Making Advanced Analytics Work for You by Dominic Barton and David Court MohitGupta714
The document discusses how advanced analytics can provide competitive advantages but many companies are unsure how to implement them effectively. It identifies that fully exploiting analytics requires the ability to identify and manage multiple data sources, build advanced analytics models, and adapt the organization. Companies must have a clear strategy for using data to compete and the right technology architecture. Managers need to focus on sourcing data, building models, transforming culture with flexibility and promoting creativity around new data sources.
This document discusses how companies can make advanced analytics work for them. It notes that companies using big data and analytics show 5-6% higher productivity. However, some companies struggle because they don't understand their existing data, programs are too complex, or insights aren't actionable. The document recommends that companies identify relevant data sources, build predictive analytics models, and transform their organization to make better decisions based on data and models. Managers must develop business-focused tools and exploit big data capabilities.
Big data and analytics have become top priorities for companies. To fully leverage data, companies need the ability to collect and manage multiple data sources, build advanced analytics models, and transform their organization to make better decisions using data and models. While skepticism exists, companies that learn core big data skills may gain a competitive advantage, as these capabilities become more important for competition.
This document discusses how enterprises can succeed with big data. It notes that the big data landscape contains a vast and complex ecosystem of different data types from multiple internal and external sources. To extract value from big data, businesses must first effectively explore this terrain to mine and refine their data resources. This will allow them to pave the road to new insights and smarter decision making by utilizing trusted information. The document outlines benefits like improving decisions, developing new business models, and gaining market presence through taming wild big data.
The big data landscape for most enterprises is a vast wilderness. It is a growing and complex ecosystem of different data types from multiple sources, including new data from social media and raw data collected from sources like sensors. Only after effectively exploring and navigating this terrain can businesses begin to mine and refine their data resources to extract value—using trusted information to pave the roads to new insights and smarter decision making.
For more information: www.ibm.com/bigdata
Art by Angela Tuminello
How can you analyze data in fragile and conflict affected states? What happens if you ignore the analytics and move on gut feeling? Read more about the three key steps for better data analytics in difficult places.
This document discusses how reducing total cost of ownership (TCO) should be a high priority for businesses. It notes that optimizing costs to do more with less can significantly impact the bottom line, as a 5% reduction in a $100 million IT budget equals $5 million. The document recommends analyzing your current IT landscape and emerging trends to identify areas for optimizing costs and investing capital through pilot projects and implementation programs across the technology stack. Finally, it lists some common strategic choices like sourcing strategy, asset costs, and consolidation that can optimize infrastructure costs and provide benefits such as shared resources, reduced operational complexity, and increased profits.
Making Advanced Analytics Work for You by Dominic Barton and David Court MohitGupta714
The document discusses how advanced analytics can provide competitive advantages but many companies are unsure how to implement them effectively. It identifies that fully exploiting analytics requires the ability to identify and manage multiple data sources, build advanced analytics models, and adapt the organization. Companies must have a clear strategy for using data to compete and the right technology architecture. Managers need to focus on sourcing data, building models, transforming culture with flexibility and promoting creativity around new data sources.
This document discusses how companies can make advanced analytics work for them. It notes that companies using big data and analytics show 5-6% higher productivity. However, some companies struggle because they don't understand their existing data, programs are too complex, or insights aren't actionable. The document recommends that companies identify relevant data sources, build predictive analytics models, and transform their organization to make better decisions based on data and models. Managers must develop business-focused tools and exploit big data capabilities.
Big data and analytics have become top priorities for companies. To fully leverage data, companies need the ability to collect and manage multiple data sources, build advanced analytics models, and transform their organization to make better decisions using data and models. While skepticism exists, companies that learn core big data skills may gain a competitive advantage, as these capabilities become more important for competition.
