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Understanding Data: Gold Nuggets And Puzzle Pieces

Paul Lewis

I regularly use the colloquial phrase “nuggets of gold in a huge pot” when describing the value obtained from understanding and analyzing data.

It seems like an easy win. The phrase is well-known and highly digestible. Most people in the audience generally appreciate that gold has immense value, and there are whole industries that exist to mine this precious metal from a variety of mountains and streams. It’s also predictable that as you collect these precious nuggets, you won’t be able to carry them around given their collective weight, and a pot is as good as anything to store them. Plus, the whole leprechaun-esque vision it likely creates might bury the phrase in long-term memory for easy recall the next day with colleagues. Like, “I went to a seminar yesterday and this dude talked about value derived from analytics as being like nuggets of gold in a huge pot.” That’s helpful.

Occasionally, like here, I even blog about it. I find repetition to be tremendously valuable in retaining content. Additionally, I also find repetition to be tremendously valuable in retaining content. (Note: embedding subliminal messages in repetitive statements is also tremendously valuable, but I will get to that content later. Trust me, you won’t object.)

Unfortunately, as metaphors go, it’s extremely weak (especially considering pots are much more likely to hold coins versus nuggets.) Let me break it down so you see what I mean:

  • Data has value the instant it’s created, for as long as you hold it, until its demise
  • The final form of data could be deletion or decade-old archiving; the effect is the same
  • The value of data changes over time
  • Adding new data to existing data, more opportunity is created to discover a potentially endless series of value (Potentially)
  • This potential value could be expressed as an undetermined number of “nuggets of gold” (I guess, if you must)
  • The more data you have, the more nuggets of gold you could discover, and the more necessary a pot to hold them (That’s a stretch)
  • The more data you have, the more precise your statistical and mathematical models and more opportunity you will have to find more nuggets (Don’t buy it, sounds complex)

Getting the picture?

The fundamental problem with the metaphor is that I’m treating value-obtained as a direct representation of data-collected; i.e., you are storing various elements of a client, therefore hidden in one or more of elements is a single purposeful and valuable answer, hidden in the fields, row and columns:

  • Data, in the sense of a database, being a single field, in a single row, in a single column, is irrelevant. It carries no weight or value beyond the knowledge of collection. It lacks context and awareness. Whether static or variable, it tells no story and solves no problem.
  • Data, in the sense of unstructured data, bytes of binary information, carrys even less value. In fact, knowing that a single bit is only a small part of a greater whole, predetermines its unlikeliness to impact the entire picture.
  • Data, as a single point in time from a stream of information, is outdated the very nanosecond it’s used, as more current data takes its place, creating a new current reality.

The concept of “nuggets of gold,” by extension, then presumes a specific and direct answer to a question; or a direct and obvious correlation to an action:

  • How many toothpicks are in the container? 173
  • What color shirt matches best with my red pants? None, don’t wear red pants
  • What’s the name of that dude with the crazy beard in that class last year? For the last time HENRY!
  • If you were to spend $5 less, you would have an extra $5 in the bank
  • If we mix these two primary colors, you would have this one secondary
  • If I build more of this product, I will sell more of this product

Lesson learned: Individual elements of data possess little to no value

There is a reason why every company (including yours) has an enterprise information management (EIM) program and a chief data officer (CDO) responsible for stewardship of your most precious technological asset, data. As a reminder, EIM is an integrative discipline for structuring, describing, and governing information assets across organizational and technological boundaries to improve efficiency, promote transparency, and enable business insight. The program includes capabilities to store, protect, architect, manage risk and compliance, manage quality, classify, and organize data. A great EIM program focuses on how organizations derive insight and value from information, either from internal effectiveness and/or growth-oriented goals and activities.

