This is part three of a three part blog series. Read part two about how predictive analytics is disrupting the finance sector and part one about how the emergence of big data has changed the way businesses make decisions.
Today with so much data coming into organizations, the majority of companies are challenged to source new insights. Increasingly our world operates in near real time, and we expect our data and our patterns to unfold at the right point in time.
Science becomes mainstream business
The discipline of advanced sciences is increasingly more relevant to business. Words like ecosystem, networks, colonization, viral, etc. are all less than 50 years old in business vernacular. We have learned that as a result of the increasing vast amounts of data, we are able to explore and analyze some of our world’s most challenging questions: how to shift the impact of climate change to reduce its impact; how to sequence DNA for replication; how to cope with the reduction in clean water supply; and how to improve farming practices to increase supply by 50 per cent.
For each of these challenges, the large surge in data volumes is shifting the world of science to be a more collaborative, open forum. This shift to “open science” is one of necessity, as data sets are too large and the problems are far too complex to be analyzed by a single discipline.
The rise of data scientists
Thomas Davenport and D.J. Patil said it right when they wrote their Harvard Business Review Article on Data Scientist: The Sexiest Job of the 21st Century.
Data scientists are the people who can understand and provide meaning to the reams of data sources both structured and unstructured: information about customers, products, and consumption patterns in order to help solve big challenges. With data coming in from so many diverse sources, the ability to see the patterns emerging to predict risk is the next holy grail in technology careers.
Businesses are trying to make sense of social data and trying to untangle over 2 billion bits of “data points” each day, notes research conducted by 140 Proof. This increase drives new skills to help do the sense making to advance businesses forward.
Between 2010 and 2020, the data scientist career path is projected to increase by 18.7 per cent, beat only by video game designers. The big data industry is expected to be a $53.4 billion industry by 2016, so organizations cannot avoid stepping up to the requirement for data intelligence (IDC 2014). Fortunately there are new degrees offer data scientist career paths, in particular Northwestern University, UC Berkeley, University of Waterloo are offering new programs in data sciences. According to a USA Today report on technology trends in the job market, if you’re deep into data analytics, you’ll be well positioned in the tech world as it moves into the “next frontier.”
At my company, SalesChoice Inc, we have invested heavily in develop a predictive analytics platform to support the demand to crunch diverse sources of data to find patterns about future(s). The initial use case we decided to solve was in large global sales data sets, as we knew there were historical patterns that could help predict futures for large multi-nationals. Sure enough after testing large global sets from leading brands, like Open Text, or Mitel, etc. we are finding that there is a natural pattern in their sales workflows that creates a unique pattern like a control chart.
By analyzing the patterns in real time, we can determine risks in sales cycles for management intervention and coaching, or help realign resourcing to focus on opportunities which have a greater likelihood of closing or help our clients focus less on priorities which are less likely to advance to success. Focus is everything in sales. This is the first time these advanced sciences have been integrated into sales practices. At this time, we are working on Salesforce.com large global data sets, as we felt this was the best entry point into the market for our advanced sciences.
We have on our core team, a chief technology officer, a chief data scientist and junior data scientists who collectively understand advanced mathematics, advanced statistics, and computing sciences (artificial intelligence and machine learning, sentiment/text mining). Bringing together these different linguistic languages from: advanced math, statistics, sciences, and computing sciences has been a real eye opener for me personally as when you are leading these brilliant scientists you realize how important the collaboration is to enable them to learn from each other.
We’re updating our organizations with newly-titled positions like chief revenue officer, replacing older terms like vice-president of sales. So why not add chief data scientist to the mix?
I do envision these types of skills will in time become a future sourcing pool for CEOs, as these types of resources will be mired in the depths of the data in their respective organizations, and will have an uncanny sense to guide their organizations through what will increasingly be murky pathways. The new business fuel is data intelligence and we’ll collect it with predictive analytics tools. Collecting knowledge from the past to learn how to improve in the future and continually mine data patterns to become stronger is the new competitive advantage. And, data scientists will be very much part of this future roadmap.
