ENLARGE YOUR IDEA

Everything begins with an eager desire [PROBLEM]. Shared with others, it expands throughout the people believing in it.

While pitching, you clarify your thoughts and share your passion for it, which, by contagion, infects others [ONE MINUTE PITCH].

GREAT TEAMMATE(S)

And because it’s always better with other people, you share energy with your teammates, gather together when it’s hard, do high-fives to relaunch after the low moments [TEAM].

You take tough decisions together and you accept them whatever they are, committed to be a great partner at any moment.

JUST DO IT

No one will tell you what to do there. You are free to stay or go, and also to say “no” too!

You are bold and do things you’ve never done [EXECUTION]. And it feels amazing.

Organizers and coaches are here to support you. They won’t tell you what or how to do things, they will just ask questions and help you reflecting. And it will be your decisions to take, and later to deal with the consequences.

Most of the time, they will just tell you to continue. Don’t worry, they keep you covered!

GET THE F*** OUT OF HERE

But the idea itself is just an idea for you, so you go out and search for people who would be ready to pay for it [CUSTOMER VALIDATION]. You don’t need to give them back your service right away, fake it until you make it they say [BUSINESS MODEL]!

You may need to change position at a moment [PIVOT]. Then, just continue!

At the very end, you wrap it up to present it to the world [FINAL PITCH].

Later, you look back and realize how much you grew, the new person you became in just a matter of hours. Inside of you something changed, and it cannot be removed. Congrats, you became an entrepreneur!

Startup Weekend Zürich, next edition on November 2nd, 2018.

Photograph by Jürg Stuker. Startup Weekend Canvas: original by Jozué Morales, translated in English and adjusted with the help of the community by Léo Davesne.

Another post about this fantastic week-end on Namics' blog.

As a reminder our key values are authenticity, accessibility and openness, so let’s put ourselves in openness-mode and have a close look into a recent Swiss blockchain project that was rewarded with the first price of the SRG /SSR Hackdaysin Zurich namely : “Blockchain Reporter” - originally conceived by Marko Nalis who teamed up with a few developers at the event and with whom he elaborated the project during the 13 hours the Hackathon took place.

“306” – in reference to the number of journalist imprisoned in Jan / Feb 2018 worldwide

Introducing his presentation with an intriguing image displaying the number “306”, Marko Nalis explained that this is the number of journalists imprisoned during the two first months of the year only. They are imprisoned in countries where free speech is not granted. Yes there are still a lot of those… ;-(

Starting from the recognition that journalists and reporters are exposed to persecution when personally identified (#1) and considering that they also suffer from a constant reduction of their financial means (#2) in a media landscape where advertising funds erode, the team came up with this cutting-edge blockchain concept : a decentralized peer-to-peer platform that enables journalists and readers to connect directly while injecting value into the ecosystem through tokenization & network effect.

There is more : through the magic touch of decentralized blockchain technology, other features are addressed too:

• anonymity of journalists,
• guarantee of safe payment,
• avoidance of censorship,
• authenticity of publications,
• prevention of fake news
• protection of the platform against arbitrary shut down by a totalitarian government (because decentralization prevents action against a unique IP server ;-).

Removal of the “middleman”

At this stage it’s already amazing to see how blockchain’s decentralization effect (i.e. removing the “middleman” – here the press agency –), simultaneously solves numerous parameters of the equation.
But let’s dive a little deeper into the functioning of the “token economy” (i.e. how publications and rewards are made possible by the community).
Nota Bene: this might become a little technical but we will try to guide you to illustrate the three elements, that are technically bound together to allow all parts to talk/interact.

Part 1 : ever heard of “Smart Contracts”?

Smart Contracts are independent, self-executing pieces of codes that are immutable, not controlled by anyone and can’t be manipulated – unless 51% on the users would own the blockchain – which is most unlikely on popular blockchains like Ethereum and Bitcoin.
Smart Contracts will fulfil some kind of “contract” a given condition is fulfilled once (it’s similar to the principle of IFTTT rules /“if this - then that” attribution rules, but more complex).
Mostly run on Ethereum which supports more opportunities than the Bitcoin blockchain (where you basically can transfer value only), these Smart Contracts don’t belong to somebody and will preserve the anonymity of journalists.

