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Entrepeneurship, Practical examples

Successful negotiations

February 20, 2019

When we talk about services industry, unless you are in a niche not explored or own an entire market, it’s hard to show trustworthy credentials and proofs that you should be the one chosen (showing you are better, you deliver in time, your quality is world class, etc). Almost everybody can say they are good at doing what you do, even if they have never done anything like that before. This affects sales directly in a market where the competition is huge like the US. Getting into scenarios where we have at least 3 competitors, and some times more than 10, is pretty common.

In 2018/2019 I’ve been successful in the biggest part of sales negotiations I got in. For those which we didn’t win, at least we figured in the final shortlist. They all have different stories like where we met people involved, the percentage of how much the negotiation was done physically or not, how many steps each one of them had, the client’s size, and many other things that influenced on the results.

The common thing for winning all of them, from what I’ve seen this far, for sure passes by how much you can show of your technical quality not using technical stuff. The one who signs the check usually is not someone technical, but wants to feel comfortable on choosing us. Making them comfortable someone appropriated is going to handle the situation is the key.

But after winning, a feeling, or even a testimonial, comes about why did we win each one of them in specific, I mean the strongest reason in particular for each of them. Below I listed some of those main reasons for some companies, described a bit the scenario involved and shown how we prepared for some of them.

Speed and coherency in communications

Being fast and coherent on your communications is something decisive. If you go too slow, your client will end up thinking they are just one more in your list. Some studies say that your first answer must be within the first hour the client contacted you. For the next steps it may vary a lot. Being fast brings the feeling that you are dedicating energy to them. Beyond that, the speed with coherency creates trust between the parties. By coherency here I mean saying NO when you have to do. When you say no for something requested you are creating anchors of truth (that you will have to proof they are true in the future), and also is decreasing the strength of vendor-client aggressive relationship that some partners try to proceed.

Dedication on understanding the scenario

Do dedicate yourself on understanding the scenario. For sure this is the step that will give you inputs for everything mentioned here. If you don’t know the partner’s scenario, you simply are not up to help them. Dedicate on understand what is the business scenario, what you really have to deliver, but also spend at least 30% of your time understanding the consequences of each scenario. Why is that company buying from you? Do they have a strategic move related? You will know that and get more information to look even more trustworthy just knowing your client’ situation.

Be flexible. Give alternatives

For many of the negotiations I got involved in, there was a mental clock running out of time for something to be solved on the partner’s organization. If your client is under pressure, he needs alternatives. Period. He needs something he can realize is better for him so he will sleep comfortably at night. If you go for a negotiation with one scenario of purchase, you are losing the opportunity of being 2, 3 or even more alternatives inside one partner at a time. If you go for the scenario with more than one alternative, you will show empathy with the situation and also will show flexibility. You solution and your knowledge may always be the best in world, but sometimes it’s more than what the partner needs. He just needs to solve a problem, not a rocket.

The setup for the moment

Unquestionably, my favorite part for every single negotiation. The moment at the round table. The table must be round. For all of those I got involved and I remember while writing this article, we wanted the long term approach. We didn’t want just to have a deal and never show ourselves again. We want that deal to be successful, because we want more deals and also referrals for more deals. A lot of techniques can be applied for this moment, but it is a matter for a single article and I will resume here in three steps:

  • Preparation – know everything. The scenario, the people involved, the implications involved, etc;
  • At the table – prepare for that. Plan ahead each move and each question that might arise at the table. Be prepared for a money negotiation;
  • After the table – you went so far, let’s now miss the deal with small mistakes;

Use credentials

For every moment of contact you are allowed to use credentials. Credentials can be a lot of things like telling a story about a project your company delivered, for how long the company exists and handle challenging projects like the one you want to talk, even cheap chat can show credentials for some unformal scenario. The goal for a credential is to increase trust in something that will help the negotiation move ahead. But the one who got my attention and was not in my speech at the beginning of my journey was telling how many years I’ve been working in my company. The perfect scenario happens if the customer ask you that, because will show he’s interested. But you always can mention that to leave one more strong anchor.

