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Top Five Big Data Challenges For CIOs

Daniel Newman

Big Data is more than just a buzzword these days – but it can be both a massive opportunity and a huge problem for companies. Digital data collection has been a practice since the dawn of computing, but the exponential increase of information, in terms of smartphones, social, search, and the Internet of Things, have created a snowball effect when it comes to the data that’s generated, collected, and stored. What’s that mean? It means lots of possibilities for smarter products and services, smarter marketing, and smarter business practices. It means 88% of executives say Big Data is a top priority for their company. But it also means lots of challenges are coming their way.

The amount of data we create each year creates a staggering set of problems for those who are in charge of managing it. Thankfully, we’re no longer in an era where one person has all the collective responsibility for an organization’s data. That said, the CIO is most often tasked with developing systems of record that can leverage tech to drive better business processes.

We know CIOs face innumerable challenges when it comes to Big Data, but what are the biggest Big Data challenges for CIOs in 2016? Let’s break down.

  1. Collection. The challenge with collection doesn’t have to be the obvious one (how do we collect it)? What if, as Clorox’s CIO Manjit Singh discussed in an interview on the very subject, it was something more high level, like “how to get insight out of the data – what questions to ask and how to use the data to predict results in the business?” Singh is right. CIOs should ask themselves what data is important to collect from a business case standpoint? What data isn’t? How is it decided? CIOs can start by looking at Big Data collection from a big picture perspective.
  1. Storage. Logic states that Big Data requires some big attention to storage, and it’s the truth. Besides the sheer volume requirements of all those bytes, certain data also needs to be available on demand at any time. This can be for operations purposes or compliance. Even though storage is more available than ever, it isn’t all created equal. CIOs should examine infrastructure and cloud options thoroughly before any checks are signed. Purchasing too much is wasting money, but too little could mean crashes and costly downtime.
  1. Organization and management. To make data useful, it needs to not just be stored and accessible but organized in some way that makes it easy to find and easy to pull. That way, data scientists and other users can locate, analyze, and apply the information in a way that is both efficient and measurable.
  1. Conversion. With companies turning to more and more chief data officers, architects, and analysts, it is critical that business intelligence tools are readily available these individuals. While they may be involved in selecting the tools for creating business intelligence, the CIO needs to have systems in place – systems for collection, storage, organization, and management – so they can actually use them.

I’m sure I don’t have to tell you that internal infrastructure costs money, and many organizations want results from Big Data initiatives sooner rather than later. In these instances where financial and analytical expectations don’t jive – and in a ton of other Big Data situations, too – CIOs who have not yet done so should consider taking a look at cloud-based options, especially in the realm of increased operability provided by software-as-a-service (SaaS).

  1. Unstructured data growth. Big Data is more than just data produced by and for a company. Even when consumers aren’t interacting specifically with a brand – when they’re making posts on social media, uploading videos, or generating any other type of personalized content – they’re giving businesses insight into their habits, preferences, and consumer behaviors. Even when it’s not explicitly tied to their business pages or endeavors – perhaps, actually, especially when it’s not – CIOs need to develop systems to collect, store, organize, and make this valuable customer data usable for operations, sales, and marketing.

Big Data has already had a substantial impact on the way businesses operate internally, interact with consumers, and navigate their respective markets. That Big Data snowball keeps rolling, though, and even more changes are forthcoming. If you’re a CIO who can conquer the challenges above without losing sight of the opportunities success will bring, 2016 will be a great year.

See The Digital Economy Infographic to learn more about how hyperconnectivity is changing the way we live and work, how businesses operate, and how society functions.

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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

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CIO

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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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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How Emotionally Aware Computing Can Bring Happiness to Your Organization

Christopher Koch


Do you feel me?

Just as once-novel voice recognition technology is now a ubiquitous part of human–machine relationships, so too could mood recognition technology (aka “affective computing”) soon pervade digital interactions.

Through the application of machine learning, Big Data inputs, image recognition, sensors, and in some cases robotics, artificially intelligent systems hunt for affective clues: widened eyes, quickened speech, and crossed arms, as well as heart rate or skin changes.




Emotions are big business

The global affective computing market is estimated to grow from just over US$9.3 billion a year in 2015 to more than $42.5 billion by 2020.

Source: “Affective Computing Market 2015 – Technology, Software, Hardware, Vertical, & Regional Forecasts to 2020 for the $42 Billion Industry” (Research and Markets, 2015)

Customer experience is the sweet spot

Forrester found that emotion was the number-one factor in determining customer loyalty in 17 out of the 18 industries it surveyed – far more important than the ease or effectiveness of customers’ interactions with a company.


Source: “You Can’t Afford to Overlook Your Customers’ Emotional Experience” (Forrester, 2015)


Humana gets an emotional clue

Source: “Artificial Intelligence Helps Humana Avoid Call Center Meltdowns” (The Wall Street Journal, October 27, 2016)

Insurer Humana uses artificial intelligence software that can detect conversational cues to guide call-center workers through difficult customer calls. The system recognizes that a steady rise in the pitch of a customer’s voice or instances of agent and customer talking over one another are causes for concern.

The system has led to hard results: Humana says it has seen an 28% improvement in customer satisfaction, a 63% improvement in agent engagement, and a 6% improvement in first-contact resolution.


