In recent years, financial providers have looked for ways to leverage the power of Big Data by implementing new digital ecosystems intended to take advantage of increased data volume. Unfortunately, many of these projects have produced lackluster returns, if not outright failure.
Our 78-page report (plus four additional data supplements) analyzes why Big Data projects in the financial services sector are often less successful than intended, and what wealth managers can do to ensure that their Big Data initiatives stand a better chance of success. The report and profiles a group of 30 vendors who cater to a range of Big Data needs of wealth managers. And presents five case studies and learning points on how the main Big Data needs are addressed in different industries.
In addition the report identifies how the success and ROI of Big Data solutions can be measured and provides a set of practical tools for wealth managers who are looking to integrate a Big Data solution into their existing digital ecosystem using a needs-based approach. The report is based on personal interviews with leading vendors for Big Data services in the financial services sector and in-depth research on their offerings.
Wealth managers, banks, IT vendors and consultants will find answers to the following questions:
- What are the characteristics of common data types of relevance to wealth managers?
- Benefits of descriptive, predictive, and prescriptive analytics for wealth managers?
- How to measure success and return of investment of Big Data projects?
- How to increase the retention and satisfaction of existing clients with Big Data?
- How Big Data can support the acquisition of potential clients and ease onboarding?
- How Big Data can help advisors function better and more efficiently?
- How to use Big Data for regulation, compliance, and risk detection?
- How does a Big Data scorecard and pre-planning checklist look like?
- How to map a needs-based Big Data strategy?
- Which are the five main needs that Big Data solutions can fulfill for the wealth management sector?
- How is Big Data implemented successfully in other industries?
- Can a failed Big Data project be rescued? How?
- Which are the leading vendors for Big Data solutions in the financial services sector?
- What do the leading vendors for Big Data solutions offer wealth managers and what are the strength and weaknesses of their solutions?
- What questions should wealth managers ask vendors in selecting the right solution for their needs?
- What are the most important actions wealth managers should take to get most out of their Big Data plans?
- Status quo and trends in the use of Big Data by wealth managers
- Drivers for Big Data in wealth management and reasons for success or failure
- The significance of Big Data for wealth managers
- Increasing the client satisfaction and acquisition through Big Data
- Supporting advisors and compliance through Big Data projects
- Five case studies showcasing how Big Data projects have been implemented properly in various industries
- Profiles of 30 vendors who cater to a range of Big Data needs of wealth manager
- Choosing the right vendor for the implementation of Big Data projects
- Strategic and practical actions for implementing successful Big Data projects
- A vendor suitability index to help choose which vendor best fits to which needs
The report includes more than 20 visuals including graphs, screenshots and charts and comes with four additional sets:
- Stats Premier Set: Overview of the basic statistical concepts at the core of Big Data in a way that is accessible and helpful for wealth managers
- Key Insights Deck: an easy-to-understand 7-slide presentation that summarizes key findings for quick sharing.
- Vendor Data Appendix Set: An excel dataset with information on the 30 profiled vendor solutions including whether there is AI/Machine Learning, Topic Specific External Data such as social media data is included, whether the solution allows for structured and unstructured data, and the integration approach.
- Big Data Scorecard Set: An interactive Excel Big Data Scorecard that automatically produces a sample Big Data Scorecard based on your individual input.
1.0 Executive Summary
3.0 Big Data: a Strong Conceptual Foundation is Crucial to Success
3.1 the Four V’s of Big Data
4.0 Why Big Data Projects Often Produce Underwhelming Results
4.1 Big Data And Predictive Analytics: Two Links in a Chain
5.0 Measuring Success And Return On Investment
5.1 General Measures of Success of Big Data Solutions
5.2 Concrete Measures of Roi in Big Data Projects
6.0 Needs Dossiers
6.1 Need One: Retention And Satisfaction of Existing Customers
Case Study: How Immobilien Scout24 Put Unstructured Data to Work for Them
Fiserv: Improving Customer Experience With Connected Advisors
Intersystems: a Potential Partner for Building a Wealth Management Recommendation Tool
6.2 Need Two: Growth And Acquisition of New Customers
Case Study: Kpmg And Smartlogic: Using Big Data to Automate Onboarding
Ngdata’S “Lily Enterprise” Solution Helps Vendors Target Potential Customers Through Highly Personalized Marketing
Mx.Com’S Widenet Provides a Novel Way to Gather Data On Potential Clients
6.3 Need Three: Using Big Data to Drive Big Results And Increase Advisor Functionality
Case Study: State Street’S Quantextual Lab Sends Advisor Functionality to the Cloud
Thinknum: Using Alternative Data to Gain Granular Insights Into Asset Performance
Quovo: Cooperation Instead of Disruption to Increase Advisor Functionality
6.4 Need Four: Regulation, Compliance, And Risk Detection
Case Study: Blackrock—Transforming a Traditional Institution Into a Big Data Powerhouse
Dimension Data: Expert Assistance With Compliance Needs for Financial Actors
Narrative Science: Leveraging Nlg for Regulation And Compliance Purposes
6.5 Need Five: General Data Solutions for a Complete Digital Ecosystem
Case Study: Insure the Box—Improving the Customer Experience Through Telematics
Addepar: An All-Around Big Data Framework for a Complete Digital Ecosystem
Datameer: a Big Data Analytics Solution That Works With Hadoop to Provide a Complete Digital Ecosystem.
Yseop: Helping Big Data Projects Go the “Last Mile” With Natural Language Generation
7.0 Short Vendor Profiles
7.1 Aim Software
7.2 Automated Insights
7.3 Bmc Software
7.5 Cisco Data Virtualization Platform
7.8 Ernst & Young
7.11 Ibm Client Insights Solution
7.12 Icapital Network
7.14 Lucena Research
7.15 Rage Frameworks
8.0 Ten Key Strategic Take-Aways
- Aim Software
- Automated Insights
- Bmc Software
- Cisco Data Virtualization Platform
- Ernst & Young
- Ibm Client Insights Solution
- Icapital Network
- Lucena Research
- Rage Frameworks