Where is data?
The world is becoming increasingly quantifiable and data is now so cheap to gather, that the relevance of continuously storing it is relevance is often questioned. Data is collected from social media, health records, hardware logs, ubiquitous sensors, scattered spreadsheets, repositories and online pages like the one you’re currently reading. 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.
Can useful things be transformed with data?
This abundance of data has made it possible for scientific principles to be creatively applied when tools and processes are designed in areas where the complexities and expenses of data collection once frustrated scientific approaches. And from all indications, it looks like this is just the beginning; quantitative methods of data collection can significantly improve our governments, businesses, NGOs and society as a whole.
How difficult is it to identify problems and their solutions?
Any data project has two major concerns: knowing how to address problems with data and knowing how to set or achieve objectives in the right way. A lot of data projects turn sour because of one or both of these concerns.
Bring on that challenge!
We are known for best practices.
Although we’re not among the first in the data science industry, we dare to boldly claim that our approach to data projects is relatively more effective.
We tailor for you
Anyone promising to give you off-the-shelf solutions for sensitive data science projects is most likely deceiving you because such don’t exist at the moment and may never. Our idea of improving organizations, people and society at large involves tailoring specific solutions to address specific needs.
A good data science team is always developing valuable services in response to market demands. This is one thing we encourage our teams to do regularly and in addition, they proactively let go of obsolete services at intervals. Our teams comprise professionals from diverse backgrounds and this is an advantage because from their various perspectives, we can better understand and tackle problems that our clients may have. In comparison with homogeneous teams, diverse teams are generally better at handling creative tasks and making decisions.
We are efficient
One thing we have come to realize is the fact that problems are always changing form. So it is common to find ourselves in situations where it seems like we have lost focus because some problems we’re trying to solve for clients give birth to more problems. Instead of retreating at such times, we hang on and brainstorm to develop the most befitting solutions as quickly as possible.
Awareness pays off
It’s good to be smart. But smart people must continually review their knowledge and be aware of trending topics around them. Otherwise, they may not stand a chance against their colleagues, competitors, or clients who may not even be half as smart.
The importance of effective communication
The results of data science projects should not be difficult to understand, and it is the responsibility of data scientists to ensure this by breaking down design tools and results into fractions that others can relate with.
An alternative to commercial apps
Open source applications are collaboratively developed, and the codes are made public so that anyone that’s interested can learn, ask questions and contribute. This model is also applicable in science and engineering, allowing people to share ideas and enhance projects originally developed by other people. One of the advantages of open source collaborations is that it encourages strangers to virtually exchange and review ideas.