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MathWorks is the leading developer of mathematical computing software MATLAB and Simulink. MATLAB, the language of technical computing, is a programming environment for algorithm development, data analysis, visualization, and numeric computation. Simulink is a graphical environment for simulation and Model-Based Design for multidomain dynamic and embedded systems.
Engineers and scientists worldwide rely on these product families to accelerate the pace of discovery, innovation, and development in automotive, aerospace, electronics, financial services, biotech-pharmaceutical, and other industries. MATLAB and Simulink are also fundamental teaching and research tools used in global universities and learning institutions. Founded in 1984, MathWorks employs more than 3500 people in 15 countries, with headquarters in Natick, Massachusetts, USA.
Driving Innovation with Real-Time Data and InsightsMATLAB Integrates Workflows
Major engineering and scientific challenges require broad coordination across teams to take ideas to implementation. Every handoff along the way adds errors and delays. MATLAB helps automate the entire path from research to production.
MATLAB Is Trusted
Engineers and scientists trust MATLAB to send a spacecraft to Pluto, match transplant patients with organ donors, or just compile a report for management. This trust is built on impeccable numerics stemming from the strong roots of MATLAB in the numerical analysis research community. A team of MathWorks engineers continuously verifies quality by running millions of tests on the MATLAB code base every day.
MATLAB Is Designed for Engineers and Scientists
Everything about MATLAB is designed specifically for engineers and scientists:
• Function names and signatures are familiar and memorable.
• The desktop environment is tuned for iterative engineering and scientific workflows.
• Documentation is written for engineers and scientists, not computer scientists.
An Experienced LeaderSunil Motwani is the Industry Director at MathWorks India office and manages sales for commercial customers in the country. He has been at MathWorks since 2008 from the time it started operations in India. Prior to joining MathWorks, Sunil worked at Hewlett Packard & Agilent Technologies managing sales of test instruments for various industry segments in India including Aerospace & Defense, Communications, Semiconductor, Automotive and Industrial Automation.
Sunil has over 25 years of experience in sales of technology products across various regions within India having been based at Mumbai, Delhi, Hyderabad & Bangalore during this time. He holds a Bachelor’s Degree in Electronics Engineering from Visvesvaraya National Institute of Technology (VNIT), Nagpur and a Post-Graduate Diploma in Software Technology from National Centre of Software Technology (NCST), Mumbai.
Sunil has been responsible for building market expansion strategies, partnerships, domain expertise and engineering capabilities across the MathWorks’ operations in India. He sees a huge potential emerging in the market for engineering data analytics that drives applications like predictive/prescriptive analytics, fleet analytics, autonomous systems (automated driving, UAVs, Robotics), IoT etc. “Data science platforms make it possible for the engineering teams that develop and maintain the equipment to leverage their wealth of knowledge about how the equipment should operate. This idea of empowering the engineers, or domain expert, is often more appealing than hiring data scientists who have little knowledge of how the equipment operates”, he says.
Addressing Data Science ChallengesSunil feels the significant challenge for the company has been helping customers get data from the source into the hands of end users, which is a common barrier for engineers who need data to formulate requirements for new products, troubleshoot field problems, and come up with new technologies. With more and more streaming data, the industry is faced with a data science challenge. We need to ensure that the speed of data analysis is keeping pace with data intake and, equally important, provide the capability to zoom into and extract insight from stored data throughout the engineering community.
To address this new challenge, one often looks for those who have computer science skills, knowledge of statistics, and domain expertise relevant to their specific engineering problems. However, domain expertise is often overlooked, yet it is essential for making judgment calls during the development of an analytic model. “Instead of searching for elusive data scientists, we’re now working with engineering teams by helping their engineers to do data science with a flexible tool environment like MATLAB, which enables engineers to become data scientists,” he added.
Future Industry PerspectivesSunil foresees there are three key trends to track when it comes to growth in big data analytics, AI, machine learning and deep learning.
Impacts of predictive analytics systems on industries like manufacturing and medical devices
• It’s well-known at this point that data analytics technologies can bring significant business benefits in areas such as predictive maintenance. However, the system architecture for such applications remains an open question. Customers are hesitant to share their data with vendors, logging all of the data from a machine is often impossible given the volume of data created, and responses to events may be needed in milliseconds – much too short of a time to wait for a response from an Internet server. All of these will drive innovation at “the edge”, or on the equipment itself.
