A Brontobyte of Data – Why I Should Care

Natalie Kenny

If the stats are to be believed, there is a veritable tsunami of data headed our way, and those who are unable to adopt the tools and technology to ride the wave will be left floundering. According to some, we are soon entering the brontobyte era of sensor data, examples of which are self-driving cars that alone will generate approximately 1 gigabyte of sensor data per second. Airplanes are already creating 2.5B terabytes of data collectively. CCTV in the London Underground generates around 2 petabytes of data every day, and the Square Kilometre Array (SKA) radio telescopes nestled away on the flat desert planes just a short 315km northeast of Geraldton, Western Australia, will pull down more data in a day than is currently produced by the Internet to date when they come online in 2020. This will require an “Exaflop-capable” supercomputer to crunch through the data created.

Source: HP ‘The Brontobyte Era in the world of interconnected digital things’

Aside from confirming a growing suspicion that data scientists and mathematicians host a wicked sense of humor when it comes to inventing new terminology for data sizing, what does this all actually mean? And why should we even care? It is a sad realization for a person like myself working in the IT industry to accept and come to terms with, but let’s be honest: data is boring for most people. Just by using the term “data” we’re creating a barrier between ourselves and the majority of people that we’d like to be communicating with, because most people will never realize or even care that they are producing or consuming data.

But this isn’t necessarily a bad thing, and it isn’t the reason why people won’t be able to capitalize on a Big Data future. In truth, this is exactly how it should be with technology. The most successful technological advancements are those that become invisible and ubiquitous to our daily lives to the point that you forget they are there.

It’s a lot like electricity. Most people don’t know how electricity works or how many kilowatts their house is consuming by the hour, but when you walk into your house and flick the switch, the lights come on, the computer hums to life, and TV wakes up like magic. Once the infrastructure has been built, all that is required by end users is to plug in and switch on to extract value from it. Data is the same. People don’t care about how much data is being produced, they only care about the valuable end user product, service, or experience that data provides them.

Without the proper context or supporting infrastructure, data on its own is meaningless and the value is trapped. It’s only when data is put into context that it becomes useful information that can be tapped into and extracted by others. Much like a stray lightning bolt of electricity cannot easily be captured, despite having the power to supply a whole household with all their energy needs for a month, the sheer quantity of a brontobyte of data being made available is meaningless if it is not inherently simple for the majority of potential users to access it, search it, put it in context, and use the data productively.

In many instances, we are still at the stage today of concerning ourselves with how much data is being produced, rather than how its maximum value can be extracted. This is typified by our current approach to data sharing, in which we are pursuing traditional forms and publishing on individual portals for specific and targeted purposes rather than looking at data as a vast, interconnected web that can and must be tapped into. There are portals for weather data, healthcare data, census data, and so on. Yet data portals only provide limited value to their users because they do not connect with one another as seamlessly as they could. Often each of these portals are isolated – within the organizations that house them, but also from the rest of the data universe including news, social media, blogs, and other relevant sources.

There are many ways in which we could strengthen our understanding and treatment of data in order to magnify the value contained within it. In the first instance we need to invent better ways to search the web of data. There is a network effect where data becomes more useful and its value is magnified as more people and machines use it, add to it, and maintain it. Searching the vast web of data requires being able to find it, access it, and understand its context. In the future, searching vast portals of data will need to become as simple as it is today to search for news or documents on the Internet.

Imagine if you could join the dots between all data sources and discover hidden connections? This would put data into context in ways today that was never possible before. An organization could move from questioning, for example, “Why did health outcomes in a particular rural area decrease last month?” to identifying, “Did the reduction in health outcomes in a particular rural area last year have anything to do with the storms and power outages?” In this future, the value of data would be maximized as information would flow more easily through an economy for users to derive new insights, find the data they need to design new products and services, and incrementally add more information to the growing knowledge base.

This is the cusp of where we find ourselves today and the challenge that those who wish to capitalize on the vast oceans of data face. As the volume of data grows exponentially, data custodians will need to start thinking about their data as an infrastructure and as an asset, contributing to public value creation in a similar manner as transportation, water, and electrical infrastructure. Doing so can provide a network of contextualised information that people, government, and business can consume to address the problems that matter and create public value.

To find out more about the SAP Institute for Digital Government visit www.sap.com/sidg, follow us on Twitter @sapsidg and email us at digitalgovernment@sap.com.

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Natalie Kenny

About Natalie Kenny

Natalie Kenny is an Industry Value Engineer at SAP, focused on digital transformation of public sector organisations across Australia and New Zealand. She is dedicated to helping governments re-imagine future ways to interact with and deliver services to citizens. Natalie's key focus area is exploring how digital technologies can help un-tap the immense value that lies in government data to extract new value and insights and help solve challenging civic problems.

