Today I’m sharing the presentation I used for the speech at 13th Brazilian congress for management, projects and leadership. It was a big pleasure to talk to many people who attended. Looking forward to be part again on next year!
This article can be faced as a step ahead of this one suggesting user experience as the main driver for successful business models.
The massification of good user experience is a trend and it makes a huge sense. Let’s discuss why and some opportunities we can easily see ahead.
People are demanding about good experiences
When you are planning a trip and want a flight with more comfort, it costs more. A special consultant to assist you during your buying experiences, will cost more (there are even stores that plan for times with closed doors to meet only one single and special buyer at a time). Product faster delivery? More money. When you want to visit a special beach in your vacations, and want a boat just to you and one more with you, it costs more. People with high acquisition power have it and look for it naturally because they can afford.
But everybody want good experience. Everybody want to feel they are different. They feel special when they receive unique treatments. But not everybody have enough money to pay for unique experiences. And it’s completely reasonable. Better experiences will require either more people involved or more specialized people involved to deliver that in a customized way.
Products and services’s experiences
It’s possible to deliver a better experience when we talk about products in an easier way. When you buy a product from Apple, the experience is incredible. It was designed by a group of high qualified people inside one of the most innovative companies in world, and then copied to everybody who buys the result. They think about the product in California (expensive), and produce it in China (cheap). When you buy a Tesla, the experience is also meaningful. It drives by itself, is secure, can be accessed through a mobile phone. No one has done that before. When you buy a piece of clothe from Nike, it is not very different than other brands, but you know it’s a Nike and you are wearing the same of many athletes.
When we talk about those services which require a lot of attention, the good experience gets way harder to achieve. Meals, travels, entertainment, financial services, in-store purchases and many other require more subjective requirements to show a good experience. And it does because trying to teach everyone inside your company to provide the same experience is the same of teaching them to be actors. Once you are delivering something from person to person, the best scenario is that (I.E.) the employee who got inside McDonald’s yesterday serve their customers the same way the owner would. The employee who was hired in the nails saloon won’t have the same experience of the owner who has been running the business for a decade. It’s impossible (for now). It requires experience and commitment to your job. Not everybody want to do that. Actually you could only hire the best people in world to serve your BigMac or to make you up but that would cost a lot of money.
Disney achieved that perfect scenario of experience in services and is a world benchmark. I can’t remember another one now.
When we start to think about how technology can assist that experience mission, some of these mentioned services start getting easier to improve.
- How much money would cost to have thousands of movies available at your home? Netflix solved through technology;
- How much money would you have to have to count with a special treatment from a bank account manager that will only offer you what is really interesting to you and not for his yearly goals? Digital banks solved that with technology;
- How much time would you spend searching for a good price for your next trip going physically from store to store? Booking.com solved that gathering everything online. And did that in a cheaper way than the stores;
- How much would cost you to have a personal consultant tracking what you like to say wherever and personalizing that to buying suggestions? Amazon solved through suggestions (AI) and Google by ads (BigData);
All the examples above used to be services that would require more time or money on customer’s side. Technology gives them autonomy to look for/do everything when they want. The autonomy is part of a good experience. The ease to use is important, the speed and all the steps required to reach a goal matter.
- Why is it so hard to deal with flights miles in some companies? It should be all automated. We do have technology for that.
- Cable TV companies could offer customized plans for people. This way people could buy only those 10 channels they are really interested in. Let’s put technology here!
- Why can’t we design our own mobile phone plan? 1h of calls, more 10gb of internet and international usage costs you (fidelity plans here) only 50 dollars a month. Let’s try to measure usage in different ways.
Netflix doesn’t have a CTO (Chief Technical Officer). Having a CTO would be a symptom of centralization of technical decisions. On Netflix they have just a CPO (Chief Product Officer), which is the chief for their products. Products and IT are the same. Netflix also is one of the most innovative teams on technology and value purpose on world. But how did they reach this point? What took them there?
Profiles and responsibilities
They were born this way. It was a mindset simple to keep while they were a startup in 1997. But this thought have been kept during the years even with company’s growth.
With a set of benefits to their employees, which can be translated simply on freedom for them to take the actions they think are needed, Netflix shares their actions on management and responsibility in their speeches. The main concern of a manager is to hire the best people on world. The main concern of all the employees is to take the best possible decisions, having all the company’s context available to rely on. Subscribers numbers, revenues, all the areas budgets are part of the routine information that the managers share with their teams. Managers require their team members to enroll to competitor’s hiring process at least once a year to ensure they are getting the money they deserve. There is no knowledge management also. When an employee leaves the company, their projects die with him. There are no junior, plenum or senior levels.
