Retail Industry Technology Trends

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Retail Industry Technology Trends

Q: What technology trends will have the biggest impact on the retail industry over the next 5 years?

A: 5 major trends will have the most significant impact on the way the retail industry will do business over the next several years. They are:

  • Mobile Computing
  • Machine to machine (M2M) or The Internet Of Things
  • Consumerization of IT
  • Data Science and Big Data
  • Cloud Computing

Q: Why will mobile computing have such a big impact?

A: The Internet has been disruptive because it allows people to access content and applications instantly, perform business transactions, or connect with each other on-demand from their homes, workplaces, or other locations where they have internet access. This makes us connected, but limited in that we need to be in places that provide the connectivity.
Mobile computing, on the other hand, allows most of the population to be connected – to be online from practically anywhere. It removes the difference between online, and brick and mortar businesses, and requires us to rethink the way we interact with customers. Smart, connected devices are accessible to a wider population, so we need to assume that all of our customers have the option to do business with us online at any time.

Q: Can you explain what you mean by Machine to Machine, Consumerization of IT, Data Science, and Big Data, as well as Cloud Computing?

Machine to Machine (M2M) or The Internet Of Things  

Machine to machine communication (or another name for it is the internet of things) is one of the interesting technology developments for me. When I read about development in this field I think about the Terminator 3: Rise of the Machines movie and I feel that name of the film has become a reality. I just hope that we will not create “Skynet” that will attack human civilization!
Internet of Things that is based on Machine to Machine communication is the highly integrated network of sensors and smart miniature devices that may be incorporated in anything from cars, appliances, toys, clothing and personal monitoring devices. The ubiquity of wireless technology allows us to create a very cost-effective integration of those devices into intelligent networks.
You may buy a wireless device today for less than $ 100 that measures steps taken, distance traveled, stairs climbed, calories burned and sleep quality and duration that syncs automatically with a computer, mobile phones or tablets. The next natural evolution is to integrate this into an intelligent health network and provide you with a health advice or alarm your physician if an anomaly is detected beyond a certain threshold.
My car talks to my iPhone today so that I can see my engine status on the phone, my routes and when I need to change oil or break pads.
One of the most amazing applications of the Internet of Things today is the smartphone Waze application that uses drivers’ GPS devices embedded in their smartphones to calculate the car speed vs. average for each street and warn areas with heavy traffic or problems. It also advises the driver with the best route to get to their destination.
We are only at the beginning of the new “Rise of the Machines” era. The internet of things opens new and almost limitless possibilities to innovate in virtually any area – from how we operate businesses, run healthcare, solve traffic congestion problems, use our home appliances, and educate and entertain our children.
We also cannot forget lessons from the Terminator movie. In reality, the Internet of Things is very complex, and we need layers of machines to create, manage and monitor this network. This makes it vulnerable to cyber crime, cyber war or even target attack on competing businesses or organizations by sabotaging critical infrastructure. You may imagine a scenario when a new cyber assassin may hack a drive by wire vehicle system or target individual to cause serious harm or even death. And this is not a sci-fi movie anymore.
We must understand the benefits and risks that are coming with the Internet of Things and provide education as well as public awareness based on scientific facts rather than hype.

Consumerization of IT

We have witnessed the recent trend where new technologies are first adopted by consumers and then gradually moved to enterprises. Those technologies include the new generation of smart mobile devices such as the iPhone, iPad, and Android phones and tablets; Bring Your Own Device (BYOD) programs; high speed wireless networks; certain Software as a Service applications (SaaS) such as social networks; cloud email; cloud storage; VOIP and webcam video (Skype); and mobile applications.

This trend sets expectations of user simplicity and the speed of change, as many employees use a blend of consumer devices and technologies to perform business tasks to become more productive. At the same time, many enterprises are still behind in the adoption of consumer technologies, mainly due to inflexible policies and procedures, security concerns and limits on IT resources.
We see a great potential to integrate consumer technologies within business organizations without comprising security. However, we also realize this would pose significant challenges for traditional enterprise IT departments that are used to the “perimeter wall” security protection model – they would have to rethink and apply new paradigms. These are not simple leadership tasks and very often require culture shifts in the IT / Business relationship.

