Category Archives: Academically speaking

Big Data and analytics – briefly

DFDDODData and data analytics is becoming more and more important for companies and organizations. Are you wondering what data and data science might do for your company? Welcome to a three-day ESP (Executive Short Program) called Decisions from Data: Driving an Organization with Analytics. It will take place at BI Norwegian Business School from December 5-7 this year. The short course is an offshoot from our very popular executive programs Analytics for Strategic Management, which are fully booked. (Check this list (Norwegian) for a sense of what those students are doing.)

Decisions from Data is aimed at managers who are curious about Big Data and data science and wants an introduction and an overview, without having to take a full course. We will talk about and show various forms of data analysis, discuss the most important obstacles to becoming a data driven organization and how to deal with data scientists, and, of course, give lots of examples of how to compete with analytics. The course will not be tech heavy, but we will look at and touch a few tools, just to get an idea of what we are asking those data scientists to do.

The whole thing will be in English, because, well, the (in my humble opinion) best people we have on this (Chandler Johnson og Alessandra Luzzi) are from the USA and Italy, respectively. As for myself, I tag along as best I can…

Welcome to the data revolution – it start’s here!


After Moore: Landauer

Very interesting blog by the very readable Ted: Is computing in reverse the next big thing?

As Moore’s law continues, it will reach certain physical limitations, such as electrons behaving less dependently the thinner the conduits become (think individual electrons instead of a more predictable stream. Another (they are linked, I suspect) is Landauer’s principle, which dictates that there is a certain lower limit on how much power that is necessary to flip a bit, and that forms a hard stop in terms of how much you can lower power consumption (and with it, heat dissipation.) (See Denning, P. J. and T. G. Lewis (2016). “Exponential laws of computing growth.” Communications of the ACM 60(1): 54-65, for an excellent discussion of Moore’s law and its remaining life.)

Turns out computing capability as a function of electric power consumption might be the next big obstacle (or at least measurement.) The BitCoin miners certainly know that.

Reverse, computing, which Ted writes about, is essentially computing where the power can be reversed, recreating the initial state. While difficult technically, it certainly would reduce power consumption to almost nothing.

To learn how, read the article. Recommended!

Made my day!

digøkskjermI just got the message that the new bachelor program Informatikk: Digital Økonomi og Ledelse (Informatics: Digital Economics and Management) is now the most sought-after study program in Norway, with 19 applicants per available place (514 first-priority applicants for 27 available places).

Since I have taken the initiative to this program and developed it with colleagues at the University of Oslo (where I have an adjunct position, this definitely made my day. Week, actually.

Just sayin’…

Notes from ACM Webinar on blockchain (etc.)

The Next Radical Internet Transformation: How Blockchain Technology is Transforming Business, Governments, Computing, and Security Models

Speaker: Mark Mueller-Eberstein, CEO & Founder at Adgetec Corporation, Professor at Rutgers University, Senior Research Fellow at QIIR

Moderator: Toufi Saliba, CEO, PrivacyShell and Chair of the ACM PB Conference Committee

Warning: These are notes taken live. Errors and omissions will occur. No responsibility whatsoever.

  • intro: old enough to remember the discussions in the early 90s about how the internet would change mail services – completely forgetting shopping, entertainment and others
  • Blockchain solves the problem of transferring value between Internet users without a third party
  • goes beyond the financial industry, can handle any kind of transaction
  • most of the world has access to a mobile phone, only about 20% has access to the banking system
  • Blockchain is the banking industry’s Uber movement
  • Blockchain much wider than Bitcoin, will facilitate new business models.
  • Blockchain transfers rather than copies digital assets, making sure there is only one instance of it.
    • settlement process: no clearing houses or central exchanges
    • peer-to-peer transfers, validation by network
  • Example: WeChat taking over payments in China, no link to banks
  • many commercial or government services are basically “databases” that are centrally managed, with one central point of failure
  • Blockchain allows a distributed ledger, information put in cannot be changed
    • Estonia thinking about a Blockchain in case of hacking or occupation
  • public (open), private and government blockchainsxx1
  • allows new services to existing customers, lots of inefficiencies up for grabs
    • estate records, voting, domain control, escrow, etc…
    • iPayYou allows use of Bitcoin
    • Walt Disney looking at Blockchain (DragonChain) for internal transfers, also use it for tracking supply chain to their cruise ships. Opensourced it.
  • 80% of Bitcoin mining done in China
  • regulation comes with a cost
  • Shenzhen want to be Blockchain Tech capital
  • 6-level security model, developed by William Mougayar (goes through it in detail: transaction, account, programming, distributed organizations, network (51% attacks, perhaps as low as 30%, smaller blockchains more vulnerable), governance)
  • Ethereum blockchain focusing on smart contracts: Hard forked in 2016, DAO issue where somebody hacked DAO code to siphon off money, hacking the program using the blockchain (not the blockchain),
  • credit card transaction can take up to 30 days, with disputes and everthing, Blockchain is almost instant
  • How “real” is blockchain technology
    • Goldman-Sachs invested $500m+
    • 15% of top global banks intend to roll out full-scale, commercial blockchain
    • etc.
  • what is holding it back?
    • difficult to use, understand, buy in; perception of risk and legality
    • difficult to see value for the individual
  • questions:
    • what are the incentives and adoption models?
      • different philosophies: computing power must be made available in the network: industrial mining vs. BitTorrent model, the amount of computing provided will be important, if we can find a model where just a little bit from every mobile phone is required
    • what are the hard costs of Blockchain?
      • you can google the costs. There are other approaches being developed, will post some links
    • can Blockchain be compromized by a virus?
      • theoretically, yes. Bitcoin is 10 years without, open source means verification (change is happening slowly because of code inspection)
      • comes back to incentive and governance model
  • and that was that…recording will be at in a few days.

