NICSA’s Technology Committee, including Dan Cwenar, President, Access Data, Broadridge, offer perspectives on the “state of play” of Big Data in the fund industry:
The history of “ Big Data”
The definition of Big Data in the context of industry applications.
The movement from descriptive towards prescriptive analytics in driving decisions
Common misconceptions about the use of predictive analytics.
La base para optimizar y potenciar la toma de decisiones en cualqueir empresa es la información. Pero no la información en bruto, sino aquella de la que podemos obtener valor tras su análisis.
Data Mining: The Top 3 Things You Need to Know to Achieve Business Improvemen...Dr. Cedric Alford
While companies have been using various CRM and automation technologies for many years to capture and retain traditional business data, these existing technologies were not built to handle the massive explosion in data that is occurring today. The shift started nearly 10 years ago with expanding usage of the internet and the introduction of social media. But the pace has accelerated in the past five years following the introduction of smart phones and digital devices such as tablets and GPS devices. The continued rise in these technologies is creating a constant increase in complex data on a daily basis.
The result? Many companies don't know how to get value and insights from the massive amounts of data they have today. Worse yet, many more are uncertain how to leverage this data glut for business advantage tomorrow. In this white paper, we will explore three important things to know about big data and how companies can achieve major business benefits and improvements through effective data mining of their own big data.
Dr. Cedric Alford provides a roadmap for organizations seeking to understand how to make Big Data actionable.
La base para optimizar y potenciar la toma de decisiones en cualqueir empresa es la información. Pero no la información en bruto, sino aquella de la que podemos obtener valor tras su análisis.
Data Mining: The Top 3 Things You Need to Know to Achieve Business Improvemen...Dr. Cedric Alford
While companies have been using various CRM and automation technologies for many years to capture and retain traditional business data, these existing technologies were not built to handle the massive explosion in data that is occurring today. The shift started nearly 10 years ago with expanding usage of the internet and the introduction of social media. But the pace has accelerated in the past five years following the introduction of smart phones and digital devices such as tablets and GPS devices. The continued rise in these technologies is creating a constant increase in complex data on a daily basis.
The result? Many companies don't know how to get value and insights from the massive amounts of data they have today. Worse yet, many more are uncertain how to leverage this data glut for business advantage tomorrow. In this white paper, we will explore three important things to know about big data and how companies can achieve major business benefits and improvements through effective data mining of their own big data.
Dr. Cedric Alford provides a roadmap for organizations seeking to understand how to make Big Data actionable.
Looking at what is driving Big Data. Market projections to 2017 plus what is are customer and infrastructure priorities. What drove BD in 2013 and what were barriers. Introduction to Business Analytics, Types, Building Analytics approach and ten steps to build your analytics platform within your company plus key takeaways.
In the analogue era information was scarce and came from questionnaires and sampling. Since the dawn of the digital age in 2012 far more data than ever before is stored and it is mainly collected passively, i.e. while people go about doing what they normally do, such as run their businesses, use their cell phones and conduct internet searches.
Analysts, policy makers and business people value business tendency surveys (BTS) and consumer opinion surveys (COS) specifically because the survey results are available before the corresponding (official) quantitative data. However, Big Data has begun to make inroads on areas traditionally covered by BTS and COS. It has a competitive edge over BTS and COS, as it is available in real-time, is based on all observations and does not rely on the active participation of respondents. Furthermore, Big Data has little direct production costs, because it is merely a by-product of business processes. In contrast, putting together and maintaining a sample of active respondents and collecting information through questionnaires as in the case of BTS and COS, require the upkeep of a costly infrastructure and the employment of people with scarce, specialised skills.
However, BTS and COS also have a competitive edge over Big Data in certain aspects. These aspects could broadly be put into two groups, namely 1) BTS and COS offer information that Big Data cannot supply and 2) BTS and COS do not suffer from some of the shortcomings of Big Data. The biggest competitive advantage of BTS and COS is that they measure phenomenon that Big Data does not cover. Big Data records only actual outcomes, while BTS and COS also cover unquantifiable expectations and assessments. Although Big Data often claims that it covers the whole population universe (and not only a selection) this does not necessarily prevent bias. For example, twitter feeds could be biased, because certain demographic or less activist groups are under-represented. In contrast, the research design and random sampling of BTS and COS limit their selection bias.