Netflix avoids rules and processes because they believe that when you tell people how to act, their creativity is restricted and it makes them to stop thinking on how to add more value to the company. It has a cost. It is common for hiring positions to take more than 6 months to be filled, because they require complex hiring process and high levels of subjectivity.
Having some benefits examples like:
- Vacation time undefined;
- Maternity leave time undefined;
- Technical subjects budget unlimited;
- Hardware and software budget unlimited;
- No work plan definition: once you get hired nobody will tell what you have to do. You create your own project, work, finishes, tests, and goes to the next. At this point your manager can help you taking the decision if you want.
This freedom goes through only one restriction: act like Netflix interests.
This way the company, which is proud of saying that hires only the best possible people for their positions, and that has more than 4000 employees, reached a level of ownership hard to compare. Each one is responsible for their projects and has autonomy to define if it is useful to the company or not.
What does it generates?
It generates an incredible freedom environment for people to produce what they consider important. Once all the employees are the best in their areas, it is understood that they will have enough knowledge to take the best decisions. It generates more than 100 projects going on simultaneously from all company’s areas and being tested at every moment. IT projects can’t take more than 2 months to be finished. Each 2h one publication to production environment is made, and nothing is activated before going through A/B testing.
The great objective of ownership also is achieved because all of these conditions also have the intention to delegate power. Every Netflix employee must be capable of taking decisions without depending on endless validation processes or unnecessary opinions. Mixing freedom, responsibility and power is how Netflix reached and keeps his very high level of ownership among their employees and keeps being one of the most innovative companies since the moment it was created.
The model and how to learn with it
Compared to other companies on different acting areas, among the most traditional ones like industries and the most recent startups, Netflix reached his ownership through the joint of some things: almost unrestricted power, almost unrestricted freedom and seniority/maturity as a premisse.
This is a unique model that should not be pursued blindly, but used as inspiration to watch the disruptive way they found to manage their company
This article was written by me, Eduardo Diederichsen and Felipe Lindenmeyer. Me and Eduardo are managers of ilegra’s Software Development area, and Felipe is a senior account manager. We all are connected daily to clients demands.
Daily we do negotiate with a lot of clients. The hardest part is not giving them a price or conducting a good presentation with beautiful slides speaking buzz words. The hardest part is to identify if the potencial clients have a challenge for real and how its challenge can be approached by our company’s potential, language, market vision, and etc. When we find that out, that client will deserve our deep focus to make a good understanding and offer something that fits perfectly to its needs, even if he doesn’t understands it, but then try helping him understand it.
But what makes a experience as perfect as it must be so a client with sign the new contract? Impossible to tell because who buy from anyone is people. People lay on different things to evaluate their experiences everywhere, giving more weight to different points based on their personality and the influences they had during their whole life.
This recent new client had a complete journey from the very beginning contact to signing a contract held by themselves in many companies. At the end he decided to buy from us.
The client scenario
It is a client from financial area, so he knows the financial area customers in Brazil are very demanding regarding the whole UX and the products flexibility. Brazil is the country with most developed User eXperience demand in entire world, so the competition and investments here are huge.
What happened: the first contact happened in a casual party, not related to work. The subject on the crowd turned to work. We explored very briefly few examples of how we are helping some important companies in Brazil to be in front of their competitors. What really happened: The attention was caught and visit cards were given. Few days later they asked a meeting to talk about our portfolio and get to know their requirements and concerns.
What happened: it happened at customer’s facilities. The goal was, as they asked, to talk about few scenarios we’ve been working and the opportunities we identify and foresee as experts at the market. What really happened: they understood we had the knowledge to be their partners and if they count on us they would have oppinions of a specialist in their market. So the next step will be send a proposal and everybody celebrates? Yes, but not so fast.
- FIRST! What happened: they are a very conservative Brazilian firm which still isn’t enrolled to digital transformation practices and doesn’t even want to get to. We are very used to work based on agile methodologies, Lean approaches, testing and discarding losses very quickly. They wanted everything predictable with very clear goals and steps. What really happened: they understood that their competitors don’t work in that way anymore and that working that way they would still be left behind in the competition scenario. Then we reached a mix of practices that would give them part of the control they were asking but giving the project part of the freedom that kind of work needs.
