This article was written by Jason Demby, the Director of Business Development, Financial Services at Datameer.
What defines the relationship in wealth management? Is it defined by the strength of a database primary key or is it about a level of trust, loyalty, skill, and white-glove service?
Supporters of the former would have you believe that your Betterment or Wealthfront accounts and their data architectures and supporting algorithms will expertly manage your wealth for a fraction of the cost. The major full-service brokers, including Morgan Stanley, Merrill Lynch (Bank of America), JPMorgan, and others, rely on a high net worth individual placing premium value on the personal relationship – the attention of the human sitting at the mahogany desk.
The question of whether the relationship is about the data or the people rages on. I’d argue that the relationships within your data – and how you use them – are critical elements to success in both robo-advisor and full-service wealth management models.
The Data Supporting the Advisor
Full service brokerage firms are investing heavily in their data platforms in order to sustain and expand their business.
Relationships are complex for these firms. Full service brokers have to worry about the business relationship with the client AND the employment relationship with the financial advisor. Both clients and financial advisors are generating a lot of data that can be critical to answering key business questions to sustain a competitive advantage.
Data sources for the full service brokerage include:
- Website/mobile application logs
- Call center engagement
- Communications (written/spoken)
- CRM systems
- Market data
- Account/transaction/portfolio management systems
- Human resources/compensation systems.
And that data can help provide answers to questions that include:
- How can we predict which advisors are considering defection to another firm?
- In the event of a defection, what action can we take to retain clients and assets under management?
- How can we predict which clients may churn?
- What upsell or cross-sell opportunities are there with our existing clients?
- How can we efficiently target our advertising or products/offers to attract new clients?
- How can I efficiently manage the client’s portfolio within a given risk tolerance?
The brokerage firms that are taking advantage of ALL of their data – and can gain insights the quickest – have a clear competitive advantage. They are able to attract and retain the right talent, who will inevitably attract and retain the right clients.
Big Data
Big Data
Big data refers to the collection of data that is too complex and too large for processing by standard database tools. There is no specific quantity of data, which is set as a minimum level to be considered Big data. Image the data collected on global credit card transactions. Many governments used Big data analysis to study the recent pandemic spread. The term Big data was first introduced in 1980 by Charles Tilly.The term Big data was primarily used in computer science, statistics, and econome
Big data refers to the collection of data that is too complex and too large for processing by standard database tools. There is no specific quantity of data, which is set as a minimum level to be considered Big data. Image the data collected on global credit card transactions. Many governments used Big data analysis to study the recent pandemic spread. The term Big data was first introduced in 1980 by Charles Tilly.The term Big data was primarily used in computer science, statistics, and econome
Read this Term architectures based on Hadoop, and the supporting Analytics
Analytics
Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy
Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy
Read this Term platforms are being implemented to provide this advantage. Hadoop is the technology of choice as it provides the ability to store massive amounts of structured and unstructured data on low cost commodity hardware.
An ecosystem of big data analytics tools can enable the ingestion, analysis, visualization, and governance of this data on top of the Hadoop data lake. As full service brokers compete with each other and with the emerging robo-advisors, the right big data strategy can make all of the difference.
The Data Instead of the Advisor
But, as wealth continues to move into the hands of a younger, more tech-savvy generation, does the advisor relationship even matter? Disruptive robo-advisor challengers don’t think so, and they believe that the objective nature of the data and the algorithms reign supreme.
Many of the data sources and business questions outlined above are universal in wealth management regardless of the model. Though, firms using the robo-advisor model can focus on client and investment related decisions without needing to invest the resources, or the risk, related to the employment of a branch network of humans.
Pure data-driven decisions are central to the robo-advisor model but it’s not just about having massive amounts of data. It’s about leveraging the data to make better business decisions. Firms need to continue to push the envelope in how they take advantage of the data available and a Hadoop-based data strategy that enables scalable, agile analytics and decision-making is key to success.
The Data Is the Advisor
The ability to gain insights quickly from the data is critical – regardless of the wealth management business model a firm employs. Firms continue to invest in big data architectures and analytics to make these decision-making insights possible. The next step is putting business user-friendly agile analytics tools in the hands of the right teams to achieve these results.
The firms that are going to beat the competition are the ones that truly understand the wealth management relationship – the relationship with their clients, the relationship with their advisors, and the relationships within their data.