To remain relevant and survive, producers of BTS and COS will have to adapt and publicise their unique competitive advantage vis-à-vis Big Data in the future. The biggest shift will probably require that producers of BTS and COS make users more aware of the value of the unique forward looking information of BTS and COS (i.e. their recording of expectations about the future).
Who needs Big Data? What benefits can organisations realistically achieve with Big Data? What else required for success? What are the opportunities for players in this space? In this paper, Cartesian explores these questions surrounding Big Data.
www.cartesian.com
Dark Data Revelation and its Potential BenefitsPromptCloud
This presentation covers benefits, use cases, practical examples, potential issues and the approach that needs to be taken when it comes to harnessing the power of dark data (a largely untapped strategic play in the big data realm).
Big data for the next generation of event companiesRaj Anand
Only on rare occasions do we consider the amount of data that our every action produces. It’s pretty overwhelming just to think about every interaction on every app on every device in our bag or pocket, in every environment and every location.
But then there’s more. We also use access cards, transportation passes and gym memberships. We have hobbies, we travel, buy groceries, books and maybe warm beverages on rainy days. We are part of multiple communities. Looking around billions of people are doing the same. Our every action produces data about us. This is big.
We believe taking an interest in this wealth of data will be the key to success for next generation Event Companies.
We are living in a fast changing world, where it’s ever more important to foresee trends and seize opportunities. A global perspective is not a strategic advantage anymore it is a necessity.
Event companies are facilitators , they create common grounds for brands and audiences, by thoughtfully connecting goals and means. Having a deep understanding of customer behaviour, group psychology, digital habits, brand interaction, communication, and awareness through unlocking the power of big data will ensure next generation event companies thrive on strategy.
BIG Data & Hadoop Applications in Social MediaSkillspeed
Explore the applications of BIG Data & Hadoop in Social Media via Skillspeed.
BIG Data & Hadoop in Social Media is a key differentiator, especially in terms of generating memorable customer experiences.
Herein, we discuss how leading social networks such as Facebook, Twitter, Pinterest, LinkedIN, Instagram & Stumble Upon utilize Hadoop.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
The Emergence of Alt-Data and its ApplicationsPromptCloud
Alternative data is information gathered from non-traditional information sources. Analysis of this data can provide insights beyond that which an industry’s regular data sources are capable of providing. Here is all you should know about alt-data.
Open Innovation - Winter 2014 - Socrata, Inc.Socrata
As innovators around the world push the open data movement forward, Socrata features their stories, successes, advice, and ideas in our quarterly magazine, “Open Innovation.”
The Winter 2014 issue of Open Innovation is out. This special year-in-review edition contains stories about some of the biggest open data achievements in 2013, as well as expert insights into how open data can grow and where it may go in 2014.
Data-Ed Webinar: Demystifying Big Data DATAVERSITY
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Takeaways:
- The means by which Big Data techniques can complement existing data management practices
- The prototyping nature of practicing Big Data techniques
- The distinct ways in which utilizing Big Data can generate business value
- Bigger Data isn’t always Better Data
This infographic is about how banks can maximize the value of their customer data using big data analytics. While the volume of data has been increasing in recent years, many banks have not been able to profit from this growth. Several challenges hold them back.
Looking at what is driving Big Data. Market projections to 2017 plus what is are customer and infrastructure priorities. What drove BD in 2013 and what were barriers. Introduction to Business Analytics, Types, Building Analytics approach and ten steps to build your analytics platform within your company plus key takeaways.
In the analogue era information was scarce and came from questionnaires and sampling. Since the dawn of the digital age in 2012 far more data than ever before is stored and it is mainly collected passively, i.e. while people go about doing what they normally do, such as run their businesses, use their cell phones and conduct internet searches.
Analysts, policy makers and business people value business tendency surveys (BTS) and consumer opinion surveys (COS) specifically because the survey results are available before the corresponding (official) quantitative data. However, Big Data has begun to make inroads on areas traditionally covered by BTS and COS. It has a competitive edge over BTS and COS, as it is available in real-time, is based on all observations and does not rely on the active participation of respondents. Furthermore, Big Data has little direct production costs, because it is merely a by-product of business processes. In contrast, putting together and maintaining a sample of active respondents and collecting information through questionnaires as in the case of BTS and COS, require the upkeep of a costly infrastructure and the employment of people with scarce, specialised skills.
