BIG DATA FOR WEALTH MANAGEMENT - … Report... · A BIG DATA TOOLKIT FOR WEALTH MANAGERS | 1 BIG DATA FOR WEALTH MANAGEMENT ... Case Study: How Immobilien ... NGData’s “Lily Enterprise”

BIG DATA FOR WEALTH MANAGEMENT | 1

A BIG DATA TOOLKIT FOR WEALTH MANAGERS | 1

BIG DATA FOR WEALTH MANAGEMENTA PRACTICAL GUIDE FOR SUCCESSFULL IMPLEMENTATION

FOCUS REPORT - ANALYSIS - STRATEGY

MAY 2017

ONAWA LACEWELL

ANALYST

REPORT EXTRACT

ORIGINAL REPORT WITH 78 PAGES

BIG DATA FOR WEALTH MANAGEMENT | 2BIG DATA FOR WEALTH MANAGEMENT | 2

CONTENT

1.0 EXECUTIVE SUMMARY 5

2.0 METHODOLOGY 7

3.0 BIG DATA: A STRONG CONCEPTUAL FOUNDATION IS CRUCIAL TO SUCCESS 93.1 THE FOUR V’S OF BIG DATA 9

4.0 WHY BIG DATA PROJECTS OFTEN PRODUCE UNDERWHELMING RESULTS 134.1 BIG DATA AND PREDICTIVE ANALYTICS: TWO LINKS IN A CHAIN 14

5.0 MEASURING SUCCESS AND RETURN ON INVESTMENT 175.1 GENERAL MEASURES OF SUCCESS OF BIG DATA SOLUTIONS 17

5.2 CONCRETE MEASURES OF ROI IN BIG DATA PROJECTS 18

6.0 NEEDS DOSSIERS 226.1 NEED ONE: RETENTION AND SATISFACTION OF EXISTING CUSTOMERS 23

Case Study: How Immobilien Scout24 put unstructured data to work for them 25

Fiserv: Improving customer experience with connected advisors 27

Intersystems: A potential partner for building a wealth management recommendation tool 29

6.2 NEED TWO: GROWTH AND ACQUISITION OF NEW CUSTOMERS 31

Case Study: KPMG and Smartlogic: Using Big Data to automate onboarding 33

NGData’s “Lily Enterprise” solution helps vendors target potential customers through highly personalized marketing 35

MX.COM’s Widenet provides a novel way to gather data on potential clients 37


6.3 NEED THREE: USING BIG DATA TO DRIVE BIG RESULTS AND INCREASE ADVISOR FUNCTIONALITY 39

Case Study: State Street’s Quantextual Lab sends advisor functionality to the cloud 41

Thinknum: Using alternative data to gain granular insights into asset performance 43

Quovo: Cooperation instead of disruption to increase advisor functionality 45

6.4 NEED FOUR: REGULATION, COMPLIANCE, AND RISK DETECTION 47

Case Study: Blackrock—Transforming a traditional institution into a Big Data powerhouse 49

Dimension Data: Expert assistance with compliance needs for financial actors 50

Narrative Science: Leveraging NLG for regulation and compliance purposes 52

6.5 NEED FIVE: GENERAL DATA SOLUTIONS FOR A COMPLETE DIGITAL ECOSYSTEM 54

Case Study: Insure the Box—Improving the customer experience through telematics 55

Addepar: An all-around Big Data framework for a complete digital ecosystem 57

Datameer: A big data analytics solution that works with Hadoop to provide a complete digital ecosystem. 59

YSEOP: Helping Big Data projects go the “last mIle” with Natural Language Generation 61

