NEW YORK—As the TV industry grapples with the measurement problems that have been produced by rapid changes in the way people watch programming, the Coalition for Innovative Media Measurement (CIMM) has issued a major new study that reviews the methodological challenges currently facing big data-based, currency-grade measurement providers, and provides guidance on how best to evaluate and address potential issues.

The new CIMM paper by industry experts Josh Chasin and Albert Lau describes how U.S. TV consumption behaviors have changed dramatically over the last decade, making measurement increasingly challenging as the landscape becomes more fragmented and complex. With the growing availability of big TV datasets reducing barriers to entry in the marketplace, the ecosystem now operates across multiple measurement vendors—meaning different data sets, methodologies and outputs.

“The U.S. TV and video marketplace is fragmented and extremely complex, presenting significant challenges for measurement and currency, across different platforms and devices,” CIMM Managing Director Jon Watts said. “Measurement vendors are working hard to address these challenges and are making tremendous progress, but there is scope to support their efforts through collaboration and cooperation. We hope this new study is a powerful contribution to the industry, helping to identify potential solutions to some of the biggest methodological challenges facing vendors.”

In addition to sharing their own perspectives in the study, titled “Solving Today’s Evolving TV Measurement Puzzle,” Chasin and Lau secured input from industry experts across the measurement marketplace—including representatives from the buy side, sell side and each of the four primary providers of currency-grade measurement—to establish broad consensus about the key elements driving differences in the outputs of big data-based measurement solutions.

With these insights in mind, the study pinpoints six critical methodological challenges faced by big-data measurement providers today: 1) assessing the impact of identity; 2) addressing footprint coverage bias; 3) onboarding, cleansing and combining of big data assets; 4) metadata; 5) integrating linear and digital streaming; and 6) processes and methods for addressing coverage gaps and shortfalls.

“In today’s alternative currency and measurement marketplace, it is essential to not only understand what the new quality parameters are, but also to identify opportunities for the industry to collectively help measurement vendors address a new set of methodological complexities,” Chasin, principal at KnotSimpler, said. “With alignment across the TV measurement ecosystem, we can develop shared assets, and codify the most effective practices and industry initiatives able to stabilize and address some of the most pressing measurement challenges faced today.”

Lau, principal of analytics at DirecTV, added: “With competing methodologies and datasets yielding different results for the same linear TV programming asset, measurement customers must be involved in the assessment and due diligence required to potentially onboard any viewership metric that would impact day-to-day business functions since there are no set industry standards or requirements. In addition, all the key stakeholders like publishers, networks, agencies, researchers, and marketers, and measurement users need to push for metric standards that will provide consistency, projectability and interoperability while providing a framework that can be scaled to accommodate media consumption and measurement changes such as the integration of nonlinear TV data like streaming, addressable and VOD.”

To address these challenges, Chasin and Lau recommend a collective and collaborative approach to the future TV measurement landscape. This includes the future of identity and the scoring and validation of identities, personification research to remediate the delay in migration to alternative currencies for demographic transactions, a single industry-accepted source and taxonomy to mitigate variations in metadata, and the creation of codified standards for ACR-based smart TV data, the authors said.

More information is available here.

NEW YORK—Nielsen has announced that LiveRamp is now interoperable with Nielsen to power Big Data + Panel advanced audience planning and measurement in Nielsen ONE.

Mike Bregman, chief activation officer for Havas Media Network North America praised the integration by explaining that "LiveRamp and Nielsen's new integration allows Havas Media to holistically plan on, buy, activate and measure custom-built advanced audiences as part of our proprietary Converged product suite to better serve our clients. This integrated solution will enable us to leverage first- and third-party data and we're excited to introduce it across our portfolio."

The deal seamlessly connects first and third-party data sources to Nielsen via LiveRamp, which enables audiences to be planned and measured across platforms by leveraging Nielsen ONE Ads, Nielsen's planning suite, and Nielsen's Data Driven Linear solutions. 

It also means that clients can create and leverage advanced audiences across the Nielsen ecosystem for end-to-end planning and measurement across screens at both the household and person-based level.

"We've seen the industry demand grow across linear and digital for highly targeted audiences powered by big data," said Stefan Maris, chief partnerships officer, Nielsen. "Our integration with LiveRamp enables seamless connectivity of both first and third-party data to plan and measure advanced audiences at scale using Big Data + Panel."

LiveRamp's integration furthers Nielsen's interoperability within the marketplace, enhancing long standing integrations with publishers, platforms, data providers, and agency holding companies, Nielsen said. 

"Our integration with Nielsen delivers a more holistic and detailed understanding of audiences across screens that empowers both brands and agencies to improve activation, and publishers to enhance the value of their inventory," said Vihan Sharma, chief revenue officer, LiveRamp. "All parties gain access to deep insights that fuel a powerful flywheel around planning and measurement."

NEW YORK—Nielsen and LG Ad Solutions have signed an agreement that creates the largest automatic content recognition (ACR) data footprint in the U.S. 

With this arrangement LG Ad Solutions joins previously announced Comcast, Vizio, Roku, Dish and DirecTV deals that increase Nielsen's big data footprint. The agreement also expands Nielsen's CTV campaign coverage via direct integrations that leverage big data. Nielsen's other direct integrations include Netflix, Sling, Samsung, Vizio, Amazon, Roku and Hulu. 

Such agreements are important because they provide advertisers with additional data that allows them to make smarter decisions around linear campaign spend and optimization.

As part of the agreement, LG Ad Solutions will provide Nielsen with its ACR data for use in Nielsen’s National TV measurement service, giving Nielsen the largest ACR data footprint in the industry. LG Ad Solutions is providing Nielsen with ACR data for both linear and CTV measurement, providing greater stability for Nielsen National TV audience estimates. Nielsen is also receiving LG ACR data at the household level, the companies explained.        

As a result of the deal, advertisers activating campaigns on LG Smart TVs will have the opportunity to receive 'Always On' streaming measurement and big data from LG Ad Solutions via Nielsen ONE Ads starting in the First Quarter of 2024. Nielsen will measure all ad impressions, inclusive of co-viewing, on advertisers' campaigns, giving advertisers a broader view of performance metrics to fuel cross-platform campaigns.

In addition to the big data expansion, the deal provides CTV data for Nielsen's Ad Intel solution, enabling an increased level of measurement and visibility into competitive advertising spend. This allows Nielsen to gain advertising intelligence capabilities on streaming platforms in addition to its existing linear capabilities, providing advertisers and agencies more valuable insight into their ad performance on the platforms, the companies explained. 

