The recent Spotfire on-demand webcast, “Data Science 2.0: Guided and In-line Analytics with Spotfire,” covers how Spotfire and data science are impacting global business across every market and industry.
For example, if you work in the retail banking industry this may mean using analytics to successfully combine data from all available sources to develop a better understanding of customer needs so you can serve them more efficiently.
Here’s a sample clip from the retail banking segment of the webcast followed by a post on how retail banks can benefit from data analysis.
Retail banks must mine customer data for actionable insight through big data analytics or face losing market share to competitors that do.
That’s according to new research from KPMG that advises banks to tap data analysis to gain insight from their internal data while integrating external data sources to effectively compete with new market entrants.
“The real competitive advantage will go to those players who are able to successfully combine data from all available sources to develop a better understanding of customer needs and, as a result, serve them more effectively,” according to KPMG.
However, many banks aren’t aware of the value that customer insight through analytics can bring to the bottom line, KPMG notes.
“The simple truth is that retail banks focus on product sales and, as a result, many see data analytics purely as a means to an end rather than recognizing the value of achieving a richer understanding of the customers themselves,” according to the KPMG research. “In the face of these challenges, banks should change tack and prioritize their investment in customer insight and analytics to fully exploit the rich data they already own but under-exploit.”
KPMG notes that banks should:
- Foster a new mindset throughout the organization with data and analytics ratcheted up to be a strategic priority
- Better demonstrate the value that analytics teams bring to the organization
- Instill leadership as champions of analytics and boost analytical literacy across the organization
“In the digital banking model of today, data is the most important asset (and will be even more so in the future),” KPMG notes. “Banks that are able to combine their internal and external data to create value can find themselves well placed to thrive in this new world. Those that are unable or unwilling will do so at their own peril.”
The companies that do embrace analytics deep into their operations can deliver productivity and profit gains that are 5% to 6% higher than those of the competition, according to McKinsey & Co.
But many companies still struggle with the first steps to harnessing the power of big data.
The solution? Start with developing a plan, McKinsey advises.
“The missing step for most companies is spending the time required to create a simple plan for how data, analytics, frontline tools, and people come together to create business value,” according to McKinsey. “The power of a plan is that it provides a common language allowing senior executives, technology professionals, data scientists, and managers to discuss where the greatest returns will come from and, more important, to select the two or three places to get started.”
A good analytics strategic plan will:
- Highlight the critical decisions or trade-offs a company must make and define initiatives that must be prioritized.
- Match investment priorities with business strategy. Companies commonly struggle with integrating their stovepipes of data, McKinsey notes. A central database will allow each bank to optimize its marketing campaigns by targeting individuals with products and services they’re more likely to want, thus raising the hit rate and profitability of the campaigns.
- Balance speed, cost and acceptance. Companies must balance the need for affordability and speed with business realities such as scheduled maintenance and buy-in among stakeholders.
- Focus on frontline engagement and capabilities. “Far too many companies believe that 95 percent of their data and analytics investments should be in data and modeling,” McKinsey notes. “But unless they develop the skills and training of frontline managers, many of whom don’t have strong analytics backgrounds, those investments won’t deliver. A good rule of thumb for planning purposes is a 50-50 ratio of data and modeling to training.”
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- Check out the on-demand webcast Data Science 2.0: Guided and In-line Analytics with Spotfire.