Part 2 : Linking the reference of the Smart Contract to a Public Address.

Let’s say a journalist filmed a given footage and wants to share it on the platform anonymously. Therefore he will use a dedicated android-based APP which allows uploading the content to a decentralized file-system called IPFS (=InterPlanetary File System).
IPFS uses a merkle tree architecture allowing the uploaded content to be split in chunks of data that are distributed on the network. The architecture ensures immutability and no single point of failure. (The blockchain itself not being efficient to store rich content, -due to transaction speed and cost-, it is used solely to publish a reference to the content in a specific Smart Contract).

Inside the Smart Contract the Public Address of a journalist is linked to his content and cryptographically signed. Thanks to the public address, the platform user will always be sure that the content of a given journalist is authentic - without even needing to know his real identity.

PART 3 : a “DAPP” / decentralized APP – linking the platform to Ethereum

Last the system needs a way to allow you “talking” to the Ethereum online wallet of the user/member.
A browser using HTML/ Javascript, adding Web 3 objects through a plug-in called Metamask. Web 3 enables you to make calls to the Ethereum network and call smart contract functions in Javascript. As these interactions with the blockchain will cost (as any open-source system Ethereum needs to be sustained) a small fee called “gas” charged to get the transactions through the network.
The system allows users to donate ether currency to journalists through the smart code. These donations are also used to rate the popularity of journalists : the more donations for a given article, the better is the rating.

Finally, a dedicated Ethereum framework called “Truffle” offers many tools to compile and deploy Smart Contracts and to set-up a test network. Its language is called “Solidity” and can be applied by any developer mastering Javascript, - in theory (although this quickly gets “a little complicated” as Marko Nalis says with a touch of understatement ;-)

In a nutshell – what’s in it for me?

So beyond the technical aspect of this analysis that will rather interest our Full Stack Developers, what innovation brings this “Blockchain Reporter” project?
Blockchain Reporter appears as a real-life use-case designing concrete and applicable value-transferring models in an interesting vertical (the press industry).

It was thrilling and uplifting to see how every parameter of the complex equation inherited its own place in the ecosystem organically (the donation/ranking logic, the token economy, the protection of the journalist). This is definitely a lesson for Liipers as this can help us design or deploy further models in other verticals.
It demonstrated that blockchain technology is not only meant for worldwide corporate companies, but it helps defend human values such a free speech and liberty.

We’re wishing long life and great success to the project “Blockchain Reporter”!

Here is the full LiipTalk from Marko Nalis : youtube.com/watch?v=P2CyfuC5EKA

For further interest about these topics of attribution and valutation of content in the medias, here is an interesting interview from a former journalist from Huffington Post who joined the startup Po.et aiming to create the first globally-verifiable record of digital media assets: https://bit.ly/2FEw2yh

We have the ambition to create a tool that provides micro-learning to train cognitive biaises. Today we have a prototype. Last spring, we had only a vision to lead us. As told in a previous post, one of my colleague detected a need in an industry and an opportunity for us to create a new tool. He gathered a small team and invited us for a kickoff meeting. We were all motivated. How could we proceed?

During the kickoff, we jolted ideas around, and used sticky notes to draw the project. It was important that we all had a common understanding of the tool we wanted to create. This kickoff meeting was also the moment when we created a team spirit and built personal commitment.

Ownership, responsibility and role

As motivated as I was to play my part, I needed to understand how I could contribute to the project and how much time it would involve. We started by writing down the outputs we expected from the meeting. The expectations were various.

Our expectations for the kickoff meeting

Kevin expected us to take ownership. The initial idea came to him and he wrote a paper about it. He expected us to work as a team and take ownership. This is what he means by ‘Co-sign Whitepaper'.

To me ownership meant responsibility. The moment I commit to a project means that I stop saying ‘Kevin's idea' or ‘Kevin decided' or ‘Kevin meant'. I start saying ‘we think', ‘we decided'. It also means that I committed myself to play my part, make time to work on the project.