"Nielsen is on a path to evolve measurement through use of big data sets, and our deal with LG Ad Solutions is a landmark moment in our journey," said Ameneh Atai, general manager of audience measurement at Nielsen. "The continued acquisition of ACR data combined with Nielsen's representative panel allows Nielsen to provide the most accurate modern audience measurement of linear ads as well as increase coverage of CTV."

"Agencies and brands working with LG Ad Solutions can now seamlessly benefit from Nielsen ONE Ads measurement data and essential metrics, including co-viewing across millions of smart TVs and connected devices," said Alistair Sutcliffe, head of product at LG Ad Solutions. "This collaboration with Nielsen exemplifies our dedication to industry interoperability and underscores LG Ad Solutions' strategy of fostering a forward-looking data approach with our strategic partners, prioritizing both consumers and advertisers for the overall benefit of the industry."

SAN FRANCISCO—The data validation-as-a-service provider Truthset has announced that Experian, a major global information services company, has joined Truthset’s Data Collective. 

The move will allow Experian to evaluate its marketing data against industry benchmarks, enhance the accuracy of its marketing data assets, and provide its clients with validated data insights to optimize data-driven marketing campaigns against business outcomes, the companies said. 

In December 2022, Truthset launched its Data Collective with 20 partner companies in order to provide an agnostic, independent platform for companies to share and benchmark the accuracy of their marketing data. 

"Experian already has a reputation for delivering trusted marketing data and insights, and by joining the Data Collective, they only reaffirm their commitment to quality,” said Scott McKinley, CEO of Truthset. "We are thrilled to welcome them to the Data Collective and to work together to advance the marketing industry’s standards for data accuracy.” 

“At Experian, we are dedicated to helping our clients make better marketing decisions through highly accurate data," said Aimee Irwin, senior vice president of strategy for marketing services, Experian. “Joining Truthset's Data Collective is a natural fit, as we share their commitment to data accuracy and transparency. We look forward to working with Truthset and our fellow Data Collective members to drive industry-wide improvements in data quality."

In addition to the Data Collective, Truthset works with a variety of stakeholders across the ecosystem including networks, brands and platforms, powering data validation, audience building and measurement. 

Truthset also works with such trade organizations as Coalition for Innovative Media Measurement (CIMM), Association of National Advertisers’ Alliance for Inclusive and Multicultural Marketing (AIMM) and the Advertising Research Foundation (ARF).

For decades, broadcasters have been forced to rely on third parties such as Nielsen Media Research for data on their audiences. Today, data is power. It shapes targeted advertising strategies and programming decisions, and broadcasters find themselves at a significant disadvantage compared to OTT services who can collect and analyze very detailed audience data sets. While standards such as ATSC 3.0 and HbbTV can level the data playing field for broadcasters, before proceeding they should look closely at experiences with user data in the recent past.

ATSC 3.0 and HbbTV are designed to bring new viewer experiences in environments where the broadcast receiver also has an internet connection. ATSC 3.0 is a digital broadcast standard that is being rolled out in North America, South Korea, and other countries. It uses IP as the transport protocol for delivery of video and other content. HbbTV is a standard that brings together digital broadcast-delivered content and IP-delivered content into a single experience. It has been in use in Europe and other locations since 2011.Using either of these standards, broadcasters can create apps for these receivers and collect data directly from their viewers via the internet. Since these are IP-based apps, broadcasters may have access to most of the viewer data an app can collect from the platform it is running on, including:

• Device location
• Device environment information (OS, browser type, etc.)
• App usage patterns
• Contextual data from other apps on the user’s device

For broadcast video, the app would collect time stamp information about when the broadcast replay was started and stopped. The broadcaster can compare the time stamp data to the broadcast time data to determine which content and commercials (if any) were watched.

Broadcasters can also work with a number of third-party services to gather profiles of user activity across multiple devices and “off-line” user data that could include such information as home and car ownership, income, purchase patterns, etc. Typically, broadcasters will also have access to data exposed by the user and the user’s device such as names, email addresses, mobile phone numbers, IP addresses and device IDs.

Access to all of this data can enable broadcasters to both provide their viewers with a more attractive experience and increase revenue. But first, they should carefully weigh those benefits against the potentially negative legal and financial consequences that could arise from its misuse.

Quite often this sort of data is referred to as “toxic.” Much of it is subject to laws and regulations that govern how companies collect, use and store consumers’ personally identifiable information (PII). The most notable is the EU’s GDPR, but there are others. For example, California and Vermont this year passed legislation that aggressively addresses data privacy that, like the GDPR, has broad-reaching ramifications around companies’ collection and usage of personal data. At the federal level, the Trump administration is reportedly crafting a set of data privacy protections to guide state and federal lawmakers as they consider similar legislation.

This data is also often targeted by cyber attackers, and a successful breach that exposes PII will also most certainly result in government action, litigation and long-term brand damage. Companies can also be damaged when consumer data they collect is misused either by themselves or third parties they do business with. Facebook’s issues with Cambridge Analytica is just the latest example.

Broadcasters have a long history of providing passive and comforting entertainment experiences. Aggressive collection and use of viewer data for targeting can feel “creepy” to viewers. Still, the ability to collect, analyze and use this data is becoming critical to improving audience engagement, creating new revenue streams and improving programming decisions. Therefore, as broadcasters go down the path of data-driven business models, they should ask themselves two key questions:

1. What are the minimally viable data-driven experiences that can easily enhance the viewing experience as well as provide business value?
2. What is the minimum amount of data we need to collect and hold to provide these experiences?

Additionally, they should take steps to ensure that any data collection and its purpose are crystal clear to viewers. Broadcasters must receive permission from their viewers to collect their data, and the good news is that consumers typically allow data collection if they perceive benefits for themselves. Any systems broadcasters that handle this data should be vetted to ensure they appropriately handle it securely and with respect for viewer privacy.

For comprehensive coverage on ATSC 3.0, visit TV Technology's ATSC3 silo.

MELBOURNE, Florida--Qligent will demonstrate its new Vision SQM platform, a cloud-based, second-generation, big data processing engine for viewer satisfaction at IBC2018, Sept. 14-18 at the RAI in Amsterdam. The platform uses real-world MVPD deployments to verify overall Quality of Experience (QoE) for subscribers watching live streaming sports or video on demand, recording a show, browsing the program guide, or accessing any other content or service.

