Autonomous vehicles: Three key use cases of advanced analytics shaping the industry

Driven by analytics, the culture of the automobile, including conventional wisdom about how it should be owned and driven is changing. Case in point, take the evolution of the autonomous vehicle. Already, the very notion of what a car is capable of is being radically rethought based on specific analytics use cases, and the definition of the ‘connected car’ is evolving daily.

Vehicles can now analyse information from drivers and passengers to provide insights into driving patterns, touch point preferences, digital service usage, and vehicle condition, in virtually real time. This data can be used for a variety of business-driven objectives, including new product development, preventive and predictive maintenance, optimised marketing, up selling, and making data available to third parties. It’s not only powering the vehicle itself, but completely reshaping the industry.

By using a myriad of sensors to inform decisions traditionally made by human operatives, analytics is completely reprogramming the fundamental areas of driving – perception, decision making and operational information. In this article, we discuss a few of the key analytics-driven use cases that we are likely to see in the future as this category, (ahem) accelerates.

The revolution of driverless vehicles

Of course, in the autonomous vehicle, the major aspect missing is the driver, traditionally the eyes and ears of the journey. Replicating the human functions is one of the major ways in which analytics is shaping the industry. Based on a series of sensors, the vehicle gathers data on nearby objects, like their size and rate of speed and categorises them based on how they are likely to behave. Combined with technology that is able to build a 3D map of the road, it helps it then to form a clear picture of its immediate surroundings.

Now the vehicle can see, but it requires analytics to react and progress accordingly taking into account the other means of transportation in the vicinity, for instance. By using data to understand perception, analytics is creating a larger connected network of vehicles that are able to communicate with each other. In making the technology more and more reliable, self-driving vehicles have the potential to eventually become safer than human drivers and replace those in the not so distant future. In fact, a little over one year ago, two self-driving buses were trialed on the public roads of Helsinki, Finland, alongside traffic and commuters. It was the first trials of its kind with the Easymile EZ-10 electric mini-buses, capable of carrying up to 12 people.

Artificial intelligence driving the innovation and decision making

In the autonomous vehicle, one of the major tasks of a machine learning algorithm is continuous rendering of environment and forecasting the changes that are possible to these surroundings. Indeed, the challenge facing autonomous means of transportation is not so much capturing the world around them, but making sense of it. For example, a car can tell when a pedestrian is ready to cross the street by observing behavior over and over again. Algorithms can sort through what is important, so that the vehicle will not need to push the brakes every time a small bird crosses its path.

That is not say we are about to become obsolete. For the foreseeable future, human judgement is still critical and we’re not at the stage of abandoning complex judgement calls to algorithms. While we are in the process of ‘handing over’ anything that can be automated with some intelligence, complex human judgement is still needed. As times goes on, Artificial (AI) ‘judgement’ will be improved but the balance is delicate – not least because of the clear and obvious concerns over safety.

How can we guarantee road safety?

Staying safe on the road is understandably one of the biggest focuses when it comes to automated means of transportation. A 2017 study by Deloitte found that three-quarters of Americans do not trust autonomous vehicles. Perhaps this is unsurprising as trust in new technology takes time – it took many years before people lost fear of being rocketed through the stratosphere at 500 mph in an aeroplane.

There can, and should, be no limit to the analytics being applied to every aspect of autonomous driving – from the manufacturers, to the technology companies, understanding each granular piece of information is critical. But, it is happening. Researchers at the Massachusetts Institute of Technology are asking people worldwide how they think a robot car should handle such life-or-death decisions. Its goal is not just for better algorithms and ethical tenets to guide autonomous vehicles, but to understand what it will take for society to accept the vehicles and use them.

