How MUSIC IS MEASURED? How many SONGS does SPOTIFY want us to listen to?

How much music have you consumed in the last week? “That’s easy,” you say probably. After all, the music is quite well countable. You can either count the number of songs, OR – if you want to be even more precise – you can count the total seconds the music sounded. So what’s the loophole, here?

Well, the music at first glance really looks as countable as cheese, potatoes or crows on a telegraph pole. But that is true only as long as you count it for yourself solely. If the music is to be traded, suddenly all traditional metrics are of no use anymore. Not convinced? Well, then next time you go to music store, try to ask for 245 seconds of rock-n-roll and 720 seconds of classical music, please, packed separately, so it does not mingled. Do you get the point?

Measuring Medieval music

In fact, the sales units of music have changed significantly throughout history. In the oldest times, music could not be stored. There was no medium on which to record the music, so the music was only sold per unit of experience. In today’s notion, it would be a unit of measure like “one concert“. As with other medieval measures (such as thumb or elbow) there was no standardized form of that measure, and so the length (and intensity of the concert) depended largely directly on standard of the individual musicians. 3-Z1-R1 J.Zick, Familie Remy in Bendorf/ 1776 Zick, Januarius 1730-1797. 'Die Familie Johannes Remy in Bendorf bei Koblenz', 1776. Oel auf Leinwand, 200 x 276 cm. Nuernberg, Germanisches Nationalmuseum.

AUDIO_kazetaThe invention of a gramophone (and soundtrack recording) had brought a fundamental revolution in music units. Music suddenly stopped being sold in experience units (which will have its historical implications, but we will revisit it later), but the the music medium has become the unit of music. Firstly (gramophone) vinyls, then magnetic tapes and CD’s ultimately. Since most of us were born already into this set-up, we don’t find it strange. However, when you get a little historic zoom-out, you may realize that selling music by a “medium unit” is like selling sausages by area of the packaging or health by number of blood cans. On other words, it is not important how much benefit you buy in the package, literally, only the size of the package matters.

Generation after generation, we had learned to live with this music measuring “defect” and kept on buying albums. There were only two units in the album metric system: 1 Album and 1 Single and it was set that the Album is bigger than Single in terms of songs or seconds. It is crucial to note (for out further discussion) one more important fact: According to the (back-then) contemporary music sales stats, the Albums to Singles ratio was 179: 1. Thus, the Single’s accounted for only about 0.5% of all the music sold.

The root cause is the Apple, not the Newton

However, as a unit of music, the Album actually only suited one player in the music market: the music studios that were recording the albums. If you happen to be one of elder, you will surely remember moment when you bought a cassette or CD and you liked very much 2, maybe 3 songs from the whole album. The rest of it was, um, sort of crap. This, of course, irritated people, and as the music industry (some what negligibly) allowed computer technology to use magnetic tapes and CD’s to record data, these media became the Trojan horse music business. Unlike the vinyls, CD’s could have been burned (and thus copied) already by the regular people, which in turn led to a significant increase in piracy and the emergence of a separate section of shabby market stalls. Moreover, compact discs (unlike tapes) did not damage the original when copying. However, the final blow for the album metric unit was yet to come.

NEWTON_appleThe figurative last nail into Album’s coffin was an apple. In order to re-balance a reputation from the gravitational law (when fame was treacherously attributed entirely to Newton, and the apple got out of it with mere proverb “didn’t fall far from the tree.”) Taught by this crisis, this time the apple left nothing to chance and under its English pseudonym “Apple Inc.” brought the World the iTunes application. (which broke the album metric system for good). In Apple products, the music was suddenly available, God help us, by pieces. The world order has turned again from the head to its feet and you could buy (in the sense of the above analogy) sausages by pieces rather than by package loads.

Those less patient could say “and here we can call it off, right?” After all, the music can still be bought in pieces (songs) until today. In fact, another important step in measuring music has made more difficult to measure again. If the iTunes step was called “unpacking music to pieces,” there has been another “re-wrap” over the past decade. Don’t worry, I don’t mean any parental responsibilities associated with distinct, uh, smell. (Although bad languages say that this re-wrapping has led to many: How is it fairly called? Poops?)

Thanks to companies like Spotify, the music began to be sold as subscription. Suddenly the pieces were no longer important, you could literally tap the whole stream of music. This move greatly simplified the music business, while the unit price of the song being in cents, collecting (remotely) a few cents from you after each song played is as if the bus driver stopped every 100 meters of ride to collect from all passengers a small fee for next 100 meters to come. (If it still seems pretty doable to you, think aircraft in very same analogy).

However, unlike a monthly transport ticket or fitness gym membership, Spotify subscription, even though linked to time interval (e.g. a month), does not assume that you will “run the service the whole month continuously” in this subscription. Thanks to great data from the book of A. McAffee and E. Brynjolfssona: “MACHINE, PLATFORM, CROWD , which depicts Spotify model in more detail, I can introduce you to Spotify’s gross margin (excl. overhead costs). For given number of songs listened to by  the regular user, their margin looks something like this:

From numbers above it is clear that Spotify firmly hopes that you will not listen to more than 45 songs per day (or 1300 per month). So, something like an eat-all-you-can restaurant that also assumes you haven’t come to her (literally) eat yourself over to the other world. The catch is, of course, that not everyone pays for Spotify. According to avaliable public data some form of the payment provides every second Spotify’s customer. Thus, leaving advertising revenue aside (and making a somewhat strong assumption that a paying user has more or less similar consumption as free user), Spotify can be profitable if its average user consumes no more than 750 songs a month.

But let’s revert from Spotify business model, back to what the emergence of music streaming services meant for the music units. The advent of iTunes brought us the easiest way to measure music, but Spotify subscriptions have made the clear waters muddy again. On top of that, though advent of iTunes and music streaming services has been massive, they have failed to cover the entire music market. And thus we have been left with somewhat strange cocktail of parallel, different music units, making the total commercial music consumption measurement a mess again. From Jake Brown’s data, (and few other music blogs) it is clear, that companies in the industry managed to agree on linking the different music units. Thus finally, at the end of this historical excursion, we worked our way to music being measured in three main units:                                                           

                                       1 500 SEA = 10 TEA = 1 ALBUM                , where

SEA = any (even unfinished) streaming of exactly 1 song, TEA = purchase (or download) of 1 paid song and ALBUM = simply the album as we know them from past.

Therefore, let me conclude this blog with some practical stats (potentially interesting to you, if you happen to swim through music topics) that arise from the music measurements units. Consumption of music with the advent of its media, digital purchasing and streaming services has increased rapidly. That might sound like it is worth being a musician, because the demand for listening to music is still growing.

In reality, this is so because we can now afford a trick, that was nor possible in “one concert unit” times: We are able to listen to the artists even after their death. However, the more crucial information is that the total amount of money people put into music (relatively steeply) fall from generation to another. According to the SoundScanData 2016 2016 study, in year of 2016 people paid for the music equivalent of 561 million albums. In 2000 it was 785 million albums. Consequently, there are 29% fewer units of money in music sales industry, which ever increasing number of living artists must gradually more and more share with the passed away ones. So if you’re considering a second career in your life, the music will probably be a very muddy path.

They invited me to write National AI Strategy. So I proposed to …

Have you ever thought about how artificial intelligence would really be at most useful for your country? I was “forced” to do so by fate. Therefore, let me briefly depict what I think should be the key invest areas in AI court; How to bring artificial intelligence even to rural areas? But first of all, how to make world-class AI here, ideally in local language?

Sometimes you dip into things you even don’t know how. This was the case also one day when a strange email landed in my mailbox. Don’t take me wrong, I really have experienced a lot of bizarre pieces in email communication. But this one was really strange. It was an invitation from the National Artificial Intelligence Forum that they would like me to join the team of experts assembled to create the National Strategy for Artificial Intelligence (AI) for our country.