Qligent works with MVPDs and other service providers to easily identify and correct network performance issues sooner. Qligent says Vision SQM serves those MVPD “silent sufferers,” or subscribers who never complain to customer service about service difficulties and just decide to drop the service.

[Read: Telestream to showcase IQ Monitoring Platform at IBC2018]

“By proactively reaching out to these suffering users—whether it’s one person or tens of millions—and telling them, ‘We see you had a problem and we’re going to make it right for you,’ MVPDs can show their commitment to delivering that optimal user experience that keeps customers happy, engaged and on-board,” said Ted Korte, Qligent COO.

Qligent will demonstrate its platform at Stand 8.E47.

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As emerging technologies go, artificial intelligence (AI) has certainly taken its time in making its presence felt on the world.

Surprising as that may be, the term AI has actually been around for almost 70 years, having been first coined back in 1955 by computer scientist John McCarthy, a.k.a the ‘father of AI’, the same year Emmett Brown invented time travel in the movie “Back to the Future”.

Since then, AI has experienced a largely stop-start existence, principally due to sporadic funding and below-par technology. In truth, the term AI has (arguably) gained more notoriety for storylines of killer robots (and the occasional Wall-e) hell-bent on destroying mankind than for its practical use and business benefits. But that’s all changing.

THE FOURTH INDUSTRIAL REVOLUTION

Thanks to breakthroughs in computing power, the advent and availability of big data, cloud hosting/storage, highly sophisticated software, and complex algorithms, the potential of AI is now starting to be fulfilled—with the business world being the biggest benefactors.

The market has reacted at pace. In recent years, billions of dollars have been invested by many of—if not all—of the world’s leading organizations into AI technologies (and companies), each of them looking to utilize some form of AI technology to future proof and improve their businesses, and/or create a competitive advantage.

Various estimates suggest more than $46 billion will be spent on AI services by 2020 by businesses, a figure rising to above $51 billion 12-months later.

WHERE WE ARE

Depending on what you have seen, heard, or read (fictional or not), you may have different ideas as to what AI is and is capable of at this stage. To offer some clarity there are three simple levels to be aware of:

1. Weak AI
AI capable of demonstrating human intelligence to carry out specific tasks.
2. Strong AI
AI capable of showing self-awareness, the ability to think and make decisions for itself to the same level as a human being.
3. AI Super Intelligence
AI showing superior levels of intelligence to human beings and fully in control of its existence.

For now—and for the foreseeable future—only Weak AI is currently relevant, so it’s time to remove any images of a leathered up, sunglasses wearing Arnold Schwarzenegger.

IT’S ALREADY HERE

Examples of weak AI have widely been adopted by many different business and are in use today—you just might not realize it.

In fact, it’s a fairly safe bet that you’ve already unknowingly encountered some form of AI and machine learning technology before you started reading this article. Maybe even before you got out of bed.

Have you ever used Apple’s personal assistant, Siri? Or Google’s Home? Perhaps you’ve noticed how your emails can often now detect questions being asked of you and automatically provide you with a readymade short reply? How about your smart phone that seems to be able to predict sentences you’re about to type as you’re typing?

For those that like a bit of online shopping (such as Amazon) or video streaming (Netflix), have you ever wondered how those personalized recommendations are determined? What about Facebook and its ability to not only recognize there are people in the photo you’ve posted, but can sometimes even identify them too?

They’re all using some form of AI, with the intention of bettering their customer’s experience, enhancing their financial opportunities and—unseen externally—improve their efficiency in workplace.

AI FOR ALL

“We are at the cusp of a new revolution, one that will ultimately transform every organization, every industry, and every public service across the world,” commented Ralph Haupter, president, Microsoft Asia. “I believe 2018 is the year that this will start to become mainstream, to begin to impact many aspects of our lives in a truly ubiquitous and meaningful way.”

You may be forgiven for thinking the implementation of AI is something exclusively for the Fortune 500’s of this world... but you’d be wrong. Today, things are so advanced that there is unlimited growth potential with AI companies designed to support business like yours.

In essence, AI is something that can be packaged up and purchased on a monthly basis, like your broadband or phone. This model reduces significant costs associated with more bespoke in-house solutions.

It also means the complexity is significantly removed (the ‘it doesn’t matter how it works as long as it works’ approach), meaning you won’t necessarily need to go and employ a qualified and expensive computer scientist.

“You don’t need to be a mathematics genius or have a PhD in software engineering to make sense of AI for your business,” said Gartner analyst Whit Andrews. “You don’t have to make massive investments in infrastructure and personnel in order to start applying AI’s potentially transformative technologies.

“These technologies will transform the nature of work and the workplace itself,” he added. “Machines will be able to carry out more of the tasks done by humans, complement the work that humans do, and even perform some tasks that go beyond what humans can do. As a result, some occupations will decline, others will grow, and many more will change.”

COMPETITIVE ADVANTAGE

So, why should you and your business consider implementing AI? Could it be just a fad? Will it really benefit you?

The answers are wide, extremely varied, and will be largely unique to your own business. There may be some obvious areas of your business you’d like to improve upon—be it financially or operationally motivated.

The magnitude and inevitability of AI cannot be ignored, nor underestimated. Many AI experts and professionals have described the potential impact of AI on businesses as being equivalent to the invention an adoption of the personal computer and email.

Some suggest that AI can help boost revenues by around 20 percent, whilst others warn that any business currently not at least thinking about adopting some form of AI could already be two years behind a rival. Further delays could even result in their demise further down the road due to losing a competitive advantage.

GETTING STARTED

The first thing to remember is what the purpose of AI actually is and identifying how it can benefit your business. From speaking to various professionals in the field, it can be narrowed down to two core reasons: solving existing problems and discovering/identifying new opportunities.

At its core, AI is fueled by data, which can come in many different forms for many different uses. Emails, newsletters, subscriptions, views to your website, downloads, and sales are just some examples of where data can be collected.

On its own, that data may not seem useful. However, contained within, it could be the difference between success and failure or profit and loss. This is where AI comes into play, and where it would be able to—in many instances—do the job of a human.

A famous quote within AI circles comes from Michael Palmer of the Association of National Advertisers sums it up well: “Data is just like crude [oil]. It’s valuable, but, if unrefined, it cannot really be used. It has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity; data must be broken down and analyzed for it to have value.”

