We improve business and society through science and design.
Data is everywhere.
From hardware logs to health records, social media to ubiquitous sensors, scattered spreadsheets to big databases to the very page you are reading now, more of our world is quantified or quantifiable than ever before. It has become so cheap to collect and store data that the question of whether it is "worth it" to store the information has become an afterthought. In 2013, research group IMC estimated that there are over 1.45 trillion gigabytes of data stored worldwide, and that number will more than quadruple by 2020.
It's possible to do transformative, useful things with data
With all this data, something amazing is happening. It's now possible to creatively apply scientific principles to design tools and processes in areas where scientific approaches have been infeasible due to the difficulty or expense of collecting data. We think this is just the beginning. We're in the midst of a Cambrian explosion of problems where a scientific approach, until now, has been too difficult, too costly, or too complex. Data and quantitative methods have the power to improve our businesses, NGOs, governments, and society as a whole.
It's really hard to identify and do these things.
There are two huge jumps to make on any data project: the gap between having a problem and knowing how you could address it with data, and the gap between knowing what you want to do and knowing how to do it the right way. Most data projects whiff on either the first one or the second.
Datascope was founded in 2009 to address this.
There's a right way to do it.
We're certainly not the first people to have these realizations, but we think our approach sets us apart.
Tailored beats one-size-fits-all.
If you've got a data science opportunity that fits the description above, off-the-shelf solutions probably don't exist yet. Depending on your problem and how common it is, they may never exist. We believe that the way to improve people, organizations, and even society is by tailoring an approach to the specific needs, culture, and goals of the problem at hand.
A team with diverse thoughts and backgrounds beats a homogeneous one.
A data science team must be able to imagine a wide array of potential solutions and continually make decisions about which ones to pursue and which ones to throw out. That's why, for us, building a heterogeneous team is as much a pragmatic decision as anything else; research has shown that diverse teams consistently outperform homogeneous ones at creative tasks and decision making. Having a team with many backgrounds allows us to understand a broader set of client problems and come up with better solutions to them.
Fast and iterative beats big and over-planned
We recognize that from the outset we often don't know exactly what problem to solve, and that in the process of solving it the problem will change. Without a willingness to periodically consider ourselves ignorant, reflect on the problem, and adapt by building something new, it's easy to struggle with deciding what to solve, or worse, blindly sink time and resources into things that don't matter.
Skeptical beats dogmatic
Smart people tend to think they know a lot, rightfully so. Smarter people continually re-evaluate what they know, and keep their ears open to what others are saying, whether those people are their colleagues, competitors, or clients.
Concise and user-centered beats jargon-filled and haphazard
Data science results can be complex and nuanced, but it should not require data science skill to understand them. We believe it is always the responsibility of the data scientist to communicate results and design tools in ways that are meaningful to others.
Open-source beats proprietary
Open source is a proven way of collaborating to create the best software. It gives everyone the freedom to see the code, learn from it, ask questions, and build on it. Science and engineering, like open source, thrive in an open environment where people share ideas and build on the work of others. Novel ideas come from connecting and remixing ideas that came before. Open source makes it happen faster and more reliably.
Mike beats Dean
This is just a generally accepted fact.
We can't do it alone.
We believe the world will be a better place with more data scientists and better trained ones, whether they work for us or somewhere else. Datascope takes active steps towards helping the data science community grow by:
- organizing and hosting the Chicago Data Science Meetup, the Southeast Michigan Data Science Meetup, and the Madison Data Science Meetup
- donating our time to data events like DataKind, Data Science for Social Good, the Civic Consulting Alliance, or NUvention Analytics
- developing and maintaining open source packages like textract or catcorr.js and actively contributing to many more.
- teaching and giving presentations at universities and conferences.