Spread happiness across the organization

Source: “Happiness and Productivity” (University of Warwick, February 10, 2014)

Employers could monitor employee moods to make organizational adjustments that increase productivity, effectiveness, and satisfaction. Happy employees are around 12% more productive.




Walking on emotional eggshells

Whether customers and employees will be comfortable having their emotions logged and broadcast by companies is an open question. Customers may find some uses of affective computing creepy or, worse, predatory. Be sure to get their permission.


Other limiting factors

The availability of the data required to infer a person’s emotional state is still limited. Further, it can be difficult to capture all the physical cues that may be relevant to an interaction, such as facial expression, tone of voice, or posture.



Get a head start


Discover the data

Companies should determine what inferences about mental states they want the system to make and how accurately those inferences can be made using the inputs available.


Work with IT

Involve IT and engineering groups to figure out the challenges of integrating with existing systems for collecting, assimilating, and analyzing large volumes of emotional data.


Consider the complexity

Some emotions may be more difficult to discern or respond to. Context is also key. An emotionally aware machine would need to respond differently to frustration in a user in an educational setting than to frustration in a user in a vehicle.

 


 

download arrowTo learn more about how affective computing can help your organization, read the feature story Empathy: The Killer App for Artificial Intelligence.


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About Christopher Koch

Christopher Koch is the Editorial Director of the SAP Center for Business Insight. He is an experienced publishing professional, researcher, editor, and writer in business, technology, and B2B marketing. Share your thoughts with Chris on Twitter @Ckochster.

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What Will The Internet Of Things Look Like In 2027? 7 Predictions

Tom Raftery

Recently I was asked: Where do you see the Internet of Things in 10 years?

It is an interesting question to ponder. To frame it properly, it helps to think back to what the world was like 10 years ago and how far we have come since then.
iPhone launch 2007

Ten years ago, in 2007 Apple launched the iPhone. This was the first real smartphone, and it changed completely how we interact with information.

And if you think back to that first iPhone—with its 2.5G connectivity, lack of front-facing camera, and 3.5-inch diagonal 163ppi screen—and compare it to today’s iPhones, that is the level of change we are talking about in 10 years.

In 2027 the term Internet of Things will be redundant. Just as we no longer say Internet-connected smartphone or interactive website because the connectedness and interactivity are now a given, in 10 years all the things will be connected and the term Internet of Things will be superfluous.

While the term may become meaningless, however, that is only because the technologies will be pervasive—and that will change everything.

With significant progress in low-cost connectivity, sensors, cloud-based services, and analytics, in 10 years we will see the following trends and developments:

  • Connected agriculture will move to vertical and in-vitro food production. This will enable higher yields from crops, lower inputs required to produce them, including a significantly reduced land footprint, and the return of unused farmland to increase biodiversity and carbon sequestration in forests
  • Connected transportation will enable tremendous efficiencies and safety improvements as we transition to predictive maintenance of transportation fleets, vehicles become autonomous and vehicle-to-vehicle communication protocols become the norm, and insurance premiums start to favor autonomous driving modes (Tesla cars have 40% fewer crashes when in autopilot mode, according to the NHTSA)
  • Connected healthcare will move from reactive to predictive, with sensors alerting patients and providers of irregularities before significant incidents occur, and the ability to schedule and 3D-print “spare parts”
  • Connected manufacturing will transition to manufacturing as a service, with distributed manufacturing (3D printing) enabling mass customization, with batch sizes of one very much the norm
  • Connected energy, with the sources of demand able to “listen” to supply signals from generators, will move to a system in which demand more closely matches supply (with cheaper storage, low carbon generation, and end-to-end connectivity). This will stabilise the the grid and eliminate the fluctuations introduced by increasing the percentage of variable generators (such as solar and wind) in the system, thereby reducing electricity generation’s carbon footprint
  • Human-computer interfaces will migrate from today’s text- and touch-based systems toward augmented and mixed reality (AR and MR) systems, with voice- and gesture-enabled UIs
  • Finally, we will see the rise of vast business networks. These networks will act like automated B2B marketplaces, facilitating information-sharing among partners, empowering workers with greater contextual knowledge, and augmenting business processes with enhanced information

IoT advancements will also improve and enhance many other areas of our lives and businesses—logistics with complete tracking and traceability all the way through the supply chain is another example of many.

We are only starting our IoT journey. The dramatic advances we’ve seen since the introduction of the smartphone—such as Apple’s open-sourced ResearchKit being used to monitor the health of pregnant women—foretell innovations and advancements that we can only start to imagine. The increasing pace of innovation, falling component prices, and powerful networking capabilities reinforce this bright future, even if we no longer use the term Internet of Things.

For a shorter-term view of the IoT, see 20 Technology Predictions To Keep Your Eye On In 2017.

Photo: Garry Knight on Flickr

Originally posted on my TomRaftery.com blog

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About Tom Raftery

Tom Raftery is VP and Global Internet of Things Evangelist for SAP. Previously Tom worked as an independent analyst focussing on the Internet of Things, Energy and CleanTech. Tom has a very strong background in social media, is the former co-founder of a software firm and is co-founder and director of hyper energy-efficient data center Cork Internet eXchange. More recently, Tom worked as an Industry Analyst for RedMonk, leading their GreenMonk practice for 7 years.