Today, the bulk of that potentially valuable data held in companies is still not being utilized. “If we look at the amount of data which is actually being analyzed today, only 20 percent of the data we have is searchable and being used productively,” said IBM chairman, president, and CEO Ginni Rometty during a discussion on the subject. “The other 80 percent is held inside companies, generally not being used.”

IMPACT ON JOBS

One of the more universal drivers for AI is centered around automation—a word often which spreads fear when discussing the risks to people’s professions. Areas like administrative work (such as data processing and data collection) are widely seen as function where AI will support—or even replace—humans.

“If your work is repetitive and not creative, you will be gone very soon,” was a stark warning Dr. Roman V. Yampolskiy, a professor in the department of computer engineering and computer science at the Speed School of Engineering, University of Louisville.

Bart Selman, a professor of computer science at Cornell University, was quoted as saying: “A lot of large companies have middle management jobs where people manage other people at a very low-level in terms of keeping track of things like vacations and sick days. Those jobs I believe are at risk. Jobs that involve a large routine component. If you’ve made the proper investment, you can develop an AI system that can take over a good fraction of those jobs. A lot of big companies have a lot of those positions and will be looking at it.”

Examples of automation and loss of jobs can be seen all over the world today. Some of the headline grabbing stories include that of a Japanese law firm, Fukoku Mutual Life, which invested more than $1.7m building an AI platform with IBM Watson, has used the technology to replace more than 30 staff members. The annual savings for the firm is predicted to be around $1m a year.

“The next wave of economic dislocation won’t come from overseas,” President Obama stated in 2017 during his farewell address. “It will come from the relentless pace of automation that makes many good middle-class jobs obsolete.”

SPOTTING AN OPPORTUNITY

Another major area for AI adoption is within sales. There are now examples of where AI, using natural language processing software and specific algorithms, is able to spot patterns (in data), to identify new opportunities and provide a level of analysis on in just a few seconds.

This can vary greatly. Examples include gaining greater knowledge on customer’s viewing and buying behavior—i.e, what they’re looking at and when they most likely buy (such as pay day).

Another might be identifying the best time to send bespoke marketing and promotional materials or newsletters to specific customers rather than en masse as part of a one-size-fits-all strategy. Another could be automatically identifying cold customers that haven’t transacted with you for a while and send them updates, reminders, or special offers to help get things over the line.

THE FUTURE OF AI

These are just a handful of examples on how and where AI is already proving significant benefits and opportunities for businesses all over the world.

It’s important to realize these are all current technologies, with this article designed to demonstrate and educate you as a businessperson, what is available today, rather the stargazing into the future.

The AI revolution is here and it will—if it doesn’t already—play a key role in the future of the way your business runs and perform. When you decide to get on board is up to you. I’ll leave you with this quote from Gartner: “As vendors exploit AI software capabilities within business suites, enterprise applications, infrastructure support services, and the customer experience, your organization will need new or updated strategies. Ready or not, AI is coming to you.”

Dateline 2014—at the time, the “digital universe” was growing at a phenomenal 40 percent annually and expected to continue “on into the next decade.” At the time, that growth rate reflected conglomerate sets of data that not only included people and enterprise, but included the relatively new term “Internet of Things (IoT).”

To a broadcast engineer, the term IoT used to mean “inductive output tube”—an alternative to the klystron, and both referencing transmitting tubes used in high-power TV transmitters, the latter in analog television and the former a most cost-effective device, which emerged full strength during the ATSC transition.

The modern day IoT may have equally as broad an impact for society as it did for the digital TV broadcast marketplace. The Internet of Things has propelled storage demands and solutions (including the object store) into the next universe, aiding and changing the perspective and dimensions of “big data” forever.

COMPREHENDING THE ZETTABYTE ERA

When the IDC conducted its study in 2014, they predicted the volume of unstructured data created and copied all over the world would reach 44 zettabytes (1 zettabyte = 2 to the 70th power bytes), i.e., 44 trillion GB, annually, by 2020. By perspective, just a year before that 2014 IDC prognostication, the amount of data created and stored in 2013 sat at a mere 4.4 trillion GB per year. If correct, the amount of data growth is outpacing Moore’s Law, and will increase tenfold in six years.

Ironically, according to that IDC report, the amount of useful data (if tagged and analyzed) grew by a much lesser amount. In 2013, only 22 percent of the data accumulated in the digital universe was considered “useful”—that is, it was relevant because it was meaningfully tagged or categorized and was searchable and retrievable.

By the year 2020, the IDC prediction reported in April 2014 stated that only 37 percent of the data collected will be useful because of that same criteria.

USELESS DATA RETENTION

So why do we continue to store data that isn’t useful? The simple answer: “Because we can.”

Irrespective of how, where, when or why we create this mass of data, we find that most companies, enterprises or individuals collect and save literally everything because, fundamentally, there isn’t the time to sort, catalog or even physically hit the delete key once the data is collected. On the personal level, think of how many VHS tapes or compact discs or DVDs you still have in boxes or on shelves in the basement or the attic.

Putting those collections into today’s perspective, all those memories are essentially just another set of data. If we digitized all those analog VHS tapes into compressed ones and zeros, we’d still have enormous sets of data that would likely remain unmanageable, ignored and probably lost in the digital quagmire of never-never land.

At least while in a tape format there was a storage container (the wrapper), information about the content (the metadata) and an easy methodology to catalog the content by orderly arrangements on shelves, boxes or with a 3x5 card catalog or even a digital picture of the box.

EXPONENTIAL EXPANSION

Production companies, news organizations, broadcasters all generate enormous amounts of data. The volumes continue to expand exponentially and will likely end up in the “no-where’s-land” of the digital landfill. For today, this enterprise digital repository is now an ambiguous, unknown depot that might be one of many ubiquitous “clouds”—some on premises, some in that atomic number 26 mountain place, some in a public cloud, and a lot more of it ending up in privately managed datacenters scattered around the globe.

For how long and what purpose do organizations intend to keep that data? It’s relatively inexpensive to hide those bits in a cloud and nearly zero cost to keep it there—until you want to retrieve it. However, to get meaningful use out of those bits, you needed to catalog it. Otherwise, you must pull it all down from the cloud, store it again (locally) and then search through it to find something usable.

For an enterprise of any size, this takes labor—which costs money. And that’s a resource that doesn’t grow automatically, like the data you and your friends and their friends are generating every second of the day.