At first, I wondered if this was not a joke from one of my friends. But since April 1 has been already pretty quite away, I started to seriously consider that someone really means it. “Why me?” I asked myself. After all, strategic documents of this kind are not my daily job and I don’t know much about national AI programs of other countries either. However, the authors of the email seemed to anticipate my embarrassment and they left nothing to chance. In the invitation, they explained that the expert team will combine 1] academic environment, 2] representatives of domestic AI research companies, and 3] business representatives who already use real AI technologies today. I quickly realized that I owed my invitation to the third group. So I exhaled in relief … and confirmed my participation.

If I decide to devout my time into something, I try to make it at least of some quality. Therefore, immediately after accepting the invitation, I started studying the AI strategies of other countries. However, I was not looking for ready-made measures that could be “borrowed”. Rather I tried to dive mainly into parts where those papers discussed ways of assessing the country’s initial situation. For I realized that for country strategy, I was just about to participate, it will not help to “invite” the measures chosen by US or China. Thus sooner that I took part in any round of measures brainstorming, I had crystallized the key AI assumptions in my head. These were (euclidean) premises I decided to defend as the starting point of group work. I am sure there could be a lot of “meditation” on the topic of AI maturity, but I tried to put my postulates into as few points as possible:

  • The country for which we are going to prepare an AI strategy is not one of the current AI leaders, nor even the leaders in AI research. Therefore, at least to start, we will have to build upon already existing foreign solutions. (it does not rule out research focus as such into the future, I just mean that we do not have enough of our national research to draw from there)
  • Although AI is a wide spread buzz-word, what it really means and how it can be used in business, is known only to limited communities of people, mostly associated in university cities. Even in mid-sized cities that do not have a university or in the countryside, no one (including a local pastor, bartender or mayor) has a clue about what true nature of artificial intelligence is and what its applications are. In other words, AI awareness is very unevenly geographically distributed across the country.
  • Most countries have a very specific language environment. If a relatively few people speak English in given country, recently available, foreign (NLP and other AI) solutions will not work well. If the implementation of AI in a country were to be based on English solutions, it would be very risky both in terms of quality of solutions and due to (possible) resistance by end-users. (If you are to understand a conceptually difficult thing, it is not very helpful if it is also primarily described in a foreign language that you do not speak)
  • The whole world runs primarily on open-source AI solutions. Even the most advanced companies publish (on GitHub) their libraries for AI solutions.
  • Let’s think for a moment. How to maximize the effect of AI on local companies in the country? I admit, this is also a bit of a personal belief dilemma, because any answer to this question can be suppressed with phrase “who knows, how it really will be”. Nevertheless, I personally believe that AI will reach its maxim, when even ordinary, small businessmen will embrace it.
  • I came to the previous conclusion  after also considering the fact that large, multinational companies will take care of the implementation of AI in their local subsidiaries. Large (abroad reaching) local companies will be pushed by competition from those other countries. The only group (in the first stage) that will not be guided to this are the national medium and small entrepreneurs. That is why, in my opinion, these should also be the focus areas of the national implementation strategy.
  • For SME companies, in addition to the language barrier, the “technological abyss” will also be important. Cloud environment is an inevitable prerequisite for most AI solutions design & maintenance today. However, it might be both costly and secondly unrealistic for a small business, as they cannot sustain their own full FTE Data Engineer and have no way to borrow a shared one either.

Enough of preparation, I police myself, and plunged into discussions about the initial strategy proposals. As it turned out later, the above three groups did not necessarily have a balanced representation in the expert team. The Task Force had to, therefore, often choke through proposals that ignored even several above mentioned premises simultaneously. Nonetheless, we ferociously adhered to the golden rule of brainstorming: every well-formulated idea would be included in the long list of possible measures. (knowing that we would have to subject those to more scrutiny later). Sometimes it was really painful for people in practice to withstand the tirades and their teeth were often clenched. After listening to all perspectives, I decided to nominate following draft proposal for the National AI strategy:

1] Create a hosted cloud solution at universities or one of the state institutions (based on a model like Amazon Web Services), as an extension on top of any of the commercially viable cloud alternatives. Using the central hosting/management of this repository, offer a ready-made, all-inclusive symbolically priced packages for AI solutions, which for end-users (and institutions) also include maintenance realized through experts paid by that central cloud provider. Simply turn-key cloud environments.

2] Localize some of the most wanted Udemy / Udacity / Cloudera / … artificial intelligence courses and basic Machine Learning and Deep Learning skills. Purchase a mass license for such localized courses for 100,000 residents in a given country and grant access to these courses for a symbolic amount (e.g. 5 EUR / person).

3] Create a moderate national community for Opensource solutions in the AI area. Collect and localize into national language information on new AI modules and their reviews by foreign experts. Create conditions for Slovak (research) teams to actively participate in the development of individual branches of these solutions.

4] Select 5-6 most important industries in SME segment of companies. Recruit at least one relevant large business per each area to provide local, anonymized data. Organize Hackathons with the participation of international teams, with aim to develop specific solutions for AI use in the industry and based directly on local data. Set conditions for participation in Hackathons so that the developed solutions can be freely picked-up and implemented in any company registered in the country and operating in selected industry.

5] After the implementation of Hackathons, for each industry recruit at least 4 volunteers from within already existing companies to be become pioneer beneficiaries of AI. In those pioneers Central AI agency, via means of a state grants, implements ready AI solutions (from Hackathons). In case of higher interest of the volunteers for pioneer status, a lottery with oversight a notary will be drawn to decide whom the grant ought to be awarded. Participation in the grant is subject to the approval of the company elaborated about in measure 6, see below.

6] Realize roadshows in the regions and district towns where AI and its concrete, ready-to-use solutions (acquired through Hackathon) will be presented. Representatives of pioneering companies with AI implemented under Measure 5 will be presenting at the meetings. instantly available, plug-and-play solution, free of charge and even in local language. If measures 4 + 5 + 6 are overly successful, repeat this procedure for other sectors.

What do you say? Does that make sense to you, or would you suggest something completely different? Well, I will not bore you with which  my actions had gained enough “support” for the final draft of the National Strategy. Some passed, others did not survive the fight and prioritization. But that’s not the point of this blog, anyway. In the sense of his unfinished blog title, I wanted to give you some food for  thought: What are the inputs for AI strategy in your country where you live? Would you suggest similar solutions for you homeland or do you have much better ideas? How to bring artificial intelligence to the rural village habitat? But first-mostly: How to build world-class AI and (preferably even) in the local language? So that ordinary John and Ann can take benefit of it important process as well.

 

NOAH 2019 inspirations – Part I. – BEST BUSINESS STRATEGIES

In the field of digital technologies and data there are some conferences in Europe worth seeing. On top of that, there are a few that you can feel really be sorry if missing them. Well, and at the top of this imaginary pyramid of know-how, there is a very short list of those conferences from which you will have goosebumps still several weeks after their closing. One such conference is the NOAH conference in Berlin, which I had the honor of attending few weeks ago. More than 500 speakers (yes, reading right) in 2 days for more than 4000 people in the audience (yes, reading it correctly again) will prepare a hailstorm that you would seek to recover from longer than over evening after-party. The conference is known to leave the presenters only 6-10 minutes, so you can hear 20 – 30 unique approaches of dealing with issues in a single 3-hour block. Pure massacre. I guess that after day one you wished you would have been rather landscaping all day long.

It is not possible to pass on such an airborne attack of ideas (if for nothing else, there are always 4-5 parallel streams running, so unless you have arrived as wide team, you have no chance to see all the agenda). However, I will try to convey to you at least the coolest thing that our team saw into a three-parts-long blog.