• Medical Devices: Predictive analytics systems will allow for more informed and personal relationships between patients and physicians and more effective diagnoses at point-of-care. It is quite possible that predictive analytics will also drive the progress of both preventative and therapeutic care with the data collected from wearables and shared on personal devices.
Machine Learning and Deep Learning
• As it becomes easier and easier to apply machine learning techniques, more products and services will incorporate machine learning models. Embedded systems, typically used for controls and diagnostics, will incorporate machine learning models that can detect previously unobservable phenomena. In 2023, we’ll continue to see machine learning models being incorporated in new places, especially in edge nodes and embedded processors.
• While deep learning continues to look promising, there is still a lot of design and tuning necessary to train a useful deep network. Techniques such as automated hyperparameter tuning appear well-positioned to reduce this work, which should ramp-up the pace of adoption of deep learning.
Domain Experts Take on Data Science
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Stefanie Lindstaedt: A Pioneer In Europe’s Data
Data-driven business is one of the most important global economic trends. Its central paradigms are seeing data as a central corporate asset and utilizing Artificial Intelligence (AI) to transform it into business value. China and the USA have fully embraced these paradigms and are rapidly driving innovation. Europe faces the challenge of finding its own way to promote the exchange and widen the use of data, maintaining high standards of data protection, security and ethics while at the same time enticing (young) AI experts to put their brains to work for European companies and Start-Ups. Stefanie Lindstaedt is a clear frontrunner in this context, on the one hand driving the data-driven business paradigm in Europe and on the other, developing AI talent. As an expert in AI she recognized the potential of data early on and has made significant contributions to the establishment of data-driven business in Austria. Stefanie is the Director of Institute of Interactive Systems & Data Science (ISDS) and the first female professor of computer science at Graz University of Technology. Since 2011 she is also the CEO of
Developing talent is most rewardingStefanie is a strong believer in the power of converging disciplines (inter-/multi-/trans-disciplinary). During her PhD at University of Colorado at Boulder she was part of the cognitive science research institute which brought together researchers from disciplines as varied as philosophy, neuroscience, and computer science. Bridging the boundaries between disciplines is an important experience which students need to learn as early as possible. This involves overcoming the “symmetry of ignorance” (Prof. Gerhard Fischer, Stefanie’s doctoral supervisor): Most people value the opinions and approaches of their own discipline more than those of others, often being ignorant and arrogant about the knowledge accumulated over centuries, expressed in different terminology, and applied in “simple” methods. To learn to respect the different disciplinary backgrounds and cultures can bring us a long way in developing truly novel approaches and theories. “Unfortunately, in Europe, we still have a long way to go to truly embrace convergence of disciplines – in academia as well as in business.” However, the ability to collaborate across different industries and disciplines is key to data-driven innovation. An important measure taken by Stefanie was therefore the development of a unique training programme for data scientists within Know-Center which particularly promotes the development of interdisciplinary cooperation and communication skills. This has enabled the development of a large pool of experience at the center in various fields of application, which also had an impressive impact on the growth of the organization. Currently, with a team of 130 people, Know-Center cooperates with over 150 international scientific organizations and maintains long-term partnerships with more than 50 industrial partners from different sectors.
Making a difference is essential for yourselfStefanie started her professional career at a large automotive group (Daimler) where she developed and led research projects. While learning a lot about corporate processes and politics, she quickly realized that it is not easy to make a difference in such a huge corporation, especially as a young person. This experience was reinforced by the fact that computer scientists and women were not particularly valued in the male-dominated automotive industry back then. What really bothered her was that she could not get close enough to internal customers in order to really make a difference. Therefore, she decided to join an American start-up (GlobalSight) and later Know-Center. In these smaller organizations she could see the change that her work was making. This gave her the foundation to believe in her own ideas and judgement. It also allowed her to understand “networking” in a different way: instead of having to impress people nonstop (which she found very tiring), networking became the much more enjoyable journey of finding like-minded people to work together and develop new initiatives.
Acting as digital innovation hub and trendsetterThis commitment has been recognized at the EU level: Since 2023, Know-Center has been awarded the silver iSpace label by the EU Big Data Value Association every year. It has now received the golden iSpace label for the first time for delivering excellence in innovation. This award highlights Know-Center as a Trusted Data Incubator, accelerating the uptake of Data-driven innovation in all economic sectors. The center so far is the only Austrian research center and one of the few European institutions to have received this award. Cooperation with innovative start-ups and spin-offs are also an important part of Know-Center’s agenda as this provides important impulses for AI research and drives the data-driven economy in Europe. They also provide vital impetus for AI research. In recent years the center has founded three spin-offs on the basis of its research results: Invenium, e-nnovation and OpenKnowledgeMaps.