INTELLIGENT DATA STORAGE

When you consider the daily couple of billion pieces of data “about” you, your friends and their friends, too, you can see the storage challenges which entities like Facebook, Google, Amazon, and the other social media or shopping platforms have on their hands. The difference is these companies have figured out how to intelligently collect the data, identifying each piece using artificial intelligence algorithms that are, incidentally, developed either by their own organization or acquired by buying another company with that expertise. Across each social group, they will cross-relationship every piece of their data and then store it in one-to-many of their “private” clouds—which are liberally dispersed data centers interconnected by networks based upon volumetric accessibility per region.

Their data is never stored just once. Instead, it is replicated multiple times for accessibility, protection and resiliency. How each organization diagnostically and dynamically protects that information and makes it nearly instantly retrievable is their secret sauce.

Yet today, some of the concepts and principles which social media companies have developed for their own applications are now becoming available to individuals and organizations. The goal in these products is to start diminishing the “uselessness” of the data by applying intelligent metadata that can then utilize more conventional search engine approaches for cataloging and retrieving those assets. These new AI-based approaches now differentiate the future from the more traditional legacy media asset management methodologies.

STRUCTURING THE UNSTRUCTURED

What we’ve learned by collecting huge sets of information about known places around the world is now supporting machine learning techniques that create accurate metadata tagged not just to a single image, but to an entire generation of data sets grouped as objects. Such information may use the angle of a shadow which then identifies a time of day, which, when coupled to a geographic (GPS) location, gives more information about the season or the atmospheric conditions. People in images can now be related to their siblings or parents, based upon data sets generated from favorites or albums. Road signs, window lettering on buildings, and other distinguishing characteristics add to the databases about the actual surroundings where that image, and those of others, were collected. What was heretofore considered useless information is now branded and repurposed by machines which “look” for this data and then catalog it without any direct human intervention.

Using these new autonomous techniques, every time a new piece of content (still image, sound or video) enters a system equipped with these technologies, the system turns that previously “unstructured” data into “structured” data that is then cataloged not just as a single image, but as collections of data sets bound into a global storage platform.

These are the roots of where we’re headed as the future of storage becomes an indirect, unsuspecting model that makes potentially useless data valuable again, for all.

Karl Paulsen is CTO at Diversified (www.diversifiedus.com) and a SMPTE Fellow. Read more about this and other storage topics in his book “Moving Media Storage Technologies.” Contact Karl atkpaulsen@diversifiedus.com.

Inscape said it made a deal to provide LiveRamp with the viewing data it collects from nearly nine million smart TVs.

The big-screen TV viewing information will be incorporated into the other data LiveRamp uses to identify consumers based on their digital activities. That means that when advertisers run data-driven ad campaigns, they’ll be better able to target TV viewers, who will be getting more relevant messages.

[Read: TiVo Report Sees New TV Data Shaking Up Measurement]

“This is a very strategic integration that brings the speed, scale and transparency of Inscape’s smart TV viewing data to LiveRamp’s ecosystem platform partners, agencies and brands who leverage LiveRamp for identity resolution,” said Allison Metcalfe, general manager for TV at LiveRamp, part of Acxiom. “By tying viewership to LiveRamp’s IdentityLink ID, LiveRamp is making it possible for marketers to take a data-driven approach to better plan, target and measure their omnichannel marketing efforts.”

Inscape’s data is being used by a number of measurement and analytic companies that are finding new ways to look at TV audiences for networks, agencies and advertisers.

“As new advanced television efforts and initiatives evolve, having a strong identity link between device-level television viewing data and first and third-party data sets is imperative,” said Greg Hampton, VP of business development at Inscape. “LiveRamp is making it possible for marketers to plan future marketing initiatives based on real data and better understand the results of omnichannel campaigns.”

Increasing content volumes, shorter turnaround times, and a growing range of outlets for content are combining to drive media enterprises to look for ways to automate their production workflows. One technology that has begun to bear fruit is Artificial Intelligence, which can be used to streamline repetitive tasks and simplify media operations.

For an in-depth analysis of the survey results, download the whitepaper titled "There's Nothing Artificial About A.I. for Broadcasters".

TV Technology recently completed survey of over 300 media professionals, sponsored by Quantum, that shows artificial intelligence (A.I.) technologies are already in use within the media and entertainment industry. Fully two-thirds of the companies which have tested A.I. have adopted it for one or more tasks. Large media organizations, those with media archives containing over 20,000 hours of content, are particularly enthusiastic adopters of A.I. technology for both metadata creation and automated clip generation and distribution.

Providing appropriate tags for new materials and content that is already stored in media libraries is growing in importance as all types of media organizations seek to maximize the value of content they already own. Quantum Corporation has developed an e-book titled “Applying the Power of AI to Your Video Production Storage” that describes how metadata can be automatically created using on-premises appliances to analyze content housed in object storage. This e-book also explains how to avoid the cost, complexity and potential security risks of uploading content to a public cloud service.

Quantum offers perspective on the value of metadata for locating and using existing video and audio files in a blog post titled “It’s 2020—Why Aren’t You Mining Metadata?” Many media companies have large and growing archives of raw and fully-produced content that can be re-purposed to generate new revenue streams. To unlock the value within these files, metadata is crucial, providing the means to locate clips that are relevant to current events or ones that can be used in new productions. This blog post describes some of the key ways in which content companies can increase their revenues and viewer mindshare by leveraging assets they already own.

At NAB 2017, Quantum showed a new A.I. system in operation that was specifically designed to process large volumes of video contained in object storage owned or operated by media companies. The aiWARE for Xcellis™ product that was demonstrated is described in detail on this web page which also includes links to the product data sheet and other useful reference material.

Additional Resources:

Ebook: Applying the Power of AI to Your Video production Storage

Find out what AI means for the media and entertainment industry, How AI can help your video production workflow and the value of running AI engines on an on-premise appliance. Click here for more.

Webinar: The Future of Artificial Intelligence in Broadcasting

Join TV Technology as they reveal results of a survey on how the M&E industry is deploying A.I. today. Sponsored by Quantum, the survey revealed that two-thirds of the organizations that have tested A.I., have deployed the technology for at least one critical role in their workflow. Find out the applications where A.I. is making a difference in M&E workflows and more.

Watch the webinar here.

ALEXANDRIA, VA.—Even as technology changes the way broadcasters operate, some legalities remain little changed. Broadcasters still have a legal requirement to log all broadcast content and ensure that the content is compliant with existing federal regulations.