A CDO, or VP of business intelligence, or manager of management information systems (MIS) understands that data, in its elemental form, does NOT equal value. They understand that value is derived from discovering patterns and appreciating the impact of change and time, and that data requires enrichment, not just discovery. The activity required to derive value is implemented in four capabilities:

  • Descriptive: MIS or reporting, focusing on hindsight (what has happened)
  • Diagnostic: Business intelligence or incident management, focusing on current-state insight or understanding “why” it happened
  • Predictive: Analytics combining models of previous data and application to new data, focusing on foresight (what will happen)
  • Prescriptive: Analytics and action, foresight algorithms to implement a business function

The EIM program also appreciates that the effort to create value focuses far less on finding a long-lost and specific piece of data, and instead focuses on studying patterns in static, changing, and moving information and researching correlations, causations, and theoretical application of mathematics and logic to create complex business value from data-centric components. Yes, it’s a science. It’s far less searching for a nugget of gold, and far more about determining that you could make money from gold jewelry… all from the same mine.

So here is my NEW metaphor

And for the sake of inconsistency, I’m not even going to use precious metals. Imagine a pile of random puzzle pieces. Each piece represents a single data point, collected from a variety of sources.

Before value can be obtained, preparatory activity is needed to curate and enrich data:

  • Extraction: Identify all the puzzle pieces in the house: under beds, in vacuum cleaners, in the dog bowl, etc. For data, discover all the sources of information: internally and externally, structured and unstructured, and classify.
  • Integration: Send out all the kids and parents to grab the pieces and bring them back to the pile. For data, connect to hundreds of sources for batch or real-time integration/ETL.
  • Enhancement and cleansing: Dust off each piece, glue back down the picture side, sharpen the edges, number the backs. For data, match and qualify, and add appropriate metadata.

This effort to convert raw data to content, and indescribable fields into describable objects, requires the capabilities of more than just a pile, a box of sorts.

A content platform (the box) allows organizations to bring together object storage (a place to put all data), data mobility (a means to abstract data from its sources), cloud gateways (ability to use multiple deployment models), and metadata (tagging and sophisticated search to create a tightly integrated, simple, and smart data intelligence solution.) You may have heard this being referred to as a “data lake.” I highly recommend this solution set, if you happen to be in the market.

For this new enhanced data set (puzzle pieces), contained in a content platform (puzzle box), the EIM value-creation activities can be described (it’s still the goal to find the Picasso):

  • Descriptive: Create a list of puzzle pieces, organized by shape/color/origin; determine which pieces closely resemble the palette of a master work of art
  • Diagnostic: visualize the current state of completing the puzzle; how far along is the process and/or discover missing pieces
  • Predictive: Given where we are in the process, and the remaining pieces still in the box, determine what picture we might be making and/or predict what might be the picture, even if we have missing pieces
  • Prescriptive: After having made dozens of pictures from these same puzzle pieces, guide the creation of existing and new completed puzzles

Both predictive and prescriptive analytics would use linear and non-linear algorithms (ways of thinking out the problem), would focus equally on the puzzle pieces that exist and the ones that are missing, and combine or use pieces from hundreds of potential sources to create hundreds of different works of art.

In a nutshell: The value obtained from understanding and analyzing data is not that you will find “nuggets of gold” of data or an individual puzzle piece that solves the problem. The value obtained from understanding and analyzing data is the millions of dollars in your bank account from building several masterpieces from all your individual puzzle pieces.

Learn how to derive more value from Data – The Hidden Treasure Inside Your Business.

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Paul Lewis

About Paul Lewis

Paul Lewis is the Chief Technology Officer in Hitachi for the Americas, responsible for the leading technology trend mastery and evangelism, client executive advocacy, and external delivery of the Hitachi vision and strategy especially related to digital transformation and social innovation. Additionally, Paul contributes to field enablement of data intelligence and analytics; interprets and translates complex technology trends including cloud, mobility, governance, and information management; and represents the Americas region in the Global Technology Office, the Hitachi LTD R&D division. In his role of trusted advisor to the CIO community, Paul’s explicit goal is to ensure clients’ problems are solved and opportunities realized. Paul can be found at his blog, on Twitter, and on LinkedIn.

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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Taking Learning Back to School

Dan Wellers

 

Denmark spends most GDP on labor market programs at 3.3%.
The U.S. spends only 0.1% of it’s GDP on adult education and workforce retraining.
The number of post-secondary vocational and training institutions in China more than doubled from 2000 to 2014.
47% of U.S. jobs are at risk for automation.