Decisive factors exposition

In long-cycle sales like my reality (having more than 6 months between knowing a company and signing a contract), it’s close to arrogance to think that what you heard for the first time when you met your client as his business goals will remain exactly the same 6 months later, when you are at the round table negotiating. Whenever you have a chance, ask key questions to check if you are delivering what the customer expects. Be straight on that. That’s simply the reason you are being hired. Ask that and double check whenever you can to check if nothing has changed. At the right time make it visual to create a mind-contract between you and who are signing for your services. This is the only way for you and the partner have the same feeling that you are going to deliver what they need and want.

I hope these thoughts I gathered during 2018 and 2019 (this far) help you to be successful in your upcoming negotiations. Use the space in comments to ask questions if you want. Let’s talk!

Digital transformation, Innovation, IT is business, Practical examples

5 reasons to start and speed up digital product management

November 4, 2018
Digital Products

One of the main objectives of a well-planned Digital Transformation journey is the creation of Digital Products. Having Digital Products means that some of the core systems of the company can be turned into a product or service. Other partners can benefit from! Or even your data will allow you to create new products that didn’t exist yet. That means new revenue streams, not necessarily aligned with the current business.

Some examples are famous and some of them are pretty new, showing that to create useful Digital Products all you have to have is the will to make a difference:

  • Retail started what we can see now. Then banks came with their apps, home banking, credit cards, insurance simulators and etc. All this Digital Transformation reality got strengthen with them;
  • BTG started in 2014 what nowadays is the SnakeBite911 app. With that people can collaborate and mark where they’ve been attacked the most by snakes. For BTG, they are offering clinical trials and have many other offers to come.
  • Vivino is a social network for wine lovers. A score for each wine and many other details can be added by the user. Vivino has been using the app to offer wine to the huge number of users they got.
  • Companies that sell farming fertilizers are mapping region’s soil, the weather forecast for the year and the next, and many other data. This way they can suggest their clients which fertilizers they should buy and when to buy it;
  • Health: apps who help runners to monitor their evolution, tracking how people feel while they train and suggest shoes, nutrition hints (and products), next events, etc. Also, smart things (like shoes and swimsuits) are already available, tracking a lot of data, and suggesting a lot of products and services to be acquired.
Be the one pushing the change

Some changes happen on how you and your team behave when you change from project to product-centric approach:

Project-centric Product-centric
Delivery – that’s the mission! Owning – change everything. The culture will change, the will to be part, motivation, etc etc etc
Stay on backstage – yeah, IT usually is just the way to reach something, not the something that must be reached Stay in the front stage – IT leaders start being at the front stage alongside with business leaders
Sensation of completion – leading to… nothing. Just more tasks; Continuous – apply the mindset of improvement, avoiding the team’s sensation that a project who completely satisfies the final client is never delivered

The 5 reasons

1st – “By 2020, 75% of digital business leaders will have abandoned Project model and adopted product management model” – opening keynote from worldwide flagship IT event in Orlando 2018. Project model is not fast enough. Every time a project ends, the team is disbanded. Then new negotiations for the new cycle of improvements start. But until its end and the new team come, is waste of time and waste of the team’s knowledge.

2nd – “By 2020, organizations that have embraced the product model will outperform the competition that has not, in both customer satisfaction and business results (Gartner)”. Since product-centric is faster, we must adopt it in order to stay ahead of the competition.

3rd – “78% of companies growing the most on American market are using a product-centric delivery (Gartner)”. Nothing to add here.

4th – “A product-oriented company’s IT department depends less on HR interventions, and takes by itself the leadership of culture and self-development initiatives with internal teams (Gartner)”. The first change is more power and influence to IT. But that doesn’t mean the other areas will be weak. It means they will put strength together to make things happen to have both visions: business-led and IT-led. None of them can exist without each other.