I needed to understand, the role that I would play, in other words how, with my competences I would contribute to the project. This is expressed as ‘Where do I position myself?' From the beginning we are a multidisciplinary team. We have learnt to contribute with our respective skills. Understanding my role leads to better planning. If I understand my tasks and how I relate to the other team members, I can organise my agenda and be available when I am needed.

During this meeting we also decided how we would communicate about the project to our stakeholders', which at this point, were internal. We finally defined the next steps and decided the content of the next workshop.

Map the idea – understanding with drawing

We were sitting down, listening to Kevin. Sitting around a table is so limiting! Ideas cannot express themselves, they keep eluding and the energy slowly runs low. We couldn't see what Kevin was explaining. After a moment of deep concentration, I tend to relax a bit, which means that I am not being this concentrated. At some point, we were all running low on energy. Thus we started drawing.

White walls are a blessing. Someone starts drawing and you can add up your idea, then everyone can see and add his/hers.

It started with a sketch, and step by step it became like a map. A map of the idea, where we could navigate, see the stakeholders, start apprehending who we needed to talk to, what we needed to understand, what remains unclear, what is our role, our strengths and weaknesses.

Let your team take ownership by drawing together the idea.

It very much looks like this: drawing, talking and gesturing. When you stand, the flow of ideas wraps you up and before you realize it, you are ‘in it', you take ownership and you belong. You stand and draw together. It has nothing to do with sitting and looking at someone talking, you are part of it.

Our drawing got more complex while our understanding of the situation got clearer.

Be kind to your blue side and deal with uncertainties

Have you ever heard of the DISC assessment? That test attributes colors to people after a test. I never took it myself, but I often heard some friends refer jokingly to it. When they refer to the ‘blue colleague', they talk about his preciseness, attention to detail and his capacity to be systematic. As I started this project, I realised that part of me, that I will call my ‘blue side' backed off, because it was unconvinced. My blue part tends to refrain the overly enthusiastic and risky part (I don't know the color of this side yet ;-)

In other words, during this meeting, my blue side realized that there is a huge part of unknown in this project. When you start an innovation project, you have to be aware of the fact that some uncertainty and risk will always be present. During my studies and work life, I have been trained to try to avoid mistakes and evaluate risk. I usually try to have a fairly good idea of the success I expect  from my actions before I perform them. Starting an innovation process is the contrary of this. It is jumping in the unknown and imagining something that does not exist… yet. You need to be open-minded and accept the risk and unknown.

To conclude: we mapped the project and I accepted the probability to fail

It was time for me to accept that mistakes are part of the game and to come to terms with the probability of failing. An innovation process is made of ups and downs, test, success, mistakes and iteration. The risk is part of the game.

During this first meeting, we mapped the project and the stakeholders It gave us the necessary common grounds to start working together. To draw the project allowed us to clearly see the expertise we needed. We planned the next steps and organized the first workshop where we would invite other experts. The project had officially started.

How to be open to innovation?

You have to be open to new challenges, which is difficult even close to impossible if you are stressed out or under tight deadlines for example. During my first year at Liip (2016), I undertook many projects that had started before I had arrived. As a result, I had little time for planning or strategies, I undertook what was already started. During this first year, everything was new, I was in the turmoil of an event, or in a middle of a project, my whole energy was focused on current tasks.

Before Christmas 2016, my knowledge of the enterprise and the field had exponentially expanded. It allowed me to grasp the necessary bigger picture of my enterprise's needs and challenges. Simultaneously, many projects came to an end, as a result, I was not under tight deadlines. In other words, I was open to new challenges and ideas. I had cognitive capacity to take on new challenges. When Kevin, a colleague I barely knew, approached me, I welcomed his project with an open mind.

Sorry, we have no time to innovate!

An idea that takes 30 minutes to explain but a clear call to action

I do not recall the exact situation of our first actual discussion. We were probably in the open space we call arena. He most probably caught me on my way to the cafeteria.

Note to self: grab people when on their way to the cafeteria, when they are neither in a hurry for a meeting, nor in deep concentration.

Sorry Kevin, the first time you explained I did not understand your idea. It took about 30 minutes of explanation for me to understand that it was about learning, innovation, banking and compliance. It probably did not help that I had no prior knowledge of these fields.