This document discusses how enterprises can succeed with big data. It notes that the big data landscape contains a vast and complex ecosystem of different data types from multiple internal and external sources. To extract value from big data, businesses must first effectively explore this terrain to mine and refine their data resources. This will allow them to pave the road to new insights and smarter decision making by utilizing trusted information. The document outlines benefits like improving decisions, developing new business models, and gaining market presence through taming wild big data.
The big data landscape for most enterprises is a vast wilderness. It is a growing and complex ecosystem of different data types from multiple sources, including new data from social media and raw data collected from sources like sensors. Only after effectively exploring and navigating this terrain can businesses begin to mine and refine their data resources to extract value—using trusted information to pave the roads to new insights and smarter decision making.
For more information: www.ibm.com/bigdata
Art by Angela Tuminello
How can you analyze data in fragile and conflict affected states? What happens if you ignore the analytics and move on gut feeling? Read more about the three key steps for better data analytics in difficult places.
This document discusses how reducing total cost of ownership (TCO) should be a high priority for businesses. It notes that optimizing costs to do more with less can significantly impact the bottom line, as a 5% reduction in a $100 million IT budget equals $5 million. The document recommends analyzing your current IT landscape and emerging trends to identify areas for optimizing costs and investing capital through pilot projects and implementation programs across the technology stack. Finally, it lists some common strategic choices like sourcing strategy, asset costs, and consolidation that can optimize infrastructure costs and provide benefits such as shared resources, reduced operational complexity, and increased profits.
This document discusses how companies can make advanced analytics work for them. It notes that while big data is attracting investment, most companies are unsure how to implement it. It recommends that companies 1) choose the right data sources, 2) build models that predict and optimize business outcomes, and 3) transform their capabilities to develop analytics that managers understand and can use daily. The key is aligning analytics with business goals and processes rather than just focusing on data itself.
Advanced analytics uses sophisticated techniques like machine learning, data mining, and predictive modeling to gain deeper insights from data beyond traditional business intelligence. While executives see the potential benefits, most companies are unsure how to implement advanced analytics. The document recommends starting with targeted efforts to build models from existing data sources and transform organizational culture, rather than massive overhauls. This balanced approach can help companies develop analytics capabilities and maintain flexibility as technologies and opportunities evolve.
Data Analytics with Managerial Applications InternshipJahanvi Khedwal
This document discusses how companies can make advanced analytics work for them. It identifies three key capabilities: 1) Choosing the right data, including both internal and external sources, and asking how available data can help key decisions. 2) Building predictive models that optimize outcomes simply, focusing on the least complex model that improves performance. 3) Transforming company capabilities by embedding analytics in tools for front-line use and making analytics central to daily operations.
Big data and analytics have rocketed to the top of the corporate agenda. Executives look with admiration at how Google, Amazon, and others have eclipsed competitors with powerful new business models that derive from an ability to exploit data.
This document discusses how business intelligence (BI) can be used to strategically manage human resources. It defines BI as collecting and analyzing large amounts of customer, vendor, market and internal data. Choosing a BI tool requires defining a strategy aligned with business goals and requirements. Human resource management aims to maximize employee performance to achieve organizational objectives through policies and systems. The relationship between BI and HR management is explored, as is how BI can influence business performance through efficiency, identifying opportunities, and empowering decision makers. The conclusion suggests that using BI can provide a competitive advantage by analyzing data and ensuring the insights reach all parts of an organization.
This document discusses the need for data unification across enterprises. It notes that while companies have invested trillions in IT and billions in big data and analytics, data remains extremely siloed within organizations. True big data and analytics require clean and unified data sources. The document advocates for a bottom-up, probabilistic and collaborative approach to data unification using machine learning combined with human insight and curation. This approach is needed to tackle the huge challenge of integrating and making sense of the massive variety of siloed data sources within large organizations. The document provides examples of how Tamr's data unification platform has helped large healthcare and biopharma companies achieve a unified view of their extremely diverse and decentralized data.