- SECOND! What happened: they told us they wanted to be in front of their competitors knowing how much it would cost and how many time would take. Well, if I knew that, I would be one of the competitors. And that’s where the Lean and experimenting mindset gets the attention. The startsups bothering giant industries don’t have this kind of answers. They won’t have it too. What really happened: we decided to go for a first deliverable (We can call it an MVP) and a further evaluation after that to redesign the plans.
- When things got warm: What happened: we had the steps above but it was taking too long for a decision and things got warm (bad news). Our decision was to invite them to come to our company’s headquarters to see with their own eyes everything we were discussing. They really got impressed with our office because it’s designed to give freedom to people think and innovate. This was a key step because they spoke to people who would actually work with their project. What really happened: the deal got hot again.
What is faced differently by everyone
There are a lot of subjective things in the explanation of the sentences above. I’m not exploring their body language, neither ours. I’m not exploring the sentences we told each other and how did we look during the meetings. So, I’m not telling how we created empathy here. Let’s get to things close to that.
The client scenario
Some companies can be afraid of innovate. It’s a whole new scenario. People fear the unknown. They don’t know what is coming and then they get afraid and just stop. For other companies, the unknown is exciting and they know from there the innovation will come. They will seek for it as a routine.
Since it happened in a social event, the thing was easier. The focus was not evaluation. The empathy came easily because we spoke about our cases in a brief (not moving the whole night focus to work only) and humble way. If we were too thirsty about their needs it could turn into something boring and we could lose that guy’s attention.
The meeting was all about showing our capabilities. With that being flexible to understand their concerns about models and what we were proposing. At that time we invested some time explaining and desmistifiyng software development practices like Dojos, Meetups, team management models, our concerns about quality (A/B Testing, Chaos testing, etc). And at that time a big thing happened: the empathy with one of the guys was so huge, he found so many value, that he started defending some of the approaches to the sponsors in a very enthusiastic way.
We had to use a lot of knowledge to tell them the differences between the way they were approaching the problem and where we see the market moving to. It was hard work to understand how they treat projects and mix it to a reality we could work being sure we would reach the results both of us were hoping regarding the new partnership. But the biggest step taken was the use of the experimentation mindset to go for the first phase. They wanted to give a shot at our suggested model. That’s the chance we had to keep things going well so we would build more trust.
When things got warm
It was the dangerous part. Calling and bothering client’s patience wouldn’t have been the efficient approach. Bringing them to a controlled environment was a good move. Sometimes people don’t get everything you say when you are presenting something. You will have to repeat it in order to get the perfect moment where your explanation will make sense to them and then their attention and interest will be caught.
Being more generalist here with a few more examples
One customer may like to hear to most recent buzz words all pronounced in english. Another client, coming from countryside may not like it because he thinks it’s something for bigger companies. The proposal: one customer may prefer a document with a set of beautiful images in a more abstract way. Another customer may prefer to receive a one page document getting straight to the point using just text. It’s unpredictable.
Good! How can I learn with this scenario?
The CX (Customer eXperience) gets more challenging to achieve when we are talking about B2B or B2B2C offers. When you have a B2C scenario you probably will have one person at the edge who you have to please with your offer and your advantages. It’s easier to ask him: “hey, did you like this new feature?”. Getting back to B2B or B2B2C the variables are countless, since you will have to deal with many people from the very beginning of the negotiation until reaching the final contract signed.
How to attack that efficiently?
Short answer: be interested. Long answer: keep the knowledge with people involved, get experience, and be interested about evolving in the process and the learned lessons. Try to understand things about body language and psychology, do know the one who is buying from you. Does that guy writes publicly? Does he give speeches? What is he speaking/writing/reading/hearing/studying that you can take in count to set up a moment for a fast approach?
Nowadays when we talk about business, companies, money, processes, and everything else, we always turn to innovation. Improve internal process? Innovation. Make a new software to cut and automatize processes? Innovation. Make more money or save more money? Innovation.
All knowledge areas talk about innovation. Cientists always look for innovation to get new formulas, theories and progress. Banking people innovate to get bigger profit margins. Architects innovate on construction to find cheaper and more resistant materials. IT professionals innovate to make faster systems that make user experiences more and more immersive.
Once all areas have to innovate, what new and traditional companies do to make that possible? How to foster an innovative environment? What results can be expected from which kind of innovation?
The hierarchy kills innovation. Face that. The person who is suggesting the innovation cannot rely on judgement of another 4 or 5 people above his hierarchical level until the idea reach those who will really understand it.