This article was written by Jason Demby, the Director of Business Development, Financial Services at Datameer.
What defines the relationship in wealth management? Is it defined by the strength of a database primary key or is it about a level of trust, loyalty, skill, and white-glove service?
Supporters of the former would have you believe that your Betterment or Wealthfront accounts and their data architectures and supporting algorithms will expertly manage your wealth for a fraction of the cost. The major full-service brokers, including Morgan Stanley, Merrill Lynch (Bank of America), JPMorgan, and others, rely on a high net worth individual placing premium value on the personal relationship – the attention of the human sitting at the mahogany desk.
The question of whether the relationship is about the data or the people rages on. I’d argue that the relationships within your data – and how you use them – are critical elements to success in both robo-advisor and full-service wealth management models.
The Data Supporting the Advisor
Full service brokerage firms are investing heavily in their data platforms in order to sustain and expand their business.
Relationships are complex for these firms. Full service brokers have to worry about the business relationship with the client AND the employment relationship with the financial advisor. Both clients and financial advisors are generating a lot of data that can be critical to answering key business questions to sustain a competitive advantage.
Data sources for the full service brokerage include:
- Website/mobile application logs
- Call center engagement
- Communications (written/spoken)
- CRM systems
- Market data
- Account/transaction/portfolio management systems
- Human resources/compensation systems.
And that data can help provide answers to questions that include:
- How can we predict which advisors are considering defection to another firm?
- In the event of a defection, what action can we take to retain clients and assets under management?
- How can we predict which clients may churn?
- What upsell or cross-sell opportunities are there with our existing clients?
- How can we efficiently target our advertising or products/offers to attract new clients?
- How can I efficiently manage the client’s portfolio within a given risk tolerance?
The brokerage firms that are taking advantage of ALL of their data – and can gain insights the quickest – have a clear competitive advantage. They are able to attract and retain the right talent, who will inevitably attract and retain the right clients.
Big Data
Big Data
Big data refers to the collection of data that is too complex and too large for processing by standard database tools. There is no specific quantity of data, which is set as a minimum level to be considered Big data. Image the data collected on global credit card transactions. Many governments used Big data analysis to study the recent pandemic spread. The term Big data was first introduced in 1980 by Charles Tilly.The term Big data was primarily used in computer science, statistics, and econome
Big data refers to the collection of data that is too complex and too large for processing by standard database tools. There is no specific quantity of data, which is set as a minimum level to be considered Big data. Image the data collected on global credit card transactions. Many governments used Big data analysis to study the recent pandemic spread. The term Big data was first introduced in 1980 by Charles Tilly.The term Big data was primarily used in computer science, statistics, and econome
Read this Term architectures based on Hadoop, and the supporting Analytics
Analytics
Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy
Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy
Read this Term platforms are being implemented to provide this advantage. Hadoop is the technology of choice as it provides the ability to store massive amounts of structured and unstructured data on low cost commodity hardware.
An ecosystem of big data analytics tools can enable the ingestion, analysis, visualization, and governance of this data on top of the Hadoop data lake. As full service brokers compete with each other and with the emerging robo-advisors, the right big data strategy can make all of the difference.
The Data Instead of the Advisor
But, as wealth continues to move into the hands of a younger, more tech-savvy generation, does the advisor relationship even matter? Disruptive robo-advisor challengers don’t think so, and they believe that the objective nature of the data and the algorithms reign supreme.
Many of the data sources and business questions outlined above are universal in wealth management regardless of the model. Though, firms using the robo-advisor model can focus on client and investment related decisions without needing to invest the resources, or the risk, related to the employment of a branch network of humans.
Pure data-driven decisions are central to the robo-advisor model but it’s not just about having massive amounts of data. It’s about leveraging the data to make better business decisions. Firms need to continue to push the envelope in how they take advantage of the data available and a Hadoop-based data strategy that enables scalable, agile analytics and decision-making is key to success.
The Data Is the Advisor
The ability to gain insights quickly from the data is critical – regardless of the wealth management business model a firm employs. Firms continue to invest in big data architectures and analytics to make these decision-making insights possible. The next step is putting business user-friendly agile analytics tools in the hands of the right teams to achieve these results.
The firms that are going to beat the competition are the ones that truly understand the wealth management relationship – the relationship with their clients, the relationship with their advisors, and the relationships within their data.