Case teaching in Vienna

quantI have been asked to give a keynote speech at a conference on case teaching in Vienna, at the The University of Applied Sciences BFI. This is quite an honor, and I am very much looking forward to it.

Should you happen to want to be in Vienna and focus on case teaching on May 19 – well, I hope to see you there!

Key myths about analytics

My excellent colleagues Alessandra Luzzi and Chandler Johnson have pointed me to this video, a keynote speech from 2015 by Ken Rudin, head of analytics at Facebook:

This is a really good speech, and almost an advertisement for our course Analytics for Strategic Management, which starts in two days (and, well, sorry, it is full, but will be arranged again next year.)

In the talk (starting about 1:30 in), Ken breaks down four common myths surrounding Big Data:

  1. Big Data does not necessarily imply use of certain tools, in particular Hadoop. Hadoop can sift through mountains of data, but other tools, such as relational databases, are better at ad hoc analysis once you have structured the data and determined what of the data that is interesting and worth analyzing.
  2. Big Data does not always provide better answers. Big Data will give you more answers, but, as Rudin says, can give you “brilliant answers to questions that no one cares about.” He stated the best way to better answers to formulate better question, which requires hiring smart people with “business savvy” who will ask how to solve real business problems. Also, you need to place the data analysts out in the organization, so they understand how the business runs and what is important. He advocates an embedded model – centrally organized analysts sitting geographically with the people they are helping.
  3. Data Science is not all science. A lot of data science has an “art” to it, and you have to have a balance. Having a common language between business and analytics is important here – and Facebook sends its people to a two-week “Data Camp” to learn that. You ned to avoid the “hippo” problem – the highest paid person’s opinion – essentially, not enough science. The other side is the “groundhog” issue – based on the movie – where the main character tries to win the girl by gradual experimentation. Data is like sandpaper – it cannot create a good idea, but it can shape it after it has been created.
  4. The goal of analytics is not insights, but results. To that end, data scientists have to help making sure that people act on the analysis, not just inform them. “An actionable insight that nobody acts on has no value.”

To the students we’ll meet on Tuesday: This is not a bad way of gearing up for the course. To anyone else interested in analytics and Big Data: This video is recommended.

(And if you think, like I do, that his sounds like the discussion of what IT should be in an organization 20 years ago – well, fantastic, then we know what problems to expect and how to act on them.)

Cases: How to prepare for and learn from them

My versatile and creative colleague Hanno Roberts and I have made a series of five videos on case learning and preparation, originally for students at the BI/Fudan MBA program. This teaching method is difficult both for teacher and student, but highly rewarding provided you give it proper attention – which means effective preparation. Hanno and I talk about the goal of case teaching, how students can prepare individually, how to prepare as a group, how to go through the case discussion in the classroom, and then we sum up with some strategies for how to retain what you have learned.

Hanno and I did these videos against a green-screen, with little preparation – we basically met, outlined a structure with some keywords, decided broadly on who should say what, and dove right into it. Most of the videos were shot once, and then the very capable Milosz Tuszko edited them, added background, logos and keywords.

The updated videos are a less wooden than the previous version, methinks, and available in high resolution and with better sound. We clarified the differences between my version of case teaching and Hanno’s (both work, by the way). Over the years the original videos have been much watched – hopefully, our students (and others) will watch them carefully, and the result will be better case teaching, more learning, and an even more enjoyable experience teaching.

Details about each video below the fold…

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