Highlights of IBM Analytics Research ReportPaul Gillin
These highlights come from the IBM report, Analytics: the real-world use of big data
(http://www.slideshare.net/pgillin/big-data-analytics-study-4-13annotated). This document is used in a blog post that shows how to write a summary of a complex research report quickly.
According to research, companies that effectively use big data and analytics show 5-6% higher productivity and profitability than their peers. To do this requires three capabilities: identifying and managing multiple data sources, building advanced analytics models, and transforming the organization so data and models improve decisions. The document provides recommendations for choosing usable data sources, establishing supportive IT infrastructure, building models to optimize business outcomes, and transforming company capabilities to develop and utilize analytics. Overall, developing capabilities around big data may become a key competitive advantage.
Marketers struggle to effectively analyze and apply big data. While big data can be used for customer optimization, engagement, and retention, most organizations rely too heavily on intuition over data-driven insights. Additionally, many marketers struggle with statistics and can become distracted by excessive data without applying critical thinking. To improve, marketers should focus on goals and filter irrelevant data, ask strategic questions, and maintain a narrow focus on higher-level objectives rather than getting lost in data details.
The big-data explosion is driving a shift away from gut-based decision making and marketing in particular is feeling the pressure to embrace new data driven capabilities.
This document discusses how advanced analytics and big data have become top priorities for companies. It argues that big data has the potential to transform businesses and deliver major performance gains. However, companies need to carefully define a pragmatic strategy for using data and analytics, focusing on how to make better decisions. The key is having a clear strategy for how to use data to compete and deploying the right IT architecture and analytical capabilities. Past failures with CRM show that analytics initiatives need to align with companies' processes and decision-making.
Big data and advanced analytics have the potential to significantly improve company performance and productivity. However, companies often struggle to realize these gains because their analytic systems remain disconnected from how managers actually make decisions. To fully leverage data and analytics, companies need to integrate multiple data sources, build predictive models, and develop analytics that directly inform business decisions and operations. Simply collecting large amounts of customer data and building new IT systems is not sufficient - organizations must transform themselves to ensure data and models yield better outcomes.
Companies that utilize big data and analytics show productivity and profitability rates 5-6% higher than peers. Leaders should invest time to align managers across the organization behind using multiple data sources to build advanced analytical models, develop capabilities for exploiting big data, and create business-relevant analytics in order to transform capabilities. Data-driven strategies, when successfully implemented, can become an important competitive differentiator.
Big data and analytics have become a top priority for companies. The document discusses how big data can transform businesses by delivering large performance gains similar to those achieved in the 1990s during business process redesign. Research shows companies using big data and analytics have productivity and profitability 5-6% higher than peers. While executives are cautious given past hype around technologies, the authors believe companies should take a pragmatic approach and focus on using data to make better decisions. This involves identifying, combining, and managing multiple data sources, building advanced analytics models, and transforming the organization to ensure data and models yield improved decisions.
Under Pressure - The state of middle offices in smaller asset management firmsStatPro Group
Smaller asset management firms face growing complexities in their middle offices due to increased trading volumes, regulations, and data requirements. While this presents challenges, it also provides an opportunity for smaller firms to take advantage of new technologies and implement more nimble, flexible solutions compared to larger firms hampered by legacy systems. The middle office is crucial for smaller firms to perform well and add strategic value through enhanced analytics that inform business strategy. New technologies allow data to be efficiently processed and transformed into a valuable asset for competitive advantage.
This document discusses how companies can make advanced analytics work for them by developing three key capabilities: 1) Choosing the right data sources creatively and getting necessary IT support, 2) Building models that predict and optimize business outcomes by focusing on business opportunities, and 3) Transforming company capabilities by developing business-relevant analytics, embedding them into front-line tools, and developing analytical skills. Fully exploiting data and analytics requires developing all three of these mutually supportive capabilities.
In it Together: why “collaboration” is now an essential skillset for asset ma...StatPro Group
Traditionally, asset management teams have worked in silos. But with asset classes and the data becoming more complex, greater collaboration is now needed. Find out why.
