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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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The Future of Cybersecurity: Trust as Competitive Advantage

Justin Somaini and Dan Wellers

 

The cost of data breaches will reach US$2.1 trillion globally by 2019—nearly four times the cost in 2015.

Cyberattacks could cost up to $90 trillion in net global economic benefits by 2030 if cybersecurity doesn’t keep pace with growing threat levels.

Cyber insurance premiums could increase tenfold to $20 billion annually by 2025.

Cyberattacks are one of the top 10 global risks of highest concern for the next decade.


Companies are collaborating with a wider network of partners, embracing distributed systems, and meeting new demands for 24/7 operations.

But the bad guys are sharing intelligence, harnessing emerging technologies, and working round the clock as well—and companies are giving them plenty of weaknesses to exploit.

  • 33% of companies today are prepared to prevent a worst-case attack.
  • 25% treat cyber risk as a significant corporate risk.
  • 80% fail to assess their customers and suppliers for cyber risk.

The ROI of Zero Trust

Perimeter security will not be enough. As interconnectivity increases so will the adoption of zero-trust networks, which place controls around data assets and increases visibility into how they are used across the digital ecosystem.


A Layered Approach

Companies that embrace trust as a competitive advantage will build robust security on three core tenets:

  • Prevention: Evolving defensive strategies from security policies and educational approaches to access controls
  • Detection: Deploying effective systems for the timely detection and notification of intrusions
  • Reaction: Implementing incident response plans similar to those for other disaster recovery scenarios

They’ll build security into their digital ecosystems at three levels:

  1. Secure products. Security in all applications to protect data and transactions
  2. Secure operations. Hardened systems, patch management, security monitoring, end-to-end incident handling, and a comprehensive cloud-operations security framework
  3. Secure companies. A security-aware workforce, end-to-end physical security, and a thorough business continuity framework

Against Digital Armageddon

Experts warn that the worst-case scenario is a state of perpetual cybercrime and cyber warfare, vulnerable critical infrastructure, and trillions of dollars in losses. A collaborative approach will be critical to combatting this persistent global threat with implications not just for corporate and personal data but also strategy, supply chains, products, and physical operations.


Download the executive brief The Future of Cybersecurity: Trust as Competitive Advantage.


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Unleash The Digital Transformation

Kadamb Goswami

The world has changed. We’ve seen massive disruption on multiple fronts – business model disruption, cybercrime, new devices, and an app-centric world. Powerful networks are crucial to success in a mobile-first, cloud-first world that’s putting an ever-increasing increasing amount of data at our fingertips. With the Internet of Things (IoT) we can connect instrumented devices worldwide and use new data to transform business models and products.

Disruption

Disruption comes in many forms. It’s not big or scary, it’s just another way of describing change and evolution. In the ’80s it manifested as call centers. Then, as the digital landscape began to take shape, it was the Internet, cloud computing … now it’s artificial intelligence (AI).

Digital transformation

Digital transformation means different things to different companies, but in the end I believe it will be a simple salvation that will carry us forward. If you Bing (note I worked for Microsoft for 15 years before experiencing digital transformation from the lens of the outside world), digital transformation, it says it’s “the profound and accelerating transformation of business activities, processes, competencies, and models to fully leverage the changes and opportunities of digital technologies and their impact across society in a strategic and prioritized way.” (I’ll simplify that; keep reading.)

A lot of today’s digital transformation ideas are ripped straight from the scripts of sci-fi entertainment, whether you’re talking about the robotic assistants of 2001: A Space Odyssey or artificial intelligence in the Star Trek series. We’re forecasting our future with our imagination. So, let’s move on to why digital transformation is needed in our current world.

Business challenges

The basic challenges facing businesses today are the same as they’ve always been: engaging customers, empowering employees, optimizing operations, and reinventing the value offered to customers. However, what has changed is the unique convergence of three things:

  1. Increasing volumes of data, particularly driven by the digitization of “things” and heightened individual mobility and collaboration
  1. Advancements in data analytics and intelligence to draw actionable insight from the data
  1. Ubiquity of cloud computing, which puts this disruptive power in the hands of organizations of all sizes, increasing the pace of innovation and competition

Digital transformation in plain English

Hernan Marino, senior vice president, marketing, & global chief operating officer at SAP, explains digital transformation by giving specific industry examples to make it simpler.

Automobile manufacturing used to be the work of assembly lines, people working side-by-side literally piecing together, painting, and churning out vehicles. It transitioned to automation, reducing costs and marginalizing human error. That was a business transformation. Now, we are seeing companies like Tesla and BMW incorporate technology into their vehicles that essentially make them computers on wheels. Cameras. Sensors. GPS. Self-driving vehicles. Syncing your smartphone with your car.

The point here is that companies need to make the upfront investments in infrastructure to take advantage of digital transformation, and that upfront investment will pay dividends in the long run as technological innovations abound. It is our job to collaboratively work with our customers to understand what infrastructure changes need to be made to achieve and take advantage of digital transformation.

Harman gives electric companies as another example. Remember a few years ago, when you used to go outside your house and see the little power meter spinning as it recorded the kilowatts you use? Every month, the meter reader would show up in your yard, record your usage, and report back to the electric company.

Most electric companies then made a business transformation and installed smart meters – eliminating the cost of the meter reader and integrating most homes into a smart grid that gave customers access to their real-time information. Now, as renewable energy evolves and integrates more fully into our lives, these same electric companies that switched over to smart meters are going to make additional investments to be able to analyze the data and make more informed decisions that will benefit both the company and its customers.

That is digital transformation. Obviously, banks, healthcare, entertainment, trucking, and e-commerce all have different needs than auto manufacturers and electric companies. It is up to us – marketers and account managers promoting digital transformation – to identify those needs and help our clients make the digital transformation as seamlessly as possible.

Digital transformation is more than just a fancy buzzword, it is our present and our future. It is re-envisioning existing business models and embracing a different way of bringing together people, data, and processes to create more for their customers through systems of intelligence.

Learn more about what it means to be a digital business.

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Goswami Kadamb

About Goswami Kadamb

Kadamb is a Senior Program Manager at SAP where he is responsible for developing and executing strategic sales program with Concur SaaS portfolio. Prior to that he led several initiatives with Microsoft's Cloud & Enterprise business to enable Solution Sales & IaaS offerings.