14 May 2019
Value from Data
Challenges with Finding Value in Data
The size of the global datasphere is expected to grow from 26 zetabytes in 2017 to 175 zetabytes in 2025 (Statista). Similarly, the global big data market is expected is to grow significantly in the coming years – $28 bn in 2016 to over $56 bn in 2020 (Statista). Considering that banks spend 8% of their budget on marketing, using big data can be a good option for generating revenue with targeted marketing. While companies collect more customer data than ever before, extracting real value from data remains a challenge.
Two Challenges with Data Analytics
Data from advanced analytics, visualization tools, social networks, and geospatial tools are easily available. However, the collected data does not often directly relate to customer needs.
Measuring the right metrics is critical in translating big data analytics into performance. Some companies make the mistake of adding too many data points instead of focusing on the points that matter, according to Accenture. Setting data collection priorities is where data experts can play their role. An experienced data analyst can deliver better customer targeting, and ultimately, a better return on investment (ROI).
However, acquiring talent is another challenge with big data analytics. According to a 2015 Accenture and MIT report, both high performing and low performing organizations lack the required skills themselves, and they also struggle to find analytics skills. The difference between high performers and low performers is that high performers are better at managing talent and they also make a greater effort to find talent. When it comes to managing talent, 88% of high performers have a “well-defined sourcing, selection and allocation strategy” compared to 34% of low performers, and 63% of high performers partner with companies that provide the needed skills compared to 33% of low performers.
However, acquiring talent is another challenge with big data analytics. According to a 2015 Accenture and MIT report, both high performing and low performing organizations lack the required skills themselves, and they also struggle to find analytics skills. The difference between high performers and low performers is that high performers are better at managing talent and they also make a greater effort to find talent. When it comes to managing talent, 88% of high performers have a “well-defined sourcing, selection and allocation strategy” compared to 34% of low performers, and 63% of high performers partner with companies that provide the needed skills compared to 33% of low performers.
Data Optimization
A data plan typically includes a data research strategy, a data optimization plan, and data presentation tools. The purpose of the optimization plan is to identify areas, professionals and solutions that will add value and ensure that organizing that data is organized in a manner that is consistent across different departments of the company.
The quantity of data to process for optimization can be enormous. For instance, manufacturing giant Rolls-Royce generates 0.5 terabyte of data on each fan blade they manufacture, resulting in three petabytes of data per year for each component.
The quantity of data to process for optimization can be enormous. For instance, manufacturing giant Rolls-Royce generates 0.5 terabyte of data on each fan blade they manufacture, resulting in three petabytes of data per year for each component.
Implementation of Big Data Analytics
Prominent credit union Webster First has combined demographic and behavioral data with big data insights to categorize customers according to home value, property features, technology use, etc. Adjusting their approach enabled them to create better wealth correlations and target specific customers with specific products and offers.
CitiBank uses data science company Feedzai’s real-time machine learning and predictive modeling expertise to identify fraud and minimize financial risk for online banks. The technology enables CitiBank to spot suspicious transactions, notify users, and identify threats. In the social media space, business-focused social network LinkedIn uses big data to develop several products, including who has viewed my profile and jobs you may be interested in.
This could well be termed the Age of Data and it is an unavoidable and intensely powerful force in how business in each sector is done. Bear Stearns is well positioned to advise our corporate clients on how best to maximize these tools to uncover new opportunities and incorporate protection and early detection strategies into their growth plan. Never before have we been better positioned to provide the most informed decisions to our clients.
CitiBank uses data science company Feedzai’s real-time machine learning and predictive modeling expertise to identify fraud and minimize financial risk for online banks. The technology enables CitiBank to spot suspicious transactions, notify users, and identify threats. In the social media space, business-focused social network LinkedIn uses big data to develop several products, including who has viewed my profile and jobs you may be interested in.
This could well be termed the Age of Data and it is an unavoidable and intensely powerful force in how business in each sector is done. Bear Stearns is well positioned to advise our corporate clients on how best to maximize these tools to uncover new opportunities and incorporate protection and early detection strategies into their growth plan. Never before have we been better positioned to provide the most informed decisions to our clients.
For more information please contact us at: info@bearstearnscompanies.com
References
References
- https://thefinancialbrand.com/82100/segmentation-big-data-banks-credit-unions/
- https://www.jpmorgan.com/commercial-banking/insights/unlocking-value-from-your-data
- https://bernardmarr.com/default.asp?contentID=684
- https://www.statista.com/statistics/871513/worldwide-data-created/
- https://www.infoworld.com/article/3289744/how-to-get-real-value-from-big-data-in-the-cloud.html
- https://deloitte.wsj.com/cmo/2017/01/24/who-has-the-biggest-marketing-budgets/
- https://www.accenture.com/us-en/~/media/accenture/conversion-assets/dotcom/documents/global/pdf/technology_6/accenture-analytics-in-action-survey.pdf
- https://www.accenture.com/sa-en/_acnmedia/accenture/conversion-assets/dotcom/documents/global/pdf/industries_14/accenture-big-data-pov.pdf
- https://www.accenture.com/_acnmedia/accenture/next-gen/hp-analytics/pdf/accenture-linking-analytics-to-high-performance-executive-summary.pdf
- https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-whats-your-plan
