‘Data Science’ was more an esoteric buzzword than an organizational necessity, leaders of fledging Data Sciences and analytics divisions focused one key goal – ‘growth’. Today, with increasing demand of data scientists across sectors and the explosive expected growth in its software and services, the same leaders have a new goal – how to drive value and impact while maximizing efficiency. Data Scientists extract value out of the massive quantities of meaningless data that we generate every day, and competition for their skills is fierce. Job postings for data scientists ballooned by more than 15,000 percent between 2011 and 2012, and entry-level salaries start at $11000 to $120,000.
As the CXO crowns and goals keep shifting and companies keep looking for Data Sciences to drive differentiation, the need for Data Science leadership is also changing. This is fast becoming the ultimate way to empower CEOs and boards to drive the innovation agenda. There is also a growing realization that the information era is leaving traditional decision making methods, and plain data visualization techniques behind to adopt a deeper look, not only at what was, but what will be. In the next few years organizations will have at least one executive in their team, if not the CEO, specializing in Data Sciences.
We are seeing a new generation CEO, who no longer relies on a hunch or gut feel to determine the future direction of their organization. It’s just all too often in data science that you see intuition, anecdote, and feeling get turned on its head by actual data, just like it was intuitive to think that the world is flat, but that intuition is dead wrong. The data-driven CEO uses numerous sources of data to make decisions with precision, which is now essential in being able to report to the board and ultimately shareholders.
Founders and execs of data companies must look employ capability from three domains: unique subject matter expertise, an understanding of machine learning, and the capability to build systems that can scale. And it’s rare to find such a resonant combination of all three.
At the CXO level, one must employ data science as a way of thinking about the world in terms of hypotheses, testing, confidence, and error margins. A background in data science tends to help CEOs ask better questions and get better feedback, because it brings conversations down to a level of reality and practicality. Facts, data, and probabilities can have a way of removing the ego, politics, and hand-waving from a conversation
CXOs often make the typical mistake of bringing in data scientists and treating them like developers, but they are not the same. Data scientists care about having an impact on the business, but companies systematically underinvest in training them in the domain and forming a linkage with other parts of the business.
Employing Data Science as a driving force for top level strategy will not only be the key in running a data-driven organization from the very core, it will also be the future of top level strategy. Hence I’d recommend the CXOs to align to the following Data Science driven strategy points:
A big trend is publishing dashboards displaying KPI information. These are well intended efforts, with green, yellow and red beacons providing 30,000 foot situational awareness. But dashboards introduce the “whack-a-mole” problem and what is missing is providing context associated with the status. If an indicator is red, the reason why must be available. Data science needs to tell a story about the data with context, offering both “the why” along with “the what.”
Teams embarking on their data science journey need to be empowered by company leadership and not treated as a half-hearted measure. The motto should be “all in or not at all.” Avoid the trap of starting a data science initiative before the CEO and CxO team are ready to commit their respective departments to the cause. Data science projects must reach across company functional silos to provide the comprehensive context and tell the complete story.
A data science journey begins with top-level management approval and internal funding. New technologies will need to be purchased and most likely new team members hired. Use the “Lean Startup Playbook” to guide the project. This means work backward from the customer (in this case the management team and department leaders), have a big vision but focus on quick deliverables and short iterations (something new must be given to customers at least every 2 weeks) and be prepared to pivot to please the customer.
Don’t create in isolation. A data science project will only be successful when business users are active co-creators in the project. Many technologists have blind spots toward the “business side of the house.” Connect business users to data scientists in one team so there is cohesion and shared sense of responsibility for success.
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