What Does IoT all mean?

The number of articles about the Internet of Things [IoT], Machine-to-Machine communication [M2M], the Industrial Internet, the Internet of Everything [IoE] and the like have been increasing since I wrote my post introducing my IoT mindmap almost a year ago. I learn from some of them, some I nod sagely in agreement, and others cause me to scratch my head in confusion. One in particular this last week fell in that last category, when they claimed that all the terms listed here all mean the same thing.

From my reading, briefings and research over the past year, I've come to a different conclusion. The following definitions are my opinion. I can't say that any authority has certified these definitions. I believe them to be accurate, and if any vendor with an interest in any of these definitions strongly agree or disagree, I would be very much interested in talking with you.

Types

The first thing to be considered is Machine-to-Machine communication. M2M is really just one of four types of interchanges that occur over the Internet, intranets and any command, control, communication, computing or intelligence network. The other types are Human-to-Machine [H2M], Human-to-Human [H2H] and Machine-to-Human [M2H]. H2M and H2H interchanges have been around since the beginning of ARPAnet, which evolved to become the Internet. From the many different protocols at the beginning, such as FTP and Gopher [among many more], two have come to dominate Internet traffic:

  • simple mail transfer protocol [SMTP] at the heart of email, and
  • hypertext transfer protocol [HTTP] at the heart of the world wide web [WWW or Web].

Every transaction made using a computer: online transaction process [OLTP] electronic data interchange [EDI], and eCommerce; every purchase you make at your favorite web store, is an example of H2M.

Of course, starting with email [still the dominant form of communication over the Internet and for businesses and individuals] and expanding to Twiter, Facebook, Waze, Yelp, Foursquare, Yammer, all the various instant messaging networks, voice over Internet protocol [VoIP] and your favorite public or private social network, we have many examples of Internet enabled H2H communication.

These two, H2M and H2H, have become so prevalent, and so important to business, governments and our personal life, that the over-hyped phenomenon "Big Data" was born. But the importance and pervasiveness of M2M, and soon, M2H data will swamp the so-called data tsunami of the past decade. Predictive maintenance, building automation, elastic provisioning, machine logs, software "phoning home" and automated decision support systems are all good examples of direct M2M interchanges where one sensor, device, embedded computer or system has a productive exchange with another such machine, without concurrent human intervention. Self-quantification, gamification, personalized medicine and augmented reality [AR] are all early examples of M2H interchanges, where sensors, devices, embedded computers or system directly provides relevant information to an individual, allowing for better informed decisions.

The Internet of Things

The Internet of Things was coined in 1999 by Kevin Ashton. Since then, the term has come to mean any device that is connected to the Internet. Most people don't consider computers, routers, edge equipment and other Internet infrastructure hardware to be a "device", and usually exclude such hardware from consideration as a thing that uses that infrastructure. For many, the devices are only smart phones, feature phones and tablets. This has led to predictions by Cisco and GSMA to declare that there will be 30 to 50 billion devices connected to the Internet by 2020. However, even these organizations, and most people with whom I speak who have skin in the IoT game, feel that my own prediction of one trillion devices connected to the Internet by 2020 is more likely. These devices span from individual, but connected sensors, to heavy machinery. However, as companies come out with Tweeting diapers, glowing clothing and other such silliness, the Internet of Things is in danger of becoming a fad. So, what is the Internet of Things? To my mind, the Internet of Things comprises any sensor, embedded sensor, embedded computer, component, package, sub-system, systems, or System that is connected to the Internet and intended to have meaningful interchanges with other such items and with humans. The Internet of Things primarily uses M2M and increasingly M2H interchange.

Smarter Planet

The first treatment of the IoT as large, complex system, to which I was exposed was at networking event in 2008… One of those events where IBM was introducing their new initiative for a Smarter Planet. The Smarter Planet brings complex systems such as the Smart Grid, building automation across facilities, water management, traffic management, Smarter Cities and Smarter Farms under one System. One approach and one initiative that raises the IoT to a new level of importance for world governments, global businesses and individuals from the poorest village to the most cosmopolitan city. The Smarter Planet initiatives go beyond IoT, beyond the individual things, to treating all such things, the Internet, the protocols, process and policies as one very large, complex, possibly cognate system.