However, BTS and COS also have a competitive edge over Big Data in certain aspects. These aspects could broadly be put into two groups, namely 1) BTS and COS offer information that Big Data cannot supply and 2) BTS and COS do not suffer from some of the shortcomings of Big Data. The biggest competitive advantage of BTS and COS is that they measure phenomenon that Big Data does not cover. Big Data records only actual outcomes, while BTS and COS also cover unquantifiable expectations and assessments. Although Big Data often claims that it covers the whole population universe (and not only a selection) this does not necessarily prevent bias. For example, twitter feeds could be biased, because certain demographic or less activist groups are under-represented. In contrast, the research design and random sampling of BTS and COS limit their selection bias.
To remain relevant and survive, producers of BTS and COS will have to adapt and publicise their unique competitive advantage vis-à-vis Big Data in the future. The biggest shift will probably require that producers of BTS and COS make users more aware of the value of the unique forward looking information of BTS and COS (i.e. their recording of expectations about the future).
Who needs Big Data? What benefits can organisations realistically achieve with Big Data? What else required for success? What are the opportunities for players in this space? In this paper, Cartesian explores these questions surrounding Big Data.
www.cartesian.com
Dark Data Revelation and its Potential BenefitsPromptCloud
This presentation covers benefits, use cases, practical examples, potential issues and the approach that needs to be taken when it comes to harnessing the power of dark data (a largely untapped strategic play in the big data realm).
Big data for the next generation of event companiesRaj Anand
Only on rare occasions do we consider the amount of data that our every action produces. It’s pretty overwhelming just to think about every interaction on every app on every device in our bag or pocket, in every environment and every location.
But then there’s more. We also use access cards, transportation passes and gym memberships. We have hobbies, we travel, buy groceries, books and maybe warm beverages on rainy days. We are part of multiple communities. Looking around billions of people are doing the same. Our every action produces data about us. This is big.
We believe taking an interest in this wealth of data will be the key to success for next generation Event Companies.
We are living in a fast changing world, where it’s ever more important to foresee trends and seize opportunities. A global perspective is not a strategic advantage anymore it is a necessity.
Event companies are facilitators , they create common grounds for brands and audiences, by thoughtfully connecting goals and means. Having a deep understanding of customer behaviour, group psychology, digital habits, brand interaction, communication, and awareness through unlocking the power of big data will ensure next generation event companies thrive on strategy.
BIG Data & Hadoop Applications in Social MediaSkillspeed
Explore the applications of BIG Data & Hadoop in Social Media via Skillspeed.
BIG Data & Hadoop in Social Media is a key differentiator, especially in terms of generating memorable customer experiences.
Herein, we discuss how leading social networks such as Facebook, Twitter, Pinterest, LinkedIN, Instagram & Stumble Upon utilize Hadoop.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
The Emergence of Alt-Data and its ApplicationsPromptCloud
Alternative data is information gathered from non-traditional information sources. Analysis of this data can provide insights beyond that which an industry’s regular data sources are capable of providing. Here is all you should know about alt-data.
Open Innovation - Winter 2014 - Socrata, Inc.Socrata
As innovators around the world push the open data movement forward, Socrata features their stories, successes, advice, and ideas in our quarterly magazine, “Open Innovation.”
The Winter 2014 issue of Open Innovation is out. This special year-in-review edition contains stories about some of the biggest open data achievements in 2013, as well as expert insights into how open data can grow and where it may go in 2014.
Data-Ed Webinar: Demystifying Big Data DATAVERSITY
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Takeaways:
- The means by which Big Data techniques can complement existing data management practices
- The prototyping nature of practicing Big Data techniques
- The distinct ways in which utilizing Big Data can generate business value
- Bigger Data isn’t always Better Data
This infographic is about how banks can maximize the value of their customer data using big data analytics. While the volume of data has been increasing in recent years, many banks have not been able to profit from this growth. Several challenges hold them back.
Transforming Customer Engagements in a Digital WorldBroadridge
Financial services companies must adapt to new communications channels as customers are more connected than ever and expect companies they do business with to cater to their preferences. Businesses must move beyond focusing on marketing channels as silos and now consider how channels and devices need to connect across the full customer experience.