PART I – BEST DIGITAL BUSINESS STRATEGIES

In following section your jaw might struggle to bounce back from the dropped position because the paths to success, chosen by the below listed companies, are not only brutally functional but also unconventional on top of it. Well, see for yourself:

NOAH_EXPONDOExpondo, as company name, will certainly not ring the bell with you. That is so, because it is precisely part of their strategy. Nevertheless, even over our ignorance, they are market leader in the production of professional poppers, micro-scales (among others allegedly used also by drug cartels) or other niche products that you might not even realize that exist. In everything they produce, they strive to be in the world’s top 3. Ehm, yet another niche, no-name business, you might think. The “problem” is that Expondo launches annually more than 1,000 (!!) new products. That is, about 5 news items every working day. What is more, only 4% percent of their releases will not turn into world-wide hits. Having a niche, inconspicuous products, they manage to produce and sell these products with a (whooping) 56% margin. So just to summarize: Do you know any other company that launches 960 world-successful products per year and sells them with margin equal to half of its revenue?

The fashion (clothing) market is rather tight. Thus, looking for a new, unique approach to business strategy resembles famous needle in a haystack. Though, OUTFITTERY.com has found its way through  rain-forest of other clothing retailers. They bet is a private label constructed purely on data basis. Collecting about 50 dimensions on each product and more than 200 data features about each client (which would not be so shocking by itself, Telcos hold thousands data points per client) gives this e-retailer chance not to leave its clients “wandering around” on the internet. Unbelievable (almost) half of all purchases originate directly from the portal’s recommendation. TheNOAH_OUTFITTERY underlying data analytics engines are so well-tuned that from over 1 million clients portfolio, incredible 40% of customers are so satisfied with what Outfitters have recommended to them that they choose to activate subscription. This works by sending items on a monthly basis that they have never seen before nor indicated any preference for them, yet they like them so much that they keep them. Clients have literally outsourced their wardrobe to the e-shop. And all this is happening in so taste sensitive and demanding industry as fashion is.

Did you know that there is a separate spice e-shop? Well, nowadays we are used to having a dedicated E-shop on everything, so the very existence of JUST SPICES will probably not surprise you. On the other hand, what raises the quills is the fact that this portal has taken on the task of bringing emotions to the spice purchases. Surveys among people have found that many shopping for spices compare to buying socks: “You choose to buy them only when you run out of them and just because you need them”. Approach they used to bring soul to purchase of these (often deemed) commodities is, in fact, remarkable. For their own brand of 150 spices, they have built a strong Instagram (over 220,000 followers), podcasts and even the Influencers’ network. However, that is not the limit of their uniqueness. Since spices are still sold primarily in brick-and-mortar stores, Just Spices has managed to introduce nearly 95 different products into some retail chains, mainly thanks to 47 different (patented) ways to (witty) place spice as a complementary item in the traditional stores.

NOAH_SupplementerThe industry, which has long been stumbling somewhere on the interface between small e-shops and (even smaller) off-line stores, is selling nutritional supplements (especially for bodybuilding or fitness). The Supplementer.com portal is a very interesting way of simultaneous (and synergistic) activity both in on-line and off-line; In addition to the private label, which is common in the industry and in CEE it is smartly operated by GymBeam (for example), the Supplementer.com has also launched several cunning ways of selling their products. The portal understood that their target group was hard to be targeted by general advertising and needs to be addressed mainly along their practicing, in the gym. However, the gyms are numerous and fragmented and often there is not much room for the placement of “own” stalls in the gym premises. Therefore, Supplementer came up with ideas like using QR walls and wending machines to overcome these problems.

Among the predominantly online-functioning portals, the NOAH 2019 conference also featured a truly traditional players. Coca Cola, confirmed that even giants can innovate. Its representatives have introduced the system developed for their drink selling partners. Using external data (such as social events in a given location, weather, competing product campaigns …) embedded directly into ordering system by Coca Cola, the application can better predict the demand for Coca Cola beverages and thus prevent demand being under-served due to low stocks. To cement the credibility of the application, the partner can review the entire history of the given recommendations (and their accuracy) for each type of partner at real stores of the providing data as the transparent partners.

If you follow the market for mobility services, names like DriveNow (from BMW) or Car2go (from Mercedes) are probably familiar to you. However, fewer people know that these hitherto competing platforms have taken an unprecedented step and merged into a shared ShareNow service. Together, in 31 cities around the world, they offer more than 20,000 cars (of one of the original brands) to unlock with mobile phones, which you can pick up at any time. Just like bike-sharing programs around the world or the newly launched Sharengo.sk service in Slovakia. The company shared interesting data by revealing that in the cities where car-sharing works, it is at least 30% cheaper than taxis. Equally interesting is the fact that the average city car is utilized only 2% of the total time. The remaining 98% of the time it is waiting for the owner somewhere in the parking lot or in garage. What is fascinating  that ShareNow’s cars aim to get their cars’ utilization up to almost 50% during the day, which means that if these services start running widely, single car-sharing vehicle can replace up to 25 passenger cars.

Even data services based on mobile operators’ data inputs are not unheard of in Europe. Instarea.com has been around for years, and has been behind several interesting studies onNOAH_Porsche human movement. However, with the advent of new trends in mobility, these data points are taking on completely new fields of application. A much larger sibling of Instarea, the Teralytics.net, for example, has from the anonymous mobile data of moving people predicted, where is the best to place e-car charging stations (and also foresees demand for charging in each part  of the day). They are also involved in launching car-, bike- and other sharing services as they can accurately identify the need for people to move and, on to of all, also what is approximate creditworthiness of those potential clients. Perhaps the most exotic use of geo-movement  data is for planning first lines and heliports for Air-taxis, which are emerging as another form of transport in advanced cities.

An old saying preaches: If you damn good in something, you better stick to your trade. But what if you can do very well something that is not the object of your business? Portal lastminute.com, primarily providing last minute holiday deals for end customers, has found itself in similar situation. Since their whole existence is based on how effectively they can do online marketing of individual destinations and hotels, they have become a hell of a good player in that area. Instead of hovering on the ego and showing their opponents a long nose, they decided to use this ability and turn it into separate line of their business. Therefore, nowadays they run also an digital marketing agency for individual resorts taking care of their marketing presentation even outside their very own portal.

The last company in this first block of inspiration from NOAH 2019 conference is Thermondo.de. They chose an interesting goal as their mission. Aspiration is to change the traditional heat supply sector, where (often) monopoly heat utilities (either on gas or electricity basis) have only very shallow relationships with the end customers. Many consider heating simply to be a commodity. However, Thermondo, which also acts as alternative supplier of gas and electricity, builds also its own boilers. You do not need to invest in purchase of boiler, neither care about their installation or maintenance. The company will provide you with heat (in a family house or apartment) as a subscription. This includes not only the heating medium at a flat rate per month, but also the full installation of heat units, boilers or radiators hardware, even their replacement for new ones throughout the subscription period. Customers can thus afford higher quality equipment, for which they would have not only to sink high initial costs, but also hope that boiler survives till real estate divestment and that they will be able to sell it (at least) partially within the property strike price.

>>>  continue READING HERE

You have just read the first part of the inspiration from NOAH 2019 conference. You can read more fascinating information from this conference in the second or third part of this mini series.

NOAH 2019 inspirations – Part II. – EMERGING TRENDS

In the field of digital technologies and data there are some conferences in Europe worth seeing. On top of that, there are a few that you can feel really be sorry if missing them. Well, and at the top of this imaginary pyramid of know-how, there is a very short list of those conferences from which you will have goosebumps still several weeks after their closing. One such conference is the NOAH conference in Berlin, which I had the honor of attending few weeks ago. More than 500 speakers (yes, reading right) in 2 days for more than 4000 people in the audience (yes, reading it correctly again) will prepare a hailstorm that you would seek to recover from longer than over evening after-party. The conference is known to leave the presenters only 6-10 minutes, so you can hear 20 – 30 unique approaches of dealing with issues in a single 3-hour block. Pure massacre. I guess that after day one you wished you would have been rather landscaping all day long.