The faster incremental innovations well succeeded are those who research for consumer behaviour. It’s a quick innovation, it’s inside every user of your tool. You just have to ask for it. It cannot wait for hierarchy.
People are afraid of getting their ideas ahead because they think they will be judged. They are afraid of the feedback in case their idea is not good. When there are too many steps to be “won”, the idea will die without reaching the ears of whoever really matters. Given that, let’s get to the next step.
Have many ears
I’ve seen many clients being proud and saying “we created an innovation area!” with big budgets! I’ve already seen the “innovation area” be the new name for R&D team (Research and Development). I recognize the innovation has to start somehow, but restricting the ears only to the voices coming from the innovation area is dangerous. Yes, it’s a step, but just that. If it’s the way to start, let’s go!
But always keep in mind that small companies with few employees don’t have innovation areas:
- Nubank has a department called “Wow! Factor”. As the name suggests, their goal is to create experiences to impress their customer. But their innovation doesn’t come only from the “Wow! Factor” department.
- Your scenario is a big company? Think about Google. With thousands of employees, 20% of their time is free for them to work on their own ideas.
- Ok… Google’s business model allow them to have idle time because their money factory is automatized? Cool, let’s take a look at Apple. It sells hardware, it’s a factory. It’s one of the most innovative companies in world.
Encourage intra entrepeneurship
The final objective is to innovate, but the innovation only will come when people think outside the box. To think outside the box they have to feel comfortable and understand they have freedom to suggest ideias and they won’t be cutted.
Focus on “How”, not on “What”
An idea whose owner judges awesome, but after evaluation be discarded, requires much care:
- If the owner don’t get any feedback in a fair time, they will demotivate. Don’t let him into limbo.
- If the owner don’t get convinced about the reasons why their idea won`t be taken ahead, he will demotivate. Don’t let him without explanations.
- Help the owner to identify the main idea. If more scenarios over their idea are not explored, they can demotivate. Help him to understand that creating a new credit card is a way to solve something, but the innovation can be if he looks for new payment methods.
There is not how-to here. It depends only on you. Big consulting companies will have ready models, costing millions of dollars about how implementing “digital transformation” i.e. It won’t work. Your culture won’t allow it to work. Innovation is incremental. It won’t have preset costs and schedule. Nothing is invented and evolved at the same time (beautiful sentence, not invented by me).
Think by yourself how to start. Establish plans, go for innovation theories and how the innovation happens. Once you get how the innovation happens, you will conclude where to start. Suggestions: Lean, design journey, agile software development methodologies.
The discipline to apply the Lean mindset must be everywhere. Last saturday I got myself into a class where we were discussing project management tools and concerns, focusing on risks.
The Lean scenario
We had this construction project as example. Almost everybody was stating that the project could be consider successful if we deliver it inside the defined scope, schedule, cost and quality planned (as PMI states for years). It is very subjective, because it will lay on the understanding of success each one has. If your goal is just to make a check on your checklist, keep that above. But let’s explore an exception.
Consider yourself as the project manager. The point here is: if during this construction a plumb is broken and your project release toxic wastes in a nearby river? It would cause an environmental damage at least. Naturally fixing the plumb turns inside your project scope. But it’s not the same for all of the rest. It’s not your responsibility to create a workaround to fix the damages to the environment (seeding trees, cleaning the river, etc), neither to care about your company’s image regarding media and the society (marketing advertising, taking water to the affected communities, etc).
To explore that we started discussing if you, as a project manager should tell your organization’s owners about what happened and the subject got into the MCSW tool to help evaluating.
Making a break, we argued about the MSCW tool when we were defining the risks also. It is a tool to classify the importance of the item you are handling. M stands for MUST, S stands for SHOULD, C stands for COULD and W for would. Clearly there’s not a Lean mindset applied to this tool. It should be just DO, and DON’T. It’s yes or no. You must or must not do something about a requirement, or a risk, or a situation. Discussing about the points in the middle will be waste of time.
The good will thought
Getting back to the plumb. It SHOULD be your concern, as a good project manager to tell, and I suggest, to help planning something to fix the environmental problem and your company’s brand’s image. The project management bible tells you shouldn’t get involved unless it turns your project scope. And here we have a difference between entrepeneurs and people who just work.