“The need for compliance loggers comes from the legal requirement to log all broadcasted content, and this need, arising from the national telecom regulators, such as the FCC, has not changed,” said Ken Frommert, president of ENCO in Southfield, Mich.

Moreover there has been ever-increasing economic pressure on broadcasters as content distribution has become fragmented, coupled with the fact that compliance loggers in essence remain mostly an economic liability with no real business benefit. This has forced what Frommet called a “technological evolution”—that could allow broadcasters to utilize the enormous amounts of data created by the loggers.

“This ‘big data’ is just sitting there for months until it is deleted and replaced by newer data, with no effective use, while the technologies are gradually enriching the value of these video records and adding a number of useful functions,” he said.

KEEPING UP
As the industry faces consolidation, there are fewer players catering to the requirements of broadcasters as well as to OTT and MVPD operators.

Hiren Hindocha, CEO of Digital Nirvana

“Every year, there are new challenges and new requirements, and it demands a constant focus from the vendors to keep up, said Hiren Hindocha, CEO of Digital Nirvana in Fremont, Calif. “As OTT takes its rightful place beside broadcast, the need to monitor OTT becomes more important. While the broadcast industry can be considered mature, the same can’t be said about the OTT industry/vertical. There are many vendors providing solutions for different parts of the workflow.”

As a result there can be much more to monitor including encoder status, start/stop streaming and monitoring of web site accessibility as well as quality control analysis. OTT confidence and compliance monitoring can also be done utilizing cloud-based monitoring services, while many network monitoring applications can also be used to monitor the process involved with OTT workflows across the content delivery network (CDN).

“Most clients monitor pre-CDN and post-CDN as a confidence check measure,” Hindocha said. “Some monitor at the end user level with embedded monitoring in the apps on the user devices.”

Compliance logging has seen a significant evolution over the past decade, but as the broadcast world becomes more cloud-and IT-based, challenges will remain in how logging is handled.

“Logging and compliance has come a long way since the early days of VHS,” said Mark Horchler, senior vice president of marketing at Mediaproxy in San Diego. “The capabilities of today’s software-based solutions, such as LogServer, are enabling broadcasters to manage and repurpose their video content, including metadata, for a wide variety of services including advertising and social media.”

At the same time, there has been increased regulatory and industry scrutiny that has been coupled with a growing plethora of viewing devices and outlets including OTT.

“Broadcast engineers now need to log and monitor multiple streams of video and metadata from each broadcast source,” added Horchler. “This challenge is being met by software-based loggers that can quickly address new formats and standards. The scalability and flexibility of software-based logging enables broadcasters to stay one step ahead of regulators and develop innovative revenue-generating services.”

NEW OPPORTUNITIES
The improvements that have come via software advances could help resolve issues on the regulatory side and create new opportunities for increased revenue.

Russell Wise, vice president of video solutions for Verizon Digital Media Services

“There remains a requirement to archive the data from compliance logging for seven years, and technology has made it easier to do so,” said Russell Wise, vice president of video solutions for Verizon Digital Media Services. “However, in recent years it has evolved into a tool set, which ensures compliance not only for the FCC, but can also allow broadcasters to produce better content. In this way compliance loggers are becoming more of a collaborative tool, not just an appeasement tool.”

For these reasons many broadcasters are now using compliance products to help in the production of content for OTT and other line services, added Wise. “The demand for content has gone up 10 times, and these tools help deliver it to the quality that the viewer expects.”

Software-based logging systems have provided the scalability and flexibility to help broadcasters to stay ahead of regulators, while also developing innovative revenue-generating services; and have also been a valuable tool for reviewing content and much more.

“Web-based user interfaces, now with HTML-5, offer distributed deployment, while multichannel capture and streaming can be integrated within broadcast automation logs and social media platforms,” said Horchler. “Software-based logging and compliance solutions such as LogServer can be scaled up and configured in tandem with broadcast expansion. Instead of working as a parallel function, software logging and compliance can be tightly integrated as part of an overall broadcast production and content delivery workflow.”

Mediaproxy’s Monwall provides monitoring of live streams from broadcast and OTT sources, and show both live video and real-time metadata being simultaneously monitored for compliance.

Additionally, as MVPDs and OTT operators respond to changing consumer habits, the opportunities to monetize on-demand content are evolving.

“Broadcasters are keeping track of metadata including ad-insertion points across multiple video streams,” added Horchler. “Mediaproxy’s extensive live stream monitoring and post-broadcast reporting tools are designed to address these requirements. Unlike hardware-based systems, logging and compliance software can be efficiently scaled in line with new service rollouts. A broadcaster can instantly allocate either on-premise or cloud-based resources for temporary coverage of major sports events.”

Mediaproxy’s headend encoders even deliver IP transport stream alongside traditional ASI outputs, while its LogServer software further enables logging and real-time analysis of both outgoing and return feeds to capture the end-viewer experience and instantly resolve on-air incidents.

In addition, the exponential development of social media and of clips for OTT presents new opportunities for broadcasters.

“The compliance loggers have the advantage of compressing the linear TV feeds into proxies of smaller size,” said ENCO’s Frommert. “These proxies are much more suitable for distribution on social media and for VOD in general. We believe that it is possible to use the ‘necessary evil’ of compliance loggers and obtain valuable data and resources that can be monetized into competitive information, such as ads income, and better and faster clips for OTT and social media.” This can include improved automated detection of commercial ad spots, and replace what has been manually intensive work. As a result this technology can scan many channels and provide very accurate affidavit reports on the number of ad spots.

“The ads watching technology can also analyze competing TV and radio channels and provide comparisons channel to channel on the number of ads sold and aired,” Frommert said. “Some TV networks are using the content detection technology, coupled with viewership data to obtain insight on the relative success of one program or another on the number of viewers of the same channel. The fact that the program managers can view the real-time viewer’s charts just above the actual recording of the TV feeds of competing channels, makes it much easier to get the right feel on what works and what is rejected by the viewers.”

INDIAN WELLS, CALIF.—It’s all right there in the numbers. From the key to interactive advertising to creating responsive content. It’s in the data, Steve Wong of Siemens and media technology consultant, Christy King, said Wednesday at the HPA Technology Retreat.

Wong talked about the necessity of having the proper metadata for interactive advertising. He said there is now “an opportunity then to license metadata rights. Think of the opportunity to negotiate now with another revenue stream.”