NOAH_Header_2_EmergingTrends

PART II – EMERGING TRENDS

As is common in grandiose conferences, a man has a chance to look beyond the bragging of presenting companies to see also bubbling. To describe in detail all of them would probably be a work competing with the length of Dostoevsky’s Crime and Punishment. Though, I would like to dive us together in detail to at least 4 major trends mentioned:

New types of E-shops and online marketplaces

NOAH_ChronextIf you feel like that there is an e-shop for anything already, ehm,  not entirely true. However, the truth is that massive innovations in the E-commerce industry are gradually filling in even the tiny white spots not covered yet. An example of this is CHRONEXT, a second-hand luxury watch market. In addition to the obligatory matching of demand and supply, they have their own workshop with more than 20 watchmakers to ensure authenticity and objectively assess the technical condition of luxury watches. So if a luxury watch owner decides to sell them, (s)he just hands them over to the delivery site (or courier) and the CHRONEXT portal will take care of the certificate of authenticity, fair pricing, sales and delivery to the new owner. Even though for many of us it might be uncommon to use this kind of service, one notable trend is still emerging from this . As the company’s representatives explained in their presentation, the cost of sending a tennis ball and a luxury watch is similar, but their transaction value is disproportionate. Hence, even seemingly “antediluvian” process via post can suddenly create new services in the field of high-value goods. Therefore, it is assumed that marketplaces of such products will be on rise soon. Just be frank to yourself, would you dare to buy “real rolexes” from an Asian-sounding name guy in the past?

Another confirmation of this trend is the StocX, which was created as an e-shop for the sale and purchase of limited series of branded sneakers. (Used ones, of course). In many ways, their processes and procedures match those of Chronext. But they added one more essential ingredient to the pot. Prices were left to decide on prices purely in a similar way as the stock market operates. This, at first glance, insignificant element, has led them to a substantial discovery when introducing their private label products. They published a description of an exclusive limited-edition product and let people specify how much they would be willing to buy the luxury product for. (Originally assumed the price could be about $ 120) When they ranked the bids by offered price, they found that even the 50th highest bid was more than $ 200. Therefore, they decided to sell 50 units at $ 200, which means that 95% of the clients received the product cheaper than originally planned. But they earned almost 70% more than expected, at the same time. Once and again and slowly: you earned 70% more than you planned, and 95% of the clients were surprised by getting it  cheaper than they expected. Doesn’t it feel like a business “perpetum mobile” ?

So-called classifiers are a separate line of E-shops development. Although, unfortunately, we do not yet know in Slovakia what classifiers are, in the West several platforms have started war on who will be “Google” in the field of classifiers. Yet these services are very useful and they literally find application in every country. Their essence is – simply put – effective shedding of unnecessary things. As if you crossed-pollinated antique store with a flea market expert and Amazon. One of emerging classifier is Lalafo. In practice, this service works by loading everything you no longer need into a box, a courier (or a robot, but it will come that point later) picking up this box and automatically sort, photo document, describe and publish advertisements of your things, pair the buyers, give them your belongings and send the money to your account (after deducting a small commission). Researches confirm that an ordinary person puts 2 items a year on e-bay or similar portals. The average Lalafo user gives away more than 30 of them. Thus, classifiers can significantly save the planet when they find yet another use for things that would otherwise end up in waste bin. As this twangs on the sensitive strings of the current society, it is clear that the classifiers are on a path for a rocket launch.

By the way, the Lalafo presentation was unique in yet another way. Lalafo officials brought a mock-up of a sorting robot to the podium, which presented part of the presentation in the synthesized human voice. In addition to the thunderous applause in the hall, it was the first powerpoint presentation in my life that have been delivered (at least partially) by the robot.

Online food market

Interesting things are happening on the food market as well. Especially in larger cities, the market for food delivery directly via applications like Lieferando or DeliveryHero has already heated up. Here in Berlin it is almost impossible to cross the street in the evening without encountering a delivery man with a meal package. As some traditional restaurants are busy enough anyway, and yet others hesitate to get involved in the delivery cycle, the demand for online food has grown much faster than the supply did. Big players, like above mentioned DeliveryHero, had to start opening restaurants that cook for them exclusively. To have something to deliver first place. This gave rise to the concepts of so-called cloud kitchens (a location with a fully equipped and staffed kitchen at a strategic transport hub ready to open brand new branch of your great restaurant chain) or ghost kitchens (kitchens that have a strong brand but only cook exclusively for delivery app, having no tables and no where to try beyond the bare delivery).

Delivery services quickly realized that their business depended on how they could stimulate the growth of cooked and order food through their portal. Therefore, almost over-the-night they have become B2B partners, who finance the opening of new restaurants, advise on marketing and expanding operations, or collect suggestions from end consumers for new recipes and meals.

However, the online food market has one more interesting branch, the so-called boxed food line, represented mainly by companies like MarleySpoon. Unlike boxes with ready-to-eat, which NOAH_entranceyou had a chance to meet in various EU regions (especially as the weight loss methods), the mission of modern boxes is to supply all the ingredients and recipe so that people can cook their own food. This approach also carries with so-called the IKEA effect, where you have a different emotional relationship to the cabinet that you put together with your own hands than the furniture you bought as a whole. MarleySpoon, nonetheless, has another important mission in mind. They recycle their (cooling) boxes. This move not only stimulates re-purchase (what you will do with empty boxes?), but also promotes the environment. That is also (one of) the reason(s) why they do not rush  to sell through most of supermarkets, but only cooperate with Fresh-Markets (they do not wish to leave the boxes on the shelf of the store for a few days or throw away those not sold during their shelf-lifespan).

Whether you want to cook or you want to come to “ready-to-eat” meal, online services are likely soon to take on a relatively large share of the food we eat at home with our family. Thus there is a clear revolution in the diet unwinding, which (a little surprisingly) is not one of the extremes of the “going to the restaurant”, “making a big shopping and cooking at home” spectrum. This introduces another hard and very significant trend. Both of the above mentioned issues will be a negative hit on conventional grocery supermarkets. In either of the ways, the customer does not buy groceries from chain store any more. It is unclear if supermarkets are aware of this down to this depth, but they are anyway trying to respond to craving of online food purchases. To be honest, I have to admit that the DeliveryHero presentation has also teased up a whole new perspective on the issue. Think buying food online: What do you consider to be more difficult: Supply the food items stipulated in the order OR ensure timely and very cheap delivery to your home? Do you also see that if any of those 2 activities turns into commodity and the other is an act of art, it will be the food itself ending up as commodity. Thus, if a delivery companies compete with an excellent foreign food delivery player AGAINST grocery chain with its own food chain , who would you bet on in the long run? Companies mastering efficient food delivery networks in cities will be able to attack the grocery purchases in online assault. You can probably  smell already the consequences of such a change. So far, subtly, but along time passing, it bubbles and becomes burning pathway for general chain stores.

Future direction(s) of digital services and markets

A lecture by FJ Labs, presented by none other than the legendary Fabrice Grinda, would most likely deserve a separate blog. It is one of those lectures where you do not know if you should take pictures of slides, write notes from things that the speaker said beyond the slides, or start thinking about how that impacts you. FJ Labs specialize in starting and investing in E-shops and online services. They set up or purchase annually more than 350 of them, with none of the transactions below level of tens of millions of dollars. Furthermore, they advise more than 1,000 projects a year that they had not managed to invest into. If you’re looking for someone who really understands the  online service most thoroughly in the world, you may have just found him.

Their presentation initially named the factors that over and over again  relentlessly separate the grain from the chaff, successful E-commerce projects from the start-ups that simply blew up. Since these topics are not necessarily the daily bread of themightydata.com audiences, I will try to depict in detail rather the second part of the presentation, which introduced what new areas of digital services are just hatching out. The first clearly documented trend was the verticalization of established services. Just as Grindr was a specific offshoot of Tinder‘s “dating service,” specific segments of demand of large services such as Uber Eatsebay or UpWork are also being gradually cut off newcomers. And this trend does not shy away even from such Titans as AirBnB (from which the Luxury Retreats steal the base).