Go Figure: Data Processing Is Needed but Analytical Insight Is the Real ValueStatPro Group
The document discusses the changing role of middle offices in asset management firms. Middle offices can no longer just process numbers and must provide strategic insights through data analysis. To do this effectively, middle offices need clean and accurate data as well as strong analytical capabilities. Smaller boutique asset management firms are well-positioned to transform their middle offices due to their flexibility and agility. Embracing new technologies, they can create a single, clean data set and provide the accurate analytics and tracking needed to inform strategic decision-making.
Making advanced analytics work for youRahul Chawla
This document outlines how companies can benefit from advanced analytics and big data. It discusses 3 key steps: 1) choosing the right data sources, 2) building models to predict and optimize business outcomes, and 3) transforming company capabilities. Specifically, it emphasizes sourcing data creatively to solve problems, using new technologies to access data, focusing models on business opportunities, developing analytics that can be used, embedding analytics in frontline tools, and upgrading analytical skills. The document argues that with the right approach, big data can significantly boost company performance and competitive advantage.
Today's Chief Data Officer: 3 Types of CDOs, Key Challenges, and Opportunitie...Scott Richardson
In this presentation we examine the 3 types of Chief Data Officers that are common in the industry today. We discuss their responsibilities, key challenges, and present ideas for having an meaningful impact. A great overview of the CDO role and how this executive position can contribute to the success of your business.
This document discusses how companies can make advanced analytics work for them. It notes that while big data is attracting investment, most companies are unsure how to implement it. It recommends that companies 1) choose the right data sources, 2) build models that predict and optimize business outcomes, and 3) transform their capabilities to develop analytics that managers understand and can use daily. The key is aligning analytics with business goals and processes rather than just focusing on data itself.
Advanced analytics uses sophisticated techniques like machine learning, data mining, and predictive modeling to gain deeper insights from data beyond traditional business intelligence. While executives see the potential benefits, most companies are unsure how to implement advanced analytics. The document recommends starting with targeted efforts to build models from existing data sources and transform organizational culture, rather than massive overhauls. This balanced approach can help companies develop analytics capabilities and maintain flexibility as technologies and opportunities evolve.
Data Analytics with Managerial Applications InternshipJahanvi Khedwal
This document discusses how companies can make advanced analytics work for them. It identifies three key capabilities: 1) Choosing the right data, including both internal and external sources, and asking how available data can help key decisions. 2) Building predictive models that optimize outcomes simply, focusing on the least complex model that improves performance. 3) Transforming company capabilities by embedding analytics in tools for front-line use and making analytics central to daily operations.
Big data and analytics have rocketed to the top of the corporate agenda. Executives look with admiration at how Google, Amazon, and others have eclipsed competitors with powerful new business models that derive from an ability to exploit data.
This document discusses how business intelligence (BI) can be used to strategically manage human resources. It defines BI as collecting and analyzing large amounts of customer, vendor, market and internal data. Choosing a BI tool requires defining a strategy aligned with business goals and requirements. Human resource management aims to maximize employee performance to achieve organizational objectives through policies and systems. The relationship between BI and HR management is explored, as is how BI can influence business performance through efficiency, identifying opportunities, and empowering decision makers. The conclusion suggests that using BI can provide a competitive advantage by analyzing data and ensuring the insights reach all parts of an organization.
This document discusses the need for data unification across enterprises. It notes that while companies have invested trillions in IT and billions in big data and analytics, data remains extremely siloed within organizations. True big data and analytics require clean and unified data sources. The document advocates for a bottom-up, probabilistic and collaborative approach to data unification using machine learning combined with human insight and curation. This approach is needed to tackle the huge challenge of integrating and making sense of the massive variety of siloed data sources within large organizations. The document provides examples of how Tamr's data unification platform has helped large healthcare and biopharma companies achieve a unified view of their extremely diverse and decentralized data.