King, who collected and interpreted data for the UFC, spoke to specific applications. She said she is working with companies introducing new content for over-the-top Internet delivery.

“We’re starting to see people looking at data around titles that already exist, and what opportunities they present,” she said.

When King says “data,” she means everything, not just the shoot date and the technology used, but information about the content within the content. E.g., the city in which action is taking place, an actor’s nationality, their brand of clothing, the time of day in a scene, the items in the scene, the age of the characters and their emotions.

On the audience side of the screen, a company by the name of “Canvs” gleaned social media and identified 4 million terms that define emotions, King said. They narrowed it down to a lexicon and created a timeline bar chart of “reactions” so clients could determine how people are responding emotionally to content based on their social media posts.

King also pointed out that genres aren’t simple anymore. She said Netflix recognizes a multitude of “discrete” genres.

“Horror with 14 knives only shot at night is a genre,” she said.

There are 597 genres that include the term, “about marriage.”

One issue here is “scope creep,” where you’re just collecting and looking at too much stuff, King said. There need to be best practices and possible a data chief to mine data and deliver the results to creative so they can, in turn, respond to audience appeal.

The growth in data is, has and continues to be a topic that influences how much and what types of storage architectures, etc., are selected and for what applications. Besides determining the storage architecture itself, another element which is affected by the continued growth in data storage is the analysis of the data to insure that anomalies are identified, located, and corrected.

Data analysis has traditionally been confined to IT departments and systems administrators. For those experiencing the dynamic growth of what is referred to as “big data,” these administrative teams must now preside over indescribable amounts of unstructured data. According to industry analysts, much of this data—in the context of media files and other information—has been largely overlooked; and therefore remains uncategorized, cataloged or in many cases still remain in numerous raw (original) formats including analog or digital videotape and film.

Traditionally, unstructured data was comprised of documents such as PowerPoint presentations, spreadsheets, social media collections used in marketing, newscasts and other trending data. Production and/or project emails among coworkers, production teams, approval management, etc., all contribute to the “unstructured/big data” domain. The explosion of video, which is largely unstructured, into all areas of information technology has complicated management and identification of anomalies, which can infect the integrity of the content immediately and well into the future.

Fig. 1: Generic workflows for media systems

CONVENTIONAL ACTIVITIES
All of this content is most likely considered valuable information. However, since the data is used throughout production processes, it should probably become part of the permanent archive for the production or project. Conventional media asset management systems have, for the most part, focused on the video cataloging, search and production aspects of the project life cycle. Functionality and broadly speaking, these workflows include: ingest, processing, preparation for distribution and archive (Fig. 1).

When you expand the requirements to include email, graphics, scripts, production schedules, approvals, rights management, etc., the system tasked with managing these extended sets of data might be called a digital asset management system. Specialists in these areas might argue where the boundaries are, and may indeed add other layers, for example, “production asset management;” and from the business side might be called “business process management.” Whatever or however one divides the subjects, all these data sets—when looked at holistically—make up the broadening dimensions of what is now called “big data.”

Managing myriad files, file formats, versions, and such, especially when much of the data is unstructured, becomes a systemwide issue, irrespective of the scale of the enterprise itself. Some manufacturers of MAM/PAM systems place constraints on just how far they take the descriptions or types of files they can include in their management solution set.

Others like to address all the issues under the umbrella of a DAM; which allow the end-user to add or modify modules, which include planning, prediction, trending, reporting and orchestration. What you need, whether as an individual or an enterprise, must be carefully analyzed, product/modules selected and assembled into an all-encompassing system, which often requires customization between ancillary components that may or may not be available from the DAM-provider’s shopping cart.

CORRUPTION, SLOWDOWNS AND PIRACY
Irrespective of the depth and breadth of the asset management solution set are issues related to monitoring, troubleshooting and analyzing data whereby systems (humans or machines) look for changes, which could result in data corruption, inconsistency, slowdowns in performance, piracy, etc. When data anomalies are located, sometimes they lead to breaches in security whereby data flow processes change abruptly; such as when large numbers of random files are transferred from one storage system to another without following rules or procedures established by the MAM/DAM or other machine- controlled systems.

Of the two major forms of data, “structured” data lends itself to easier analysis because the information (and its flow) consists of well-defined and expected sets of content within each record. Structured data are items like order entry, ATM transactions and such. In structured data, messages usually contain an expected set or sequence of information; for example, the IP address of the client, the URL being requested, the code sequence or the kind of browser being used. Because the events are machine-readable, i.e., the event can be easily parsed into fields (such as in a database) and then are made sense of easily by a computer program, the structure is generally consistent and predictable.

Fig. 2: Structured vs. unstructured data flows

When dealing with “unstructured” data, the content is more free form, with the meaning of the text within an event being somewhat arbitrary (see the generic comparison of structured versus unstructured data flows in Fig. 2). For example, it is hard for a machine to predict search decisions, content frame boundaries, or whether an accompanying document is related to a single element or many elements.

Often the data set information may be nothing more than a timestamp and then all the rest of the information. The timestamp can be database-anchored, but the remaining information can—and usually does—relate to many other locations, storage sources, storage destinations, processes, etc.

The type and range of unstructured data is arbitrary, thus making it much more difficult to utilize machine-reading approaches (such as search or advanced analysis) that can derive meaning. The result often means that humans must visually sort through the data looking for the changes that could indicate the cause of performance issues or even a security breach. Relying on human analysis of large data sets is risky, often leading to missing anomalous messages that could have provided the exact information one was trying to extract from the data. Automated analysis on unstructured data is also difficult, however, it can be accomplished if there is an additional step inserted.

STRUCTURE TO UNSTRUCTURED DATA
By bringing structure to the unstructured data via dynamic classification (sometimes called “categorization”), tasks that a properly configured MAM or DAM are designed to do, the identification of data anomalies becomes easier.

Advanced machine learning algorithms are employed in this process. Once unstructured data is classified, algorithms can then identify message similarities and assign like messages to one of the dynamically generated category names.

Once this background task is completed, users can begin to analyze events for anomalies by baselining, trending or locating deviations in the rate of the events by each category over time.

The concepts employed in this type of analysis take time to set up. Once started, they begin the collecting of category trending information. Upon completion of the initial data runs, the now “categorized-unstructured data” can be treated in similar fashion to that of structured data.