The second major trend is the expansion of B2B markets. Unlike end-user portals, B2B marketplaces offer not only more valuable shopping carts, a higher margin, but also a higher likelihood of re-purchase (the company buys things for its operation that is continuous). Examples are portals such as RIG UPknowde or FLEXPORT.

The 3rd important trend is that successful online services are increasingly focusing on the OFF-premise model, i.e. operating without physical operations (and assets). Examples include not only the above-mentioned ghost kitchens, but also travel services and legal services such as AirHelp.

Another emerging trend is that while marketplaces were bilateral in the first wave of portals, they are  operating rather unilaterally today. In the past, both the demand side and the supply side had to reconcile each other before the service or goods were launched. Moreover, matching and quality management were purely market mechanisms. Modern online markets are moving to a model where the client expresses their preference and the portal chooses for him/her, confirms the transaction and starts service implementation directly. (Remember the Outfittery from Part 1 of our NOAH series) IN long term, this indicates a greater need for AI and a worse negotiating position for the supply side.

No less fascinating was hearing from Fabric Grinda’s mouth (and slides) where our journey of online services was heading. Based on the data available to them, he predicted a sharp increase in areas such as digitization of real estate sales and further fragmentation of financial services through Lending clubs (i.e. peer-to-peer loans directly between people without the presence of banks). Strong changes are also awaiting us in the digitization of the labor markets, both in the search for a full-FTE job as well as in the development of micro-work assignments (a form that we would label as a free-lance today). Just as some people got their second job in the evening thanks to Uber, other micro-services will come to turn individuals’ free time into an additional income. This trend is directly related to another growth impulse, this time for EduTech (which we will address directly in the last chapter of this blog.) The last area, where FJ Labs is expecting significant changes, is the automotive sector, which will be hit not only by car-sharing services,  by car-pooling and other forms of mobility (e.g. air transport), but also change of guard in the most dominant car sales channel. (Anybody e-shops?)

Quo vadis Transport?

NOAH_travel_trendsPerhaps less noticeable, but equally interesting, was the presentation of the French company Bla-Bla-Car whose mission could be called guaranteed hitch-hiking. They have built a carpooling portal that connects car drivers who want to share the ride with “hitch-hikers” willing in return to contribute to the gasoline costs of driver. And  managed they did really a large scale of that mission. Their service had already provided passenger transport over 22 billion kilometers. As the service fills with passengers the cars that would drive their route anyway, they save 1.6 million tons of carbon dioxide annually. Nevertheless, the presentation of Bla-bla car was interesting for another reason. They decided to announce the launch of the new Bla-Bla-Bus service, which introduces a bus version of service. Of course,  buses do not just wander around the country and do not take random hitch-hikers, the service literally creates regular bus lines. However, it will deliver 3 essential differences compared to traditional bus carriers. The first major distinction is that Bla-Bla-Bus will not own its buses, but will be a kind of UBER for bus service. Thus It is a direct competitor of the German FlixBus, which already successfully operates this model throughout Europe, including CEE. Individual bus owners can subscribe to Bla-Bla-Bus on a voluntary basis, giving the platform a chance to acquire a much larger fleet (in much shorter time) than if relying on sourcing of its own vehicles. The second major distinction of Bla-Bla-Bus (which also gives it a competitive edge over the FlixBus), is the fact that it uses data from the Bla-Bla-Car transport history. This way the service knows how many people need to move from where to where at what time. As illustrated on example of France, their home country and the strongest market, there are 200 large bus stations for important intercity lines in France, but their Bla-Bla-car users have used 250,000 different pickup points. This enables company to plan routes with unconventional stops (where normal buses would not stop) in areas with a huge demand to get into (or out of) the car. The third essential factor of the distinction is fact that all bla-bla-buses automatically report their location. Thus, from the customer’s point of view, one gets again similar information comfort as with Uber. You can see exactly where your bus is, whether it’s on time, and if your partner or family should be ready to pick you up when you get off the bus.

The presentation of LakeStar, respected Venture Capital (also behind Spotify, AirBnB or Skypom), confirmed that although Silicon Valley dominates many digital services, but Germany NOAH_travel_trends_2and its neighboring countries are very strong in transport start-ups. Of the top 20 such services in the EU, up to 11 of them come from this region. And full 7 of them operate precisely from Germany. (see slide) Another interesting piece of knowledge is the digitization is likely to finally push the last nail into the coffin for travel agencies. By comparison, the value of TUI (probably Europe’s largest travel company) dropped from $ 24 billion in 2000 to barely $ 6 billion today, while booking.com has risen from zero to $ 71 billion over the same period. Even in less than half of the same time window, AirBnb grew from zero to 40 billion. This brings us to the 4 trends Lakestar foresees for the industry to unveil soon:

a] Travelers are increasingly looking for branded offerings that are not a cocktail of no-name providers, but rather a comprehensive experience like service of GetYourGuide.

b] Most likely already in forthcoming year, the value of accommodation rental portals (like Airbnb) will exceed the value of all hotel chains. The hotels officially become only Plan B, unless you happen to get accommodation differently.

c] dthe market of so-called Smart Travel Advisors is also developing dynamically; Customized experiences that you can take part in mainly thanks to local enthusiasts and (orchestration) of tiny service providers.

d] apparently under the pressure of ever-accelerating life pace, the demand for holiday packages, such as sightseeing round trips or thematic tours, is surprisingly booming. However, unlike in the past, these tours are not compiled by travelers on their own, but users are expect them to be intelligent ensemble (and suggestion) of  accommodation booking portals from the “components they have in inventory”.

If you thought everything had been already digitized in travel and transport, presentations by ZizooCampenda and Sailogy would correct your view. While digital services already dominate the classic search for accommodation, airline tickets or buses, there are still sectors that are only at the beginning of this digitization movement (and therefore are only experiencing their boom right now). Probably the most prominent representatives of this second wave are the services that offer the rental of small cruise ships and caravans. Few of us have the opportunity to own our own motorboat, small sailing boat or comfortably furnished caravan. Therefore, if you long for this kind of rest, you have in the past been reliant on renting such a boat or caravan from small, local owners or few-pieces providers. The above named services in this paragraph, in matter-of-fact, gradually create a kind of booking.com solely for these types of holidays. A yacht cruise is no longer just a fancy treat, as you can order a boat with a captain and food from the warmth of your living room, at any seaside location, on your chosen days, for a price as low as 50 EUR per person and a day. The skyrocketing number of orders from these portals prove that cruises will probably be a hit in the nearest tourist seasons to come.

EduTech = education in completely different way

A separate section within the NOAH conference was also carved out for the EduTech industry; in other words, digital services in education. At first glance, again a topic that we do not read from the front pages of our newspapers and magazines in CEE, but which is experiencing a massive boom in developed markets. If you are in the field of data analysis, you have certainly noticed the boom of portals such as Coursera or Udemy, which allow you to learn specific skills.

However, in addition to these trends, relatively less conspicuous services are coming to the forefront, starting to compete with traditional education in various forms and shapes. You live in a small town or village and you like the group-based language study model? But in the surrounding area happens to be no language school that opened course for the language of your choice? Do you want to spare yourself from commute several times a week to the nearest regional city for that matter only? Well, then Lingoda, which provides online group language courses, can be a solution for you. You have your permanent group and you can join from anywhere around the globe. Even from a business trip or from a vacation (if you really prefer so first place).

Are you planning to take a bold step in your business that you are not sure if has been tried by someone else before? Don’t you just want to learn from your own mistakes? Thus, try using the ReserachGate portal, which is unique in a sense that it brings together the results of experiments and first attempts at breakthroughs, no matter how (bad) they turned out. Unlike conventional “scientific” portals, where you will find only boasted successes (and discrete silence about mis-hap’s), on ReserachGate you can also view failures and projects that worked quite differently than originally intended. You can save not only a lot of funds but also some of a professional shame.