7.0 SHORT VENDOR PROFILES 637.1 AIM SOFTWARE 63

7.2 AUTOMATED INSIGHTS 63

7.3 BMC SOFTWARE 64

7.4 BYALLACCOUNTS/MORNINGSTAR 64

7.5 CISCO DATA VIRTUALIZATION PLATFORM 65

7.6 CONTIX 65

7.7 ENVESTNET/YODLEE 66

7.8 ERNST & YOUNG 66

7.9 FACTSET 67

7.10 HEDGESPA 67


7.11 IBM CLIENT INSIGHTS SOLUTION 68

7.12 ICAPITAL NETWORK 68

7.13 KENSHO 69

7.14 LUCENA RESEARCH 69

7.15 RAGE FRAMEWORKS 70

7.16 SIGFIG 70

7.17 SMARTLOGIC 71

7.18 WEALTHTECHS 71

7.19 XIGNITE 72

8.0 TEN KEY STRATEGIC TAKE-AWAYS 73

AUTHORS 74

DISCLAIMER 75

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Know Your Data 13

Building on a Big Data Foundation 14

Normal Distributions are key to predictive models 16

Prescriptive models help understand where to go next 17

Big Data Scorecard 20

A Pre-Planning Checklist 21

A Big Data planning schematic 22

Screenshot: from the Fiserv website 28

Screenshot: From the Intersystems website 30

Screenshot: from the NGData website 36

Screenshot: MX.com’s WideNet solution 38

Screenshot: from the Thinknum website 44

Screenshot: from the Quovo website 46

Screenshot:from the Dimension Data website 51

Screenshot: from the Narrative Science website 53

Screenshot:from the Dimension Data website 58

Screenshot: From the Datameer website 60

Screenshot: from the Yseop website 62

Vendor Suitibility Matrix 74-75

TABLE OF CHARTS


EXECUTIVE SUMMARY

>> 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 underwhelming returns, if not outright failure. <<

Our report analyzes why Big Data projects in the financial services sector are often less successful than intended, and what can wealth managers do to ensure that their Big Data initiatives stand a better chance of success. For the report, MyPrivateBanking conducted personal interviews with leading vendors for Big Data services in the financial services sector and in-depth research on their offerings. In total 30 vendors offering Big Data solutions for financial services providers are identified and the strengths and weaknesses of their products and services analyzed.

The report shows that Big Data projects fail because they are not implemented in a way that focuses on needs first. Too many financial actors focus on the “how” of implementing Big Data projects rather than the “why” and this is a sure way to set Big Data projects up for failure. Rather than finding a vendor and solution first, they must strive for a needs-based approach with the primary business objective always at the center of the implementation process.

Therefore, the report includes five “Needs Dossiers” which are comprehensive subchapters highlighting five common needs that wealth managers can address with Big Data:

■ Serving existing clients better with Big Data

■ Finding new clients using Big Data

■ Increasing advisor functionality with a Big Data solution

■ Using Big Data for regulation, compliance, and risk detection

■ Rescuing failing Big Data projects

For each need, the report offers a detailed set of questions to ask vendors, suggestions about how to best measure success/ROI, and a detailed real-world case study of an organization that has used Big Data to address a similar need.

We see that the technological abilities are growing rapidly year by year and the current flood of data is, in reality, only the very beginning of the Big Data phenomenon. Wealth managers should keep a close eye on developments in offerings suiting their individual needs and not be afraid to think outside the box when it comes to using Big Data.

The report also includes:

A supplementary stats primer to help wealth managers brush up on statistical concepts commonly related to Big Data projects. The report also includes:

■ Data appendix that includes an overview of information on the 30 vendors profiled in the report including whether the vendor provides AI/Machine Learning, whether the solution allows for structured and unstructured data, and whether there is an internal data management solution.

■ Big Data Scorecard that is accompanied by an excel table that automatically produces a ready-to-use score card based on input.

■ A “pre-planning” checklist to help strategize Big Data projects

■ Needs schematic to help plot Big Data projects

■ Two-page suitibility matrix that maps all 30 vendors according to the needs they fulfill.