At this point, what Kevin expected was clear ‘come to a workshop'. As I had no urgent deadlines at the time, I accepted. I assumed that I would understand the idea at the some point.

The importance of the team and the setting

At Liip, we have the possibility to undertake innovation projects when we see a business opportunity. Nothing forces us to join or undertake one. I could have decided to perform other tasks that I saw more fitting

Why did I accept to join the workshop? Three important factors simultaneously played a role there:

• Firstly, I had an open mind, I was cognitively open to something new.
• The second thing that convinced me then was Kevin's enthusiasm: he had a vision and he convinced me. I saw potential in this project.
• Thirdly, I felt valued and trusted that my competences were needed. Honestly, this is flattering and energizing. Who is not appealed to have the possibility to make an impact?

Additionnally, I also not only got along, but liked the other colleagues invited to the workshop. In such an innovation process, trust and kindness are necessary. The team is meant to go through uncertainties. Though the vision is clear, the way to reach it is not. The composition of the team is important and this fact should not be underestimated.

Note to self: notice the importance of a vision in the very first step of an innovation project. Someone has to have an idea, and has to be able to share this vision with others to onboard them.

The next steps: the initial workshop

As planned by Kevin, the next step was to organise a workshop, where we would meet and test the idea. I expect it was a vulnerable moment for him. As long as you think something for yourself and plan it in your head, it is all fine. The day you open your mouth, it is for the worse or the better. After this workshop we could have all backed off and turned to other projects. It was a turning point.

To conclude, you guessed already?

It turned out well for Kevin's idea, in the sense that we shared his enthusiasm and we saw business potential in his idea. In other words, the story was just starting. At this very early stage of the innovation process, my ability to be open-minded to something completely new and the fact that we saw business potential in the idea mattered most . The fact that Liip provides an environnement prone to innovation is also highly relevant. As a team, we know, we are supported to dedicate time to investigate new opportunities.

And yes, I take notes to myself about what a leader with a vision is, because I think everybody is someone's leader and someone's follower. It is neither good, nor bad, it is just a role, where you have to play your best part. I am learning good practices to be both.

But when we sat down recently with our team, I realized that we indeed use a myriad of different tools, frameworks, and SaaS solutions. I thought it would be useful to organize them in a meaningful data stack. I have not only included the tools we are using, but I sat down and started researching. It turned out into an extensive list aka. the data stack PDF.  This poster will:

• provide an overview of solutions available in the 5 layers (Sources, Processing, Storage, Analysis, Visualization)
• offer you a way to discover new tools and
• offer orientation in a very densely populated area

So without further ado, here is my data stack overview Click to open PDF. Feel free to share it with your friends too.

Liip data stack version 1.0

Click here to get notified by email when I release version 2.0 of the data stack.

Let me lay out some of the questions that guided me in researching each area and throw in my 5 cents while researching each one of them:

• Data Sources: Where does our data usually come from? For us, it's websites with sophisticated event tracking. But for some projects the data has to be scraped, comes from social media outlets or comes from IoT devices.
• Data Processing: How can we initially clean or transform the data? How and where can we store the logs that those events create? Also from where do we also take additional valuable data?
• Database: What options are out there to store the data? How can we search through it? How can we access big data sources efficiently?
• Analysis: Which stats packages are available to analyze the data? Which frameworks are out there to do machine learning, deep learning, computer vision, natural language processing?
• Visualization, Dashboards, and Applications: What happens with the results? What options do we have to visually communicate them? How do we turn those visualizations into dashboards or whole applications? Which additional ways of communicating with the user beside reports/emails are out there?
• Business Intelligence:

What solutions are out there that try to integrate the data sourcing, data storage, analysis and visualization in one package? What solutions BI solutions are out there for big data? Are there platforms/solutions that offer more of a flexible data-scientist approach?