Do you have a child for whom school is going smoothly? Or on the contrary, would your child benefit from a little help of his/her peers? Then check out Brainly portal, which brings together students who help other peers to swallow their curriculum. This viral growth trend offers more than a “template paper” (or exercises) from which you can “inspire” for completing your ones. You will find a person from flesh-and-bones who had passed the course more smoothly and can give you a explanation closer to you than that of your real teacher. In addition, if you were absent or ill, you do not have to rely solely on the notes of your classmates; you can have the substance fully explained by someone who has already understood it. Whenever it suits you.

More and more online educational portals are applying for official accreditation to offer their students full academic degrees on course(s) completion. At a point when there is a desperate demand for certain professions (like Data Analysts), portals like OpenClassRooms can go extra mile and guarantee their students employment after graduation from the course. They aggregate companies’ desperate demand and, as being able to guarantee strict training quality standard, they are able to place their entire ‘class’ into specific job offers. Hungry firms even agree to refund part of student tuition fees of newly trained employees, so it’s literally a win-win-win model.

>>>  continue READING HERE

You have just read the second part of the inspiration from NOAH 2019 conference. You can read more fascinating information from this conference in the first or third part of this mini series.

NOAH 2019 inspirations – Part III. – AI and FUTURE SUPERSTARS

At expert conferences, it is most valuable to hear well-founded views of where it is all heading. Especially from people who know (better than you do). Therefore, in our three-part summary of the NOAH 2019 conference in Berlin, there have to be a separate blog on the topic of ARTIFICIAL INTELLIGENCE (AI) and FUTURE STARS in digital services across Europe.

NOAH_Header_1_AI_futureVisions

In the field of digital technologies and data there are some conferences in Europe worth seeing. On top of that, there are a few that you can feel really be sorry if missing them. Well, and at the top of this imaginary pyramid of know-how, there is a very short list of those conferences from which you will have goosebumps still several weeks after their closing. One such conference is the NOAH conference in Berlin, which I had the honor of attending few weeks ago. In a short three-part series I offer you a compacted summary of the most important of the 2019 edition.

NOAH_DID_no_identityOn the first day of the conference, I saw my jaw dropped at D-ID presentations. As their name suggests, they deal with the depersonalizing of personal data (which would not be a novelty itself), but now listen what. They focus on providing such a subtle alteration of your photos that all humans will still know it’s you, but none of the top image recognition tools on the market will be able to match your face with you. For example, if you add a photo to Facebook, the app won’t be able to tag you automatically. Even more fascinating, you can choose whether you want a “version of you” that preserves your physio-gnomic features (that is, you will not become a no-name and the photo will preserve your gender, race, approximate age) or you decide to suppress even those. Even in the highest “secrecy mode”, these are only pixel-level changes, any person can still recognize you from that photo, but no (publicly) available algorithm can. Truly fascinating.

The presentation of the French company PROPHESEE (although from a different angle) also dealt with the field of optical recognition using AI. The long-term video analysis research has found that only less than 0.9% of the video image is needed to detect what is happening in the image. If you watch an CCTV camera, most of the image remains static (buildings, solid objects, …) and important for judging what is happening are moments when something moves in the image. This makes 99% of the transferred (pixel) data useless, especially if need to upload the image somewhere for processing. This may not seem important when having the luxury of large megabit optics to transfer it. But if you have an IoT device and images need to be sent through a low bandwidth, it becomes an insurmountable obstacle immediately. This problem has its origins in the fact that all currently available cameras are designed for human eye scanning, not for machine processing of the signal. The principle of frames (and their incremental comparison) is very computationally demanding. Prophesee has designed a camera (and supporting software) that reduces the need for video transmission by several orders of magnitude. What is more, it enables also fast and more accurate recognition what is happening on screen. They already have 51 patents on the subject and, being aware of the nature of convolutional networks, I believe that the method can soon be emulated to a certain extent solely by software. So we may expect a revolution in image recognition again.

If there are technologies from which you are both fascinated and embarrassed, then applications from CHORUS.AI is definitely one of those. This US-Israeli start-up has created a truly remarkable Natural Language Processing system that transcribes phone calls into text. So far, it would be nothing worldly. However, the real strength of CHORUS is that it analyzes also the call transcripts in relation to what the call was supposed to achieve. Thus, it can extract from the calls of a particular sales representative the way of selling, success of individual arguments, client objections and their handling. This slowly brings us to the slightly scary part of the system. For each salesman system can identify which words and sentences work for him, whether he gives reasonable discounts and how he works with information extracted from client. Consequently, it calculates the usefulness of the individual sales representatives. For the defense of the system it is necessary to say that it picks from the database past real calls those passages that correctly address agent’s weaknesses. It also automatically selects training calls to listen for a newcomer to rapidly teach him/her the skills needed. However, not fool ourselves, the fact that CHORUS.ai can increase call center efficiency by up to 30% makes it clear that some workers “are sent home”, too. And all of this happening in fully automated mode, with relentless data finger pointing on individual people. As I wrote above, expertly fascinating, socially a little, um, embarrassing.

While many fumble on how to turn AI into business benefits, AirHelp is quite clear about it. At the same time, this company is one of the use-cases of artificial intelligence that makes a clear sense, though you wouldn’t be looking for it in first place. The essence of their business is to help people recover from airlines reimbursement for missed and canceled flights. According to their estimates, claims for compensation of aircraft passengers amount to EUR 15 billion a year, but in fact only around EUR 2.3 billion, so around 15%, are paid. The business story of Airhelp is magical in a way that they used solely human Claim Handlers to represent their clients in the beginning. However, the influx of new cases for solicitation (filed via easy web form) has steadily increased, until the company suddenly found itself with an “army” of 500 people working as claim handlers. This was the moment when the owners realized it was not scalable and developed 3 AI systems to a] initially assess the merits of the case (and prioritize), b] estimate the probability of success of the case, and c] determine the most appropriate jurisdiction to claim the case. A single case handling lasting for human expert about 20 minutes can now be processed via AI solution in 23 milliseconds! Of course, they had to also arm themselves with a large number of API’s to various auxiliary systems. But thanks to this automation, they have become the largest law firm in the world, based on the number of cases represented in court. It is also remarkable that they have found new use in the company for those 500 people replace by computers , but on that note probably more details in some other blog posts later.

NOAH_PorscheFrom time to time, you will come across news that will surprise you, perhaps make you even wonder what it really means. For me, this was QWANT presentation. If someone tells you that they are creating an Internet search engine to be an alternative to Google, you get at least puzzled if not directly burst into laughter. However, these people are serious about it and I have to admit that their chances neither hopeless not necessarily high. If you browse to QWANT web-page and search for some key-words, you get results that are, indeed, truly comparable to Google. However, Qwant’s competitive advantage is that it does not collect any meta data about the users themselves and does not hue (or censor) the search results according to who is running the search. If you don’t believe in how important this really is, try a little experiment: pick up a friend and make a request to Google for the same key-words from your own computers and his/her computer at the same time. Go through the first few pages of Google search results on each of the computers and you will be surprised to contrast how different the results on both computers were. According to the presentation of the team, the Qwant search engine will probably have a much more friendly conditions for systematic crawling of information (using searches as crawler inputs). At the same time, it will be the European alternative, i.e. it will be guided by a stricter view of the digital privacy of people on the Internet, compared to its American counterpart(s).