Industrial Internet

The Industrial Internet is a term coined by General Electric [GE] in 2011. At a very simple level, the Industrial Internet can be thought of connected industrial control systems. But the impact is much more complex, and much more significant. The first thing to be realized is that connected sensors and computing power will be embedded in everything, from robots and conveyor belts on the factory floor, to tractors and irrigation on the farm, from heavy equipment to hand drills, from jet engines to bus fleets; every piece of equipment, everywhere. The Industrial Internet also primarily uses M2M and M2H. While this sounds much like the Internet of Things, the purpose is much different. The Industrial Internet is about changing business processes and making data the new coin of the realm. GE is very serious about the Industrial Internet, and while they don't use the term yet, Sensor Analytics Ecosystems. Data Marketplaces are rapidly becoming core to GEs businesses, as proven by their recent 140 million dollar investment in Pivotal, the new Big Data Platform as a Service [PaaS] by EMC. Another excellent example of the importance of the Industrial Internet comes from Salesforce.com use of The Social Machine by Digi International and its Etherios business unit, in bringing sensor data into customer relationship management [CRM] by allowing sensors embedded in industrial refrigerators, hot tubs, and heavy and light equipment of all types to open SFDC chatter sessions and to file cases.

Internet of Everything

Cisco has recently started two initiatives related to the IoT, the Internet of Everything [IoE] and Fog Computing. IoE seeks to bring together H2H, H2M, M2M and H2H interchanges. On June 19th of this year, Cisco introduced their IoE Value Index [link to PDF]. By bringing together people, processes, data, and things, and with some impressive research to back it up, Cisco feels that the IoE, in 2013, could bring 1.2 Trillion Dollars in added value, and by 2022, 14.4 Trillion dollars in added market value to business around the world. Fog Computing tends more to the infrastructure of the IoE, bringing the concepts of Cloud Computing, such as distributed computing and elastic provisioning, to the edge of the network, with an emphasis on wireless connectivity, streaming data, and heterogeneity.

Industry Overview

While some of the above are corporate initiatives, they each represent important and distinct concepts. In addition to these from IBM, Cisco, GE, EMC and Salesforce.com, there are other initiatives and products, in this sphere, coming from HP, Oracle, SAP, MuleSoft, SnapLogic, Nuance, Splunk, Mocana, Evrythng, Electric Imp, Quirky, reelyActive, Ayla, SmartThings, Withings, Fitbit, Jawbone including BodyMedia, Nike, Basis, Cohda Wireless, AT&T, Verizon, Huawei, Orange, Belkin, DropCam, Gravity Jack, Alcatel-Lucent, and Siemens. Platforms, software, sensor packages and services, are being developed by a wide variety of innovative companies:

These innovative companies, and others, are implementing one or more of these concepts in a variety of ways. As I stated at the beginning, I don't think that these concepts are the same. While the IoT was first named 14 years ago, it is still early days in its implementation. There are many ways that the Internet of Things might evolve, and many missteps that could lead the IoT to be a passing fancy, leaving some important changes in its wake, but never reaching its full potential. I think there is one way, and one way only, that all of the concepts and initiatives will come together and change everything that we do, how we make decisions, how we think about ourselves, how governments make policy, how businesses make money: The Sensor Analytics Ecosystem [SAE]. Here's a tease of a mindmap giving a hint of what I mean by the SAE. Look for my upcoming report "Sensor Analytics as an Ecosystem" and a series of research reports delving into each area introduced therein. The companies listed above are building out parts of the SAE, and will feature heavily in these reports.

Pentaho Acquires Webdetails for Great UX

Today, Monday, 2013 April 22, Pentaho completed the acquisition of long-time partner, Webdetails.

Pentaho offers one of the most complete data management and analytics suites available both as an open source solution, its Community Edition, and as an Enterprise Edition:

  • included target database [HSQL or MySQL], with the ability to use any RDBMS,
  • extract, transform and load servers and clients, KETTLE, Carte, Pan and Spoon,
  • online analytical processing server, Mondrian,
  • metadata management,
  • report development,
  • schema development,
  • dashboard development,
  • data mining, WEKA,
  • and a BI server to tie it all together.