BI & Big data use case for banking - by rully feranataRully Feranata
Big Data and all about its business case in banking industry - how it will change the landscape and how it can be harness in order organization to stay ahead of the game
Notes from the Observation Deck // A Data Revolution gngeorge
Notes from the Observation Deck will provide you with an examined look at the interesting phenomena and trends taking place around us today. We present them to you with the hope of sparking broader conversations, debates and ideas. Please use this as a resource for knowledge, inspiration and enjoyment.
Analytics 3.0.pdfArtwork Chad Hagen, Nonsensical Infographic .docxSHIVA101531
Analytics 3.0.pdf
Artwork: Chad Hagen, Nonsensical Infographic No. 5, 2009, digital
Those of us who have spent years studying “data smart” companies believe we’ve already lived through two eras in the use of analytics. We might call them BBD and ABD—before big data and after big data. Or, to use a naming convention matched to the topic, we might say that Analytics 1.0 was followed by Analytics 2.0. Generally speaking, 2.0 releases don’t just add some bells and whistles or make minor performance tweaks. In contrast to, say, a 1.1 version, a 2.0 product is a more substantial overhaul based on new priorities and technical possibilities. When large numbers of companies began capitalizing on vast new sources of unstructured, fast-moving information—big data—that was surely the case.
Some of us now perceive another shift, fundamental and far-reaching enough that we can fairly call it Analytics 3.0. Briefly, it is a new resolve to apply powerful data-gathering and analysis methods not just to a company’s operations but also to its offerings—to embed data smartness into the products and services customers buy.
I’ll develop this argument in what follows, making the case that just as the early applications of big data marked a major break from the 1.0 past, the current innovations of a few industry leaders are evidence that a new era is dawning. When a new way of thinking about and applying a strength begins to take hold, managers are challenged to respond in many ways. Change comes fast to every part of a business’s world. New players emerge, competitive positions shift, novel technologies must be mastered, and talent gravitates toward the most exciting new work.
Managers will see all these things in the coming months and years. The ones who respond most effectively will be those who have connected the dots and recognized that competing on analytics is being rethought on a large scale. Indeed, the first companies to perceive the general direction of change—those with a sneak peek at Analytics 3.0—will be best positioned to drive that change.
The Evolution of Analytics
My purpose here is not to make abstract observations about the unfolding history of analytics. Still, it is useful to look back at the last big shift and the context in which it occurred. The use of data to make decisions is, of course, not a new idea; it is as old as decision making itself. But the field of business analytics was born in the mid-1950s, with the advent of tools that could produce and capture a larger quantity of information and discern patterns in it far more quickly than the unassisted human mind ever could.
Today it isn’t just online and information firms that can create products and services from analyses of data. It’s every firm in every industry.
Analytics 1.0—the era of “business intelligence.”
What we are here calling Analytics 1.0 was a time of real progress in gaining an objective, deep understanding of important business phenomena and giving managers.
1.Introduction
2.Overview
3.Why Big Data
4.Application of Big Data
5.Risks of Big Data
6.Benefits & Impact of Big Data
7.Conclusion
‘Big Data’ is similar to ‘small data’, but bigger in size
But having data bigger it requires different approaches:
Techniques, tools and architecture
An aim to solve new problems or old problems in a better
way
Big Data generates value from the storage and processing
of very large quantities of digital information that cannot be
analyzed with traditional computing techniques.
FATCA Compliance: Riding a Roller Coaster of Regulatory ChangeBroadridge
FATCA will impose new due diligence, withholding, and reporting requirements on financial institutions. This paper outlines the significant regulatory change FATCA brings to provide the IRS with an increased ability to detect U.S. tax evaders—specifically, those among U.S. “persons” (individuals or entities) who maintain foreign accounts and investments either directly or indirectly, through their ownership in foreign entities.
A guide to help advisors understand the proposed Department of Labor changes to the fiduciary definition regulations.
The DOL’s proposed changes to the fiduciary definition regulations are causing financial advisors to re-examine their business models and to determine whether they may be a fiduciary to the plan and participants under the proposed regulations. These proposed changes will not only impact qualified retirement plans, but non-qualified plans too, such as IRAs. There could be major implications for how advisors will work with IRAs if these changes are implemented. This Practice Guide provides a framework to help advisors understand this issue by addressing the following questions:
What are the rules today?
What is being proposed?
How would some of the proposed changes impact an advisor’s practice?
Are there any action steps an advisor can take today in anticipation of the new rules?
The availability of electronic solutions, the regulatory framework within which to offer them and an increasing investor appetite for digital information has created new opportunities in investor communications. Today, companies can provide information to investors when, where, and how they want it – and that makes for more engaged investors.