The heart of the data analyst mush have cheered up at the presentation of  FRESH ENERGY, which (quite in the spirit of its name) brings a new wind to the otherwise relatively dull industry of utilities and energy. In addition to being an alternative energy provider (mainly for electricity), they bring innovation in form of very interesting use of smart meetering sensors installed by them into homes. (they already have more than 1.7 billion sensors) They summed up their mission in the slogan “From MegaWatt to MegaValue” while they really do not perceive digital consumption meters merely as a practical substitute for human meter readings. From the time series of consumption pattern in individual household circuits, besides the obligatory proper billing, they are able to optimize electricity consumption (e.g. floating electricity price during the day). By being able to report the consumption down to level of specific household appliances, they can point to anomalies in consumption (which are often a precursor to appliance failure). They also came up with very unique solution to monitor seniors, for whom through their regular consumption patterns can be identified that are breaking some of the crucial habits (e.g. it’s 11 am already and senior has not yet brewed his regular coffee or has not put on the radio that belongs to his morning rituals. While raising these alerts senior’s relatives can call the him/her to see if (s)he was really okay. You may be surprised, but trust me the combination and intensity of use of specific electrical appliances will tell a lot about the household lifestyle as such. Therefore, the data derived from these smart meters can soon turn also into fairly high commercial asset.

If I asked you in what area of life the data analytics had hardly ever taken strong stand, many of you might have guessed the traditional areas of human manual work (e.g. construction) where digitization has not yet (fully) arrived. After all, the shovel and pickax have only a wooden handle and you won’t create too much data by using them. However, the CORRUX ‘s contribution refuted this stereotype by documenting that machine learning and deep-learning models already exist also in the construction industry. As the construction of new buildings and infrastructure accounts for almost 2% of world GDP, any savings or efficiency gains in this industry have widespread financial implications. The CORRUX company integrates into APIs already built into construction mechanisms and tools, reads the data from these devices & machines  and then subjects the data to AI processing. In addition to (relatively) primitive indicators like the machine’s up-time efficiency, CORRUX also offers more sophisticated models for predictive maintenance or for quantifying the added value of various machines on the total cost of construction.

NOAH_filipThe figurative icing on the cake were the presentations of VILLAGE POWER and PTScientists. The former deals with the problem of electricity shortages outside the main capital cities of the African continent. We may not even realize it, but the sheer availability of electricity supply allows us to significantly prolong the effective part of the day. For example, if you want to broaden your expert horizons through reading professional book after work, you can easily turn on the bedside lamp and read into that book until you snooze. However, in most rural areas of Africa, you do not read anything after dark, as there is pitch-dark and you have no light to read. More than 124 million households live without a permanent electricity supply. Lighting with candles or Diesel power generators is not only financially burdensome, but also dangerous for the environment. VILLAGE POWER therefore came up with the concept of installing portable photo-voltaic units via efficient network of local mobile operator branches. The pay-back time on such an investment for a family is approximately 15 months. I was also interested in the approach of this company because it contributes to the gradual electrification of households by the gamification of the use of individual household appliances (e.g. light first, then a washing machine, etc.). recalls Maslow’s pyramid of needs (in electricity).

Although Elona Muska‘s recent “haggies” in the field of space colonization somewhat tore this impression, I assume that if I asked who would be behind the first data network covering the Moon, you would probably guess some of the developed nations or their space organizations (like NASA or ESRO). Perhaps that’s why it will (like did to me) take your breath away that the “colonization” of the Moon will be taken mainly by private companies such as PTSCIENTISTS. Not only do they have scheduled several flights to Moon in the coming years, they are also able to sell about 300kg of cargo carrying capacity each flight they offer. The first mobile phone network on Moon is in the process of real preparation. This allows in further due easier installation of other technologies, and also would allow the robots brought there to survive on the moon for a long sustained time (which in turn will be forerunners for the arrival of the first humans for permanent missions there.) Even if you are not universe enthusiast, trust me, the opportunity to talk personally to the actual people who are planning these projects is a goosebumps experience. It gives you near touch with the future and it is fascinating that the NOAH conference can bring this to its participants. Therefore, if you have even a little chance, do not hesitate to look at it next year. I am sure you will leave full of impressions as I do this year. So maybe see you at NOAH 2020!

You have just read the second part of the inspiration from NOAH 2019 conference. You can read more fascinating information from this conference in the first or second part of this mini series.

4 TYPES of BOSSES WHO DO NOT UNDERSTAND analytics

When I wrote a blog about the Analyst Loneliness Syndrome a few weeks ago, I knew I wasn’t talking about isolated cases. However, the magnitude of readers’ responses have completely knocked me out. It is bitter-sweet mixed feeling of sadness and joy that you nailed something right, but you feel sorry that so many people are suffering from this syndrome. So I decided to talk to some of those who contacted me about that blog and to write extension of original post. This time about one of the three core factors of analytical loneliness: the management side of things.

The classic HR maxim says: “Employees don’t leave companies, they runaway from their superiors.” Although I do not quite agree with this generalization, I have to admit that in 4 out of 5 cases when I changed my job, it was true (this is my greeting, Rasto, you are the exception). One can leave boss behind for a variety of reasons, but in most cases it’s a combination of some of the following “evergreen’s“: He can’t appreciate my work; He does not understand the area and therefore I get mostly nonsense tasks; He does not believe me and hints me so; No inspiration or development from him, I only rot; His/her moral standards and deeds are in deep contrast with my beliefs.

Certainly, managerial superficiality and incompetence can affect you in almost every industry, but I would like to specialize in a typical example of this ailment in Data Analytics and Data Science. The traditional managerial characters have gained some additional spicy ingredients in this industry. After all, judge for yourself, here are 4 TYPES of MANAGERS that don’t understand analytics:

1] Don’t drag me into details

BOSS_type_1Managerial Profile: It’s incredible, but even today there are still many companies where data analysts is “stuck” under the Head of Business or Marketing. It is often a tragic consequence of widely spread belief that data can  significantly influence company’s revenue. As a result, analysts are moved under the Head of business or marketing to make this influence happen . However, these are usually managers who have a mathematics aversion, developed already at their (primary or secondary) school. Anything more complicated than percentages leave them restless. Simple numbers like sum and average (of course, they only mean arithmetic average) ok, but everything else is already far too complicated. Any statistics beyond the correlation are just “academic curls” (or crap). Their phobia from numbers and more sophisticated analyses comes from the fact that they have never understood this area, are not in control of it and thus are afraid of it. They don’t believe in the power of calculations nor AI, they solve everything intuitively and on the basis of proven approaches (read as: it worked once in the past). He prefers human speech as communication, simplifies every schema or spreadsheet into 2-3 sentences. However complicated the analysis is, at the end everything has to end up in Excel, which can be filtered by columns, and must not be more than 50 lines. When you try to “dive into” the results of your work, (s)he will tell you “Let’s do not get too technical” (just tell me the essence)

Implications for your work: If you work for such a manager, you will be probably having a very frustrating working life. Since this kind of managers never did any analytical work, (s)he doesn’t know what IS real and what’s NOT. Neither in terms of procedures and results, but especially not in terms of time you need. So get ready to receive ridiculous tasks in gallows deadlines (what could take so long, right?). He has intuitive expectations about every assignment he gives you. If you fail to match it with real data, a tough week is waiting for you. No matter how thoroughly you prepare your analysis output, (s)he will take one or two of the most obvious (= most primitive) conclusions, thereby gradually discouraging you from coming with more sophisticated procedures first place. Sooner or later, there will also come attempts to censor “illogical” analysis outcomes. If it is necessary to present to “seniors” conclusions, (s)he will let you do it (while throw half of the slides out of deck as useless). Because if the top management did not like it by chance, (s)he will drown you with “it not making sense even to him/her”, but it just happen to be the calculation result. In the area of expert or personal development, you are down to pure fate of Robinson Cruse.

What should you do about it: I hate to be an evil prophet, but if you are serious about your analyst career, run away from there. In fact, this kind of manager is unrealistic to improve, because he considers more complicated analytics to be a necessary evil that suits him only when it confirms his intuitive hypotheses. Otherwise it is unnecessary “trying to look smart” that has no support in (his/her) reality. The only alternative to fleeing would be to attempt a coup d’état (whistle-blow him to a higher level of control and they might exchange him). But honestly, this kind of managers have a stiffer root and they have more “merits” than you have of convincing arguments. So sooner or later you just leave (with great relief).

