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.

Comments

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.

Comments

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

Comments

Human Skills for the Digital Future

Dan Wellers and Kai Goerlich

Technology Evolves.
So Must We.


Technology replacing human effort is as old as the first stone axe, and so is the disruption it creates.
Thanks to deep learning and other advances in AI, machine learning is catching up to the human mind faster than expected.
How do we maintain our value in a world in which AI can perform many high-value tasks?


Uniquely Human Abilities

AI is excellent at automating routine knowledge work and generating new insights from existing data — but humans know what they don’t know.

We’re driven to explore, try new and risky things, and make a difference.
 
 
 
We deduce the existence of information we don’t yet know about.
 
 
 
We imagine radical new business models, products, and opportunities.
 
 
 
We have creativity, imagination, humor, ethics, persistence, and critical thinking.


There’s Nothing Soft About “Soft Skills”

To stay ahead of AI in an increasingly automated world, we need to start cultivating our most human abilities on a societal level. There’s nothing soft about these skills, and we can’t afford to leave them to chance.

We must revamp how and what we teach to nurture the critical skills of passion, curiosity, imagination, creativity, critical thinking, and persistence. In the era of AI, no one will be able to thrive without these abilities, and most people will need help acquiring and improving them.

Anything artificial intelligence does has to fit into a human-centered value system that takes our unique abilities into account. While we help AI get more powerful, we need to get better at being human.


Download the executive brief Human Skills for the Digital Future.


Read the full article The Human Factor in an AI Future.


Comments

Dan Wellers

About Dan Wellers

Dan Wellers is founder and leader of Digital Futures at SAP, a strategic insights and thought leadership discipline that explores how digital technologies drive exponential change in business and society.

Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation.

Share your thoughts with Kai on Twitter @KaiGoe.heif Futu

Tags:

How Manufacturers Can Kick-Start The Internet Of Things In 2018

Tanja Rueckert

Part 1 of the “Manufacturing Value from IoT” series

IoT is one of the most dynamic and exciting markets I am involved with at SAP. The possibilities are endless, and that is perhaps where the challenges start. I’ll be sharing a series of blogs based on research into knowledge and use of IoT in manufacturing.

Most manufacturing leaders think that the IoT is the next big thing, alongside analytics, machine learning, and artificial intelligence. They see these technologies dramatically impacting their businesses and business in general over the next five years. Researchers see big things ahead as well; they forecast that IoT products and investments will total hundreds of billions – or even trillions – of dollars in coming decades.

They’re all wrong.

The IoT is THE Big Thing right now – if you know where to look.

Nearly a third (31%) of production processes and equipment and non-production processes and equipment (30%) already incorporate smart device/embedded intelligence. Similar percentages of manufacturers have a company strategy implemented or in place to apply IoT technologies to their processes (34%) or to embed IoT technologies into products (32%).

opportunities to leverage IoTSource:Catch Up with IoT Leaders,” SAP, 2017.

The best process opportunities to leverage the IoT include document management (e.g. real-time updates of process information); shipping and warehousing (e.g. tracking incoming and outgoing goods); and assembly and packaging (e.g. production monitoring). More could be done, but figuring out where and how to implement the IoT is an obstacle for many leaders. Some 44 percent of companies have trouble identifying IoT opportunities and benefits for either internal processes or IoT-enabled products.

Why so much difficulty in figuring out where to use the IoT in processes?

  • No two industries use the IoT in the same way. An energy company might leverage asset-management data to reduce costs; an e-commerce manufacturer might focus on metrics for customer fulfillment; a fabricator’s use of IoT technologies may be driven by a need to meet exacting product variances.
  • Even in the same industry, individual firms will apply and profit from the IoT in unique ways. In some plants and processes, management is intent on getting the most out of fully depreciated equipment. Unfortunately, older equipment usually lacks state-of-the-art controls and sensors. The IoT may be in place somewhere within those facilities, but it’s unlikely to touch legacy processes until new machinery arrive. 

Where could your company leverage the IoT today? Think strategically, operationally, and financially to prioritize opportunities:

  • Can senior leadership and plant management use real-time process data to improve daily decision-making and operations planning? Do they have the skills and tools (e.g., business analytics) to leverage IoT data?
  • Which troublesome processes in the plant or front office erode profits? With real-time data pushed out by the IoT, which could be improved?
  • Of the processes that could be improved, which include equipment that can – in the near-term – accommodate embedded intelligence, and then communicate with plant and enterprise networks?

Answer those questions, and you’ve got an instant list of how and where to profit from the IoT – today.

Stay tuned for more information on how IoT is developing and to learn what it takes to be a manufacturing IoT innovator. In the meantime, download the report “Catch Up with IoT Leaders.”

Comments

Tanja Rueckert

About Tanja Rueckert

Tanja Rueckert is President of the Internet of Things and Digital Supply Chain Business Unit at SAP.