That’s never been more important. as governance, transparency and accountability in capital markets become more closely scrutinized and more rigorously measured, engaging investors, encouraging voter participation and demonstrating leading communication practices is vital.
ERISA Fiduciary Issues: A Guide for AdvisorsBroadridge
The role, expectations and legal requirements for ERISA fiduciary advisors is changing. Plan sponsors are increasingly looking to retirement plan advisors for guidance. This brings potential business opportunities but also more regulatory scrutiny. This paper provides advisors with guidelines to understand the plan sponsor role as fiduciaries and the steps to take to avoid breaching their duties.
Managing Big Data: A Big Problem for BrokeragesBroadridge
Reliable mutual fund invoicing and analysis has been challenging the industry for years due to the regulatory environment and other factors, and in this report we explore key concerns, current approaches, and the way forward. Based on in-depth interviews with financial services executives, this paper uncovered the significance of a data management and analytical challenge facing brokerages, which has led to lost revenue, increased compliance and reputational risk, and lost sales opportunities.
The Multi-Asset Class Conundrum: Solving Post-Trade Complexities Across Busin...Broadridge
As trading across multiple asset classes increases, operating in silos is no longer an effective strategy for optimizing post-trade efficiency, mitigating risk and capitalizing on market opportunities. This paper uncovers how leading firms are consolidating their operations, data and technology infrastructures to create a center of excellence for multi-asset post-trade processing.
Rethinking Reconciliation: How a Global Center of Excellence Can Enhance Risk...Broadridge
In two years, outsourced reconciliation solutions have grown exponentially as increased focus on risk, regulations, and cost reduction has heightened the need for greater transparency and efficiency across all areas of financial services operations. Discover how leading financial institutions are enhancing risk management and reducing costs through a global center of excellence for reconciliations.
Fee and Commission Management in Global MarketsBroadridge
Look at key trends, challenges and solutions in fee and commission management. The challenge for any global financial institution is the sheer complexity and number of relationships and fees. The ongoing financial crisis has intensified the need for transparency and risk reduction. Explore the potential benefits of automation of commission and fees management and consider ways to quantify costs savings and other gains.
Global Investing: Considerations for Building an End-to-End SolutionBroadridge
US clients are missing out on 90% of the world’s investment opportunities. Traditionally, firms have faced cost, complexity, and time-to-market hurdles when considering how to offer foreign securities to their clients. The paper defines the critical capabilities brokerage firms need to support international investing and provides best practices and a questionnaire to help design a roadmap for global expansion.
The New Hedge Fund-Prime Broker RelationshipBroadridge
The financial crisis has changed the relationship between hedge funds and prime brokers. With the default of some leading providers, funds have realized that they should diversify their prime broker relationships and require more transparency on operational processes of prime providers. However, as the funds industry regains momentum, they are looking to their prime brokers to provide services that will support business expansion. Hence, prime brokers need to adapt their offering and IT infrastructure to respond to the changing market.
Broker-Dealer Outsourcing: Key Regulatory Issues and Strategies for ComplianceBroadridge
Due to the efficiencies and economics of outsourcing, broker-dealers are relying more and more on outsourcing for a broader range of tasks. However, because of new and stricter regulations, outsourcing presents ever-growing compliance and oversight challenges. This paper explores how to retain regulatory controls while gaining the maximum benefit from outsourcing.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Big Data in the Fund Industry: From Descriptive to Prescriptive Data Analytics
1. BIG DATA IN THE FUND
INDUSTRY
From Descriptive to Prescriptive Data Analytics
NICSA Technology and Innovation Committee
January 2014
2. BIG DATA IN THE FUND INDUSTRY
From Descriptive to Prescriptive Data Analytics
Big Data in the Fund Industry
Big Data is big news in the fund industry. Every industry publication seems to include an article on
how Big Data is being used to increase marketing effectiveness, and every industry conference
seems to have a panel focused on the operational support it requires.
While the media has plunged into Big Data, the industry’s implementation of data analytics has
been much more deliberate. The fund industry has never really been a data-driven industry, at least
not when it comes to customer interactions. In fact, a recent survey by industry consulting firm
kasina found that fewer than half of firms in the industry rely on customer knowledge to develop
the corporate strategy for distribution, product development, reputation management and
customer care.* As a result, Big Data has represented a significant change in mode of thinking for
many firms.
