Wealth Management firms are spending billions on IT to differentiate in the market place. Yet the question remains, can “Big Data” have a material impact on the business? Can it deliver business outcomes by reducing risk, increasing assets under management, driving profitability, client satisfaction, products per client, client and financial advisor retention, all while improving the cost/income ratio and return on equity?
These are questions that are being discussed in board rooms across the financial industry and topics that I will cover in this blog series.
In order to answer these questions we need to put the wealth client at the center and understand changing client needs and expectations around how the client wants to be served by the firm. We need to examine external factors such as the impact of game changing consumer technology and unprecedented client access to information, as well as understand how new market entrants are challenging the traditional financial advisor value proposition and business model as a new round of Robo-Advisors hit the market.
Up until recent years, banks enjoyed an account centric transactional business model. What is changing is the onset of unstructured social interaction data as smart mobile devices and mobile broadband Internet usage reach high penetration levels. Device proliferation is leading to the availability of “data exhaust” from mobile phones, tablets, automobiles, video cameras, and from sensors in buildings, streets, consumer wearables and footfall traffic counters. Correlation of such data to better attract, retain, and serve clients can create market advantage.
The “Big” in Big Data comes from the fact that worldwide data volume is doubling every two years with unprecedented volume, variety, and velocity. Ninety percent of the all data in the history of the world was created in the last two years (SINTEF)! The concept of Big Data is about the correlation and analysis of transaction data, social interaction data, and machine/sensor data in a way that can turn data into knowledge, knowledge into insights, and insights into actions in real-time.
So what does this all mean for wealth managers?
As a wealth manager, what impact would it have on your business if you were able to increase the understanding of your client exponentially? Actions derived from data are informed by highly personalized needs predictions that can arm wealth managers with deep insights about their clients, increase their relevance in every interaction, and directly contribute to business outcomes. Big Data can help wealth managers transform the client value proposition and re-imagine the client experience.
The new vision for financial services is that a firm must be present in the financial lives of its clients, any time, any place, on any device, and across any channel.
The firm can no longer wait for the client to come to it. It must be proactive in delivering highly relevant value-added services in real-time and anticipate client needs. The firm needs to aspire to creating a “market of one” experience for each wealth client, understand the needs of and the hierarchy within the household, and move to a client centric versus account centric go-to-market approach.
When it comes to Big Data in Wealth Management start with the foundation, put the client at the center, and define business outcomes. Focus on building capabilities around what is possible while re-imagining the client experience.
Wealth management firms can take concrete steps in the form of measurable business outcome based projects to significantly enhance the client experience. These include:
- Define a roadmap for wealth client data analytics maturity. This will identify gaps that can be addressed resulting in more relevant advisor-client interactions.
- Establish a wealth client listening system across all channels. Early detection of client behaviors can lead to the identification of issues and sales opportunities.
- Create a real-time single view of wealth client data with data virtualization. Substantial savings can be had by leaving disparate data in place while providing managers with a single view.
- Establish an analytics driven financial advisor collaboration platform. This helps create market differentiation by maximizing advisor productivity, sharing best practices daily.
- Deploy mobile virtual advisor video capability and establish branch analytics. This improves client experience and gives advisors more minutes per day with clients increasing cross-selling opportunities.
- Empower advisors with real-time client insights to drive business outcomes. This helps the advisor manage to client life events with much greater granularity and speed.
The choices that wealth management firms make around data analytics in the next two years will determine their position in the marketplace. Can Big Data help wealth managers? With a client centric and business outcomes solutions approach, the answer is an astounding YES!
I will discuss each of the above steps in more detail in my next blog. As always I welcome your suggestions, stories, and feedback!
Tags: analytics, Big Data, financial advisor, Financial Services, video, wealth management
In the US, this is the time of year when holiday shopping kicks into high gear. From Black Friday to Cyber Monday and beyond, retailers begin their big push to drive nearly a quarter of their annual revenue. And whether their customers are online or inside a traditional store, retailers today have the ability to understand shopper behavior better than ever before. This information – from purchasing patterns and advertising effectiveness to dwell times and foot traffic – allows retailers to provide their customers with a more personalized, richer digital experience that’s more likely to result in a sale. It’s a win-win. And it’s made possible through an intelligent network that manages the data analytics, location information, security, and mobility applications that drive a more enhanced and personalized user experience.
Software plays an integral role in this intelligent network. Whether it’s driving data virtualization and analytics, for example, or enabling an application-centric private and hybrid cloud, or providing comprehensive threat protection – software plays a vital role. But even more than that, software enables businesses to be more agile and innovative with market and technology transitions.
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Tags: analytics, digital experience, purchasing patterns, retail, shopper behavior, UX
Ten large oil refineries produce about 10 terabytes of data each day, which equates to the entire printed collection of the U.S. Library of Congress.
One modernized city the size of Singapore can generate about 2.5 petabytes of data every day, which translates to all U.S. academic research libraries combined.
And with more than 14 billion, data-transmitting devices connected to the Internet today, growing to 50 billion by 2020, it is little wonder that most of us are overwhelmed by this mind-boggling explosion of data.
Turning this flood of raw data into useful information and even wisdom for better business decisions and quality of life experiences is what the Internet of Everything (IoE) is all about. This is a daunting task. According to IDC Research, just .5% of all data is used or analyzed, and online data volumes are doubling every two years from a combination of mobile devices, videos, sensors, M2M, social media, applications and much more.