But for many times, the complete support to the organization needs may conflict with the Lean mindset. In this case, telling the problem to people is Lean. But getting involved in the resolution is not, unless it is added to your scope. This is the time when the entrepeneur has to have maturity to let the problem be treated by other people and get distance to focus in its real challenges. Otherwise getting involved in unplanned activities, will be faced as micro managing.
Keeping knowledge and good intention close to you
One important thing to explore after this fact occurs is the learned lessons here. Forget about creating a document, writing many pages of FCA (fact, cause, action) analysis, and throwing it to the repository. The most important thing is to make sure the right people understood what happened and what it caused. Because of that plumb broken, our stocks dropped 20% in six months, and that affect the employees there in the edge because we will loose many projects for our competitors due to uncertainty about our service quality. It may cause people leaving the company and knowledge being lost.
The most important thing after this things happen is to keep this kwowledge. For sure you, as the project manager, won’t ever again be discplicent about the soil analysis and researches because you will be concerned with environmental impacts. It’s a chain and everything is connected. From the very beginning, the environmental problem, to the labor force working there.
Now that we understood the difference about the three approaches we can have over AI with the last article, let’s dive in into the mid-term approach, always keeping in mind the borders. Given the explanation of the differences between Machine Learning approaches: a) ready-to-use APIs, b) training a model, and c) creating a model, let’s talk about training (using) a model.
Training a model
This is the mid-term approach to AI (Machine Learning) problems. Once you found out your problem can’t be solved by any ready-to-use API, try this approach. Just because there is no ready-to-use API, it doesn’t mean nobody ever tried to solve your problem generically and widely speaking. There is a high probability that your problem already can be solved by an existing model. Using this approach you will have to look for three things. It’s not a dependency, since the third step can be left away in some cases (example below), but they are a sequence.
Finding the best model
This is the part where you will have to have someone with good experience in this subject. There are different models to solve the same problem, as an instance. And there are also many problems without models covering it. You will have to find the best model that fits best to your needs. You will have to check the trust percentage that model gives, if it checks all the information you have, if you will have to adapt any information you already have to use that model, and many other things;
We can split the models into three different groups:
Models for supervised training
It happens when you have the information that the algorithm must reach conclusion X (objective) after evaluating A (info 1), B (info 2) and C (info 3) information; Example: you know that sneezing (info 1), high body temperature (info 2) and pain over all the body (info 3) means you have the flu (objective). Here I present some well-known models:
o Linear regression – https://docs.aws.amazon.com/machine-learning/latest/dg/types-of-ml-models.html – It is good to work with number prediction. Examples: What will be the weather for tomorrow? For how much this house will be sold?
o Decision tree – https://www.ibm.com/support/knowledgecenter/en/SS3RA7_15.0.0/com.ibm.spss.modeler.help/nodes_treebuilding.htm – Find out the disease: are the sympthoms A, B and C true? Then disease X; Are the sumptoms A, B and D true? Then disease Y;
o Bayesian network – https://pt.slideshare.net/GiladBarkan/bayesian-belief-networks-for-dummies – When we have an evidence and want to reach its cause. Belief propagation. The same health scenario above can be applied, but in an inverse way. I have the flu. I must identify in this patient we have all the sympthoms, or even without showing sneezing, he still has the flu.
Models for unsupervised training
It happens when you don’t have the conclusion the algorithm must reach. You will have to check it every time it runs. Example: if the customer bought product X and Y, he may be interested in product Z. You won’t know if it is true, because the customer may get interested, but even with that, don’t buy the product. Here I present some well-known models:
o Association – https://en.wikipedia.org/wiki/Association_rule_learning – Same example above of suggesting things to be bought;
o Anomaly detection – Any chart control or information where anomalies have to be alerted. Stock market or the temperature inside a factory’s chamber, as instances;
Models for semi supervised training
When sometimes you know what you will reach. Your problem will set if you will be able to use this model. More models can be found at https://en.wikipedia.org/wiki/Outline_of_machine_learning#Machine_learning_algorithms
Configuring a model
It happens when you have a known model and must configure it. Sometimes you won’t have to train the model.