Another big challenge is determining how long fully automated vehicles must be tested before they can be considered safe. They would need to drive hundreds of millions of miles to acquire enough data to demonstrate their safety in terms of deaths or injuries. That’s according to an April 2016 report from think tank RAND Corp. Although, only this month, a mere 18 months since that report was released, professor Amnon Shashua, Mobileye CEO and Intel senior vice president, announced the company has developed a mathematical formula that reportedly ensures that a "self-driving vehicle operates in a responsible manner and does not cause accidents for which it can be blamed."

Transforming transportation and the future

In many industries, such as retail, banking, aviation, and telecoms, companies have long used the data they gather from customers and their connected devices to improve products and services, develop new offerings, and market more effectively. The automotive industry has not had the frequent digital touch points to be able to do the same. The connected vehicle changes all that.

Data is transforming the way we think about transportation and advanced analytics has the potential to make driving more accessible and safe, by creating new insights to open up new opportunities . As advanced analytics and AI become the new paradigm in transportation, the winners will be those who best interpret the information to create responsive, learning, and connected vehicles capable of making autonomous vehicles as simple as getting from A to B.

iottechnews.com: Latest from the homepage

Wipro Launches Industry specific Solutions on SAP Leonardo

Wipro Limited has announced the launch of industry-specific solutions on SAP Leonardo. SAP Leonardo is a holistic digital innovation system. It comprehensively integrates future facing technologies and capabilities into SAP Cloud Platform.

Initially, the duo will focus on developing use cases that leverage blockchain technology for the utilities industry. While, the first set of solutions will target the utilities industry, the next will cover the consumer and manufacturing sector.

Now, Wipro will develop industry-specific analytics “apps” on its insights-as-a-service solution. The apps will also be built around Data Discovery Platform, Big Data and data intelligence capabilities.  Read more…

The post Wipro Launches Industry specific Solutions on SAP Leonardo appeared first on Internet Of Things | IoT India.

Internet Of Things | IoT India

Why the IoT industry needs to pay attention to ePrivacy Regulation

Why the IoT industry needs to pay attention to ePrivacy Regulation

How is the EU’s ePrivacy Regulation, a companion law to GDPR, likely to impact the IoT industry? David Meyer reports. 

The European Union’s ePrivacy Regulation, currently under development, is controversial for many reasons. A companion law to the incoming General Data Protection Regulation (GDPR), it will extend confidentiality rules for traditional telecommunications players to internet-based services such as Gmail and WhatsApp, and entrench “Do Not Track” anti-cookie preferences in law.

However, due to its scope, the regulation is also likely to have a major effect on the IoT industry. But as for how great of an effect, and what the implications will be, there still remains considerable disagreement.

For the doomsayer side of the argument, look no further than a study released on Thursday by Berlin-based lawyer Niko Härting, for Hunton & Williams’s Centre for Information Policy and Leadership. Härting argues that the ePrivacy Regulation (as proposed by the European Commission) would make life very difficult for anyone providing machine-to-machine [M2M] communications, wearables, connected cars and other IoT services.

Härting raises the example of the Fitbit Surge, a wearable fitness tracker. The regulation explicitly covers the data sent in machine-to-machine communications. However, it defines communications data as being either content such as, but not limited to, text, voice, videos, images, and sound, or metadata about that content.

The Fitbit sends raw data that isn’t text, voice, videos, images or sound, and certainly isn’t metadata about those types of content, but might or might not still be defined as “content”.

Read more: Connected healthcare may violate user privacy, warns Forbrukerradet

Protecting personal data

If it is communications content, then the ePrivacy Regulation would demand consent for its transmission. However, as it is also personal data (data that can be linked with an identifiable individual), it’s also covered by the GDPR, which allows other legal justifications for its transmission, such as the performance of a contract.

So is the Fitbit’s raw data communications content? Does its transmission require explicit consent, or is the contract between the user and Fitbit sufficient? The answers, according to Härting, are worryingly unclear.

