A New Year means new beginnings and self-improvement – and the inevitable New Year’s resolutions that follow. The analytics on the top New Year’s resolution for 2015/16 were:
- Lose weight
- Get organized
- Spend less, save more
- Enjoy life to the fullest
- Stay fit and healthy
- Learn something exciting
- Quit smoking
- Help others achieve their dreams
- Fall in love
- Spend more time with family
According to a recent Ipsos poll only three in 10 Canadians will set a New Year’s resolution, and of those, 73 per cent eventually break them. Would analytic insights such as these make people move away from making any resolution?
While it would be interesting to explore the data analytics of people that keep their resolutions and people that don’t, I am curious to know how much of a leap companies take in the age of analytics.
McKinsey reported that since their analysis in 2011, only a fraction of the potential value from data and analytics was captured to date. The most progress has occurred in location-based services and in retail. However, areas like manufacturing, the public sector, and health care have captured less than 30 per cent of the potential value highlighted five years ago.
Even the analytics trends suggested that universities and colleges cannot keep up with the business demands. International Data Corporation (IDC) predicts a need for 181,000 people with deep analytical skills in the US by 2018 and a requirement for five times that number of positions with data management and interpretation capabilities.
Currently, companies are facing challenges in organizing themselves on the value of analytics. It is one thing to develop a proof of concept however, trying to be insight driven as an organization takes a cultural shift to achieve that. In order to extract value out of data and analytics and incorporate data-driven insights into day-to-day business processes, companies have to look at strategy, people, process, data and technology as a whole.
The other common challenge that companies continue to face is attracting and retaining the right talent; it is not about hiring only data scientists but having those enablers who can translate the needs of the business for data and analytical systems into high-level designs for those systems.