team – THE MIGHTY DATA

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.

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LONELINESS of data analyst

Higher salary. New challenges. Misfit with recent boss. Desire to get hand on much larger sets. Or pure world peace. None of the above is the main reason for data analysts to change their job. But what is then?

Not just American beauty contests, but also job interviews have quotes like “I wish a world-wide peace”. These are ridiculous statements on why you decided to change the job. I interviewed more than 350 people in my career and thus there is no shortage of even bizarre statements like “My father greats you, he thinks Allianz is still very good company and hopes you gonna get me the job.” Since 2017 I have been doing the interviews in a new country. I thought I would be learning some new “world peace” equivalents of this market. However, my surprise has been much bigger.

Loneliness as the denominator

I felt sorry for the guy, when it appeared in first interview. But when second, third and then forth candidate has come up with same reason of leaving recent job, I got on my toes. I thought if this market is some kind of fairy-tale kingdom cursed by mighty wizard. I could not comprehend, why so many people have loneliness as the common denominator of their life. This was not relationship- or friend- kind of loneliness. Rather this was all about work, the analytical loneliness.

How the story goes …

You graduated from some technical or IT-savvy economic university. You love puzzles and quizzes, you loved Math competitions and finding new interesting links between things. Thus, you look around the job market, where would you be able to put your talent into use. You attend several interviews and find out, that your potential employer has serious data conundrums ahead and that analysts would be measured against demanding criteria. You are thrilled. This is exactly what you are longing for: Let some task beat the f**k of you, so you can learn something new.

The first weeks on job is really fun, indeed. You discover new data that nobody analyzed before you. (even after you, but this you will find only later on). Demanding analytical tasks turn out to be a rather trivial data queries, often falling into reporting only category. But work is fun, so never mind. Your boss does not really understand the nitty-gritty of your work, he would not be up to finding you mistake in your queries. But he is really nice. Time flows, you achieved your quarter, year one, two … oh, man, how the time flies.

To your second Christmas in the company you beg Santa for a new boss, new project or at least the new version of the software you are working for. Just to get some progress ahead in your work life. You try to switch the job, try Start-up, Large corporations, family-driven business… Always few moments of thrill and then Yo-Yo effect of frustration hit as with diet. In the turmoil of internal fight, you sign up for expert conference. After all, one can get inspired externally as well. You meet a lot of stand-ins with same spark fading in the eye.

Loneliness of data analysts

With a bit of the alteration this was the common plot of all 4 candidate stories. None of them asked for salary of company benefits. None of them was investigating the chances of career growth in future. All they were interested to hear was WHAT and UNDER WHO’S LEADERSHIP will I work on? The usual comment went like “In my recent job I am the only one doing data science. Nobody understands that matter, I have no one to consult my approaches. I only know what I googled out on stackoverflow.com or similar portals. I feel lonely!”

It is interesting. When I gave it a deep thought, I realized I can find stories like that in my friend circles, too. The job of analyst is now going through strange wave. There is enormous demand for data-analyzing people. However, since Data Science is very young branch of analytics, there is very few managers, who actually did the Data Science themselves. Analysts often report to managers, you are not experts in the area. Thus, the young data scientists are doomed for the path of the bonsai: Nobody expects you to grow big, they water you time-to-time, but you are just cartoon of the real tree. The limitations of the expert growth of analytics (subject to another blog coming soon) are dire. These candidate stories only remind me of that again.

The same chorus 3 times again

Now it is clear to me that these were not isolated outliers. Sadly, the analysts’ loneliness is indeed a bitter interplay of three factors. Firstly, there is still very few data scientists. Therefore, it is unlikely that more people with this same job description bump into each other in the same company. Controlling, reporting, data engineers, these are all “multiple repeating’s” jobs in the same firm. But sophisticated analytics is often rather lonely. As a result, deserted Data Scientists do not experience team spirit, they have nobody to consult with their assumptions of uncertainty.