An example for linear regression, which came from another customer: he wanted to mix many different information from many different sources and reach out how it would affect their product pricing. For each of their products, you configure the algorithm in order to understand that supply A affects 10% of final product pricing, supply B affects 50%, and etc. Knowing that, the algorithm would be able to “predict” changes on their prices and warn them to buy more or less of each supply. This way they would be in front of their competitors, saving money at the right time;
And then… Training a model
Once you have a problem requiring a model to be trained to identify your target, you will have to have data to train your model. The image analysis that cloud providers provide through APIs are great examples. Once you upload an Eiffel Tower image there, the algorithm already knows there is an Eiffel Tower within your image. But how do they do that? They have already trained the model to understand patterns on the image and then classify it. It’s the same thing that Facebook does every time it recognizes faces on your uploaded photos. For the Facebook example, it gets even more impressive because Facebook trains their algorithm with everybody’s faces. Then they know that your image has a photo of your specific friend, and suggest you to tag that guy! It’s not just a generic person recognition like other models do.
How to do that?
At last, there are many tools, such as Google AutoML, Amazon Machine Learning, Watson and TensorFlow (open source tool). The providers solutions allow you to send a given model to cloud and then use their infrastructure to run, train and consume it;
Everybody has been talking about Machine Learning, and everybody wants to get benefits of Artificial Intelligence. It is a new thing IT managers grabbing that old problem from inside the old locker and thinking: “hey! Maybe new Watson can solve it for me!”. But every time I hear someone new asking about how to solve a problem with AI, the problem looks like something never seen before. Every day a new solution is researched to a new problem. If every day a new will comes up, how can we identify what are the borders for AI? Since AI stands for “Artificial Intelligence” what is the “intelligence” border? What can and what cannot be solved with what we have today?
How to identify how hard it will be to find a AI to your scenario
Machine Learning projects can be split into three groups:
Using a ready-to-use open API (this first article focuses here)
- What is it? This is the fastest approach. There are a lot of APIs ready to be accessed and to be added to your solution. There are a lot of benefits, and you just have to pay for that. There is a table and more details below;
- How long will it take? You can get results from some tests within one day;
- Some benefits:
- a) They are ready to use! You just have to plug them to your app. Anyone can do that;
- b) Their suppliers will keep training the model as you go! So, it won’t ever be outdated;
- c) The competition between suppliers will grant you always well trained models and non-stop improvements and updates;
- The items above will be very expensive to reach when outside this approach;
- Some restrictions:
- a) It doesn’t belong to you. It means you can’t change anything on how it works. It’s just you asking: “hey, please classify this image!”. Then the answer would be: “cool! Your image has a woman on it”. But you can’t ask back: “what’s the woman’s hair color?”;
- If the open APIs fit your needs, don’t ask again and start using them by now! Don’t worry about suppliers grabbing your information or whatever like that. Once you pay for the tool, you have a contract where they say they won’t use your data. It’s the same thing of cloud;
- If it doesn’t fit exactly your needs, try to understand how important is to have that 1% more of trust over that AI judgement. If you really need something more precise, jump to “training a model”;
Training a model
- Mid-term approach. There are a lot of different models ready to be added to a project, configured, trained and then used. I will talk about this approach in the next article;
Building a model
- Keep in mind it won’t be an IT project for a while. You will have to have people from physics, mathematics and specialists on your business, and really good information to add to your project in the very beginning. Once they finish the model (it can take up to 2 years. Maybe more), then it will turn into a regular IT project starting from “training a model”. Both of this projects (building and training) will take for sure more than 2 years of research and testing. If it is a key process to your company, don’t waste one more second and start this project. The sooner you start, the sooner you will get the benefits;
Cool! What are the ready-to-use APIs?
My suggestions are inside this table below. But the options are not limited to it. You can find many others. They are maintained by the biggest cloud and AI players. It means you can trust it, and probably for what they focus, they are the best you will find.
|Chatbot related||DialogFlow||Watson Assistant and Virtual Agent||Bot||Lex|
|Video Analysis||Video Intelligence||Intelligent Video Analytics||Video Indexer||Rekognition|
|Image Analysis||Vision||Visual Recognition||Computer Vision API||Rekognition|
|Speech to Text||Speech||Speech to Text||Bing Speech||Transcribe|
|Text to Speech||Text to Speech||Bing Speech||Text to Speech|
|Natural Language Classifier||Natural Language||Natural Language Classifier, Natural Language Understanding, Personality Insights and Tone Analyzer||Language Understanding||Comprehend|
|Trends search and analysis||Trends||Discovery and IBM’s Discovery News|
|Find patterns over unstructured text||Knowledge Studio|
|Content moderator*||Anomaly Detection|
|Jobs discovery||Job discovery**|
* Google, IBM and Amazon have content moderator built-in their products. Microsoft has this specific product looking for anomalies only.