1.0 SUMMARY


METHODOLOGY

The purpose of this report is to provide wealth managers a toolkit with which to inform themselves about the opportunities that Big Data solutions can provide for their firms. We have argued throughout this report that wealth managers should take a “needs-first” approach to Big Data, which means choosing a vendor based on their specific needs, the data they currently have available, and their expected results. For this reason, the five Big Data-related needs covered in this report

■ Existing Customers

■ New Customers

■ Advisor Functionality

■ Regulation, Compliance, and Risk Detection

■ General Solutions / Rescue of Failing Big Data Projects

VENDOR SELECTION

These five needs are also among the main criteria for the selection of the featured data and technology vendors. Overall, the five needs constitute the axis around which we gathered and profiled a group of 30 vendors who cater to a range of needs (and on occasion several at once) and levels of digitalization of a business. We then included a set of thirty vendor profiles that were either long or short profiles. The long profiles highlight vendors who we see as providing a solution that fits well with the exact need-case and are therefore included in the individual need dossier. The short profiles are intended to present additional vendors who offer similar solutions and these are found in a separate chapter at the end of the document.

We did our best to include a highly varying set of vendors. While some of the bigger vendors are included, we also highlight lesser-known vendors who provide very targeted solutions—vendors such as Thinknum, for example. The factors we took into consideration when choosing these vendors were varied:

■ The need targeted by the solution we highlight

■ The reputation of the vendor

■ A history of dealing with financial providers and offering financial industry-specific solutions.

VENDOR PROFILING

We understand and readily acknowledge that these vendors are only a subset of possible vendors, and that many of these vendors offer additional solutions and services not included in these profiles. Our approach is to highlight the solution rather than the vendor overall. For the long profiles, in addition to providing basic background information, we include information on:

■ Who the vendor is,

■ How their solution works,

■ How their solution fits into a Big Data project,

■ Additional products and/or solutions that the vendor offers,

■ Unique aspects of the solution and anything we see as a possible weakness.

For the short vendor profiles, we also include the following information:

■ If there is an Artificial Intelligence (AI) or machine learning component to their solution.

■ If the vendor provides a data analytics solution that can manage (aggregate, mine, analyze, visualize, etc.) a customer’s internal data.

■ If the vendor provides a solution that will collect external data from other institutions, social media, etc., which is relevant to wealth managers, such as customers’ accounts and held-away assets, online habits, and product preferences, purchase habits, social media data, relevant information on a given company, among others.

■ If the solution can (easily) integrate with the existing digital ecosystem; meaning if the vendor’s product can be made to work as part of/within a firm or organization’s existing digital structure and it can be managed from there.

2.0 METHODOLOGY


A STRONG CONCEPTUAL FOUNDATION IS CRUCIAL TO SUCCESS

Big Data has quickly become a term devoid of real meaning. Originally, “Big Data” was coined as a descriptor of the seemingly endless and ever growing stream of data arising from advancements in digital technology. However, Big Data has become such an all-encompassing term that some argue it is no longer useful. Therefore, the search for an adequate definition of Big Data—and the need for any report on the topic to work on the basis of a clearly defined concept—is paramount.

(...more in full report pages 9-12)

3.0 BIG DATA: A STRONG CONCEPTUAL FOUNDATION IS CRUCIAL TO SUCCESS

The purpose of Big Data is clear: to help drive innovation and profits for businesses, to apply a fact-based business strategy, and to uncover insights to help increase organizational efficiency and improve client relations. However, for many firms, the reality of Big Data programs is often underwhelming. The expectations going into implementation are high, but then the outcomes are mediocre at best.


4.0 WHY BIG DATA PROJECTS OFTEN PRODUCE UNDERWHELMING RESULTS

5.0 MEASURING SUCCESS AND RETURN ON INVESTMENT

One of the more difficult tasks for financial providers who have implemented Big Data projects is developing adequate measures to gauge success. Big Data projects are often integrated into the organizational structure in such a way that mutually exclusive measures of “success” are difficult to identify. ROI is not so easily pinpointed for Big Data solutions as it is for things like targeted advertisement and marketing campaigns.




A key part of this report is a series of Needs Dossiers that are intended to provide need-specific information to wealth managers who are thinking about a Big Data solution but may not know where to start. The dossiers are intended to be “stand-alone” chapters that provide all the information about the specific need in one place without needing to wade through information on needs that may not fit your organization at this time.