Highlights of IBM Analytics Research ReportPaul Gillin
These highlights come from the IBM report, Analytics: the real-world use of big data
(http://www.slideshare.net/pgillin/big-data-analytics-study-4-13annotated). This document is used in a blog post that shows how to write a summary of a complex research report quickly.
According to research, companies that effectively use big data and analytics show 5-6% higher productivity and profitability than their peers. To do this requires three capabilities: identifying and managing multiple data sources, building advanced analytics models, and transforming the organization so data and models improve decisions. The document provides recommendations for choosing usable data sources, establishing supportive IT infrastructure, building models to optimize business outcomes, and transforming company capabilities to develop and utilize analytics. Overall, developing capabilities around big data may become a key competitive advantage.
Marketers struggle to effectively analyze and apply big data. While big data can be used for customer optimization, engagement, and retention, most organizations rely too heavily on intuition over data-driven insights. Additionally, many marketers struggle with statistics and can become distracted by excessive data without applying critical thinking. To improve, marketers should focus on goals and filter irrelevant data, ask strategic questions, and maintain a narrow focus on higher-level objectives rather than getting lost in data details.
The big-data explosion is driving a shift away from gut-based decision making and marketing in particular is feeling the pressure to embrace new data driven capabilities.
This document discusses how advanced analytics and big data have become top priorities for companies. It argues that big data has the potential to transform businesses and deliver major performance gains. However, companies need to carefully define a pragmatic strategy for using data and analytics, focusing on how to make better decisions. The key is having a clear strategy for how to use data to compete and deploying the right IT architecture and analytical capabilities. Past failures with CRM show that analytics initiatives need to align with companies' processes and decision-making.
Big data and advanced analytics have the potential to significantly improve company performance and productivity. However, companies often struggle to realize these gains because their analytic systems remain disconnected from how managers actually make decisions. To fully leverage data and analytics, companies need to integrate multiple data sources, build predictive models, and develop analytics that directly inform business decisions and operations. Simply collecting large amounts of customer data and building new IT systems is not sufficient - organizations must transform themselves to ensure data and models yield better outcomes.
Companies that utilize big data and analytics show productivity and profitability rates 5-6% higher than peers. Leaders should invest time to align managers across the organization behind using multiple data sources to build advanced analytical models, develop capabilities for exploiting big data, and create business-relevant analytics in order to transform capabilities. Data-driven strategies, when successfully implemented, can become an important competitive differentiator.
Big data and analytics have become a top priority for companies. The document discusses how big data can transform businesses by delivering large performance gains similar to those achieved in the 1990s during business process redesign. Research shows companies using big data and analytics have productivity and profitability 5-6% higher than peers. While executives are cautious given past hype around technologies, the authors believe companies should take a pragmatic approach and focus on using data to make better decisions. This involves identifying, combining, and managing multiple data sources, building advanced analytics models, and transforming the organization to ensure data and models yield improved decisions.
Under Pressure - The state of middle offices in smaller asset management firmsStatPro Group
Smaller asset management firms face growing complexities in their middle offices due to increased trading volumes, regulations, and data requirements. While this presents challenges, it also provides an opportunity for smaller firms to take advantage of new technologies and implement more nimble, flexible solutions compared to larger firms hampered by legacy systems. The middle office is crucial for smaller firms to perform well and add strategic value through enhanced analytics that inform business strategy. New technologies allow data to be efficiently processed and transformed into a valuable asset for competitive advantage.
This document discusses how companies can make advanced analytics work for them by developing three key capabilities: 1) Choosing the right data sources creatively and getting necessary IT support, 2) Building models that predict and optimize business outcomes by focusing on business opportunities, and 3) Transforming company capabilities by developing business-relevant analytics, embedding them into front-line tools, and developing analytical skills. Fully exploiting data and analytics requires developing all three of these mutually supportive capabilities.