However, NICSA’s Technology Committee believes that continued cost pressures will push more
firms toward data-driven decision-making. In other words, data analytics will be increasingly used
as a prescriptive tool.
This white paper summarizes what the Committee sees as the “state of play” of Big Data in the fund
industry today. It has 4 sections which will review:
1. The history of the concept of Big Data.
2. The definition of Big Data in the context of industry applications.
3. The movement toward prescriptive analytics driving future decision-making in the fund
industry.
4. Common misconceptions about the use of predictive analytics.
For those who’d like to delve into Big Data in more detail, the Appendix contains the Committee’s
recommendations for further reading.
* The kasina study referenced throughout this white paper is by Julia Binder. Digital, Data-Driven,
Differentiated: The Future of Marketing for Asset Managers and Insurers. kasina. 2012
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From Descriptive to Prescriptive Data Analytics
A Little History of Big Data
Big Data seems like a new-fangled invention, the ultimate product of the Internet era. Certainly, the
terms “Big Data” and “data analytics” weren’t much used before 2011, as this trend chart of Google
search data shows.
Big Data
search term
Data Analytics
search term
2005 2007 2009 2011 2013
Of course, people have been keeping and counting records since the dawn of civilization, and many
activities predating the computer involved lots of data – just look at the United States Census.
However, while the advent of computers in the 1950s and 1960s dramatically speeded up our
processing of those records, our basic idea of what data essentially is – what it consists of and how it
should be analyzed – has remained remarkably stable.
It took the Internet to change our very concept of data. The worldwide Web itself was the product of
a massive investment in technology in the late 1990s and early 2000s that created the platform for a
new kind of business conducted electronically. Suddenly, consumers were able to shop from the
comfort of their desk – whether at home, school, hotel or office.
In addition to convenience, this e-business, as it came to be known, offered unparalleled
transparency. Shoppers online didn’t just learn about prices and product features. They could check
inventory levels, track the status of their order, find out when it was going to be delivered and read
reviews of products (and write them, too!) Even more amazingly, this information was updated in
something close to real time, rather than in a “batch” process overnight.
At the same time, businesses were worried about the potentially catastrophic effect of the Y2K
problem. In a worst case scenario, this “millennium bug,” as it was dubbed in the media, would
cause computers around the world to crash because they couldn’t distinguish the year 2000 from
the year 1900. To avert it, many companies embarked on massive systems upgrade projects.
The combined investment in e-commerce and Y2K focused attention on data – while also
dramatically expanding the quantity of information that could be captured. And this information
wasn’t just standard numerical data – it was words, music and pictures as well.
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From Descriptive to Prescriptive Data Analytics
In February 2001, Doug Laney, an analyst for META Group (later acquired by Gartner,) soon
brought definition to this effusion of data. In his whitepaper “3D Data Management,” Laney argued
that businesses needed to think of data as having multiple dimensions. He proposed three: volume,
velocity and variety.
WEEK of Feb 5, 2001:
NASDAQComposite
Even though he was writing in the midst of the bursting of the Internet bubble (as the NASDAQ
chart above illustrates,) Laney’s concept captured the attention of the information technology
community. They ultimately gave this multifaceted river of information the name “Big Data” – and
suggested additional dimensions to add to the original three. The one additional dimension that has
become most broadly accepted is “veracity.”
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The 4 Vs in the Fund Industry
Together, the 4 Vs of volume, velocity, variety and veracity make up the most widely-used definition
of Big Data. Let’s take a closer look at each of these four dimensions and how they apply to the fund
industry.
Volume
The first dimension of Big Data is the most obvious: its sheer volume.
Data used to be collected judiciously – at certain times (maybe at point of sale) and by specified
means (often through employee data entry). Today, Big Data is created 24x7 and through channels
like social media that are often outside a firm’s control.
Data is also being retained for longer periods given the low costs of storage. Thumb drives take up a
lot less space than file cabinets.
And, data collectors are more likely to see data as a matrix of rows and columns. Rather than just
viewing data as one-dimensional, they are appending additional data elements. For example, a
retail firm may want to know where customers are making purchases, so they will add geo-
positioning information to sales transaction records.