Data Science and Big Data

Data science incorporates many different elements and builds on techniques and theories from several fields, including math, statistics, data engineering, pattern recognition and learning, advanced computing, visualization, uncertainty modeling, data warehousing, and high-performance computing. The purpose of Data Science is to extract meaning from data and to create new data products. Data science (which is often used interchangeably with competitive intelligence or business analytics) seeks to use all available and relevant data to tell a story that can be easily understood by non-practitioners.
I was educated as a scientist and spent the first decade of my career applying the scientific methodology in my daily research job. From the prospective data science methods of using math, statistics, pattern recognition, visualization, and uncertainty, modeling was typical in our fundamental experimental science. We applied statistical models in understanding patterns in solar activity, solar- terrestrial relationships, physics of a planet’s atmosphere, and climate. The computing power was much more limited and expensive at that time, and our visualization techniques had been limited to plotting graphs. Statistical methods were also very wieldy used in socioeconomic analysis and modeling.
As computing power became more affordable, the opportunity to apply statistical modeling to different aspects of our life was extended. Data mining, pattern recognition, and artificial intelligence techniques moved beyond fundamental science, defense and intelligence projects to a broad spectrum of business applications.
Since the mid-1990s, we have seen new industries applying data science, particularly with the growth of internet and e-commerce. The leading internet e-commerce and online advertising companies began applying personalization based on predictive modeling and collaborative filtering. More business leaders then started to understand the business benefits of using data science methods in digital marketing and e-commerce. Search engine marketing provided an additional boost to contextual marketing based on predictive modeling.
The growth of social media and mobile technologies in the last five years have dramatically increased the amount of available contextual data – at the same time, it created a new challenge around our ability to convert this volume of data into actionable knowledge.
We continue to experience exponential data growth as the volume of business data worldwide, across all companies, almost doubles every year – or grows 1000 times within a decade.

Big Data

The new term Big Data has recently emerged and is used when the volume and complexity of data exceed our ability to apply traditional database architecture, data storage and analytics software to handle processing and analytics tasks. We cannot solve problems just by brutal computing force such as adding more computing processors, memory, and disks. We have to come out with new approaches by dividing data into parallel streams and using distributed parallel processing to analyze it.

Cloud Computing

We apply the term Cloud Computing to more than one technology delivery model. Cloud computing includes Software as a Service (SaaS), Platform as a service (PaaS) and Infrastructure as a Service (IaaS), which may be further divided into Computing on Demand, Storage on Demand, Security or other services on demand.
As consumers, we are very well familiar with Software as a Service cloud computing when we use Hotmail, Gmail, Google Search, Google Maps, Facebook, Apple iCloud and many other consumer SaaS applications. Businesses have also widely adopted Software as Service and Platforms as service delivery models. The most known example is
Infrastructure as a Service is primarily used by businesses or by consumers indirectly. When they use software running on top of cloud computing infrastructure that is provided by Amazon, Microsoft, Google or other vendors, they are using IaaS.
The benefits of cloud computing are the economy of scale and flexibility. The leading providers of cloud infrastructure can create significantly more efficient data centers than a typical enterprise, and provide clear economic benefits of using shared resources compared with in-house data centers. A significant factor that has accelerated the adoption of cloud computing is the speed of our networks and internet connections, which have grown dramatically in the last 10 years.

Q: Are there any emerging technological innovations that retail businesses should hold off adopting right away?

A: I don’t see any reason to hold off. Companies have to think about how their core business can benefit from integrating new mobile, machine to machine communication (M2M) or big data technologies to execute their innovative strategies. This takes some time, but they should not hold off and wait. If businesses apply the lean start-up methodology to test and tune applications of technology innovation, they will achieve the best results. Instead of copying others, they can enhance their unique value position with new technologies, improve their brand, and position themselves as leaders in their customers’ eyes.
Take the Starbucks mobile loyalty card, as an example. This is the natural progression of their traditional plastic card loyalty program but is much more powerful and engaging.

Q: What companies do you feel have done the best job integrating new technology into the way they do business?

A: Leading technology companies like Apple, Google, Salesforce, Amazon, and others are the obvious frontrunners. But we also see a huge impact in the automotive industry with companies such as BMW, Ford and others who have integrated digital technologies not only in vehicles but in the way they deliver their overall customer experience.
We also see non-technology brands like Starbucks, and other leading retailers, successfully adopting mobile technologies for payments and customer loyalty management.

Q: Igor, you have been an inventor and innovator for over 20 years. Describe your innovation process.

A: It’s not a complicated process. To start, I keep my eyes open for new ideas. I also try to follow key trends in science and technology, and most importantly watch and understand cross-discipline synergies.
Most of the innovative technologies that become known in the last 10 years are the result of research and development conducted over the previous few decades in multiple disciplines: physics, microelectronics, materials, mathematics and computer science. For instance, when Apple released its iPhone, it was the result of technological achievements of multiple industries, such as screen glass, touch screen sensor technology, video compression, voice recognition, Lithium battery, accelerometer, and hundreds of others.
I also always ask why do we do things in a certain way and how can we do it better. I like to understand why I have certain experiences when interacting with a product or a service, and what was good and what was missing in my knowledge – and how it may be designed and provided differently.
Last but not least, it is always very hard work. You have to work smarter and still harder to find new ways to work smarter.

By Igor Nesmyanovich

Igor Nesmyanovich, Ph.D., CISSP

Igor Nesmyanovich, Data Scientist and CEO of Eradium. Igor began his career as a space scientist, and for the more than two decades applied the unique art of science to the emerging digital world. Igor is a Certified Information Systems Security Professional since 2004. Today Igor’s focus is on helping clients to realize business value from using data science and technology in their core business operations and marketing.

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