“We would get rules that contradict what we have in GDPR, because the GDPR is not consent-oriented,” Härting told Internet of Business. In the GDPR, consent is one of six alternative options for how data processing can be lawful, he points out, “whereas the draft ePrivacy Regulation is totally focused on consent…”

“If this draft comes through, we [lawyers] will get a hell of a lot of new work, because all the processes that are now shaped in a way that are compliant with the GDPR have to be looked at again to see if they comply to ePrivacy. That is crazy,” he adds.

There are other concerns that IoT companies should bear in mind, too. The ePrivacy Regulation would effectively outlaw Wi-Fi and Bluetooth tracking, unless the operators of the tracking services display “prominent notices” at the edge of the covered areas – so no more traffic monitoring on the go, for example.

Again, the GDPR might allow this on the basis of “legitimate interest” that outweighs the interest of the data subjects, so again, there is a potential clash between the two pieces of legislation.

privacy concerns

There’s are important questions around how we protect users’ privacy while making IoT viable

Practical issues

With connected cars, you get both the raw data and Wi-Fi/Bluetooth tracking issues – consider sensor-laden roads that can communicate with connected cars, or the iPhone safe-driving mode that uses connection to a car’s Bluetooth network to establish that the user is in their car. That could mean cars festooned with notices warning of their networks.

And then there’s the consent issue. “The development of any application that enables cars or components to communicate would be burdened with the necessity of creating processes that allow for consent on both ends,” Härting’s analysis reads.

“The transmission of signals from a car to a garage for maintenance purposes would require consent of the owner of the garage as well as the driver’s consent. Whenever there is a new driver, consent would need to be renewed.”

However, the regulation’s champions disagree that it will cause confusion. Jan Philipp Albrecht, the German Green MEP who was the lead rapporteur on the GDPR and is a shadow rapporteur on the ePrivacy Regulation, told Internet of Business that the new regulation’s definition of electronic communications “doesn’t cover everything”.

“There is clearly also the application of ‘electronic communications’ to situations where two machines are communicating with each other, but it has to be in the context of human-to-human communication, between two natural and legal persons,” Albrecht insisted. “M2M can be part of that interpersonal communication… That is to make sure there is no broken link [in people’s privacy protections].”

Read more: Amazon Echo murder case marks the death of privacy as we know it

Interpreting ePrivacy Regulation

Albrecht gives the example of automatic translation services. Without the ePrivacy Regulation’s protection, he says, there “would be a missing link of confidentiality of communication. That is why this whole definition is written in a technically neutral way…”

“Of course that means many other M2M processing activities, [involving] either personal data or non-personal data, is not covered by the ePrivacy Regulation. Personal data would then be only covered by the GDPR, and non-personal data would be covered by none of the privacy rules.”

“People have many misunderstandings and fears about this which may not be appropriate… It’s only data processed in the context of interpersonal communication,” Albrecht adds. “As long as it’s just the procedures between IoT services, you are not in the scope of the ePrivacy Regulation.”

Härting, however, strongly disagrees. “I would love to ask Jan where he finds anything of what he said in the text of the draft regulation.”

“If you search for M2M you will find the recital that clearly states that M2M communication comes into the scope of the regulation.”

Of course, all this refers to the Commission’s original proposal. This week, the European Parliament will vote on its own version of the text, as amended last week by its civil liberties, justice and home affairs (LIBE) committee. And only then will ‘trilogue’ negotiations begin between the Commission, the Parliament and the EU’s member states.

Read more: Privacy and IoT: innovative regulations needed to regulate innovation

The need for clarity

The amendments passed last week in committee don’t necessarily clear up the confusion that IoT services might face as a result of the new regulation, which is supposed to come into effect alongside the GDPR in May next year (although this timescale is looking increasingly unlikely for the slow-moving ePrivacy Regulation). Raw data still doesn’t fall easily into any definition, for example.

And, while an amendment limits the regulation’s applicability to M2M to cases where “the information can be related to the identifiable end-user receiving the information”, that still means consent will be required for many types of IoT. “Smart homes, wearables [and] connected cars will always ‘relate to end-users’,” said Härting.