Data Analysts And Scientists More Important Than Ever For The Enterprise

Daniel Newman

The business world is now firmly in the age of data. Not that data wasn’t relevant before; it was just nowhere close to the speed and volume that’s available to us today. Businesses are buckling under the deluge of petabytes, exabytes, and zettabytes. Within these bytes lie valuable information on customer behavior, key business insights, and revenue generation. However, all that data is practically useless for businesses without the ability to identify the right data. Plus, if they don’t have the talent and resources to capture the right data, organize it, dissect it, draw actionable insights from it and, finally, deliver those insights in a meaningful way, their data initiatives will fail.

Rise of the CDO

Companies of all sizes can easily find themselves drowning in data generated from websites, landing pages, social streams, emails, text messages, and many other sources. Additionally, there is data in their own repositories. With so much data at their disposal, companies are under mounting pressure to utilize it to generate insights. These insights are critical because they can (and should) drive the overall business strategy and help companies make better business decisions. To leverage the power of data analytics, businesses need more “top-management muscle” specialized in the field of data science. This specialized field has lead to the creation of roles like Chief Data Officer (CDO).

In addition, with more companies undertaking digital transformations, there’s greater impetus for the C-suite to make data-driven decisions. The CDO helps make data-driven decisions and also develops a digital business strategy around those decisions. As data grows at an unstoppable rate, becoming an inseparable part of key business functions, we will see the CDO act as a bridge between other C-suite execs.

Data skills an emerging business necessity

So far, only large enterprises with bigger data mining and management needs maintain in-house solutions. These in-house teams and technologies handle the growing sets of diverse and dispersed data. Others work with third-party service providers to develop and execute their big data strategies.

As the amount of data grows, the need to mine it for insights becomes a key business requirement. For both large and small businesses, data-centric roles will experience endless upward mobility. These roles include data anlysts and scientists. There is going to be a huge opportunity for critical thinkers to turn their analytical skills into rapidly growing roles in the field of data science. In fact, data skills are now a prized qualification for titles like IT project managers and computer systems analysts.

Forbes cited the McKinsey Global Institute’s prediction that by 2018 there could be a massive shortage of data-skilled professionals. This indicates a disruption at the demand-supply level with the needs for data skills at an all-time high. With an increasing number of companies adopting big data strategies, salaries for data jobs are going through the roof. This is turning the position into a highly coveted one.

According to Harvard Professor Gary King, “There is a big data revolution. The big data revolution is that now we can do something with the data.” The big problem is that most enterprises don’t know what to do with data. Data professionals are helping businesses figure that out. So if you’re casting about for where to apply your skills and want to take advantage of one of the best career paths in the job market today, focus on data science.

I’m compensated by University of Phoenix for this blog. As always, all thoughts and opinions are my own.

For more insight on our increasingly connected future, see The $19 Trillion Question: Are You Undervaluing The Internet Of Things?

The post Data Analysts and Scientists More Important Than Ever For the Enterprise appeared first on Millennial CEO.

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Daniel Newman

About Daniel Newman

Daniel Newman serves as the Co-Founder and CEO of EC3, a quickly growing hosted IT and Communication service provider. Prior to this role Daniel has held several prominent leadership roles including serving as CEO of United Visual. Parent company to United Visual Systems, United Visual Productions, and United GlobalComm; a family of companies focused on Visual Communications and Audio Visual Technologies. Daniel is also widely published and active in the Social Media Community. He is the Author of Amazon Best Selling Business Book "The Millennial CEO." Daniel also Co-Founded the Global online Community 12 Most and was recognized by the Huffington Post as one of the 100 Business and Leadership Accounts to Follow on Twitter. Newman is an Adjunct Professor of Management at North Central College. He attained his undergraduate degree in Marketing at Northern Illinois University and an Executive MBA from North Central College in Naperville, IL. Newman currently resides in Aurora, Illinois with his wife (Lisa) and his two daughters (Hailey 9, Avery 5). A Chicago native all of his life, Newman is an avid golfer, a fitness fan, and a classically trained pianist

When Good Is Good Enough: Guiding Business Users On BI Practices

Ina Felsheim

Image_part2-300x200In Part One of this blog series, I talked about changing your IT culture to better support self-service BI and data discovery. Absolutely essential. However, your work is not done!

Self-service BI and data discovery will drive the number of users using the BI solutions to rapidly expand. Yet all of these more casual users will not be well versed in BI and visualization best practices.

When your user base rapidly expands to more casual users, you need to help educate them on what is important. For example, one IT manager told me that his casual BI users were making visualizations with very difficult-to-read charts and customizing color palettes to incredible degrees.