Webdetails is a 20-person strong consultancy based in Portugal, founded by Pedro Alves, focused on building Pentaho solutions for its customers, and on data visualization. In addition to the consulting work, Webdetails has become the major committer for the open source Community Development Framework project, originally developed by Ingo Klose. In the course of their work, as inspired by the muse of customer needs, Webdetails has grown the original CDF project into a full suite of OSS data visualization and dashboard projects, CTools. Over the past year, the talented web details user experience teams, seems to have put out a new CTool almost monthly.

  • CDF - community dashboard framework
  • CDE - community dashboard editor
  • CBF - community build framework
  • CDA - community data access
  • CCC - community chart components
  • CST - community startup tabs
  • CGG - community graphics generator
  • CDC - community distributed cache
  • CDB - community data browser
  • CDG - community data generator
  • CDV - community data validation

Pedro Alves is an extremely well-respected member of the Pentaho community, leading community events and training, appearing often in the forums and IRC, and staying connected through Twitter and Skype. Recently, Pedro was highly active in helping to create the Pentaho Marketplace, which provides direct access from the BI Server web interface for users, to a series of plug-ins for the BI Server, including CTools, and other community and third-party projects.

I have the pleasure of knowing Pedro, and several other of the Webdetails and Pentaho teams. This week I was able to speak with Pedro, as well as Davy Nys, Vice President, EMEA & APAC at Pentaho, and Doug Moran, one of Pentaho's Founders.

Pedro doesn't feel that the acquisition will change Webdetails, in that both the UX and consulting teams will continue as before. However, both community and enterprise users of Pentaho will feel the impact of both teams, as the lessons learned from Webdetails consulting projects are implemented by the UX team, not only in the Dashboards and data visualizations tools, but also, per Davy, in the overall UX throughout all the Pentaho products. Having worked with Pentaho tools as a practitioner in the past, I know that business users will appreciate this as Pentaho becomes both easier and more pleasant to use. The data scientists will also appreciate more and better tools to draw the story out of the data, and present it to the subject matter experts and business leaders in an Agile fashion.

As Pedro mentioned, most things won't change, such as the fact that CDF is the underpinning of all of Pentaho Dashboards, or the pace of development of new CTools. Several are currently underway. One that I can mention grew out of a request by the Mozilla Foundation, for a file and data browser for the Hadoop distributed file system [HDFS] that would be as easy as the file browser in any modern operating system. The result is CVB - community VFS browser. One thing that will change is that more of the CTools will make their way into the main branch of the EE product as they reach the appropriate state of maturity and stability.

Pedro has many plans for CTools, and for facilitating data visualization through Pentaho. But in addition to continuing his role as the general manager of Webdetails, and Chief Architect of CTools, Pedro will also be assuming the role of Senior Vice President of Community for Pentaho. As a long time friend of the Pentaho community myself, I have to say that there couldn't be a better choice.

One of Pentaho's Founders, Doug Moran, was the "Community Guy", who stayed in this role until the start of 2011, following the original community guy, Gretchen Moran. Doug's philosophy is that any open source community needs to stand on its own to be organic and strong. The Pentaho community is one of the strongest in the OSS DMA space, and as a result, Doug felt comfortable focusing elsewhere, and assumed management of all of Pentaho's "big data" products and Instaview initiatives. As SVP of Community, Pedro will be mostly focused within the company to integrate the community internally and help drive the corporate strategy for community. He'll continue to participate in the community, but as the Pentaho BeeKeeper model, developed by Pentaho CTO & Chief Geek, James Dixon, his main concern will be to assure that there is a rich environment for community innovation. As part of that, Pedro will also be actively pursuing ways to grow and leverage the Pentaho Marketplace. Doug also pointed out that the Pentaho community is also hugely valuable for QA and as a training ground for the best Pentaho developers. This is sure to continue with Pedro in his new role. Doug and Pedro have worked together since the early days of Pentaho, when Pedro decided to quit his job, and, with his wife, create a company devoted to professional services for Pentaho projects and products. This strong relationship between the original Community Guy and the new SVP of Community can only help to make an already strong community even better and more creative.

Davy pointed out to me that there has been an increase in customer demand for Dashboards that were in essence, apps within Pentaho. This might happen through a plan that Pedro has to make it very easy to create such dashboard-based apps without any programming ability, and then publish them to the Marketplace. This planned community plugin kick-starter [CPK] will use CDE to create the front-end, and the Pentaho Data Integration software, KETTLE and Spoon, for the backend logic. I believe that both internal and external consultants, integrating Pentaho into an organization's decision making process, will find this ability exciting, as many of these system integrators are not Java developers. The ability to push such apps to the Marketplace will also be embraced by both CE and EE users, as most customers are excited by the idea of openly sharing their solutions, and enjoy the resulting community recognition.