Legislators dealing with the ePrivacy Regulation may be primarily concerned with issues around cookies, tracking and the modernization of EU law to cope with services like WhatsApp superseding SMS, for example, but there will almost certainly be serious consequences for the IoT industry. So it would be a good idea to pay attention not only this week, but over the coming months.

The post Why the IoT industry needs to pay attention to ePrivacy Regulation appeared first on Internet of Business.

Internet of Business

Golden State Foods and IBM Watson IoT set new standards in foodservice industry

At this year’s Genius of Things (GoT) event in Boston, IBM announced that we are working with Golden State Foods to embrace two big opportunities for growth and change in the food services industry. Golden State Foods are using Watson IoT to assist fleet management and safety for their 2,000+ trucks, and creating connected restaurants in over 125,000 locations.

To say that Golden State Foods operate on a large scale is something of an understatement. They are one of the largest diversified suppliers to the food service industry, servicing around 125,000 restaurants in over 60 countries from their 50 locations, and producing 400,000 hamburger patties per hour. Many of the restaurants they supply are quick serve and rely on speedy, safe and quality food production and delivery to meet their customers’ expectations.

Innovating with IoT and Blockchain

Innovation is something of a watch word for Golden State Foods. And for them, it means something very different from exotic menus, cocktails in jam jars and pickling unusual foodstuffs. Instead, it means adopting cutting-edge technology like IoT and Blockchain to optimize operations, drive efficiency and cut unnecessary costs.

When time is of the essence and operating scales are vast, the IoT can add tremendous value. Golden State Foods is using insights from connected things to improve the management and safety of its 2,000+ trucks, to ensure that its customers receive their food supplies on time and in top condition.

A Golden State Foods truck

A Golden State Foods truck

Transporting perishable materials such as raw beef comes with its own set of challenges. Delivery delays caused by broken down vehicles or scheduling flaws mean spoiled produce and disappointed restaurants. When these vehicles are IoT-enabled, however, they become easier to track and maintain. Sensor data collected and analysed by the Watson IoT Platform ensures that issues are automatically reported and addressed before they can cause bigger problems down the line.

The supply chain has a big part to play here, too. To achieve operational efficiency, Golden State Foods are using IBM Blockchain to create visible, secure and immutable ledgers, that can be viewed by individual stakeholders, improving visibility and accountability for all parties. This in turn means fresher ingredients, improved food safety and fewer operational costs – all benefits that are passed on to Golden State Foods’ customers.

IoT for Retail: the Connected Restaurant project

Away from the supply chain, there are other, more ‘front-of-house’ opportunities to connect and evolve – in the restaurants themselves, for example. The Connected Restaurant project is using IBM’s Connected Store solutions to revolutionize the way Golden State Foods’ customers run their restaurants. Door hinge sensors, digital signage, shelf weight sensors, gesture recognition and Wi-Fi tags collect valuable data that help managers understand how their restaurants consume energy, manage inventory, and even keep clean – and help them identify ways to do those things better. For example, temperature sensors in food storage facilities could trigger alerts if food reaches an unsafe temperature, to prevent spoilage. And with data from occupancy sensors, heating and lighting can automatically be adjusted to reflect the fluctuations in need at peak times, and provide only what is needed.

With more insights into how the restaurants operate, managers have the information they need to improve operational efficiency – automating some processes and managing others remotely.

From fresher ingredients to reduced costs, Golden State Foods are helping their clients deliver fast, effective service and great food to anyone who has grabbed a bite on the run.

Learn more

The post Golden State Foods and IBM Watson IoT set new standards in foodservice industry appeared first on Internet of Things blog.

