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How Small Companies Can Use Big Data To Grow And Improve

Jennifer Horowitz

Small businesses can cost-effectively analyze large data sets to improve their marketing and product quality and accelerate customer relationships. Leaders from every business sector must learn how to grasp its changes for the future as Big Data becomes the key basis of competition.

Big Data is for organizations of any size, with data management having developed into an important skill to competitively differentiate today’s market leaders from those that are no longer influential. Signals and Systems’ mid-2014 report found that the Big Data market is expected to total $76 billion by 2020, an increase of 17%.

Technically, Big Data refers to technologies and initiatives that are too massive for traditional skills, technologies, and infrastructure efficiently address.

More than 70 years ago, in 1941, the first attempt to quantify the volume of data growth known as the “information explosion” was used, according to the Oxford English Dictionary.

Big Data was initially a unique resource only for large corporations and statisticians. With the growth in the Internet, smartphones, wireless networks, sensors, social media, and other digital technologies, small businesses and companies of all sizes are now able to leverage this trend.

As Big Data grows, MSPs can even connect to SMBs in offering their services as they look for new opportunities. Markets and Markets predicts that third-party MSPs cut recurring in-house costs by 30-40% and can add as much as a 60% improvement in efficiency. Small businesses face a big problem today with finding data storage, due to the increased growth and data volume of devices.

MSPs can expand their cloud services as SMBs look for bigger and better data storage alternatives. This means new growth and partnerships for MSPs that choose to expand their suite of services.

In addition to expanding storage options, MSPs can look to analytics performance and database management. By helping small businesses better evaluate their data, SMBs can provide a streamlined recovery and backup system to ensure data is not cluttered on a user’s mobile device.

Big Data leaders and laggards

A.T. Kearney, a global management consultancy firm, and Carnegie Mellon University investigated the corporate use of Big Data in its first-ever Leadership Excellence in Analytic Practices (LEAP) July/August 2014 study. They divided companies into four categories: leaders, explorers, followers, and laggards. Here’s what the leaders were doing with Big Data.

An inclusive atmosphere: This begins with a hands-on, dynamic policy of executive sponsorship and mindshare about Big Data. This fosters team-building, cross-functional collaboration, and company-wide confidence in data-driven methodologies.

The need for speed: Leaders used approaches that focused on rapid experimentation, mobilization, and deployment. This was primarily through pilot programs and proof-of-concept modeling.

Forward-thinking: These policies bred innovation, growth, and better operational efficiency. While Big Data was used for reporting on past efforts, leaders focused on future endeavors. They evaluated risks. They studied costs and benefits and balanced the tradeoffs between them. Then they charted a course.

Building on Big Data

According to the IBM Institute for Business, 26% of companies see returns from Big Data after 6 months. 63% see returns after one year. 40% reported that they use Big Data to solve their operational challenges.

The world will become more and more reliant on data-driven metrics in the years to come, and businesses need to recognize that fact. Using the power of analytics can shift a company into high gear, while failing to do so could leave them stuck in neutral.

Want more strategies to help your business tap the power of analytics? See Top Five Big Data Challenges For CIOs.

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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.