and the actionable. It is strength in the latter that makes Blue CRUSH stand out from its predecessors. Blue CRUSH lays bare underlying crime trends in the way that promotes an effective fast response, as well as a deeper understanding of the longer-term factors (like abandoned housing) that affect crime trends. It happens at the precinct level. Looking at multilayer maps that show crime hot spots, commanders can see not only current activity levels, but also any shifts in such activities that may have resulted from previous changes in policing deployment and tactics.

At each weekly meeting, commanders go over these results with their officers to judge what worked, what didn’t and how to adjust tactics in the coming week. They might see, for example, how burglaries are down in one ward, but up another, or where thieves are stealing cars in one ward and dumping them in another. What’s striking, says MPD Chief Godwin, is the granularity. “We’re catching this immediately and we’re doing it every day,” he explains. “On short notice, we’re able to shift officers to a particular ward, on a particular day, right down to the shift level. It’s a bit like a chess match and it’s enabling us to make arrests we never could have before.”