5th – Evolve the IT to turn into a hub to attract internal and external ideas, business partners and talent attraction (Gartner).

Having Digital Products in mind, the benefits above are natural, and the reasons to do it are compelling. It’s the fastest way to approach traditional companies to how the unicorns that are changing our economy are.

 

Recently I attended the Gartner Symposium ITxpo in Orlando. Gartner shared some of the insights here (properly mentioned on the links). The rest of them I had while listening to the keynotes.

Digital transformation, IT is business, Practical examples

Why do digital transformation initiatives fail?

September 21, 2018

I’ve been looking more and more for reasons why Digital Transformations fail. Here I’m talking specifically about the steps regarding launching products straight related to experience. Many of the articles are too abstract saying that it’s lack of culture. The culture is hard to change, etc. Some of them are about lack of empowerment, but again it turns to culture.

After reading many of them (some of the most important listed below), I came to this conclusion: it’s lack of will. I’ll explain. Before everything, the articles:

 

They say it’s culture

The most present thing is always related to people. Culture, engagement, communication, expectations clarity, etc. I couldn’t find a single mention saying that the project wouldn’t reach the expected ROI.

Beyond the results that consumers can see, some of big companies’ moves went through internal automation and changes. The great challenge in these scenarios have been to IT and business areas capacities together. If one of the areas had the knowledge of both, they would be only one. This is Digital Transformation.

Clearly the suggestion here is to put them to work together. One cannot live without the other. But the main thing is to remove the competition between them. Now that IT saw that it can add value to business just like the product areas, a competition started. The shortest way to put an end to that: have a single manager. A CPO and a CTO working together, with different responsibilities but with the same results expectations, is a good match.

It’s an internal change

Ford is having issues to real engage with Digital platforms. Tesla is not. What’s the difference?

The will is one step before putting hands to work. When we are talking about the involved teams, the will is the output of contextualization and engagement processes. Leaders that didn’t buy the sponsors idea, won’t be able to pass it on. They won’t engage their teams properly.

Then many things may come to leaders and employees minds when acting in a new initiative. It’s all new for them. They don’t know what will come. Maybe nobody is sure about that yet:

  • Leaders and employees: afraid of what’s new. I know some companies that chill when they hear about agile development. They are afraid of that since the 90’s. Even after watching more and more successful benchmarks, it’s never enough for a real change;
  • Product areas: and that goes all around the company. The product areas don’t know the IT and fear their guys;
  • Operational areas: the operational areas, if aren’t properly engaged, will sabotage everything they see. Sometimes it isn’t even rational;
  • The board: the directors are afraid of doing a new investment that won’t bring the expected ROI, and for many times will decide to keep the organic growth instead of doing something that can be exponential;
So why it is lack of will and not lack of culture?

The will is base for culture. If the leaders are not convinced of going Digital, they won’t do anything. But I’ve already seen some people well convinced that this is the future, and even after that, they won’t make a thing. But if the needed leaders are well convinced, and willing to change, they will be able to change their own culture and their team’s culture to achieve what they are looking for.

Digital transformation, Innovation, IT is business, Practical examples

Health awakes for DT

September 21, 2018

During the past week I had the opportunity to attend to an important trade fair in USA for IT decision makers in midmarket companies. It was an incredible experience to talk to many people who are in charge of the future of their companies.  I got really impressed to know what they are looking for and what are their real concerns by the end of 2018.

Special mention:

Before getting to the real point of this article, I have to mention that I got to know many important and intelligent people. They were looking for the same old solutions to solve problems that were already solved a million times. I saw people looking for solutions that were created in 2007. That’s impressive but that’s also a sign that we all are inside a cycle. And we all are evolving. That’s the coolest thing about the event.