Data-driven business needs new leadership skillsStefanie’s personal mission is to help European businesses to gain a clear competitive edge in the global marketplace by building up new, data-driven business models in parallel to the established ones. For companies, this requires looking at their business from the data point of view, which often feels like turning everything upside down. Having worked in the fields of AI, Big Data, and Data-driven business, Stefanie has learned that it is tough to help people see their own organization from a different (data) perspective and to realize what opportunities can be gained. Transforming your own business is no doubt the most challenging endeavor one can engage in since it questions all the rules, beliefs, and what the business stands for. This change of perspective requires new leadership skills at the top and middle management levels. Traditionally, leadership is seen as the art of managing the triangle consisting of (1) strategy, (2) structures and processes, and (3) culture and values. With the current wave of digitalization, a fourth dimension enters the picture: disruptive technologies such as AI which is no longer a tool to support or enact the other dimensions of the leadership triangle but a force which influences them all and potentially transforms them. In a continent where programming is not taught universally at school (unlike in Asia and the US), and people are more than critical (afraid) towards new digital technologies, this is a major undertaking. Many of the management boards today do not have sufficient computer science know-how to even ask the right critical questions, let alone to identify technology trends to plan for your business development five years down the road. Therefore, Stefanie urges that there is a need for computer scientists in each management board.
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XPLAINWhy Should Hiring Big Data Talent Become A Key For Businesses In 2023?
Big Data in the current world of business is gaining a lot of buzz as it describes the large volume of both structured and unstructured data that is essential for any company to drive success. Big data is typically larger, more complex datasets, particularly from new data sources. These are so abundant in volume and impossible to manage by traditional data processing software. However, this massive amount of data today can be leveraged to address business issues, that wouldn’t have been able to tackle before. Thus, here are the reasons why should businesses need to hire Big Data talent in 2023.
Improving CybersecurityThe recent data breaches across the globe are a major concern for companies to thwart themselves from these threats. And keeping sensitive business data secured against malware and hacking is one of the biggest challenges for modern businesses. Fortunately, big data here comes into play, making cybersecurity better. It can store a huge volume of data and assist analysts to scrutinize, observe, and identify anomalies within a network. The tools of big data analytics can be utilized to spot cybersecurity threats, including malware or ransomware attacks, compromised and weak devices, and malicious insider programs. With these capabilities, big data analytics seems the most promising approach to improve cybersecurity.
Data LiteracyIn the modern business landscape, almost every business is dealing with an enormous amount of data and managing it is a crucial task, indicating a critical skills gap. However, many organizations now are thinking of data literacy as a spectrum of related skills. And nourishing data literacy skills in each employee will be well worth for them. In a recent study, over half of potential employers, with 59 percent, ranked job experience or an interview requiring a candidate to demonstrate their abilities as top indicators of a person’s data literacy. Conversely, just 34 percent of companies provide data literacy training to their current employees, reports found.
Spurring Better Decision MakingAs businesses have lots of operations to perform and drive success, they need to take well managed and analyzed information to make informed and managed decisions. Earlier, organizations were lacking such methods, but the evolution of big data has provided them the direction and analyzed information that can be used to make better decisions. Big Data also assists enterprises in decision making for better and enhanced customer engagement through real-time data; increased efficiency of business operations; and no extra investment with enhanced capacity.
Solving Business IssuesAnalysis of data is typically essential to pinpoint what is happening in an organization, and what’s going wrong and why. For instance, UPS, the leading package shipping company, has deployed big data to not just boost profits, but to become more efficient and environmentally friendly. The company spends a staggering US$1 billion each year on the technology. Considering reports, by leveraging the On-Road Integrated Optimization and Navigation, or Orion, system, UPS is able to craft optimal routes that lessen distance, time, and fuel.