My observations when compiling the list:

Data Sources

• For scrapers, there are actually quite a lot of open source projects out there that work really well, probably because those are used mostly by developers.
• While there is quite a few software as a service solutions with slightly different focus, capturing website data in most cases is done via google analytics, although Piwik offers a nice on-premise alternative.
• We have been experimenting quite a bit with IoT devices and analytics, and it turns out that there seems to be quite a few integrated data-collection and analysis software as a service solutions out there, although you are always able to to use your own (see later) solutions.
• For social media data, the data comes either from the platforms themselves via an API (which is probably the default for most projects), but there are some convenient data providers out there that allow you to ingest social media data across all platforms.

Data Processing

• While there are excellent open source logging services like graylog or logstash, it can sometimes save a lot of time to use those pricey saas solutions because people have solved all the quirks and tiny problems that open source solutions sometimes have.
• While there are some quite old and mature open source solutions (e.g. RabbitMQ or Kafka) in the message queues or streams category, it turned out that there a lot of new open source  stream analytics solutions (Impala, Flink or Flume) in the market and almost all of the big four (Microsoft, Google, Facebook, Amazon) offer their own approaches.
• The data cleansing or transformation category is quite a mixed bag. While on one hand there are a number of very mature industry standard solutions (e.g. Talend), there are also alternatives for end users that allow them simply to clean their data without any programming knowledge (e.g. Trifacta or Open Refine)

Databases

• Databases: If you haven't followed the development in the databases area closely like me, you might think that solutions will fall either in the SQL (e.g. MySQL) or the NoSQL (e.g. MongoDB) bucket. But apparently a LOT has been going on here, probably among the most notable are the graph based databases (e.g. Neo4J) and the Column Oriented databases (e.g. Hana or Monet DB) that offer a much better performance for BI tasks. There are also some recent experimental highly promising solutions like databases in the GPU (e.g. Mapd) or ones that only sample (e.g. BlinkDB) the whole dataset.
• The distributed big data ecosystem: It is mostly populated by mature projects from the Apache foundation that integrate quite well in the Hadoop ecosystem. Worth mentioning are of course the distributed machine learning solutions for large scale processing like Spark or Mahout, that are really handy. There are also a lot of mature options like Cloudera or Hortonworks that offer out of the box integrations.
• In Memory Databases or Search: Of course you the first thing that comes to mind is elastic(search) that proved over the years to be a reliable solution. Overall the area is populated by quite a lot of stable open source projects (e.g. Lucene or Solr) while on the other hand, you can now directly tap into search as a service (e.g. AzureSearch or Cloudsearch) from the major vendors. The most interesting projects I will try follow are the fastest in-memory database Exasol and its “competitor” VoltDB.

Analysis / ML Frameworks

• Deep Learning Frameworks: Obviously, on one hand, you will find the kind of low-level frameworks like Tensorflow, Torch, and Theano in there. But on the other hand, there are also high-level alternatives that build up upon those like TFlearn (that has been integrated into Tensorflow now) or Keras, which allow you to make progress faster with less coding but also without being able to control all the details. Finally, there are also alternatives to hosting these solutions yourself, in services like the Google ML platform.
• Statistic software packages: While maybe a long time ago you could only choose from commercial solutions like SPSS, Matlab or SAS, nowadays there is really a myriad of open source solutions out there. Whole ecosystems have developed around those languages (python, R, Julia etc.). But also even without programming, you can analyze data quite efficiently with tools like Rapidminer, Orange or Rattle. For me, nothing beats the combination of pandas and an ipython notebook.
• General ML libraries: I put the focus here on mainly the python ecosystem, although the other ones are probably as diverse as this one. With scipy, numpy or scikit-learn we've got a one-stop shop for all your ML needs, but nowadays there are also libraries that take care of the hyperparameter optimization (e.g. REP) or model selection (AutoML). So again here you can also choose your level of immersion yourself.
• Computer vision: While you will find a lot of open source libraries that rely on OpenCV somehow a myriad of awesome SaaS solutions (e.g. Google CV, Microsoft CV) from big vendors have popped up in the last years. These will probably beat everything you might hastily build over the weekend but are going to cost you a bit. The deep learning movement has really made computer vision, object detection etc.. really accessible for anyone.
• Natural language processing: Here I noticed a similar movement. We used NLP libraries to process social media data (e.g. sentimentanalysis) and found that there are really great open source projects or libraries out there. While there are various options for text processing (e.g. natural for node.js, or nltk for python or coreNLP from Stanford), it is deep learning and the SaaS products built upon it that have really made natural language processing available for anyone. I am very impressed with the results of these tools, although I doubt that we will come anywhere close in the next years to computers really understanding us. After all its the holy grail of AI.