In it Together: why “collaboration” is now an essential skillset for asset ma...StatPro Group
Traditionally, asset management teams have worked in silos. But with asset classes and the data becoming more complex, greater collaboration is now needed. Find out why.
Go Figure: Data Processing Is Needed but Analytical Insight Is the Real ValueStatPro Group
The document discusses the changing role of middle offices in asset management firms. Middle offices can no longer just process numbers and must provide strategic insights through data analysis. To do this effectively, middle offices need clean and accurate data as well as strong analytical capabilities. Smaller boutique asset management firms are well-positioned to transform their middle offices due to their flexibility and agility. Embracing new technologies, they can create a single, clean data set and provide the accurate analytics and tracking needed to inform strategic decision-making.
Making advanced analytics work for youRahul Chawla
This document outlines how companies can benefit from advanced analytics and big data. It discusses 3 key steps: 1) choosing the right data sources, 2) building models to predict and optimize business outcomes, and 3) transforming company capabilities. Specifically, it emphasizes sourcing data creatively to solve problems, using new technologies to access data, focusing models on business opportunities, developing analytics that can be used, embedding analytics in frontline tools, and upgrading analytical skills. The document argues that with the right approach, big data can significantly boost company performance and competitive advantage.
Today's Chief Data Officer: 3 Types of CDOs, Key Challenges, and Opportunitie...Scott Richardson
In this presentation we examine the 3 types of Chief Data Officers that are common in the industry today. We discuss their responsibilities, key challenges, and present ideas for having an meaningful impact. A great overview of the CDO role and how this executive position can contribute to the success of your business.
The data management procedure employed by your firm is capable of building your brand or breaking it all over. So, be wise in choosing the right strategy.
This document provides information on data governance and discusses several challenges and approaches to data governance. It discusses that 80% of enterprise data is unstructured and spread across many sources like web data, enterprise applications, emails, and social media. Governing such diverse data assets is a complex long-term journey. It also discusses why data governance is needed, challenges of data governance, and different routes and frameworks to conduct data governance assessments and develop solutions. These include using cases studies, lean six sigma methodology, enterprise data architecture approaches, and linking data governance with machine learning. The document concludes by emphasizing structure of data, experimenting with different assessment and solutioning methods, and leveraging machine learning as a new capability.
Welcome to the Chief Analytics Officer Forum Europe
On 7th – 9th March 2016, over 80 Chief Analytics Officers and senior analytics leaders met in London for intimate, top-level discussions; dissecting the role of the CAO, exploring innovative case studies and addressing mutual cross-industry challenges. To learn more, visit http://www.caoforumeurope.com/
This event is organised by http://coriniumintelligence.com/
Demonstrating Big Value in Big Data with New Analytics ApproachesJulie Severance
This document discusses IBM's journey to establishing an Analytics Center of Excellence (ACE) to overcome challenges with big data analytics. It describes how IBM previously had siloed analytics groups across its 400,000 employees and 200 locations. To address this, IBM developed a strategic plan through organizational readiness. This included standing up a virtual ACE team to develop standards, provide services, and align analytics with business strategy across the enterprise. The ACE also focused on quickly enabling a cloud prototype and user community to start realizing value from big data.
This document provides an overview of business analytics concepts. It defines business analytics as the skills, technologies, and practices used to explore past business performance and gain insights to drive planning. The document outlines a business analytics model with layers moving from strategic goals to technical data sources. It describes types of analytics including descriptive, predictive, and prescriptive. Finally, it discusses the importance of business analytics in decision making, understanding data, providing results, and managing data.
Data-Analytics-Essentials-Building-a-Foundation-for-Informed-Business-Choices...Attitude Tally Academy
Unlock the power of informed decision-making with our guide, "From Data to Decisions: Building a Solid Foundation for Business Success" Explore the essentials of data analytics, empowering your business to thrive in a data-driven era. Discover strategic insights, navigate through information overload, and transform raw data into actionable intelligence.Whether you're a startup or an established enterprise, this resource is your roadmap to making sound business choices and charting a course toward success.Dive into the world of data-backed strategies and position your business for growth in today's competitive landscape.