As the innovations related to the big data and data science movements become more important, Davy told me that Pentaho has seen great interest in four areas:

  • EDW optimization,
  • exploratory analytics,
  • machine learning [WEKA, mahout & R], and
  • leveraging Hadoop to scale.

Webdetails fits very well into creating a finer exploratory analytics experience for the customers, and will make Pentaho a superior choice for big data. Combined with Instaview, and with the proper roadmap, it may even push Pentaho into the new Data Grok market, not only helping users answer the questions they have, but actually pointing out the questions that the data set can answer, even if the user didn't think of it.

Both CE and EE users and customers of Pentaho should welcome this acquisition, and look forward to the better UX and data visualization. Most importantly, they should plan on how they can contribute to, and benefit from the Pentaho Marketplace, as it becomes an important part of the Pentaho ecosystem.

Other Resources:

  1. Pedro Alves on Business Intelligence: "A new challenge - Webdetails Joins Pentaho"
  2. Pentaho page about announcement: Bringing the art of the possible to life
  3. Webdetails page about the announcement: The future of business analytics changes today
  4. Pentaho Blog: Webdetails and the Art of the Possible
  5. Press Release English: Pentaho Acquires Dashboard and UI Specialist Partner Webdetails

IMRSV, Inc Provides Facial Analytics

Cara is coming to a brick-and-mortar store near you. But don't be insulted when she doesn't recognize who you are.

Recently, I met with Jason Sosa, the CEO and Founder of IMRSV, Inc… twice. What came through to me was his passion for understanding the societal and human impacts of the technologies he creates and brings to market. This passion makes their mantra of and adherence to "privacy by design" very real and central to their approach.

Cara is the core software product from IMRSV, Inc. Cara analyses your face, and determines demographic, attention and emotive statistics about you, without attempting to identify you. As IMRSV states, Cara turns any connected camera into an intelligent sensor, but does so anonymously. Move from one Cara camera to another, or move away and back again to the same Cara camera, and the temporary ID number associated with you changes.

While Cara is pre-launch, I'm excited both by the technology, and by the IMRSV, Inc business model. The business model is very simple, whether a small shop owner or a developer interested in using Cara as part of a sensor analytics ecosystem, you pay $39.95 per camera, which includes the stand-alone Cara software and the Cloud-based data-as-a-service. The possibilities presented by Cara are what really got me going, fueling both an exciting initial briefing and a follow-up four-hour "lunch" and demo.

  • small shop owners now have demographic information available to them that was previously beyond their reach
  • larger organizations can better understand why campaigns in the physical world succeed or fail, just as they can online
  • developers can build attention and emotions into their sensor platforms

What's does any of this mean? Here's a few examples.

  • While Cara doesn't recognize a person, it can say that the person who bought X at the register was a young adult male or the person who bought Y in the drive-through was a senior female, based upon time stamps.
  • Online retailers have long established ways of determining who is buying what online. Brick-and-mortar stores have tried to do the same through loyalty programs. Privacy concerns have affected adoption of loyalty cards. By anonymously providing demographic and attention data, physical stores can glean the same understanding of their customers as the online versions, without violating privacy.
  • End-cap displays at retailers or kiosks at events can provided targeted messages, increasing effectiveness.
  • A simple toy, using a smartphone to draw a face and its camera to record the smile of a child, can smile back.
  • A car can respond to a driver glancing away from the road, perhaps "kicking" the driver in the butt through a vibration motor in the seat.
an image of the Cara software player
The Cara Player

In addition to starting companies, Jason is very interested in the Singularity, and the impending impact of technology upon human employment and self-identity. This has led both to the "Privacy by Design" and "Principles of Good Use" for developers/partners. If you don't believe me, maybe you'll believe Jules Polonetsky.