Connected Analytics Portfolio
Last Thursday, however, Cisco unveiled our Connected Analytics portfolio for the Internet of Everything, a unique approach that includes software packages to bring analytics to the data, regardless of its location or whether it is in motion or at rest. This new generation of analytics tools for IoE can convert more and more data into valuable intelligence — from the inter cloud, to the data center to the network’s edge.
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Tags: analytics, Big Data, Cisco, Fog, Internet of Everything, internet of things, IoE, IoT, Process Improvement, Wim Elfrink
Over the past few weeks, I’ve shared how we are helping our customers address one of their toughest challenges brought on by the Internet of Everything (IoE), Big Data and hybrid IT environments: effective management of the massive amounts of data, types of data and in various locations. With solutions like Data Virtualization , Big Data Warehouse Expansion and Cisco Tidal Enterprise Scheduler, we give our customers the tools to address this challenge head on.
Once you have access to all of your data…what next? The second challenge is to extract real-time valuable information from data in order to make better business decisions. As I’ve said before, more data is only a good thing if you use that data to better respond to opportunities and potential threats. Our customers certainly understand this and, in a recent Cisco study, 40% of surveyed companies identified effectively capturing, storing and analyzing data generated by connected “things” (e.g., machines, devices, equipment) as the biggest challenge to realizing the value of IoT.
The majority of data analysis has historically been performed after moving all data into a centralized repository, but digital enterprises will have so many connections creating so much widely distributed data that moving it all to a central place for analysis will no longer be the optimal approach. For insights needed in real-time, or data sets that are too large to move, the ability to perform analytics at the edge will be a new capability that must be incorporated into any comprehensive analytics strategy.
Analytics 1.0 was all about structured data, in centralized data repositories. Analytics 2.0 added unstructured data and gave rise to Big Data. Analytics 3.0 will require all of those existing capabilities but will also require data management and analytics capabilities closer to where the data is created…at the edge of the network.
With this new approach in mind, today we announced Connected Analytics for IoE, packaged, network-enriched analytics that leverage Cisco technologies and data to extract real-time valuable information called:
- Optimize the fan experience – Connected Analytics for Events monitors Wi-Fi, device and application usage along with social media to deliver insights on fan engagement and business operations.
- Improve store operations and customer service – Connected Analytics for Retail supports analysis of metrics, including customer and operational data in retail environments, to help stores take new steps to assure customer satisfaction and store performance.
- Enhance service quality, customer experience and unveil opportunities for new business – Connected Analytics for Service Providers provides near real-time operational and customer intelligence from patterns in networks, operations, and customer system data.
- Understand how to get the most out of your IT assets – Connected Analytics for IT provides advanced data management, data governance, business intelligence and insights to help align and get the most out of IT capabilities and services.
- Reveal hidden patterns impacting network deployment and optimization – Connected Analytics for Network Deployment analyzes devices, software, and features for inconsistencies that disrupt network operations and provides visualizations and actionable recommendations to prioritize network planning and optimization activities.
- Understand customer patterns in order to meet quality expectations and uncover monetization strategies – Connected Analytics for Mobility analyzes mobile networks to provide network, operations and business insights for pro-active governance to Wi-Fi solution customers.
- Gain a holistic view of customers across data silos – Cisco Connected Analytics for Contact Center delivers actionable customer intelligence to impact behaviors and outcomes during the critical window of customer decision making. Having the right offer at the right time will drive market leadership.
- Measure the impact of collaboration in comparison with best practices – Cisco Connected Analytics for Collaboration measures the adoption of collaboration technologies internally. It leverages data collection using the Unified Communications Audit Tool, from sources such as WebEx, IP Phones, Video, Email and Jabber.
The portfolio also includes Cisco Connected Streaming Analytics, a scalable, real-time platform that combines quick and easy network data collection from a variety of sources with one of the fastest streaming analytics engines in the industry.
In the world of IoE, data is massive, messy, and everywhere, spanning many sources – cloud, data warehouses, devices – and formats – video, voice, text, and images. The power of an intelligent infrastructure is what brings all of this data together, regardless of its location or type. That is the Cisco difference.
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Tags: analytics, connected analytics, data, IoE, IoT
Cisco today announced a data and analytics strategy and a suite of analytics software that will enable customers to translate their data into actionable business insight regardless of where the data resides.
With the number of connected devices projected to grow from 10 billion today to 50 billion by 2020, the flood tide of new data — widely distributed and often unstructured — is disrupting traditional data management and analytics. Traditionally most organizations created data inside their own four walls and saved it in a centralized repository. This made it easy to analyze the data and extract valuable information to make better business decisions.
But the arrival of the Internet of Everything (IoE) — the hyper-connection of people, process, data, and things – is quickly changing all that. The amount of data is huge. It’s coming from widely disparate sources (like mobile devices, sensors, or remote routers), and much of that data is being created at the edge. Organizations can now get data from everywhere — from every device and at any time — to answer questions about their markets and customers that they never could before. But IT managers and key decision makers are struggling to find the useful business nuggets from this mountain of data.
As an example, take the typical offshore oil rig, which generates up to 2 terabytes of data per day. The majority of this data is time sensitive to both production and safety. Yet it can take up to 12 days to move a single day’s worth of data from its source at the network edge back to the data center or cloud. This means that analytics at the edge are critical to knowing what’s going on when it’s happening now, not almost 2 weeks later.
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Tags: analytics, analytics at the edge, connected analytics, data, Internet of Everything, internet of things, IoE, IoT