Internet of Things blog

Integrating Analytics in Your Organization: Lessons From the Sports Industry

Shane Battier, retired National Basketball Association champion, was recently named the director of basketball operations and analytics for the Miami Heat. A player transitioning to the front office in sports is nothing out of the ordinary. It’s the last word — analytics — in his new title that is noteworthy. In the past, a statistics major who never wore an NBA uniform filled that type of role. Now, an analytically minded former player is mining the spreadsheets for game-changing insights. Battier’s new position symbolizes a watershed moment in the sports data revolution. Or, as Dave Cameron, managing editor of FanGraphs, a baseball statistics website, said, “Now the jocks are becoming the nerds.”1

Analytics in sports was not always accepted and embraced. Over the past 15 years, however, pioneering general managers — like Billy Beane of the Oakland A’s baseball team, whose analytical approach to team building with undervalued players was famously chronicled in The New York Times best-selling book and film Moneyball; Daryl Morey of the Houston Rockets, who helped usher in the 3-point shooting revolution in the NBA; and Theo Epstein, who led both the Boston Red Sox and Chicago Cubs baseball teams to World Series championships after decades-long droughts — introduced scientifically based approaches to team building. But they faced skepticism from the “old school”: a group of players, coaches, scouts, and executives who were successful without advanced metrics. Perhaps the most extreme voice was NBA Hall of Famer Charles Barkley, who once asserted, “Analytics don’t work at all. It’s just some crap that people who were really smart made up to try to get in the game because they had no talent.”2

Barkley and other naysayers are being proved wrong. Teams on all levels — professional, college, and even high school — are using data to construct rosters, develop game plans, and improve athletes’ health and wellness. Because of its effectiveness, use of analytics is expanding beyond the early-adopting sports of baseball and basketball to football, hockey, soccer, tennis, golf, and mixed martial arts. Analytics has also crossed over into the business side of sports organizations, helping executives operate more efficiently and drive more ticket, sponsorship, and merchandise revenue.

What can executives in other industries learn from studying the sports data revolution? Although there are many lessons,3 one especially valuable innovation is analytics integration: the ability of an executive to integrate an analytics program within the rest of the organization. It might be tempting to introduce an analytics program as a separate corporate function, one free to innovate and do things differently. But while that might make things easier at the start, it will ultimately undermine the effectiveness of analytics, which needs to flow across organizational decision-making to provide its true value.4 A better approach is to integrate from the beginning, even if leaders face pushback from analytics skeptics.

How can leaders integrate analytics effectively into their organization? This article explores three integration strategies that have been successful in the sports industry: practicing collaborative analysis, establishing a common language, and deploying accessible technology. In doing so, we draw on the experience of three executives in distinct sectors of sports analytics, interviewed for this research: Ben Alamar, director of production analytics at sports media company ESPN Inc.; Dr. Marcus Elliott, founder of P3, a sports performance facility in Santa Barbara, California; and Jessica Gelman, CEO of Kraft Analytics Group, a technology spinoff of the Kraft Group LLC, which also owns the New England Patriots football team and the New England Revolution soccer team. Each has led analytics transformations at organizations and observed firsthand the challenges and opportunities of analytics integration.

Practicing Collaborative Analytics

The critical step in any analytics project is to identify the problem that needs to be solved. For sports organizations, the problems are straightforward: They want to win more games on the field, and drive more revenue and lower operating costs off the field. In solving these problems, analytically minded sports executives do not view analytics as the only thing that can chart the path forward to better decisions. Instead, they see it as another source of information (albeit a powerful one) to aid in the decision-making process.

This inclusive approach to information is key to integrating analytics throughout the organization. Ben Alamar, director of production analytics at ESPN and author of Sports Analytics5, calls this “collaborative analysis.” It involves combining advanced quantitative metrics with other types of information, including qualitative analysis, unstructured data, and survey data, to inform the decision-making process. Importantly, the people responsible for collecting and analyzing these different types of information are included in the decision-making process. This approach ensures all decision-makers are operating from the same set of information and gives them a holistic view of the problem at hand.