"Privacy by design solutions are critical to implementing new technologies in a world were data collection has become ubiquitous. Steps that Cara takes such as not collecting any personal information, and not storing, transferring or recorded any images are key to ensuring privacy concerns are addressed as these technologies are rolled out.”
- Jules Polonetsky
- Facebook.com/FutureofPrivacy
- @JulesPolonetsky

There are various pieces of research out there that show that the Internet of Things will be a 15 trillion dollar market right now. By 2020, I strongly believe that there will be over a trillion sensors deployed and that if your "thing" isn't connected, it won't be a viable product. Companies like IMRSV, Inc are providing the ecosystem to allow sensor analytics from everyday objects at very affordable prices. This will push this market even further and faster than the pundits anticipate. So, let me put on my tinfoil hat and stand on my soap box:

  1. Every conceivable facet of everyday life will take advantage of connected data for informed decisions
  2. Current sensor, internet of things, and data management & analytic companies will be assimilated into sensor analytics ecosystems or die
  3. Privacy will be a major concern for those who care, but the majority don't know enough to care. Individuals like Jason and Jules will protect the masses and ensure adherence to privacy by design guidelines.

New Hope from Big Data

Big Data is a catchy phrase. Unfortunately, it is often misused and misunderstood. Often, Hadoop and Big Data are used interchangeably; as if the Apache Hadoop family of projects are the only solutions for Big Data, or that that only use for these projects is from Big Data. Neither is true.

As an EDW/BI practitioner, I watched the Hadoop, or really, the Map/Reduce framework, be embraced and forced into being by software developers who were frustrated by Structured Query Language (SQL) and the need to create Entity-Relationship Diagrams (ERD) as data models or schæmas. They were equally unhappy with the various work-arounds to access Relational Database Management Systems from within their programs, such as Object Relational Models (ORMs) and Data Access Objects (DAOs). At first, I felt that these developers were simply lazy.

However, as I worked more with these so-called NoSQL technologies, it helped to clarify the dissatisfaction that I felt during the years I was leading EDW and BI projects. Thirty years ago, I worked in Aerospace System Engineering, developing methods and algorithms for risk assessment using Bayesian statistics. But, by 1996, I became involved in my first EDW project. Since then, the actual structure and functions associated with the data - defined by the data, became less important than fitting the data into an artificial structure imposed by business process models.

Don't get me wrong. Relational algebra, relational calculus and the DBMS technologies that came out of this mathematics, are all very useful. And, in the right hands, SQL is a very powerful language. ERDs provide a wonderful way to map data to business processes and to both transactional and analytic systems.

But… There is so much more that can be done with the data coming from traditional human-to-machine (H2M) interactions, but increasingly from human-to-human (H2H), machine-to-machine (M2M) and machine-to-human (M2H) exchanges. The interweaving of the flows of data from such disparate sources is what drives my research today.

  • Gamification driving the adoption of smart meters for utilities
  • Self-quantification use cases in the workplace
  • Sustainability for increasing the bottom line
  • Combing social media and sensor data for profitability
  • Sensor analytics as an ecosystem

These, and over 70 other use cases that I'm cataloguing, come from the innovation surrounding hype of Big Data, and the Data Science movement. In a recent Quark, I've classified this innovation into 11 areas. A compete mindmap is linked from the initial mindmap shown below, and in the report.

A Mind Map of the 11 Big Data Innovation Trends
A Mindmap of the 11 Innovation Trends from Big Data

The Quark covers the trends coming from these innovations, and develops the four keys required to bring valuable decision making processes into your organization from these innovations. It's entitled "Big Data: It's Not the Size, It's How You Use It". For such a simple report, it took over 8 months to develop. Mostly this delay was caused by the fast-paced evolution of the innovations. The executive summary from the Quark is linked from the title.

I hope that you find that information, as well as the mindmap, useful in incorporating inference, prediction, insight and performance with intuition for making better decisions.

DataGrok

For all the silliness surrounding Big Data and Data Science, all the hype and all the controversy, there are actually very innovative and disruptive technologies coming from this area, this new approach to data management and analytics [DMA]. How do we categorize the vendors or the technologies that have never existed before?

Predictives

One new area is Predictive Analytics, also called Predictive Intelligence. Since predictions are not analytics, as the term is used in BI, and certainly not the Intelligence used in BI, I don't like either, but prefer the simpler "Predictives". Four companies with which I've had briefings, fall into the Predictives category, but each of these companies have very different approaches and technologies for performing predictives. These companies are Opera Solutions, Alpine Data Labs, INRIX and Zementis. There are other companies that I'll include in a full report after receiving briefings, such as KXEN, Soft10 and Numenta. By the way, Numenta's product is named "Grok". Given their differences, do they really all belong in the same category?