Big Data in the current world of business is gaining a lot of buzz as it describes the large volume of both structured and unstructured data that is essential for any company to drive success. Big data is typically larger, more complex datasets, particularly from new data sources. These are so abundant in volume and impossible to manage by traditional data processing software. However, this massive amount of data today can be leveraged to address business issues, that wouldn’t have been able to tackle before. Thus, here are the reasons why should businesses need to hire Big Data talent in chúng tôi recent data breaches across the globe are a major concern for companies to thwart themselves from these threats. And keeping sensitive business data secured against malware and hacking is one of the biggest challenges for modern businesses. Fortunately, big data here comes into play, making cybersecurity better. It can store a huge volume of data and assist analysts to scrutinize, observe, and identify anomalies within a network. The tools of big data analytics can be utilized to spot cybersecurity threats, including malware or ransomware attacks, compromised and weak devices, and malicious insider programs. With these capabilities, big data analytics seems the most promising approach to improve chúng tôi the modern business landscape, almost every business is dealing with an enormous amount of data and managing it is a crucial task, indicating a critical skills gap. However, many organizations now are thinking of data literacy as a spectrum of related skills. And nourishing data literacy skills in each employee will be well worth for them. In a recent study, over half of potential employers, with 59 percent, ranked job experience or an interview requiring a candidate to demonstrate their abilities as top indicators of a person’s data literacy. Conversely, just 34 percent of companies provide data literacy training to their current employees, reports chúng tôi businesses have lots of operations to perform and drive success, they need to take well managed and analyzed information to make informed and managed decisions. Earlier, organizations were lacking such methods, but the evolution of big data has provided them the direction and analyzed information that can be used to make better decisions. Big Data also assists enterprises in decision making for better and enhanced customer engagement through real-time data; increased efficiency of business operations; and no extra investment with enhanced capacity.Analysis of data is typically essential to pinpoint what is happening in an organization, and what’s going wrong and why. For instance, UPS, the leading package shipping company, has deployed big data to not just boost profits, but to become more efficient and environmentally friendly. The company spends a staggering US$1 billion each year on the technology. Considering reports, by leveraging the On-Road Integrated Optimization and Navigation, or Orion, system, UPS is able to craft optimal routes that lessen distance, time, and fuel. So, as the number of people trained to assess data will not grow according to the demand companies raise, this will create a challenge for them to hire experts. And as more and more companies become aware of the benefits of gleaning and analyzing data, demand for big data expertise will continue to rise.
A Big Building For Big Ideas
A Big Building for Big Ideas At 17 floors, Data Sciences Center would be hub for collaboration
The stretch of pavement between BU’s College of Arts & Sciences and Sargent College will, if all goes according to plan, soon give way to a towering addition to the Boston skyline: the BU Center for Computing and Data Sciences. Encompassing mathematics and statistics, computer science, and the Rafik B. Hariri Institute for Computing and Computational Science & Engineering, the 17-floor building will orient this intersection as the academic heart of BU. It will also be awe-inspiring.
In spring 2013, the University’s leadership team held a design competition to “find an architect that would make a statement,” says Robert A. Brown, BU president. They selected Toronto-based KPMB Architects to construct a building that would “mark the dynamic change in the University and talk about the century we’re in”—one driven by computational and data sciences.
“Every industry is being formed by new and novel uses of data,” from medicine to media marketing, Brown says. “And those uses of data are going to keep transforming the way society works. It’s becoming inculcated in every discipline, so every one of our fields is developing a data science piece. As a university, we asked how we’ll meet that demand.”
Situated at 645 Commonwealth Avenue, at the corner of Granby Street, now the site of parking lot, the proposed 345,724-square-foot building would be the tallest on campus, at 297 feet high, with a footprint of 20,500 square feet. (By contrast, nearby Warren Towers is 174 feet tall and the Prudential Center is 750 feet high.) “By putting this building at the nexus of campus, we’re making the statement that it’s central to the University,” Brown says.
Following an approval process with the city of Boston that could take up to a year, the project could begin site preparation, including drilling test geothermal wells, in spring 2023. The team anticipates full construction to be under way in fall 2023. When the building is completed, approximately 60 percent of all BU classes will be taught within a five-minute walk of the Data Sciences Center.
“This will be a significant building that will change the architectural fabric of the University, integrating a cutting-edge design into the existing campus and enhancing BU’s—and Boston’s—skyline. People will know where Boston University is,” says Walt Meissner (CFA’81), associate vice president for operations. “You can have modern and old right next to each other if they can work well together.”
