OBSTACLES TO SUCCESS What Stands in the Way of Data Science Strategies?
With such an impressive list of use cases, data science is clearly making its mark on the financial services industry. At the peak of its powers, its tools and technologies can help organizations significantly increase their competitive edge by improving customer engagement, reducing operational costs, and optimizing marketing outreach, risk management, and pricing. 17 What could possibly go wrong? As a discipline in its infancy, data science is an art that business is still learning to master. So, what stops big data analytics providing the true picture that organizations need of their opportunities, risks, and customers? In a survey of more than 16,000 data scientists, Kaggle identified the top ten barriers that data science strategies come up against. 18
1
3
LACK OF MANAGEMENT/ FINANCIAL SUPPORT
DIRTY DATA
49.4%
37.2%
Inaccurate, incomplete or inconsistent data sullies results—and cleaning it up can cost companies up to 25% of possible revenue. 19 With so much big data to work through, the need to cleanse dirty data—before even starting to analyze it—wastes the time and expertise of data scientists.
For a data science initiative to fulfill its potential, an organization must be willing to make significant investments in people, infrastructure and platforms. To get the most payback from its expenditure, though, it also needs senior management to get behind the initiative and make sure it is wholly in line with the firm’s overall business strategy. The CRO must play a leading role in encouraging executive buy-in and prioritizing projects that will offer the greatest return on investment. 21
2
SCIENCE TALENT
41.6%
4
The skills of data scientists are in high demand, but the world’s well-publicized shortage of qualified candidates is making posts increasingly hard to fill. With 59% of openings coming from the finance, insurance, professional services, and IT industries, it is forecast that the number of data science jobs available will increase by 364,000 by 2020, bringing the total to 2,727,000. 20
LACK OF CLEAR QUESTION TO ANSWER
30.2%
To get valuable, usable results from data science that solve a real business problem, your first task is to identify exactly what the problem is and define each aspect of it. Data scientists have the talents and tools to answer all kinds of questions; but without a clear definition of what the business needs, they can’t design an effective solution. 22
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