** Job Discovery is a private tool available for only few partners.
Two examples to talk about the borders again
Just like the example above: a customer came to me asking about a solution to identify people on images. Great! Let’s use Google’s Vision! Vision identifies people on photos and gives a lot more information about the colors on that image, about places that image may contain, and etc. But then the customer asked me: I want to recognize if it is a woman. I said ok! And then: I want to recognize the woman’s hair color. Ok, all open APIs are off the game. Let’s find a model, train it and then get hair colors. For you to be able to answer those questions there is no shortcut. You will have to read the documentation of each open API you find and run tests on it.
Language defect recognition
Another customer came to me asking if they could give a microphone to their employees in order they could operate a system just giving voice commands. Ok, it’s not new. We could use a mix of speech-to-text and natural language processing APIs, let’s move ahead! But then the customer said the system should recognize internal terms like acronyms and words they invented to communicate with each other. Erm… it’s not possible. You can’t train ready-to-use APIs to understand your very own specific terms. The easiest way there was to suggest the operators to change the words for some others the system would recognize. Otherwise they would have to grab models, configure and train them to understand the new words.
Then, why don’t you give your first AI step over ready-to-use APIs evaluation? The sooner you start, the sooner you will understand how to approach that old problem.
I heard “cloud is the new black” during a training session inside a Google’s office. You can guess it means that cloud is basic. But why did they say it? Google didn’t forge this beautiful sentence. Gartner did.
What Gartner means by “cloud is the new black”? In short terms, Gartner said the cloud Market is a US$ 1 trillion Market. Right now it’s just US$ 56 billion.
Google repeats that for comercial purposes of course. The reasons are the same Microsoft, AWS and any other cloud provider do: scalability, stability, abstraction of infrastructure processes, and the same bla bla bla. And I totally agree with their reasons. But without considering this technical questions, to the final business results $$$, why is the cloud so basic?
How does that come to our business?
Yes, there are already a lot of companies moving to cloud and starting their applications inside the cloud. But I still can easily find many companies still not even considering the cloud move. Inside my reality it’s hard to understand. How can they not see cloud benefits? How can they still use their machines and spend millions of dollars buying more and more storage every 6 months? For me it’s waste of time. I’ll explain why.
The rooms where the machines are hosted. They cost Money. For few companies, I’ve already seen very expensive entire blocks inside noble areas like São Paulo being used to host… machines. They don’t need the datacenter to be that close to the offices. The latency doesn’t matter that much. I’m 100% sure. For sure if they remove everything there, and rent the space, the renting revenues will pay for a big slice of cloud cost monthly. What if they sell it? It would mean investments for areas that are needing that Money to innovate and be in front of their competitors. Because of that lack of money, their areas are wasting time. It’s waste of time.
Recently a datacenter, close to the company where I work had a fire issue. Many governmental and private companies, core and non-core systems, were affected. And where were the backups? Inside the same building. Because of the fire, the fireman and police didn’t allow the technical team to get there and move the information to another datacenter. The replication wasn’t automatized. It caused more than 12h of unavailable systems. Can you imagine any company inside any industry without receiving transactions during 12h? Imagine the financial area. Hard. Now imagine a factory without systems for 12h. They won’t sell for a whole day? You could answer “Yeah, but we can ‘take notes’”. The employees don’t remember how to hold a pen this far. They also won’t know how much they produced of what they produce. They won’t know how much they spent producing things. But the main thing here is the overhead that will be created inside those companies to put everything back to systems. Talking about Brazil they can even get a ticket from government because of some missing transactions on that day. What all that means? Waste of time.
Why not cloud?
I have a client who runs a retail solution on cloud. The solution has been running for the last 2 years without downtime. Is it a core solution to their business? No, it’s not. But the fact that they don’t have headaches with that small system saves them time to think on other things. Saves tickets on traditional infrastructure teams. Saves their mental health also. Of course the cloud itself is not the only answer for the application stability. They do care about quality on their development process. Then all these benefits come easily.
When will companies decide to migrate to cloud?
It all makes me think that using cloud is related to maturity. Now a quick link with internet-related startups: companies who grow unbelievable percentages every year in the entire world. The biggest part of them has the cloud in common. Most of their business models wouldn’t be possible without the cloud.
The traditional companies, which already felt startups “bothering” their market shares, are moving, or already moved to cloud.
Why does that happen? Because the cloud gives them the speed they need. Things I’ve already seen in on-premise VS cloud environments:
- A new environment to create a new app can take up to 1 month to be released by the infrastructure team to the development team to start work. It’s one month less on that project. Within cloud it’s solved in less than an hour.