The proposed design for the Data Sciences Center picks up elements of the surrounding buildings, like the warm reds of Bay State Road’s brick townhouses, and it will change color, depending on the direction of the sun as it passes across the building’s fins. These fins “likely would be metal, a screening device that would help animate the building,” says Marianne McKenna, founding partner of KPMB. In addition to being architecturally unique, the fins are essential to the building’s energy efficiency. “The profile of the building will be quite extraordinary,” she says. “It’s very timely for BU to step out to have a landmark.” The fins echo the ridged face of the Rajen Kilachand Center for Integrated Life Sciences & Engineering building, which opened in spring 2023, and its expansive windows reflect those of the new Joan & Edgar Booth Theatre and the College of Fine Arts Production Center.
The ground floor is designed to be a public space, incorporating a café, informal lobby spaces, and general-purpose classrooms, as well as BU’s Early Childhood Learning Lab. The second floor—which may be connected to the first by “collaboration terraces” and a grand staircase—would house the BUild and the BU SPARK! programs, as well as additional classroom, collaboration, and study spaces.
The higher, more specialized floors will be organized into departmental neighborhoods connected by a central stair. “Each department has developed a common language of modular offices clustered around open collaboration and computing spaces,” according to the KPMB executive design summary. Each department’s technology-enabled active learning (TEAL) classrooms and collaborative spaces will be tailored to its individual needs. For example, computer science (floors 6 through 10) will likely be designed on an open plan, while mathematics and statistics (floors 3 through 5) may have enclosed offices. The Hariri Institute will be housed on levels 11 through 17.
“The building is designed to have flexible spaces for students and faculty to gather informally and have opportunities to collaborate,” says Jean Morrison, BU provost and chief academic officer. “It’s really state-of-the-art space that is responsive to the needs of highly collaborative and interconnected work.”
Central to this initiative are the proposed interconnected collaboration terraces that form a ramp connecting the ground and second floors. These platforms may include furnished seating areas and walls and windows intended to serve as writing surfaces. Other floors may also feature terraces, event spaces, and cafés to establish the building as a public facility, and an indoor-outdoor conference room on the 17th floor will offer dramatic views of Boston and the Charles River.
“We’ve spent quite a bit of time thinking about how this building will interact, on the street level, with its surroundings,” Morrison says. The design is intended to transform adjacent Granby Street into a two-way landscaped thoroughfare that will improve access to the new Dahod Family Alumni Center and BU Admissions at the Alan and Sherry Leventhal Center, both on Bay State Road.
The proposed plans also call for redesigning the park behind the building with terraced lawns, pedestrian ramps, and bicycle storage. “You’ll be able to walk through it to Bay State Road,” Morrison says. “We want the building to be a seamless part of Commonwealth Avenue so students can easily flow in and out of it.”
If all goes according to plan, the Data Sciences Center will promote sustainable practices like reducing potable water use through low-flow and high-efficiency plumbing fixtures and mitigating light pollution by way of light features that comply with LEED requirements. It would also have between 40 and 55 geothermal wells that use the earth’s natural heat to control the building’s temperature. These, and many other aspects of the design, would ensure that the Data Sciences Center would be a 90 percent carbon-free building.
When the BU campus emerges from the snow in spring 2023 and this new building opens its doors, it will mark the University’s new architectural era and its investment in the burgeoning industry at the center of society.
“For us to build a beautiful building at the heart of our campus attests to the University’s growing strength and impact,” Morrison says. “It’s important to our continued growth as a world-class research university that we build the Data Sciences Center, and that it be architecturally significant.”
Read more about the new Data Sciences Center here.
Lara Ehrlich can be reached at [email protected].
Big Data Vs. Privacy: Striking A Balance
Apple’s secretive nature is legendary. Though key part of its history, all the various projects would be highly compartmentalized and no one knew what other groups did. Employees working on different projects would refuse to sit together in the campus cafeteria for fear of being accused of sharing details on their projects.
And this secretiveness extends to data collection from the iPhone. Apple, like Google, wants to improve on machine learning, particularly as it extends to Siri, but according to a recent report from Reuters, its strict control over data collected by the iPhone is hampering the ability of data scientists to get anything done.
Machine learning experts who want unfettered access to data tend to shy away from jobs at Apple, former employees told Reuters. Apple’s data retention on user-centric information gathered by Siri is six months, while information from Apple Maps expires after only 15 minutes. So it’s rather difficult to gather data from iPhone’s using the Maps function.
This gives Google and even Microsoft’s Cortana an edge in spotting larger trends and – to the extent this one metric is a factor – Apple’s predictions may be further from precise.