Collaborative analysis is an especially important tactic for executives who are up against analytics skeptics. Presenting the analytics viewpoint alongside qualitative assessments, self-reported surveys, and other data sources signals to key stakeholders that the traditional ways of making decisions are still valid, but new forms of information should be considered as well. Thus, while key stakeholders might disagree about how to interpret all the information, they are starting from the same foundation. Over time as the analytics viewpoint gains more credibility, decision-makers may start to weight it more heavily than other sources of information.

Alamar saw firsthand the value of collaborative analysis when he was a consultant for the NBA’s Seattle Supersonics (now Oklahoma City Thunder). For the 2008 draft, the team had a need at point guard, a position typically charged with leading an offense and putting teammates in a position to score. They were evaluating a number of college and international basketball players, including Russell Westbrook of the University of California, Los Angeles. The scouting report on Westbrook emphasized his stellar athleticism and work ethic. He also showed steady improvement as a college basketball player, which suggested he would continue to progress in the pros. The problem was that he primarily played shooting guard, a position that focused on scoring himself rather than helping others find chances to score.

The scouts and coaching staff could make an approximation of his ability to switch positions. But for the team to expend an early round draft pick, it would need better information. And this is where analytics played a key role. To help address the critical unknown about Westbrook, Alamar developed a new metric to better understand the effect of Westbrook’s passes. Did they lead to teammates shooting a higher shooting percentage? Alamar’s analysis revealed that they did. Moreover, according to the metric, Westbrook compared favorably with other known and well-respected veteran point guards currently in the league.

Seattle ended up drafting Westbrook fourth in the draft as one of its two picks. The decision was the result of collaborative analysis; scouts, coaches, and analytics staff contributed data that led to the decision. Alamar’s work was crucial in not only answering an unknown question but also, as he says, in “reducing risk and providing another piece of confidence.” Moreover, in the long-term, his analysis helped affirm the value of analytics across the full decision-making team in Seattle. The Sonics (now Thunder) undoubtedly got this decision right; at the end of the 2016-2017 season, Westbrook was one of the best point guards in the league and won the most valuable player award.6

When integrating an analytics program into an organization, make sure that all relevant sources of information — not just advanced metrics — are brought into the fold and shared across stakeholders. This collaborative analysis approach can be applied to a range of decisions. Human resources professionals can pull together a variety of qualitative and quantitative data on recruitment decisions. A marketing team considering a new brand campaign can take into account multiple data streams on consumer behavior and brand perception. Or a nonprofit looking at ways to drive more donations could pair traditional metrics with other predictive analytics. Ultimately, the introduction of analytics is not a replacement but an enhancement of the existing decision-making process. Greater diversity of information and interpretation will lead to better decisions — and a permanent seat at the table for the analytics team.

Establishing a Common Language

Analytics has its own language, which can be confusing or downright intimidating to those outside the field. As a result, organizations can find themselves in communication gridlock, with potential insights from analytics going unused or unnoticed. Similar to how global companies invest in improving cross-cultural communication or even mandate English as an official language, executives looking to maximize their analytics program must establish and reinforce a common language for analytics. This does not mean all employees should learn how to program the statistical software R — rather, they should have a fundamental understanding of how data is collected, analyzed, and used.

Dr. Marcus Elliott of P3 has taken a novel approach to establishing a common language. P3 uses data and analytics to improve athlete performance and prevent injuries. It employs sports scientists, biomechanists, performance coaches, and strength coaches, all of whom have different orientations and biases toward analytics. Having a multidisciplinary team is a major strategic advantage, but it also presents a communication challenge for the organization. Elliott acknowledges, “We’ve got people who are really confident in their field and are asking them to venture into a field where they are novices. But it’s essential to this whole operation working well. The hand has to know what the foot is doing, even if it is not an expert in the foot.”

