In the simplest terms, Data Science is the transformation of Data into actionable insights. Raw data must be processed and transformed to produce information. That information is then analyzed to derive knowledge. The end goal of the exercise is for the knowledge to drive action.
Although using data effectively to drive decision-making is critical for competitiveness, too few organizations do it well. In a recent study published by The Economist, only 10% of leaders self‐identified their decision‐making style as primarily intuitive. In contrast, a study published by Gartner reports that through 2020,
“More than 95% of business leaders will continue to make decisions using intuition, instead of probability distributions, and will significantly underestimate risks as a result.”
Why the disconnect? Being truly data‐driven is a complete paradigm shift for companies that were formed prior to the public release of the WWW in the 90’s. Data‐driven does not mean informing ourselves with information and then making the decision based on our intuition. To be truly data‐driven, we must step out of the way and allow the data through algorithms to make routine decisions. For more complex decisions, we must seek insights from data and weigh the results of data analysis heavily in decision‐making.
There is huge competitive advantage available to those who can leverage data to adapt products and services to better meet customer needs, optimize operations and identify new sources of revenue.
Data Science focuses on future outcomes, decision making and the reduction of human intervention in the process. Traditional analytics describes what happened in the past and diagnoses why it happened. Data Science continues this process organically; analyzing data to predict what will happen next and prescribe what action should be taken.
The emerging field of data science is the intersection of social science and statistics, information and computer science, and design. Many of the tech‐centric breakthroughs we consider commonplace today began as little more than a heap of unstructured data.
- Amazon's recommendation engine suggests items for you to buy, determined by their algorithms.
- Netflix recommends movies for you to watch, established by their algorithms.
- Spotify recommends music you will enjoy, defined by their algorithms.
- Gmail's spam filter is the product of data – an algorithm behind the scenes which processes incoming mail and determines if a message is junk or not.
- Computer vision used for self‐driving cars is also a data product – machine learning algorithms recognize traffic lights, other cars, pedestrians, and obstacles on the road.
What should Data Leaders do?
Being truly data‐driven is a complete culture shift for many companies. Data Literacy is fundamental for everyone in the global data age. Therefore, similar to the way in which Jack Welch made Quality the job of everyone at GE, a key element of successful digital transformation is making data literacy the job of everyone.
Data Science is really a Team Sport; a cross-functional, collaborative effort. Data leaders should form data science teams to address specific business problems by focusing on the skills required for the project. This type of collaboration helps to ensure organizational success and exponentially increases the odds of ground breaking innovation.
The lowest cost of entry and the quickest path to success will be reserved for those who can successfully inventory employee skills and leverage that intelligence to form high functioning Data Science Teams. Job roles do not tell the whole story. Many employees have hidden aptitude and propensity that must be discovered.