Our overarching approach to education is top down, inflexible, and front loaded in life, and does not encourage collaboration.

Smartphone apps that gamify learning or deliver lessons in small bits of free time can be effective tools for teaching. However, they don’t address the more pressing issue that the future is digital and those whose skills are outmoded will be left behind.

Many companies have a history of effective partnerships with local schools to expand their talent pool, but these efforts are not designed to change overall systems of learning.


The Question We Must Answer

What will we do when digitization, automation, and artificial intelligence eject vast numbers of people from their current jobs, and they lack the skills needed to find new ones?

Solutions could include:

  • National and multinational adult education programs
  • Greater investment in technical and vocational schools
  • Increased emphasis on apprenticeships
  • Tax incentives for initiatives proven to close skills gaps

We need a broad, systemic approach that breaks businesses, schools, governments, and other organizations that target adult learners out of their silos so they can work together. Chief learning officers (CLOs) can spearhead this approach by working together to create goals, benchmarks, and strategy.

Advancing the field of learning will help every business compete in an increasingly global economy with a tight market for skills. More than this, it will mitigate the workplace risks and challenges inherent in the digital economy, thus positively influencing the future of business itself.


Download the executive brief Taking Learning Back to School.


Read the full article The Future of Learning – Keeping up With The Digital Economy

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Dan Wellers

About Dan Wellers

Dan Wellers is the Global Lead of Digital Futures at SAP, which explores how organizations can anticipate the future impact of exponential technologies. Dan has extensive experience in technology marketing and business strategy, plus management, consulting, and sales.

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Why Millennials Quit: Understanding A New Workforce

Shelly Kramer

Millennials are like mobile devices: they’re everywhere. You can’t visit a coffee shop without encountering both in large numbers. But after all, who doesn’t like a little caffeine with their connectivity? The point is that you should be paying attention to millennials now more than ever because they have surpassed Boomers and Gen-Xers as the largest generation.

Unfortunately for the workforce, they’re also the generation most likely to quit. Let’s examine a new report that sheds some light on exactly why that is—and what you can do to keep millennial employees working for you longer.

New workforce, new values

Deloitte found that two out of three millennials are expected to leave their current jobs by 2020. The survey also found that a staggering one in four would probably move on in the next year alone.

If you’re a business owner, consider putting four of your millennial employees in a room. Take a look around—one of them will be gone next year. Besides their skills and contributions, you’ve also lost time and resources spent by onboarding and training those employees—a very costly process. According to a new report from XYZ University, turnover costs U.S. companies a whopping $30.5 billion annually.

Let’s take a step back and look at this new workforce with new priorities and values.

Everything about millennials is different, from how to market to them as consumers to how you treat them as employees. The catalyst for this shift is the difference in what they value most. Millennials grew up with technology at their fingertips and are the most highly educated generation to date. Many have delayed marriage and/or parenthood in favor of pursuing their careers, which aren’t always about having a great paycheck (although that helps). Instead, it may be more that the core values of your business (like sustainability, for example) or its mission are the reasons that millennials stick around at the same job or look for opportunities elsewhere. Consider this: How invested are they in their work? Are they bored? What does their work/life balance look like? Do they have advancement opportunities?

Ping-pong tables and bringing your dog to work might be trendy, but they aren’t the solution to retaining a millennial workforce. So why exactly are they quitting? Let’s take a look at the data.