 

To the point: Digital Transformation one more time

It’s not new for everybody that financial industry is heavily affected by Digital Transformation. I can’t find a single bank that isn’t looking for that. Some of them are looking for experience enhancements; others are all about regarding automation. And the majority of big companies already past this phase and are already looking for omnichannel. It’s not that strong when we look for insurance, mortgages and some other industries. They have a different pattern. But they are all in similar situation: evolving.

Beside financial, we’ve heard some real cool moves from Amazon (a benchmark of course) in retail, and many others. But after financial and these moves in retail, it’s being kind of difficult to find the next industry witness for DT.

The health woke up

I had an incredible opportunity to talk to three chairmen for health. They are concerned about some things but they all mean the same thing. Their concerns were:

  •         Stop developing apps without previously validating if that is the real thing their end-user is in need;
  •         Generate business value from IT because of competitor’s moves;
  •         Take care of the little monsters that shadow-IT created and now they have to support;

If we look outside the box, those gentlemen were concerned about the same thing, as their next step for Digital Transformation: engage business areas;

Their initiative is great. But it’s also late. I’ve been discussing the third symptom with financial companies for the last 6 years. That’s always the same: an isolated initiative from a business area innovating creates something new that gets strength in the organization. But they are not IT. They don’t have the needed experience to support this new product/whatever else. Then the IT is called and they say: “hey, now you have to support this”. The nightmare starts. For the first two points, it looks like the company already got some good insights and is trying to actually act seriously towards DT.

What’s their scenario

Since they are looking for generating more value from their business areas, I thought quickly about some issues when I have to do something related to health, also did a very quick research (not more than 15 minutes – Forbes source) for the most common issues on health that now can be solved with IT and will to do:

  •         Disabled assistance: If there’s a disabled patient, let’s automatically call an Uber at the end of his appointment to take him home;
  •         Market knowledge: get to know what the clients are thinking. Let’s monitore social networks with AI and check if they are complaining after an appointment;
  •         Accessibility: a lot of paper is sent to people when they have to pay for health services or when they just use it. Let’s group all of that digitally;
  •         Accessibility: there isn’t a single repository that a doctor can access all of their patient’s history in diseases, symptoms and treatments. It forces people to carry a lot of paper whenever they are going to a doctor to talk about something recurring;
  •         Omni-experience (Forbes): I guess I’ve already heard that in financial and somewhere else. There it’s called omni-channel. And it’s coming to health. Here’s what it is: everytime your customer gets in touch with our brand, either online or offline, we must assure that he’ll have the best possible experience. Every point of contact matters;
  •         Health-techs: let’s connect to them. There are many coming and many more to come. Let’s send our doctor the information of our home-based check-up system. Blood pressure, weight and some other stuff can be checked remotely. And will mean a huge difference for some patients.

The same things that are very clear in financial are starting to happen in financial. The main thing is the mindset: having the will to solve the same old problems, digitally. Forbes says: it must come from above. If the health company’s sponsors don’t buy it, nobody will.

Digital transformation, Entrepeneurship, IT is business, Office, Practical examples

How did Netflix reach the nirvana of ownership?

May 15, 2018

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.

 

No rules

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

Customers, Practical examples

A good customer experience performance for sales at financial area

May 7, 2018

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.

 

The experience

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.

 

The scenario

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.

First contacts

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.

The meeting

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.

The challenges
  • 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.

First contacts

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

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.

The problems

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?

Digital transformation, IT is business, Practical examples

Using Machine Learning in real life part 2/2

April 5, 2018

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;

Digital transformation, IT is business, Practical examples

Using Machine Learning in real life part 1/2

March 30, 2018

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:

  1. 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?”;
    • Conclusions?
      • 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”;
  1. 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;
  2. 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.

Feature Google IBM Microsoft Amazon
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
Translation Translate Translator Translator Translate
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
Image recognition

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.

Digital transformation, IT is business, Practical examples

Cloud is the new black

March 15, 2018

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.

Physical space

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.

Rework rate

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.