Dashboards / Visualization

• Visualization: I was really surprised how many js libraries are out there, that allow you to do the fanciest data visualizations in the browser. I mean its great to have those solid libraries like ggplot or matplotlib, or the fancy ones like bokeh or seaborn but if you want to communicate your results to the user in a periodic way, you will need to go through the mobile / browser. I guess we have to thank the strong D3 community for the great developments in this area, but also there are a lot of awesome: SaaS and open source solutions that go way beyond just visualization like Shiny for R or Redash that feel more like a business intelligence solution.
• Dashboards: I am personally a big fan of dashing.io because it is simply free and it's in ruby, but plotly has really surprised me as a very useful tool to just create a dashboard without a hassle. There is a myriad of SaaS solutions out there that I stumbled upon when researching this field, which I will have to try. I am not sure if they will all hold up to the shiny expectations, that those websites sell.
• Bot Frameworks: Although I think of bots or agents more of a way of interacting with a user, I have put them into the visualization area because they didn't fit in anywhere else. P-Brain.ai and Wit.ai or botpress turn out to be a really fast way to get started here when you just want to build a (slack)-bot. I am however not sure if chatbots will be able to deliver the right results, given the hype with those.

Business Intelligence

• Business Intelligence: I thought I knew more or less the alternatives that are out there. But having researched a bit, boy was I surprised to find how much is actually out there. Basically, every vendor of the big four has a very mature solution out there. Yet I found it really hard to distinguish between the different SaaS solutions out there, maybe it's because of the marketing talk, or maybe because they just all do the same thing. It's interesting to compare how potentially business intelligence solutions are offering the capabilities of the before mentioned data stack, but given the variety of different solutions in each layer, I think more and more people will be tempted to pick and chose instead of buying the expensive all in one solution. There are however open source alternatives, of which some feel quite mature (e.g. Kibana or Metabase) while others are quite small but really useful (e.g. Blazer). Also don't judge me too hard, if I put Tableau in there, some may say it's just a visualization tool, others perceive it as a BI solution – I think the boundaries are really blurry in this terrain.
• BI on Hadoop: I had to introduce this category because I discovered that a lot of solutions are particularly tailored to working on the Hadoop stack. It's great to see that there are options out there and I am eager to explore this terrain in the future.
• Data Science Platforms: What I noticed too is that somehow that data scientists are becoming a target group of integrated business intelligence solutions or data science platforms. I had some experience with BigML and Snowplow before, but it turns out that there is a lot of different platforms popping up, that might make your life much easier. For example, when it comes to deploying your models (like Yhat) or having a totally automated way of learning models (e.g. Datarobot). I am really excited to see what things will pop up here in the future.

What I realized that this task of creating an overview of the different tools and solutions in the data-centric area will never be complete. Even when writing this blog post I had to add 14 more tools to the list. And I am aware of the fact that I might have missed some major tools out there, simply because it's hard to be unbiased when researching.

That is why I created a little email list that you can sign up to, and I will send you the updated version of this stack somewhere this year. So sign up to stay up to date (I promise I will not spam you) and write me a comment to let me know of new solutions or to let me know in the comments how you would have segmented this field or what your favorite tools are.

Click here to get notified by email when I release version 2.0 of the data stack.

Google at Work for email, file exchange and live streams

We are not entirely happy with Google but overall it works quite ok. Some issues we run into are related to how Google Drives deals with changes in large binary files which has resulted in our UX team getting Dropbox. We also frequently run into issues where sharing policies are set too tightly by default when people are sick or on vacation. Finally Google Hangout overall works ok-ish but we preferred a hardware solution for our meeting rooms and the Google Chrome for Meeting device UI is too limited (f.e. it doesn't integrate with Hangout Live for recording and its quite cumbersome to join calls in other organizations). For this reason we adopted Highfive last year and we are anxiously awaiting their 2.0 release which should provide Linux support and live streaming and recording. Hangouts are therefore only used for our weekly Liiptalks that are streamed to all locations and recorded on Youtube.