Second factor heating the loneliness is absence of managerial leadership. Sophisticated analysts often find themselves within teams that deal with data only marginally (e.g. sales or marketing). Or the teams work with data, but just to report on structured databases. As a result the Data Scientists do not receive proper feedback on their work and are set to learn only from own mistakes (which they have to detect themselves the first place). Some torture themselves with self-study portals and courses, but few have the iron self-discipline to do these for several years in row. Sooner or later, the majority just throws the towel.

The two above mentioned forces are joined by the lack of external know-how as the third factor. No offence here, but if you want to experience conference where each speaker of the day contributed to your growth, you probably have to organize one by yourself. No jokes here, trust me, my own experience speaks here. Expert conferences are rarely organized by people who can tell if the speaker is beneficial or not. Most of the Data Science events are vendor-brainwashing or people-headhunting traps. That is why (in the popularity ever rising) Meetup’s are often the only remedy for the expert conference hunger.

How to brake the vicious circle?

Even though our dispute might not suggest so, there is a happy-end for the 4 lonely candidate stories. Although they represent a sad probe into soul of the contemporary data analyst, they also show a way how to brake the vicious circle. Based on their talks and my own expert experience I would suggest you following 3 steps out of the loneliness:

Step 1: Self-diagnostics. The real change can happen only if participants admit there is an issue to address. This makes the change needed and unavoidable. Thus, please give yourself following 3 questions: 1] Do I have a boss, who understands my job to extent that (s)he would be able to temporarily step in during my absence? 2] Is there somebody else in our company that I can ask if I am doing my Data Science tasks correctly or give me tips if I got stuck? 3] Did I have a chance to get my hands dirty on at least 2 new analytical approaches that I never tried before over the last 6-9 months? If at least one “NO” emerged for you in previous three questions, you should seriously think about your analytical future.

Step 2: Deep breath before the dive. If you self-diagnosed yourself for a move ahead, do not run to job portals to look for new job ads. Before you embrace the switch, get ready for the leap. If you start looking for a new job now right away, you will most likely fail to get one. Grab a bit of self-discipline. Take some on-line courses, watch YouTube videos on your new desired expertise. But foremost, force yourself to really try by your own hands the new skills that you will need in new job. Maybe start here.

Step 3: Look for real Data Science leader in real Data Science team. To escape the analytical loneliness for real, one has to solve all the three underlying factors. Therefore, .. (a) you have to find a job, where you will be .. (b) part of the larger team working on Data Science and the team … (c) will be led by person with own, real analytical experience. Team that has ambition to work on plenty of new, analytics relying projects. Don’t get fooled by sexy offers of companies where one of the 3 aspects is hyped. Start-up’s often look as cool place to work, but often is accompanied with inexperienced founder who “just” had a great idea OR dire DYI conditions that leave you weak to unreliable systems or unskilled neighbor teams. I advise you to start the search by nailing down interesting projects and check on who is leading them. Alternatively, you can start your search from respected analytics manager and check is his team works on something that would make you dream big again. With high quality managers do not bother to revisit your former bosses’ profiles as well. As they say in airplane security instructions: “Look around, as your nearest emergency exit may be located behind you as well”.

On final note, let me emphasize that analytical loneliness might be a cyclic phenomenon. As the Data Science industry takes traction, teams will grow and finally form generation of nature Data Science leaders. However, in our region it make take well 5-7 years before happening so. Hence, probably a bit too long of a period to “shelter against the storm” in your recent hiding. Thus, if you identified yourself with some aspects of analysts’ loneliness, do not sooth yourself, it will get better sometime. Repeatedly do the same stuff, in stable, well-payed job, where my boss does not know enough to mess into my job or to fire me, can sound like recipe for nice life. So rather than galvanizing you to action, I ask you to give it a thought: How will the whole industry shift in the meantime? How do my chances to switch to more sophisticated analytics improve/worsen as I enjoy the “invincible” times? No matter if you decide to stay or get ready for the leap, let me wish you months (or years) without analytical loneliness.

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