2] Scared rabbit

BOSS_type_2Managerial Profile: This type of manager stems from the first type and often represents a generational shift or personality development from “Don’t-drag-me-into- details” type. What remains the same, (s)he never did an analytical work himself, so things are not understood. The “move forward”, however, is that they do not reject a more sophisticated analysis because it has come upon them that they cannot do without it anymore. To this “improvement” he was pushed most likely by the CEO / shareholder attitude or the fact that he noticed all the competitors around already using analytics, so we must have it, too. However, as (s)he does not understand things him/herself, he is only trying to follow very elementary steps, often mimicked from professional conferences or buzzwords (anybody Big Data?). (S)he is stiff whenever you enter her/his office, because (s)he knows that the debate with you will revolve around an important subject (s)he doesn’t control. Nevertheless, in order to survive (s)he must feed to levels about him/her (who forced the analytics first place) the illusion that (s)he is not only interested in analytics but also orientates well in it.

Implications for your work: The consequences are similar to situation when you have to get out of a dark room filled with things. (S)he only progresses slowly through the familiar outlines, (s)he first gropes everything thoroughly to make sure we don’t bump into something hard.As a result you will only get elementary assignments, everything will have to be tested in a small pilot (= no effect anyone could notice). Concept that to you train a model first on 1,000 people and then scale to 100,000 doesn’t make sense, does not ring a bell with her/him. Therefore, most projects will die after the pilot. He’ll never fight for better software or a more powerful computing engine, “let’s try first with what we have. When we do, then we can ask for more money.” (S)he’s too soft, because (s)he can’t steer you by essence (since (s)he doesn’t understand it) and so (s)he will try to do it in a moderate way. You won’t get strong decisions or quality feedback from them. Do not expect a vision where to follow, you often will be firefighters of issues that fell from top (and which (s)he cannot conceptualize and prioritize). Since (s)he is uncertain in your area, he will explain everything from Adam (sometimes repeatedly, as it has been overwritten by other issue in his/her head). Most probably he will never let you present the results of your work, so that it is not revealed to leadership that he does not understand even half of what you do.

What should you do about it: If you do not mind (or even prefer) that this kind of managers isolate you from contact with the top management, you can survive in this setting quite comfortably. However, you will have to educate your direct superior continuously (sometimes repeatedly on the same topics). Do not expect any career growth or expert development, at most you will be left with the space to self-tune. As a intermediary station, this is not a completely unbearable. But primitive and repetitive tasks and professional stagnation will catch you up sooner or later. If you have lived with such a manager for more than 3 years, look around where your peers have moved. Your train might be running away.

3] When we in ’95 did this …

Boss_type_3Managerial Profile: It is a manager who once worked as an analyst. Of course, when data analysis meant OLAP and mainly SQL data reporting. (S)he didn’t get too wild with predictive models, Monte Carlo simulations, or neural networks. So (s)he did not realize that data analytics is done completely differently today. In addition, his/her abilities are more of a memory-optimism that is often transformed into “When we tried this way in ’95, it worked”. In a sense, this type of manager is more dangerous than the first 2 named types. If some tries to convince you of something that is true, it is always worse when (s)he thinks being right rather than being not sure about the issue. In addition, this type of manager wants to be involved in every detail because he remembers that it was exciting to reveal new connections (maybe (s)he is nostalgic about it even). In fact, (s)he does not realize that “is no longer playing the same league as the young ones”.

Implications for your work: Perhaps the biggest risk of this type of managers is micromanagement. By living in belief that they understand the area and by having nostalgic memories of the times when they did something real with the data, they will seize every opportunity to “engage in the project.” This can sometimes go so as far as to “volunteer to help” and take parts of the project on their shoulders. (what is to be avoided by far, if for nothing else at least to meet the project deadline). Speaking of those deadlines, the second major risk of working with such a manager is unrealistically optimistic time-frames. After all, when we did it in the 95s, it took just … The biggest risk in the long run is that it will slow down (or “torpedo” by expert “arguments”) your introduction of the modern trends (to keep up with you). Maybe (s)he won’t even do it consciously, but if you take two steps back, after a few years you’ll find that you are more or less spinning in a vicious circle.

What should you do about it: For some people, such a job can be comfortable and they let themselves to be fooled that it might have turned out much worse off (see the first and second type of manager). If you are at the end of a career or are among those who prefer traditional to innovative, just enjoy a comfortable life there. However, if most of your working life is still ahead of you, you need to foster space for professional growth. And the pace should at least match the market growth to avoid becoming “unnecessary junk in the labor market”. Therefore, I recommend that you sit down with such a manager and ask for autonomy: part of your working time (e.g. 1 day a week) to test new trends (which (s)he does not hints you to). If the manager does not agree, (s)he is probably well on his/her way to transform into Type 1], and so should your answer to it be in the spirit of advice for that type (see above).

4] Jules Verne

Jules_VerneManagerial Profile: To avoid wrong impression that a manager is a problem only when (s)he knows less about the issue than you do, there is also the opposite case. I personally hate the principle, when the best surgeon is nominated to be the hospital director, with the argument that others appreciate and respect him. Regrettably, even in analytics, the most skillful (or the most powerful) analyst becomes a team leader or department manager. It happens so often because the levels of control over it are some of the first three types, and so they need someone to cover the technical side of things. Jules Verne is a type of manager who once was Data Scientist or at least a sophisticated data miner. After (s)he stops officially being responsible for direct performance, and is charged with task to manage other analysts, often one of the following things usually happens: 1) Becomes lazy and realized that (s)he no longer wants to return to writing queries or code (resulting in a gradual loss of touch for analyst’s work) or 2) will finally take the chance to do those cool types of analysis that the nobility did not allow him to do before. Often both of these transform into a non-critical acceptance of “hype news” in the industry. After all, he also wants to brag on the beer with other data managers what cool things we are in our company. As the consequence the journey becomes a goal. Trying this-or-that is more important than making something easier to really work. While (s)he is no more responsible for time spent on the individual steps, rather (s)he already determines the strategy for future.

Implications for your work: The assignments become increasingly confusing, because “Try to plug in a neuron net and let’s see what it brings.” Of course, half-successes go into drawer immediately to free up the runway for yet another new approaches to try. The result is a frequent change of priority and a gradual absence of a sense of real effect. The absence of value added gets noticed soon also by the “those up”, as will the time pass working in the Jules Verne’s team also means an increased risk that some organizational change will wipe out the entire team from Earth’s surface (read org chart) without any warning. At the same time, this kind of managers push their people into the position of generalists rather than specialists, which must not necessarily suit everyone well. Projects’ track record might look impressive in CV, but when you gonna by interviewed by someone who really did that (and not just tried as your team did), you will badly grill on your own barbecue stick.

What should you do about it: If you are JUNIOR in this area, it is paradoxically more advantageous for you to stay for a few years. Getting a broad (and shallow) outlook at the beginning of a career is not necessarily a bad choice. However, do not take too high a mortgage so that you do not bleed when your team suddenly ceases to exist one nice morning. If you are a SENIOR, confront the manager with the flicker that (s)he shows. Give him/her a feedback that you want to finalize the projects and that one new idea a week a probably enough. If he doesn’t understand or laugh at you, go to his supervisor to describe the situation and say either HIM/HER (or YOU). Both answers will be the right choice for you. If you are the first to do this, you probably save the rest of the team, but you will not regret the possible departure (possibly with handsome severance pay to get rid of you quickly).

Have you stumbled across one of these 4 types in the workplace? Have you ever experienced yet another type of dysfunctional Data Manager? Share your impressions at info@mocnedata.sk. I keep my fingers crossed for you to avoid those types of people. And if you happen to meet them, try to follow the advice from this blog. Bon voyage!