Opera Solutions: Acting on petabytes of data, Opera Solutions provides a signal hub stack starting with data management, going through pattern matching in the signal layer, and, enhanced by their own Data Science teams, resulting in predictions and inferences for better decisions for enterprise advantage, understanding the "signal" is more important than the underlying technology, to actually create front line productivity through signals manifesting and adjusting "gut feel" where machines don't direct humans but do the heavy lifting.

Alpine Data Labs: Alpine Data Labs brings mathematical, statistical and machine learning predictive methods to the data in situ, no matter how small nor how big the data sets, within a variety of RDBMS technologies and Hadoop distributions. Alpine Data Labs helps data science teams address the data where it lays, across data types and functional areas, working with all the data to bring insight to bear on better decisions.

INRIX: INRIX data science teams and technology provides unique predictives using connected cars, connected devices and connected people.

Zementis: Zementis brings predictive modeling into decision management through their data science teams, Adapa product and strong commitment to the predictive markup modeling language [PMML]. Through partners and customers Zementis works with traditional and innovative data sources to provide decision management from predictives, data mining and machine learning for marketing solutions, financial services, predictive maintenance and energy/water sustainability.

DataGrok

One of the more interesting things to come out of data science is how do you really understand the data that is being gathered and presented. Two of the companies with which I've recently have had briefings, challenge the categories of Data Discovery or Data Exploration. However, each of these companies have different technologies, and different approaches to fully, deeply understanding your data, and to being able to draw conclusions from the data before doing other, more formal analytics. Over the past month, I've had the good fortune of having very in-depth, in-person briefings by both of these companies. Both of these companies are helping those who need it most to truly, fully, deeply, easily understand their data. These approaches, while very, very different, both constitute an entirely new category. Beyond data discovery, beyond data exploration, I call this new category Data Grokking.

"Grok" as I wrote in 2007, means to

"to fully and deeply understand"; [but to you need some background on the word's origins]. It's Martian and not from any Terran language at all. It comes from the fertile mind of Robert A. Heinlein, and was brought to Earth by Valentine Michael Smith in Heinlein's wonderful 1961 novel Stranger in a Strange Land.

One of these companies is still in stealth mode, and I won't mention their name here. The other is Ayasdi, and Ayasdi takes a very, very interesting approach to grokking your data.

These two very different technologies, based upon very different science and mathematics, do indeed allow us to fully and deeply understand our data. Much like the Martian ceremony, the DataGrok allows us to mentally ingest our data, to realize creative insights from our data sets, and to recognize the fundamental interweaving among the data, that, prior to these two innovative firms, could only come about through a long, arduous struggle with the data sets.

As I mentioned, the one company is still in stealth mode, so I'll write about Ayasdi here.

Ayasdi

Ayasdi comes out of the intersection of Topology and Computer Science, as brought together by a Stanford Professor, Gunnar Carlsson, and Gurjeet Singh. The project started as a DARPA contract that has spanned more than four years, comptop. The CompTop project included Duke, Rutgers & Stanford nodes. Topological methods discover the structure of the data - this is somewhat analogous to, but not the same as the probabilistic or cumulative distribution or density functions [pdf, PDF, cdf or CDF].

Ayasdi is focused on four markets:

  1. Pharmaceuticals, Healthcare and Biotech
  2. Oil & gas
  3. Government
  4. Financial Services

From this, you can see that Ayasdi customers go after expensive data, i.e. expensive to collect, expensive to use. Iris is the front end to the Ayasdi Platform, and while available as a private cloud, their offering is primarily SaaS.

The analyst community is trying to figure out where to put Ayasdi, thus my category of DataGrok. Another area of confusion is "What is the right tool of each step of the process from DataGrok to inferences and predictions?" Some of this stems from mistrust of machines, but we need machines that do more than count and sort, we need machines that help us to find insight and improve performance.

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The TeleInterActive Press is a collection of blogs by Clarise Z. Doval Santos and Joseph A. di Paolantonio, covering the Internet of Things, Data Management and Analytics, and other topics for business and pleasure. 37.540686772871 -122.516149406889

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