To enable collaboration and “cross-talk” around analytics, Elliott has prioritized three measures. First, he makes sure that all team members are focused on the same goal, which is “to make athletes’ lives better.” In doing so, the goal serves as the rallying point around which everyone on his team, regardless of discipline or expertise, communicates. He says, “If someone works in our biomechanics or sports science side, their job is not just to collect data. The data needs to end up making the player’s life better.” For example, if a draft prospect visits the facility to train for an upcoming tryout, the P3 team members will be aligned to getting him ready for the most important phase of his professional career to date. When doing research, it can be a natural tendency to get lost in the minutiae. Elliott’s goal-centered approach forces the individual experts to stay focused on the larger mission and consistently translate how their work is helping improve an athlete’s life.

Second, Elliott limits jargon. He has a simple rule: “Do not speak in the vernacular of your profession, but in a language that people will understand and use.” A physician, Elliott has spent his career learning how to “talk doctor,” using terms that his peers comprehend but patients do not. But at P3, he has focused on simplifying his team’s vocabulary, and, as he put it, “democratizing the conversation” so that information flows through all people associated with the team. Let’s say the draft prospect goes through an initial round of tests and the sports scientists reveal that he has “valgus deviation” — a technical term no one else understands. They eliminate the term from the discussion and focus instead on the actual problem: While running, the athlete’s knee buckles inward 15 degrees on the right side and 4 degrees on the left side. The sports scientists, biomechanists, and performance and strength coaches now have a commonly understood condition to correct and can work together on a plan to rebalance the athlete’s weight distribution.

Finally, throughout the process of helping an athlete, Elliot organizes focused educational opportunities for the diverse members of his team. In the example of the draft prospect, he exposes the performance coaches to the lab work of the biomechanists on the athlete’s progress redistributing his weight, and vice versa. Importantly, this is not a one-time job shadowing: His company is committed to consistently immersing its various professionals in one another’s disciplines. The by-product is an organization with a better-understood and constantly evolving common language.

To maximize the potential of analytics, executives must establish a common language for their analytics program — one that is grounded in the organization’s goals, jargon free, and reinforced through immersive education. The lessons from P3 are applicable to a wide variety of decisions and disciplines. For example, supply chain optimization can be an analytics-heavy function that only analysts and managers fully comprehend. But if executives in other departments, frontline workers, and human resource professionals understood the fundamentals of using analytics to lower operating costs, the organization overall would be in a better position to design and implement better solutions.

In the end, the language of analytics can be easy to hide behind, making your team look smart at the expense of others. This doesn’t help your colleagues or your clients. A common language will help executives integrate analytics more holistically and foster the information sharing that will lead to more effective decision-making.

Deploying Accessible Technology

Technology is the foundation of every analytics program. When analytics technology is integrated effectively, all key stakeholders have access to critical information in an understandable format and can make data-driven decisions on a regular basis. But getting to this point can be easier said than done. For starters, there’s a wide range of data management, data analysis, and data visualization technologies from which to choose. The technologies have to work well together and be easy for the end users across the organization to use. How should leaders think about adopting analytics technologies and deploying them across their organization to drive business impact?

The Kraft Group has been at the forefront of this question for the last decade. Robert and Jonathan Kraft have long been known as innovators in the sports business, and the strategic implementation of analytics has been a key to their success. On the field, their five-time Super Bowl-winning New England Patriots football team are among the best at using data to win games. On the business side, the Kraft Group has invested in and built its own proprietary analytics technology capabilities to better engage customers through their behaviors at the stadium on game day and throughout the year via digital engagement or with partners.

The business analytics side of the Kraft Group had become so successful that in 2016 it spun off the Kraft Analytics Group. KAGR, as it is known, is run by CEO Jessica Gelman, who has worked for the Kraft Group for more than 15 years and is also the cofounder of the MIT Sloan Sports Analytics Conference. KAGR’s clients include other sports organizations and major players in the sports industry, as well as entertainment companies.