Each dossier consists of a set of descriptive points, questions to ask vendors, some need-specific measures of success and a real world case study. This case study is selected to highlight a company or organization who has faced a similar need and solved it using a Big Data solution.

These case studies run the gambit from the insurance industry to a German online real estate searh engine who increased their conversion rates significantly by implementing a Big Data solution. Additionally, the needs dossiers highlight to vendors who provide a Big Data solution that addresses this specific need. Each vendor profile includes detailed information about who the vendor is, the strengths and weaknesses of their solution, and how this specific solution can help solve your Big Data needs.

6.0 NEEDS DOSSIERS

6.1 RETENTION AND SATISFACTION OF EXISTING CUSTOMERS

(...more on pages 23-30)

6.2 GROWTH AND ACQUISITION OF POTENTIAL CUSTOMERS

(more on pages 31-38)

6.3 USING BIG DATA TO DRIVE BIG RESULTS AND INCREASE ADVISOR FUNCTIONALITY


6.4 REGULATION, COMPLIANCE, AND RISK DETECTION


6.5 GENERAL DATA SOLUTIONS FOR A COMPLETE DIGITAL ECOSYSTEM

(more on pages 54-62



These short profiles cover the solutions offered by 30 Big Data vendors including information on the needs coverd, whether the vendor solution offers AI/Machine Learning, topic specific external data, an internal data management option, and a Software as a Service (SaaS) option. Additional information includes the type of integration and whether the solution accepts both internal and external data.


7.0 SHORT VENDOR PROFILES

This chapter summarizes the content and findings of the report into ten general recommendations to help guide wealth managers on their Big Data journey.

(...more in full report page 75)

8.0 TEN KEY TAKE-AWAYS


AUTHORS

AUTHORS

Onawa Promise Lacewell, Analyst, has a research focus on the impact of disruptive technologies on the financial services sector as well as on consumer behavior and global regulation. A particular focus in her work is the rise of automated investments services and breakthrough technologies such as augmented reality. Previously she worked as a Senior Researcher for the WZB Berlin Social Science Center. She holds a PhD in Political Science with specializations in comparative politics and quantitative research methods and a Bachelor’s degree in Political Science with a secondary focus in English.

Carolina Cabrera John, Analyst, has a research focus on the topics of Big Data solutions for disruption and optimization in the wealth management and private banking sectors. Carolina holds a Lienciatura in ModernLanguages and Translation from the University of the Andes in Venezuela. She previously worked as an analyst for a market research SaaS company and her specific areas of interest are the intersections of macroeconomics, economic policy, and sociocultural issues and theory as well as entrepreneurship in the Berlin startup boom.


DISCLAIMER

DISCLAIMER

IMPORTANT NOTICE AND DISCLAIMERS:

NO INVESTMENT ADVICE

This report is not an offer to sell or the solicitation of an offer to buy any security in any jurisdiction where such an offer or solicitation would be illegal. This report is distributed for informational purposes only and should not be construed as investment advice or a recommendation to sell or buy any security or other investment, or undertake any investment strategy. It does not constitute a general or personal recommendation or take into account the particular investment objectives, financial situations, or needs of individual investors. The price and value of securities referred to in this report will fluctuate. Past performance is not a guide to future performance, future returns are not guaranteed, and a loss of all of the original capital invested in a security discussed in this report may occur. Certain transactions, including those involving futures, options, and other derivatives, give rise to substantial risk and are not suitable for all investors.

DISCLAIMERS

There are no warranties, expressed or implied, as to the accuracy, completeness, or results obtained from any information set forth in this report. MyPrivateBanking GmbH will not be liable to you or anyone else for any loss or injury resulting directly or indirectly from the use of the information contained in this report, caused in whole or in part by its negligence in compiling, interpreting, reporting or delivering the content in this report.

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BIG DATA FOR WEALTH MANAGEMENT - … Report... · A BIG DATA TOOLKIT FOR WEALTH MANAGERS | 1 BIG DATA FOR WEALTH MANAGEMENT ... Case Study: How Immobilien ... NGData’s “Lily Enterprise”