- Analytics information being generated only with the data considering the day before the current. In cloud you can have live information to take your decisions.
- Analyzing petabytes of data without having to do that on the weekend, when there’s no concurrency with other systems running. In cloud you can do that whenever you want without buying millions of dollars of infrastructure in advance.
All these examples want to say the same thing: when the companies start feeling they are being left behind because they are slower than their competitors (either it is a startup or not), they will change.
So, why cloud is the new black?
So… Getting back, why cloud is the new black? Because it means saving time. Because if this text made you remember of any issue you are having, or you may have inside your company, it means you will run after it to solve. It won’t be a short run to find all the responsible for everything and asking them to change to your new conceptions. It will take weeks. Months at least. Those weeks or months spent by the team looking to fix or prevent something to happen, means weeks or months not looking to improve the business, not looking to be in front of competitors. The IT area is not the support anymore. It can’t JUST be prepared to whatever the other areas will demand. It HAS to be the one of the leading business areas. And why that is true? The IT guys know what technology can do. The other areas don’t.
Machine Learning (ML) is the word for the moment. Just like SOA has already been, just like DevOps has already been. Now everybody talk about ML. Everybody want to get its miraculous benefits but just few of them all really know what it is, its possibilities, and the shortest path to start getting its results and leveraging with it.
What is ML?
Wikipedia says “Machine learning is a field of computer science that gives computer systems the ability to “learn””.
Ok, got that. But what is Machine Learning? What does it can achieve? Let’s get to some examples:
Netflix told, during Cassandra summit on 2016, that everything it shows are recommendations. The slides are available here (there’s also a Youtube video at the end). And people like it! 80% of what we watch are recommended. We don’t actively look for it. It’s one click far.
They got 5.2 million new users from may to june in 2017. If you were on of them, you didn’t fill a long question set giving them information about which genders you like the most, not even what you don’t. Also, they don’t have the largest number of interns in the world to read 5.2 new user’s data and humanly suggest movies for them to watch. Those recommendations were made and keep always being made and updated within few minutes after you show any kind of sign you like something. Watched something? Flagged as “watch later”? Flagged as “like” or “dislike”? It all counts. It all updates your profile and new suggestions may come.
Now take a look at this second example. This Google’s Street View image shows a parking lot in Primeiro de Março Street, at São Leopoldo (south Brazil small city):
I’m 100% sure the parking lot named “estacionamento beto” didn’t send their data to Google Maps team. They didn’t fill a form telling they are at São Leopoldo. Their owner probably have a mobile phone with internet access. But probably they didn’t even know it is possible, and maybe they didn’t even know what is Google Maps. But they are there:
The Machine Learning here is something already touchable using a Google API to analyze images. They analyzed the Street View image, and the machine knew it was something with commercial purposes. Then the algorithm automatically “registered” it as something to be shown in the map. If you look at it at Street View, you will see that this street block is almost entirely residential. Google’s algorithms knew what’s the different between residential buildings and commercial buildings.
- Facebook suggests you to tag friends at their faces when you upload a new photo. It’s Machine Learning. Facebook doesn’t have one picture of each people in world to compare when they receive new pictures;
- Almost all e-commerces we know nowadays do that, but Amazon started: products suggestion based on the last purchases you done;
- Getting information from your bank account straight on Facebook’s chat. It can recognize many different questions for “whats my balance?”
How to benefit?
It’s very easy to think on new scenarios, more or less complex. Automatizing predictions or anything that today requires a human mind, is, or will be, possible.
Example! We know our stock market has patterns on its behavior and it’s affected by many things. Today many people spend their whole day (or life) analyzing data and giving predictions to others about what to do with their stocks (buy/sell orders).
The red dots (mine) on data above (from tradingeconomis.com) show moments when the market started cycles of loss over the last 10 years. During all of these examples we already had a set of technical analysis that helped us to identify when it’s more or less likely to happen again. But it (still) takes a lot of people distributed across many companies to MAYBE find something significant out. Having a good Machine Learning model can change this entire market. It can change into something unfair to put money, since parts of the analysis MAY become predictable.
But how to start?
A good new Machine Learning model invariably goes through a huge data quantity. The data is required to train the model and then achieve the real business goal. A good start is to set up a structured base of data to be used. You will also need a data scientist. Don’t worry, this scientist is not an alien. They are potentially very easy to find. But it’s subject for a new post.