Millennials’ common reasons for quitting

In order to gain more insight into the problem of millennial turnover, XYZ University surveyed more than 500 respondents between the ages of 21 and 34 years old. There was a good mix of men and women, college grads versus high school grads, and entry-level employees versus managers. We’re all dying to know: Why did they quit? Here are the most popular reasons, some in their own words:

  • Millennials are risk-takers. XYZ University attributes this affection for risk taking with the fact that millennials essentially came of age during the recession. Surveyed millennials reported this experience made them wary of spending decades working at one company only to be potentially laid off.
  • They are focused on education. More than one-third of millennials hold college degrees. Those seeking advanced degrees can find themselves struggling to finish school while holding down a job, necessitating odd hours or more than one part-time gig. As a whole, this generation is entering the job market later, with higher degrees and higher debt.
  • They don’t want just any job—they want one that fits. In an age where both startups and seasoned companies are enjoying success, there is no shortage of job opportunities. As such, they’re often looking for one that suits their identity and their goals, not just the one that comes up first in an online search. Interestingly, job fit is often prioritized over job pay for millennials. Don’t forget, if they have to start their own company, they will—the average age for millennial entrepreneurs is 27.
  • They want skills that make them competitive. Many millennials enjoy the challenge that accompanies competition, so wearing many hats at a position is actually a good thing. One millennial journalist who used to work at Forbes reported that millennials want to learn by “being in the trenches, and doing it alongside the people who do it best.”
  • They want to do something that matters. Millennials have grown up with change, both good and bad, so they’re unafraid of making changes in their own lives to pursue careers that align with their desire to make a difference.
  • They prefer flexibility. Technology today means it’s possible to work from essentially anywhere that has an Internet connection, so many millennials expect at least some level of flexibility when it comes to their employer. Working remotely all of the time isn’t feasible for every situation, of course, but millennials expect companies to be flexible enough to allow them to occasionally dictate their own schedules. If they have no say in their workday, that’s a red flag.
  • They’ve got skills—and they want to use them. In the words of a 24-year-old designer, millennials “don’t need to print copies all day.” Many have paid (or are in the midst of paying) for their own education, and they’re ready and willing to put it to work. Most would prefer you leave the smaller tasks to the interns.
  • They got a better offer. Thirty-five percent of respondents to XYZ’s survey said they quit a previous job because they received a better opportunity. That makes sense, especially as recruiting is made simpler by technology. (Hello, LinkedIn.)
  • They seek mentors. Millennials are used to being supervised, as many were raised by what have been dubbed as “helicopter parents.” Receiving support from those in charge is the norm, not the anomaly, for this generation, and they expect that in the workplace, too.

Note that it’s not just XYZ University making this final point about the importance of mentoring. Consider Figures 1 and 2 from Deloitte, proving that millennials with worthwhile mentors report high satisfaction rates in other areas, such as personal development. As you can see, this can trickle down into employee satisfaction and ultimately result in higher retention numbers.

Millennials and Mentors
Figure 1. Source: Deloitte


Figure 2. Source: Deloitte

Failure to . . .

No, not communicate—I would say “engage.” On second thought, communication plays a role in that, too. (Who would have thought “Cool Hand Luke” would be applicable to this conversation?)

Data from a recent Gallup poll reiterates that millennials are “job-hoppers,” also pointing out that most of them—71 percent, to be exact—are either not engaged in or are actively disengaged from the workplace. That’s a striking number, but businesses aren’t without hope. That same Gallup poll found that millennials who reported they are engaged at work were 26 percent less likely than their disengaged counterparts to consider switching jobs, even with a raise of up to 20 percent. That’s huge. Furthermore, if the market improves in the next year, those engaged millennial employees are 64 percent less likely to job-hop than those who report feeling actively disengaged.

What’s next?

I’ve covered a lot in this discussion, but here’s what I hope you will take away: Millennials comprise a majority of the workforce, but they’re changing how you should look at hiring, recruiting, and retention as a whole. What matters to millennials matters to your other generations of employees, too. Mentoring, compensation, flexibility, and engagement have always been important, but thanks to the vocal millennial generation, we’re just now learning exactly how much.

What has been your experience with millennials and turnover? Are you a millennial who has recently left a job or are currently looking for a new position? If so, what are you missing from your current employer, and what are you looking for in a prospective one? Alternatively, if you’re reading this from a company perspective, how do you think your organization stacks up in the hearts and minds of your millennial employees? Do you have plans to do anything differently? I’d love to hear your thoughts.

For more insight on millennials and the workforce, see Multigenerational Workforce? Collaboration Tech Is The Key To Success.

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