I had a similar experience when I was a technical writer. One of our lead writers was so concerned with readability of every sentence that he was going through the 300+ page manuals (yes, they were printed then) and manually adjusting all of the line breaks and page breaks. (!) Yes, readability was incrementally improved. But now any number of changes–technical capabilities, edits, inserting larger graphics—required re-adjusting all of those manual “optimizations.” The time it took just to do the additional optimization was incredible, much less the maintenance of these optimizations! Meanwhile, the technical writing team was falling behind on new deliverables.

The same scenario applies to your new casual BI users. This new group needs guidance to help them focus on the highest value practices:

  • Customization of color and appearance of visualizations: When is this customization necessary for a management deliverable, versus indulging an OCD tendency? I too have to stop myself from obsessing about the font, line spacing, and that a certain blue is just a bit different than another shade of blue. Yes, these options do matter. But help these casual users determine when that time is well spent.
  • Proper visualizations: When is a spinning 3D pie chart necessary to grab someone’s attention? BI professionals would firmly say “NEVER!” But these casual users do not have a lot of depth on BI best practices. Give them a few simple guidelines as to when “flash” needs to subsume understanding. Consider offering a monthly one-hour Lunch and Learn that shows them how to create impactful, polished visuals. Understanding if their visualizations are going to be viewed casually on the way to a meeting, or dissected at a laptop, also helps determine how much time to spend optimizing a visualization. No, you can’t just mandate that they all read Tufte.
  • Predictive: Provide advanced analytics capabilities like forecasting and regression directly in their casual BI tools. Using these capabilities will really help them wow their audience with substance instead of flash.
  • Feature requests: Make sure you understand the motivation and business value behind some of the casual users’ requests. These casual users are less likely to understand the implications of supporting specific requests across an enterprise, so make sure you are collaborating on use cases and priorities for substantive requests.

By working with your casual BI users on the above points, you will be able to collectively understand when the absolute exact request is critical (and supports good visualization practices), and when it is an “optimization” that may impact productivity. In many cases, “good” is good enough for the fast turnaround of data discovery.

Next week, I’ll wrap this series up with hints on getting your casual users to embrace the “we” not “me” mentality.

Read Part One of this series: Changing The IT Culture For Self-Service BI Success.

Follow me on Twitter: @InaSAP

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Diving Deep Into Digital Experiences

Kai Goerlich

 

Google Cardboard VR goggles cost US$8
By 2019, immersive solutions
will be adopted in 20% of enterprise businesses
By 2025, the market for immersive hardware and software technology could be $182 billion
In 2017, Lowe’s launched
Holoroom How To VR DIY clinics

From Dipping a Toe to Fully Immersed

The first wave of virtual reality (VR) and augmented reality (AR) is here,

using smartphones, glasses, and goggles to place us in the middle of 360-degree digital environments or overlay digital artifacts on the physical world. Prototypes, pilot projects, and first movers have already emerged:

  • Guiding warehouse pickers, cargo loaders, and truck drivers with AR
  • Overlaying constantly updated blueprints, measurements, and other construction data on building sites in real time with AR
  • Building 3D machine prototypes in VR for virtual testing and maintenance planning
  • Exhibiting new appliances and fixtures in a VR mockup of the customer’s home
  • Teaching medicine with AR tools that overlay diagnostics and instructions on patients’ bodies

A Vast Sea of Possibilities

Immersive technologies leapt forward in spring 2017 with the introduction of three new products:

  • Nvidia’s Project Holodeck, which generates shared photorealistic VR environments
  • A cloud-based platform for industrial AR from Lenovo New Vision AR and Wikitude
  • A workspace and headset from Meta that lets users use their hands to interact with AR artifacts

The Truly Digital Workplace

New immersive experiences won’t simply be new tools for existing tasks. They promise to create entirely new ways of working.

VR avatars that look and sound like their owners will soon be able to meet in realistic virtual meeting spaces without requiring users to leave their desks or even their homes. With enough computing power and a smart-enough AI, we could soon let VR avatars act as our proxies while we’re doing other things—and (theoretically) do it well enough that no one can tell the difference.

We’ll need a way to signal when an avatar is being human driven in real time, when it’s on autopilot, and when it’s owned by a bot.


What Is Immersion?

A completely immersive experience that’s indistinguishable from real life is impossible given the current constraints on power, throughput, and battery life.

To make current digital experiences more convincing, we’ll need interactive sensors in objects and materials, more powerful infrastructure to create realistic images, and smarter interfaces to interpret and interact with data.

When everything around us is intelligent and interactive, every environment could have an AR overlay or VR presence, with use cases ranging from gaming to firefighting.

We could see a backlash touting the superiority of the unmediated physical world—but multisensory immersive experiences that we can navigate in 360-degree space will change what we consider “real.”


Download the executive brief Diving Deep Into Digital Experiences.


Read the full article Swimming in the Immersive Digital Experience.