In a way, Apple should be applauded. It analyzes its users’ behavior under some very strict self-imposed constraints to better protect the data from outsiders. But it is leaving Apple data scientists with less data, which means they can’t do their job as well.
One Word: Trust
It’s a problem that other companies may face if they don’t strike a balance between analytics and privacy. After monster breaches as Home Depot, Target, Anthem Blue Cross, UCLA Health and Community Health System, people are understandably edgy about the security of their personal information.
Privacy is considered sacrosanct, but it also has its price, notes Tim M. Crawford, CIO Strategic Advisor
and president of his consultancy AVOA. “Forget privacy for a second. If we all took our medical records and diagnostics data where we took this pill for this symptom and what result we got, if we took all the data and could compile it, imagine how much further we’d be because it would be a science because of all the data points. But we are apprehensive to do something like that because we have things like HIPPA,” he said.
This could never happen in the U.S., in part because of so many past breaches have shredded confidence in patient privacy and also because many Americans are less trusting of their government, with more than a few reasons why.
But building trust is the next big challenge, said Crawford. “Culturally, how do we get comfortable with data and how data is used? There is a direct relationship between that statement and trust. So if I trust that Apple will only use this data for their purposes to make Siri better, then that might be okay. But having Apple sell the data and Apple benefitting financially or making it publicly available and potentially compromising my behavior, that’s where you lost trust,” he said.
Mark Thiele, executive vice president of ecosystem evangelism at data center provider SuperNAP, said trust is a core tenant to making the most of people’s personal information for data mining and business intelligence.
“[Companies] need to build trust with their customer base over the data they are custodians of, and they do that by leveraging data in appropriate ways and not abusing it, and taking great care with how they protect it. As soon as you violate that trust you are done. Look at the data breaches and the results we’ve had,” he said. Companies have to figure out on their own how they become a good custodian of the data.
This is not something that can be rushed, either. “It has to happen over time because trust has to play a significant role and the culture has to change as well. More times than not the data is about individuals and behaviors, we have to be comfortable sharing that data. Also we have to have trust in those who are storing and leveraging that data. So the company has a responsibility but so does the individual,” said Thiele.
More Instances?
Thiele thinks there will be more incidences such as Apple’s challenge, but it’s more dependent on the culture of the organization. “It goes back to what the company stands for and what they hold valuable and how they leverage it. Privacy is a core tenant for Apple. Companies that follow along those lines will follow along what Apple does,” he said.
Buytendijk said Gartner has some stats to back this up. He said 59% of respondents in Gartner’s CIO Agenda Survey 2023 said that they are already experiencing digital ethical dilemmas, most prominently around privacy and security.
“At the same time, from information surveys we have learned that around 70% of people indicate that in their organizations there is no logical moment or logical place to raise these digital ethical dilemmas,” he added.
But Buytendijk disagrees. “Even if you apply all kinds of masking, Big Data has certainly complicated things,” he said. “If you know someone’s gender, age and zip code, this is enough already to re-identify the vast majority of people. Big Data most often adds all kinds of contextual information that makes it harder to be anonymous.”
There is a class of technology out there, called dynamic data masking, which replaces identifiable fields with meaningless but consistent codes. So the data can still be used for all types of analysis, and will show the same results, just some fields are meaningless. Once put into action, you can change the information back to being meaningful, only in those cases where needed.
However, that’s not an ideal solution. He cited Georgetown University Professor of Law Paul Ohm’s maxim about privacy, which states “Every perfectly anonymous data set is perfectly unusable.” “The more personal identifiable information you strip, the less opportunity the data gives to provide value for individual customers or individuals,” he said.
Regardless, Thiele thinks it will happen more often among companies that view privacy as a core tenant, as Apple does. He believes Apple’s findings from Siri and Maps data may be the motivation for its strict policies.
“The reality is when you understand how people are using data, how you want to use it is irrelevant,” said Thiele. “Once you start to expose trends, you might start to expose data you’d rather not know. I’m sure they have some analytics, like the most common words used with Siri. That could be an indicator as to why they are taking such a hard stance.”
Crawford said he thinks it will come down to the different markets. “Agencies have an increasing amount of data. I think they swing toward being overly protective of data. Retail tends to swing the other direction. They tend to be looser about info. They share information on loyalty cards and ad campaigns and there’s a lot of behavioral data that comes with it,” he said.
Photo courtesy of Shutterstock.
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