Gelman’s team focuses on technology and consulting services to help organizations become data-driven and fully use analytics to drive business impact. For example, to drive season ticket renewals for the New England Patriots, KAGR integrated multiple data systems and built a model to understand which fans were most likely to renew as season ticket holders. They identified the behaviors most significantly correlated with nonrenewal and found that if fans missed four games, they were not likely to renew. Her team then developed messaging outreach after the first, second, and third missed games. All of the messaging was automated through technology. As a result, the retention rate for fans was 3% higher than for those who did not receive the messaging. Gelman and her team used a number of technologies for this initiative, including a data warehouse, data visualization, customer relationship management software, marketing automation, appended data, and proprietary algorithms. Importantly, they developed an analytics-based technological solution that could be applied to other similar business challenges within the Kraft Sports Group as well as to external clients.

With analytics programs often requiring multiple technologies, being strategic about technology deployments is imperative. Gelman suggests the following questions to help with these decisions:

  1. What is your business need, and who will the technology impact (both positively and negatively)?
  2. What is your underlying approach and strategy with regard to technology? Does the historical approach fit with the new needs?
  3. Is the technology translatable from the sales process into the final product delivered by the vendor?
  4. Is the technology flexible and expandable, and can it grow with you as a business?
  5. Is the technology company/partner able to keep pace with the needs of their clients?

Let’s say a company is evaluating data warehouse technologies from several vendors to support a new digitally focused customer service initiative. Here’s how the company might apply Gelman’s questions:

  1. The business need is to develop more personalized, efficient, and effective customer service on digital channels. The technology will positively impact customers and the analytics team, but have a short-term negative impact on the customer service team, which will need to learn new procedures.
  2. The company uses technology to be customer-centric in service offerings. With shift to digital channels, it must change its historical approach to capture and analyze new data to better serve customers.
  3. The company prioritizes vendors who can show evidence of their product’s capabilities. A proof-of-concept test is preferred.
  4. The company prioritizes vendors who can accommodate and integrate new data into the warehouse over time.
  5. The company prioritizes vendors who have a track record of evolving with their clients’ needs.

Once your organization makes a decision on and deploys an analytics technology, don’t overlook the importance of training. The ease of using analytics technology can make or break the successful integration of analytics across the organization. If all key stakeholders have access, can easily incorporate the tools into their daily work, and expect data-driven arguments, the chances of success with an analytics program rises substantially. If the opposite is true, failure is all but guaranteed.

Get Small Wins

Analytics can be a powerful tool that helps organizations make better decisions more efficiently. By using analytics, executives in the sports world have achieved a variety of benefits for their organizations both on and off the field. But these benefits do not accrue overnight. As you develop your own analytics program, follow Gelman’s advice: “Get small wins that have big impact…. Identify the opportunities where there are challenges today and investing in an analytics program will help.” Major organizational transformations are hard; they can consume tremendous amounts of time and effort. So take the advice of change experts and look for analytics wins you can achieve quickly. But not just any wins: high-profile wins, like the Patriots season-ticket retention test.

When identifying your own opportunities, prioritize integrating your analytics program into the rest of your organization. This article offered three possible integration strategies that have worked in sports and can translate to other industries:

Practice collaborative analytics. For your next key decision, make a point to bring both qualitative and quantitative information into the process. Increasing your key stakeholders’ exposure to the analytics viewpoint is often a critical step in becoming a more data-driven organization.

Establish a common language. Assess your organization’s analytics terminology. Do all key stakeholders understand it? If not, what educational efforts can you put into place to increase the language proficiency of your key stakeholders?

Deploy accessible technology. Audit your analytics technology to ensure that it not only serves your business needs but will also be used on a consistent basis and by a variety of people within your organization.

Designing a technically sound analytics program is one challenge. Equally important is creating the environment in which analytics is integrated across the organization. Executing on these three strategies will help your analytics program become core to how your organization operates.


MIT Sloan Management Review