Rows(records)
Columns (data elements)
How big is Big Data? It’s a relative concept, especially since volumes that created processing
challenges a decade ago are a walk in the park today. Doug Laney gave a very apt definition recently,
when he said, “Big Data is data that’s an order of magnitude bigger than you’re accustomed to.”
However, volume by itself does not define Big Data.
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Volume in the Fund Industry | Exchange Traded Products
A great example of the growth of data volume in the fund industry involves
exchange-traded products. Traditional open-end mutual funds are priced,
purchased and redeemed only once per day. With just five sets of data per week,
fund managers found it relatively easy to analyze purchase and sale trends. By
contrast, the intraday indicative value for exchange-traded products is updated
continually, and shares can be bought and sold throughout the day. With so many
more data points, identifying patterns in sales requires much more sophisticated
tools.
Velocity
Not only is more data being collected, it’s being updated more frequently – often almost constantly.
For instance, an online retailer will now update inventory records as sales occur, rather than
overnight in a batch process.
Smart devices – whether phones, thermostats or traffic cameras – are capturing a steady stream of
information from our everyday activities.
Consumers also actively create the raw material of Big Data, in the form of user-generated content
on social media sites and in other online venues.
Velocity in the Fund Industry | Books of Record
Velocity is driving change in the way many funds keep track of portfolio positions.
Traditionally, funds have updated portfolio positioning records just once daily, after the
end of trading. The fund would take the custodian’s official “accounting book of record”
from the prior day and adjust it for the trading that took place during the day. This
procedure gave the investment team an accurate statement of positions at the start of
each day’s trading – a statement that would be out of date by the end of the day. As
computing has become more powerful and systems more integrated, some funds have
begun to use an “investment book of record” that is updated as trades occur, giving
portfolio managers and traders position data that is accurate in real time.
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Variety
Big Data doesn’t have to be alphanumeric. “Unstructured” videos, recordings, pdfs and emails are
now as much fodder for analysis as the numbers that we’re used to manipulating in databases and
spreadsheets.
Digitalization has made this possible. Take the example of how music recording has evolved over
the past 50 years, since cassette tapes were invented. Analog cassette tapes could only be analyzed
one by one. The introduction of the digital compact disc in the early 1980s made music easier to
store, but playback was still limited to specialized devices and analysis wasn’t scalable. It wasn’t
until the late 1990s – when the digital data was moved to virtual formats like MP3 – that music
became independent of its medium and analyzable en masse.
Analog Digital
CaptiveContent Free Content
From a business perspective, the ability to combine data sets and data types in novel ways shows
great promises in illuminating insights that were simply not possible in the past.
Variety in the Fund Industry | Voice Recognition
Call a fund transfer agent today, and you may be asked to prove your identify by
providing a PIN code or answering challenge questions. Call a fund transfer agent
tomorrow, and they may recognize you by the sound of your voice. Digitalization –
coupled with more efficient data retrieval and analysis methods – allow call centers
to match your current call with past recordings with an extremely high degree of
accuracy. Big Data variety is enhancing both customer service and security.
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Veracity
Combine volume, velocity and variety – and you’re likely to get lots of inconsistency. Maybe some of
the data is collected at a different time, or definitions aren’t consistent throughout the sample.
Frankly, it’s hard to keep this much data clean.
As a result, Big Data analytics starts with some amount of data scrubbing. Algorithms must sort
great data from not-s0-great data from garbage – and then correct the problems that can be fixed.
This analysis must be able to handle ambiguity.
Put simply, Big Data is imperfect.
Veracity in the Fund Industry | Omnibus Accounts
Analyzing sales through omnibus accounts maintained by intermediaries is an
example of the challenge of veracity in Big Data. Funds may have omnibus
relationships with hundreds of intermediaries – and each may provide slightly
different data in a slightly different format. To see trends in this stew of data, fund
managers first have to invest in smoothing out as many inconsistencies as possible.
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From Descriptive to Prescriptive Data Analytics
Overall, the industry is using data to describe the past rather than guide future strategy.
Of course, implementing a data-guided approach is far from easy. Data analytics is far from an exact
science – in fact, it’s really an exercise in probabilities. Number crunching can fairly easily identify
correlations between sets of data items. But determining whether these statistical relationships are
the result of cause and effect – and not just coincidence – requires industry expertise.