Slack for constant exchange information during the day

Slack was chosen via company-wide vote after previously having used Skype. We also tested HipChat and a few others at the time and have taken a look at Mattermost (which might deserve another look). Overall Slack is probably the tool that gets used the most and sees the least criticism and has killed off probably around 80% of emails. To some degree Slack is now also yet another way to exchange files though usually with a more temporary character. That being said Slack search is amazing. The amount of integrations rises continuously and we now have a few bots of our own. For example we have one that integrates with Google Calendar to send a message every morning who in the team is available on that day. Actually this is provided via Presence a open source simple tool we created that allows to group Google accounts into teams. We have another one which is a slack channel recommender, since we have almost 900 channels.

Zebra our custom time tracking solution and data hub various SaaS tools

Zebra is our time tracking tool and much more. Unfortunately its not open source as it build around Liip specifics. Basically all hours are entered there. But there are tools in there for product owners to track time spend on specific tickets (via an integration with Jira), for teams to see who they are impacting company-wide finances (you can see an explanation about them in a talk Tonio and I held last year at DrupalCon), to track which tools are used in which project and who has knowledge in these tools and how much they like them. There is also a tool called Taxi which allows submitting time sheets via the command line. We have integrated Zebra with Run my Accounts for billing and Pipedrive for our CRM needs and we might eventually integrate it with   10000ft for rough capacity planning (especially across team, which we try to minimize but sometimes you still need to load balance between teams). Overall this homegrown solution is a vital tool for sales, scrum masters and product owners and is in many respects the data hub that integrates our various tools.

Jira ticketing and Confluence as our knowledge database and contract generator

Jira is our authentication source for all of our Atlassian products. Otherwise our use of Jira is pretty obvious, not much more to say there. We are moving more and more topics related to other things than projects away from Jira (leads are now in Pipedrive and our recruiting process is in the process of  moving to Greenhouse).  Confluence is our key resource for knowledge sharing though with Google Drive and Dropbox things have become more fuzzy. Generally if we do place content outside of the wiki we try and link to it so that it can be found. We have considered adding plugins to the wiki search to span across tools but so far this has not been done. We use to generate all our offers and contracts via a custom integration with Prince XML. We also make use of lots of different plugins that let us pull data from different sources. We actually have 2 separate instances: one for internal use, where we are making increasing use of spaces to separate out the content to make it more searchable (we are also experimenting with the archive plugin to help us get rid of old content) and another one for client projects, where each project gets a separate space.

Gitlab, Gitlab CI and Github for version control and CI

Gitlab and Gitlab CI have become our default tools for git hosting and CI. We still have lots of projects on Github, mostly when customer developers join on the development or if we want to use a specific SaaS tool (like Travis-CI). In most of our projects we use vagrant (increasingly also docker) and with Gitlab CI we can use the same vagrant box for the CI. It runs amazingly smooth and feature set increases steadily.

Holaspirit to manage our holacracy process

We briefly tested Glassfrog but found the UX and feature progress much better on Holaspirit. As such it is becoming kind of mandatory but in theory one could get by without it. We have only been doing holacracy for less than a year now but steadily we will integrate Holaspirit via its API more and more into our internal tools. Like for example we might allow lead links to define time budgets in Holaspirit which then automatically create projects and activities in Zebra.

Closing notes

This was just a quick overview of the most used tools at Liip. The rate of adoption of new SaaS tools is quite high and so is the monthly bill. That being said the costs is still a tiny fraction compared to salaries paid. So generally as a Platform Gardener if someone says a tool will make them more productive, I whip out the credit card and get it for them. If there is interest we can follow up talking a bit more about specific tools. Especially our UX and Analytics teams are using dozens of tools for their specific uses and of course our developers too. Generally for a startup I think SaaS tools are a great way to quickly get functionality without distracting too many resources with setup and maintenance on things which are not the secret sauce.