The automated analysis of unstructured data provides time savings. It can help mitigate humans from the chores of data mining by automatically providing them with awareness into behaviors or changes in recorded data. Diagnostic analysis times (i.e., forensics) are shortened considerably through this process.

The concepts of data analysis and anomaly detection are a growing science. Enterprise- class systems serving many locations and which utilize centralized (or cloudbased) architectures may find value in applying these techniques and technologies to their operational practices, especially when sensitive or compartmentalized information structures are involved.

Karl Paulsen, CPBE and SMPTE Fellow, is the CTO at Diversified Systems. Read more about this and other storage topics in his book “Moving Media Storage Technologies.” Contact Karl atkpaulsen@divsystems.com.

The future for video, IP and storage have at least one common foundation amongst them: Ethernet networking. Recent changes and future roadmaps for networking and storage systems are, without a doubt, about to alter the architectures of data-and-media storage as well as video transport in short order. We’ll look at some of these changes, both historically and in future tense, to provide some vision for what you should prepare for.

In the start-up days of Ethernet—around the early to mid-1980s—the strategy of “if you build it, they will come” propelled Ethernet’s development from a data-rate (ie., speed) perspective. In those days, if the storage community needed faster Ethernet, it was usually ready and waiting.

The novelty of PC computing extended to those who could afford the various components of the PC, including storage, network interfaces and memory. Storage networking was in its infancy and used only by those who needed shared data on a broader scale vs. in a PC workstationonly environment.

The cost to update or modify the infrastructure to support Ethernet was still relatively expensive; thus, major changes to networks were often delayed and were based upon the need and economics associated with those modifications.

The increases in speed for storage media weren’t occurring particularly fast either. Until the early 2000s, the significant changes in spinning media yielded only modest overall throughput improvements: faster and more reliable mechanics, rotational speed increases and reductions in latency. The big differences came in raw storage capacities, which increased many-fold from the early uses of rotating magnetic media. Ethernet, except for network transport, had little impact on storage systems as a whole.

Then, circa 2010, Flash memory as cache emerged to further improve the overall spinning-disk storage equation. As previous TV Technology articles have demonstrated, Flash dramatically improves storage system IOPS. And it doesn’t take a lot of Flash to make a significant difference in overall storage system improvements. Studies have shown that by adding as little Flash as 1 percent of the total storage capacity, in a tiered form, can yield increases in IOPS of 25 percent or more. Furthermore, as 3D NAND (Flash) was incorporated into SSDs, the cost/benefit ratio skyrocketed in many dimensions.

Fig. 1: Common Ethernet speed rates (link speeds) show current, in development and future standards with their dates of implementation. In the past, networking generally stayed ahead of other supporting system technologies, including storage. As storage systems have evolved, networking may now actually be lagging behind the storage (SSD) performance side, despite the fact more than a billion ports of Ethernet have shipped to enterprise, residential, data centers and industrial users.

Enterprise class SSDs, available today, can do sequential reads at around 2.8 GBps (22.4 Gbps). Essentially, this is faster than what a 10 GbE adaptor can support by a two times factor. It’s not difficult to see that in short order even 10 GbE network infrastructures will lag in performance to the level that storage throughput and/or system processing on network attached storage could be seriously affected; especially given the fact that 1 GbE has only recently peaked in terms of the total number of port shipments actually sold through this year.

As for 10 GbE, it is becoming cheaper and far more prevalent than it was two to three years ago. We’ve had 100 GbE since 2010, yet the 100 GbE growth rate remains much lower than 10 GbE is today. That said, 10 GbE is expected to reach saturation (peak port shipments) somewhere around 2018. By that time even faster mid-speeds (e.g., 25 GbE and 40 GbE) are expected to become common, and probably necessary for video systems where the transport shifts from SDI-for-video to IP-for-video (Fig. 1).

This gives rise to some serious considerations into how video and broadcast facilities will need to plan, network infrastructure wise, for the inevitable paradigm shift to IP.

NOT JUST FASTER
Putting the future for IP video aside, “Big Data” storage and the networking of that data is still predicted to increase by 50 percent a year. Once we seriously begin to pump video over IP, that number is surely to expand and possibly be incalculable by the 2017–2018 timeframe. Therefore, the perspective on network speed won’t be “how fast is your network,” but instead may be “how much more networking will you need?”

Not unlike what we’re finding for higher resolution video (UHD/4K)—“more, faster and better” pixels—is the question regarding storage networking solutions now “more, faster and broader” networks? Some, such as the Ethernet Alliance, believe that is the case and are in turn making a much stronger argument for addressing network improvement development.

The firestorm that is happening depicts an Ethernet ecosystem expanding by the second. Adding fuel to that fire, discussions are underway related to new direct connect 2.5 GbE and 5 GbE interfaces for HDDs. This, in part, appears necessary in order to address huge media storage systems, such as at Facebook, where billions of pictures and videos are now being housed on Ethernet-connected storage platforms. In addition, 2.5 Gig, 2.5 GigbaseT and 5 GigbaseT will allow existing Cat5e and Cat6 outlets to support new 802.11ac WiFi technologies; revitalizing cabling infrastructures in place worldwide.

The Ethernet roadmap has objectives for 400 GbE; with 100 Gb-single lane and Terabit Ethernet (TbE) as possible future speeds further on the horizon.

The reality of these higher-speed networks won’t become clear until the industry has reached a successful implementation of the 400 GbE and 100 Gb-single lane systems. Not forgetting that the products necessary to support IP-video must also include multiterabit fabrics in order to switch the 10-to-20 Gb full bandwidth (or even lightly compressed) signals for UHD/4K and beyond.

PARITY ACHIEVED
Some say that parity in SSDs and HDDs will be achieved somewhere in 2016; meaning the cost and capacities of the devices will be essentially equal. This will certainly change the landscape of storage systems forever going forward. As for networking, including Ethernet, this won’t be reaching that kind of parity for some time.

To the visionaries of tomorrow, keep these perspectives in mind as you plan your next “big” network system update; or as you consider the move towards an all-IP infrastructure.

Note: The author wishes to acknowledge appreciation to SNIA (www.snia.org) and the Ethernet Alliance (www.ethernetalliance.org) for providing background and statistics used in this article.

Karl Paulsen, CPBE, is a SMPTE Fellow and chief technology officer at Diversified Systems. Read more about this and other storage topics in his book “Moving Media Storage Technologies.” You can contact him atkpaulsen@divsystems.com.