One of the most important realizations in the last two years has been the power of building a problem focussed company. Whenever the right data, technology, and humans came together, we believed that we can solve any world problem in any domain. That's a great place to be in as a team because it reflects both the ability and ambition we have as a team to solve these challenges. Some of the clear learnings have been on how making data easy to access and consume helps leaders make informed decisions. We have realized how data in itself has no value. Insights and knowledge derived from datasets are the real assets. Knowledge derived from internal and external data can help change how some of these organizations have been functioning for decades. We have together learned and unlearned so much as a team as we set out to solve some of these challenges.

Despite the best of the tools and technologies available, data preparation is still a challenge for so many organizations. Discovering the right data in a cleaned and standardized format is an aspiration for most of the teams. Another important learning has been around people working on data. We have realized that if data teams have to solve business problems effectively, the right mix of skills and approach is required. While a data scientist might be working on the great model, someone needs to research the domain in depth. Size and high velocity of datasets need a person to think of a system and scale approach. One needs to solve for reducing the complexity of the visualization of the data so that more people within the organization are able to derive benefits. Equally important is the person who works with business to help translate the solution back to the relevant audience. Whatever names you might give these different roles, they all have different DNA and approach to their work. This is very similar to how product teams need the right mix of skills and approach to come together to solve a need for the user. Though the major difference between data and product teams as of today is that while products teams over a period of time have built their own vocabulary, tools, and own unique verbal communication language which helps them communicate effectively and efficiently, data teams of the world are still finding the right definitions and terminology to use which makes communication extremely difficult especially when you have people from diverse background coming together. Data teams are still exploring the best way to collaborate and share knowledge with one another.

Trust is another important element as questions like protection of data, access levels, ethical use of personal information and encryption are not part of the daily discussions for most of the teams unless the organization has a GDPR forcing them to do the same. If we believe that in the coming future data will help empower organizations, one emotion to solve for is 'trust' - for everyone people contributing data, working on data, using data and getting benefits because of data.

Over the course of next 12 months - we will be working on some of these problems -  helping data teams collaborate in effective and efficient manner, retaining knowledge as organizations work with data and making trust an important emotion of the data teams. These problems are important not just for our team but also for organizations around the world who are looking to build their data teams and have started seeing the value in them. Empowering their data teams will be critical for companies of today's generation so that can keep up with the pace of ever-changing landscape and disruption across industries. How product thinking has become an important part of companies in order to scale - in the online and connected world - data will be a key driver for innovation of tomorrow and data thinking will have to become the new culture. Building agile and efficient data teams will become moats for business. As a company, we have always believed in empowering others - from our customers to our own fellow team members - this will give us an opportunity to empower teams across the world and industries to confront some of their biggest challenges.

What are some of the challenges we see upcoming - the one major challenge we see is customers trusting us with their data (Trust is my new favourite word after chaos). We will have to together find unique ways of solving this - and it will go beyond a feature or engineering solution. Being attached to the most mundane, non-sexy, boring tasks of data teams will help us to create workflows that were never imagined before.

If we look internally - one major challenge will be delivering a world-class product experience. All the teams need to raise their quality bar for every pixel, word, code, emotion, thought, critic. It will need some of us to get out of our comfort zone and bring our best in us and some of us to go back to the drawing board to start from scratch. If you are thinking about possible competition as one of the challenges, don't overthink. In general, it's important to know about them and learn from them to ensure that we are not duplicating something already solved. But don't fear the world as long as you are clear on the value-add you provide to your customers.

In terms of execution, the first phase for us will help everyone understand the vision beyond words - when you are working in the unknown of tomorrow - the best way to translate your thoughts is to go beyond words. We will be creating a visual flow for the experience of the product. We will also be talking to a bunch of data teams across the world and individuals who have led data teams to just listen and hear from them - it's very important for us to not create our own bubble around the solution. We might discover a new approach to the same problems within these teams or it will help us prioritize the problems based on complexity, frequency, value-add or any other parameter.

The second stage for us will be converting that visual flow into a full-fledged alpha product with some workflows which will be enough to get us into some organizations. This will be polished with full product experience but with the most critical user stories which we will discover during our interviews. We might have to leave out and be very harsh on killing some of the most exciting and innovative user stories of the product early on in order to get good alpha customers who see a clear and distinct value-add.