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Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation. Share your thoughts with Kai on Twitter @KaiGoe.heif Futu

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Jenny Dearborn: Soft Skills Will Be Essential for Future Careers

Jenny Dearborn

The Japanese culture has always shown a special reverence for its elderly. That’s why, in 1963, the government began a tradition of giving a silver dish, called a sakazuki, to each citizen who reached the age of 100 by Keiro no Hi (Respect for the Elders Day), which is celebrated on the third Monday of each September.

That first year, there were 153 recipients, according to The Japan Times. By 2016, the number had swelled to more than 65,000, and the dishes cost the already cash-strapped government more than US$2 million, Business Insider reports. Despite the country’s continued devotion to its seniors, the article continues, the government felt obliged to downgrade the finish of the dishes to silver plating to save money.

What tends to get lost in discussions about automation taking over jobs and Millennials taking over the workplace is the impact of increased longevity. In the future, people will need to be in the workforce much longer than they are today. Half of the people born in Japan today, for example, are predicted to live to 107, making their ancestors seem fragile, according to Lynda Gratton and Andrew Scott, professors at the London Business School and authors of The 100-Year Life: Living and Working in an Age of Longevity.

The End of the Three-Stage Career

Assuming that advances in healthcare continue, future generations in wealthier societies could be looking at careers lasting 65 or more years, rather than at the roughly 40 years for today’s 70-year-olds, write Gratton and Scott. The three-stage model of employment that dominates the global economy today—education, work, and retirement—will be blown out of the water.

It will be replaced by a new model in which people continually learn new skills and shed old ones. Consider that today’s most in-demand occupations and specialties did not exist 10 years ago, according to The Future of Jobs, a report from the World Economic Forum.

And the pace of change is only going to accelerate. Sixty-five percent of children entering primary school today will ultimately end up working in jobs that don’t yet exist, the report notes.

Our current educational systems are not equipped to cope with this degree of change. For example, roughly half of the subject knowledge acquired during the first year of a four-year technical degree, such as computer science, is outdated by the time students graduate, the report continues.

Skills That Transcend the Job Market

Instead of treating post-secondary education as a jumping-off point for a specific career path, we may see a switch to a shorter school career that focuses more on skills that transcend a constantly shifting job market. Today, some of these skills, such as complex problem solving and critical thinking, are taught mostly in the context of broader disciplines, such as math or the humanities.

Other competencies that will become critically important in the future are currently treated as if they come naturally or over time with maturity or experience. We receive little, if any, formal training, for example, in creativity and innovation, empathy, emotional intelligence, cross-cultural awareness, persuasion, active listening, and acceptance of change. (No wonder the self-help marketplace continues to thrive!)

The three-stage model of employment that dominates the global economy today—education, work, and retirement—will be blown out of the water.

These skills, which today are heaped together under the dismissive “soft” rubric, are going to harden up to become indispensable. They will become more important, thanks to artificial intelligence and machine learning, which will usher in an era of infinite information, rendering the concept of an expert in most of today’s job disciplines a quaint relic. As our ability to know more than those around us decreases, our need to be able to collaborate well (with both humans and machines) will help define our success in the future.

Individuals and organizations alike will have to learn how to become more flexible and ready to give up set-in-stone ideas about how businesses and careers are supposed to operate. Given the rapid advances in knowledge and attendant skills that the future will bring, we must be willing to say, repeatedly, that whatever we’ve learned to that point doesn’t apply anymore.

Careers will become more like life itself: a series of unpredictable, fluid experiences rather than a tightly scripted narrative. We need to think about the way forward and be more willing to accept change at the individual and organizational levels.

Rethink Employee Training

One way that organizations can help employees manage this shift is by rethinking training. Today, overworked and overwhelmed employees devote just 1% of their workweek to learning, according to a study by consultancy Bersin by Deloitte. Meanwhile, top business leaders such as Bill Gates and Nike founder Phil Knight spend about five hours a week reading, thinking, and experimenting, according to an article in Inc. magazine.

If organizations are to avoid high turnover costs in a world where the need for new skills is shifting constantly, they must give employees more time for learning and make training courses more relevant to the future needs of organizations and individuals, not just to their current needs.

The amount of learning required will vary by role. That’s why at SAP we’re creating learning personas for specific roles in the company and determining how many hours will be required for each. We’re also dividing up training hours into distinct topics:

  • Law: 10%. This is training required by law, such as training to prevent sexual harassment in the workplace.

  • Company: 20%. Company training includes internal policies and systems.

  • Business: 30%. Employees learn skills required for their current roles in their business units.

  • Future: 40%. This is internal, external, and employee-driven training to close critical skill gaps for jobs of the future.

In the future, we will always need to learn, grow, read, seek out knowledge and truth, and better ourselves with new skills. With the support of employers and educators, we will transform our hardwired fear of change into excitement for change.

We must be able to say to ourselves, “I’m excited to learn something new that I never thought I could do or that never seemed possible before.” D!

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