Useful Link:- https://www.attitudetallyacademy.com/class/pythonda
5 top reasons why data governance needs to business successshopiawilson
The data governance approach is a combination of processes and practices which help to ensure the management od data assets within an organization and enterprise.
The document discusses the importance of developing a big data plan. It states that while exploiting big data is an important source of competitive advantage, many companies struggle due to technical and organizational challenges. It recommends that companies craft a big data plan that focuses on three elements: assembling and integrating data from various sources, selecting analytic models that can optimize operations and predict business outcomes, and creating intuitive tools that help employees make use of the analytic outputs. Developing such a plan will help companies prioritize investments and initiatives to harness big data effectively.
At Axtria, we provide world-class training, support and growth prospects - all crafter to build on your unique skills and outline your success. You will be in a highly collaborative culture among a bunch of the most talented and visionary folks in the industry.
Data come from everywhere and everyone, but we need to be able to analyze them in a way to improve performances
Companies need to work faster but they also have to use those data to anticipate and innovate
Knowledge management and business intelligenceAzmi Taufik
1) Business intelligence is a set of tools and processes that analyze raw data to provide useful information to make business decisions. It includes technologies that transform data into meaningful insights.
2) Key aspects of business intelligence include allowing organizations to get a more accurate view of business and customers, increasing visibility, and enabling analysis of customer behavior.
3) Strategic knowledge management helps identify business needs, organize information flow, implement plans, and evaluate to improve by addressing goals, competitive advantage, and organizational performance.
The Aligned Agenda -Insights From Cfo And Cio Studiesmanish gupta
The document summarizes findings from multiple studies conducted by the IBM Institute for Business Value on CIOs and their roles. It analyzed interviews with over 2,500 CIOs between 2004-2010. Key findings include:
1) Successful CIOs balance complementary roles like being an insightful visionary and able pragmatist.
2) High growth CIOs are more actively involved in business strategy and innovation initiatives.
3) CIO roles need to be adapted based on industry and business priorities in order to drive innovation, increase IT value, and expand business impact.
The document discusses the role and importance of a Chief Data Officer (CDO). A CDO champions data as a strategic business asset and driver of revenue. They identify how data can support business priorities, ensure the right data is collected, and wire the company to make data-driven decisions by eliminating semantic differences in enterprise data.
The document discusses the role and importance of a Chief Data Officer (CDO). A CDO champions data as a strategic business asset and driver of revenue. They identify how data can support business priorities, ensure the right data is collected, and wire the company to make data-driven decisions by eliminating semantic differences in enterprise data.
Dynamic Talks: "Data Strategy as a Conduit for Data Maturity and Monetization...Grid Dynamics
Organizations need to tap into the huge potential of their vast volumes of data, but a use case tactical approach is not going to work. Instead, they need to work in the definition of a data strategy linked to the most relevant goals for the enterprise.
The document provides guidance on designing a data and analytics strategy. It discusses why data and analytics are important for business success in the digital age. It outlines 13 approaches to a data and analytics strategy organized by core business strategy and value proposition. It emphasizes the importance of data literacy, governance, and quality. It provides examples of how organizations have used data and analytics to improve outcomes. The overall message is that a clear strategy is needed to communicate the business value of data and maximize its impact.
The document discusses how organizations are facing challenges managing the growing amounts of data and information from various sources. This includes extracting insights from large amounts of structured and unstructured content across the enterprise. It also talks about the need to connect people and processes to improve collaboration, decision making and customer service. New approaches to enterprise information management are needed to gain control of data, drive business optimization and enable organizations to adapt quickly to changes.
Data has become a key focus for corporate leaders today. Chartered Global Management Accountant (CGMA) designation holders are well placed to help translate data into commercial insights and value.
Similar to Role of the Chief Data Office Infographic by IBM (20)
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.