Implementing predictive data analytics effectively requires more than just hiring a team of data
analysts to work with the current team. It involves a change in mindset. In data-driven
organizations, everyone from senior executives to front-line staff knows exactly what is happening
inside their companies and in the marketplace. They have up-to-the-minute knowledge of events
and the power to anticipate and respond to opportunities, trends and anomalies. There’s broad
demand throughout the organization for actionable intelligence, which is met with interactive
dashboards and data visualization applications for real-time decision-making.
In sum, everyone at the firm uses data to improve customer experience, manage risk, develop
products, and enhance productivity.
WHAT A DATA-DRIVEN ORGANIZATION LOOKS LIKE
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From Descriptive to Prescriptive Data Analytics
Myth vs. Reality
Even with the challenges, implementing prescriptive analytics is within reach of any firm. Here are
three common misconceptions about what it takes to become a data-drive organization:
1. You need perfect data.
Firms that don’t yet have a predictive analytics strategy often believe that they can’t get started
without perfect data – and they’re convinced that they’re the only firm struggling with this issue.
However, data-driven firms recognize that:
• A subset of the data available can still provide tremendous insight. For example, when
analyzing sales data, even if you don’t have information on every transaction every day,
there’s still value in getting some of your data to better understand what’s happening in
your distribution channels.
• No one has perfect data. In fact, Big Data is, by definition, imperfect. (Remember that
fourth V for “veracity.”)
2. You need a big team.
Many firms assume they need an army of rocket scientists to parse the mountains of data, and
their executives wonder if they’ll be making regular trips to India to visit the tech team that they
set up there.
True, many firms who are deep into predictive analytics have often committed significant
resources to the effort.
But it’s ok to start small. Most firms can work with their existing marketing and sales resources
– or by calling in the aid of an outsourced solution provider or consultants.
In fact, the essential resource in many cases is leadership. A predictive analytics effort requires
a leader to drive the effort, manage expectations and marshal needed skills.
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3. Predictive analytics is a black box.
Many firms think Big Data is a black box: put in lots of data, and out comes a fully-formed sales,
marketing or customer service strategy.
Nothing could be further from the truth. There’s no magic here at all.
Yes, predictive analytics does use complicated algorithms. And it can be applied to an incredibly
large and complex data set.
But the analytics simply examine the questions that users ask. For example, maybe the sales
team wants to know if higher click through rates on the video content on the website lead to
more productive wholesalers. Or maybe the transfer agent needs to know how voice response
menus relate to caller anxiety.
Predictive analytics is an objective assessment of the industry expertise that we think we have.
In short, don’t assume you can’t use or aren’t ready for predictive analytics. Jump in. The 2% of
firms who have done so already are seeing spectacular results.
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Appendix | Further Reading
Top picks for the general reader
Big data: The next frontier for innovation, competition, and productivity
Great overview from McKinsey, with insights on how Big Data is being used today and the human
resources constraints on increasing data analytic capacity. Includes a useful glossary.
Smart Data Collective
Online magazine devoted to data. Accessible to the non-techie.
The Four V’s of Big Data
Seminal infographic from IBM
Big Data University
Website with training material. Check out the “What is Hadoop?” video if you want an
understandable description of a critical Big Data tool.
Making effective use of Big Data
Big data Analytics and Predictive Analytics
An overview from Gartner
The Forrester Wave™: Big Data Predictive
Analytics Solutions, Q1 2013
Making Big Data a useful business tool
Thinking Data, Talking Human
Integration of Big Data and behavioral finance
from the Lateral Group
Visualizing Big Data
Data Visualization and Discovery for Better
Business Decisions
Webinar. Requires registration.
FILWD (Fell in Love with Data)
Blog dedicated to data visualization
Measurement Drives Behavior
Series from top data visualization software
provider Tableau.
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From Descriptive to Prescriptive Data Analytics
Websites dedicated to Big Data
Big Data Gal
Beye Network
Big Data Landscape
IBM: Big Data at the Speed of Business
What’s the Big Data?
For a deep dive: LinkedIn groups
Advanced Analytics
Advanced Business Analytics, Data Mining and
Predictive Modeling
Big Data and Analytics
TDWI Business Intelligence and Data
Warehousing Discussion Group
Big Data use today
Analytics: The real-world use of big data
2012 IBM survey on the state of the art. Registration required to download full survey.
Mastering Big Data: CFO Strategies to Transform Insight into Opportunity
Overview of current use in several industries, including financial services
The Evolution of Decision Making
SAS and Harvard Business Review study. Requires registration.
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