Data and analytics leaders should make mission-critical investments to accelerate their ability to predict, transform, and respond based on these ten trends.
Unleash Your Data While Ensuring Governance and Security: Reporting, Prism, a...Workday, Inc.
For IT leaders, unlocking data is foundational to organizational success in a digital-first world. But what can you do to deliver data and insight to reduce the digital acceleration gap within your organization?
View this slide deck to learn:
How to unlock data for faster insights
How Workday Prism Analytics gives you the analytics you need in one secure place
How Workday strengthens partnerships with HR and finance
The Workday Financial Management Certification Training Program in IQ online will provide you with in-depth knowledge of Accounting and Finance, Revenue Management, Financial Reporting and Consolidation, Financial Planning, Project Billing and many more. Training at IQ Online will help you to become a certified Workday Financial Management professional with Real time projects and use cases. You will get the best support and guidance from our team of experts.
Embedded Analytics for the ISV: Supercharging Applications with BIBirst
Embedded analytics vendor Birst presented on embedding business intelligence (BI) capabilities into independent software vendor (ISV) applications. The presentation discussed Aberdeen research finding that embedded BI improves organizational performance, with leaders embedding BI across various applications like CRM and ERP. Case studies were presented of companies using Birst's embedded BI to increase revenue, optimize operations, and accelerate product development. The presentation concluded with a discussion of how embedded BI can benefit various industries and transform ISVs into forward-looking, data-driven organizations.
Give your customers what they want, with SaaS embedded analytics Powered by GoodData. Read this guide to learn why Zendesk says “Advanced analytics are the #1 reason why customers upgrade.” Get a better understanding of:
1. How embedded analytics can help you differentiate in a crowded SaaS market
2. Why Forrester identifies the cloud and analytics as two key drivers of future business applications innovation
3. How you can practice agile revenue development, monetizing the data you already have within your core application
4. The unique benefits of becoming a Powered by GoodData embedded analytics partner
5. How GoodData is driving revenue, retention and relationships for software vendors across operations, martech, health, travel and other sectors
Trying to figure out if embedded analytics are for you?
According to Gartner Research, more than 90% of business leaders view content information as a strategic asset, yet fewer than 10% can quantify its economic value. Read this guide to learn why you should be leveraging an asset you already own--data--to build relationships, increase retention, and drive revenue.
This 66-page guide goes over everything you need to know about embedded analytics - targeted for software executives and product managers looking to build product value with embedded analytics. Learn more at www.logianalytics.com.
Future-Proof Your Contracts for 2021 and BeyondWorkday, Inc.
How are you safeguarding your business against surprise renewals and deadlines?
Without an effective digital contract management strategy, procurement teams lack the control they need to optimize contract obligations and empower the business.
View this slide deck to learn how Workday can help you:
Reduce cycle times
Prevent contract delays
Proactively manage obligations
Unleash Your Data While Ensuring Governance and Security: Reporting, Prism, a...Workday, Inc.
For IT leaders, unlocking data is foundational to organizational success in a digital-first world. But what can you do to deliver data and insight to reduce the digital acceleration gap within your organization?
View this slide deck to learn:
How to unlock data for faster insights
How Workday Prism Analytics gives you the analytics you need in one secure place
How Workday strengthens partnerships with HR and finance
The Workday Financial Management Certification Training Program in IQ online will provide you with in-depth knowledge of Accounting and Finance, Revenue Management, Financial Reporting and Consolidation, Financial Planning, Project Billing and many more. Training at IQ Online will help you to become a certified Workday Financial Management professional with Real time projects and use cases. You will get the best support and guidance from our team of experts.
Embedded Analytics for the ISV: Supercharging Applications with BIBirst
Embedded analytics vendor Birst presented on embedding business intelligence (BI) capabilities into independent software vendor (ISV) applications. The presentation discussed Aberdeen research finding that embedded BI improves organizational performance, with leaders embedding BI across various applications like CRM and ERP. Case studies were presented of companies using Birst's embedded BI to increase revenue, optimize operations, and accelerate product development. The presentation concluded with a discussion of how embedded BI can benefit various industries and transform ISVs into forward-looking, data-driven organizations.
Give your customers what they want, with SaaS embedded analytics Powered by GoodData. Read this guide to learn why Zendesk says “Advanced analytics are the #1 reason why customers upgrade.” Get a better understanding of:
1. How embedded analytics can help you differentiate in a crowded SaaS market
2. Why Forrester identifies the cloud and analytics as two key drivers of future business applications innovation
3. How you can practice agile revenue development, monetizing the data you already have within your core application
4. The unique benefits of becoming a Powered by GoodData embedded analytics partner
5. How GoodData is driving revenue, retention and relationships for software vendors across operations, martech, health, travel and other sectors
Trying to figure out if embedded analytics are for you?
According to Gartner Research, more than 90% of business leaders view content information as a strategic asset, yet fewer than 10% can quantify its economic value. Read this guide to learn why you should be leveraging an asset you already own--data--to build relationships, increase retention, and drive revenue.
This 66-page guide goes over everything you need to know about embedded analytics - targeted for software executives and product managers looking to build product value with embedded analytics. Learn more at www.logianalytics.com.
Future-Proof Your Contracts for 2021 and BeyondWorkday, Inc.
How are you safeguarding your business against surprise renewals and deadlines?
Without an effective digital contract management strategy, procurement teams lack the control they need to optimize contract obligations and empower the business.
View this slide deck to learn how Workday can help you:
Reduce cycle times
Prevent contract delays
Proactively manage obligations
Emagia Master Class 3 | Integrated Order-to-Cash (OTC) Transformation for Glo...emagia
Integrated Order-to-Cash (OTC) Transformation for Global Shared Service Organizations. Emagia Master Class 3. Automated consolidated receivables – in total and by customer from multiple ERP’s
https://www.emagia.com/master-class/
5 Steps To Measure ROI On Your Data Science Initiatives - WebinarGramener
1. Measuring ROI from data science initiatives is challenging for many organizations as the outcomes are often not clearly defined, quantified, or attributed to the initiatives. Breaking the chain from data to insights to actions to outcomes is common.
2. A framework is presented for quantifying the value of data science initiatives using 5 steps - define success metrics, measure the metrics, attribute outcomes to causal factors, calculate net costs and benefits to determine breakeven, and benchmark results.
3. The framework is applied to a case study of a beverage manufacturer that used analytics to optimize plant costs. Key metrics like cost savings, employee productivity, and process efficiency were defined and attribution methods like A/B testing were used
NLB is a technology, analytics and advisory services company founded in 2007 with over 1500 resources in the US and Canada. It helps clients innovate and improve processes through best practices from Fortune 1000 companies. NLB provides services including analytics, process re-engineering, staff augmentation, and vendor environment optimization to help clients reduce costs and realize measurable business impacts. It takes an end-to-end approach to predictive analytics from identifying patterns to developing prescriptive actions and embedding insights into client systems and culture.
Business Intelligence - Why Data Visualization is Only the Tip of the IcebergPaul Campbell
Clear concise access to data is critical in today's rapidly changing business environment. However, there are pitfalls in reviewing data trends only. This presentation contains thoughts on how to complement your data visualization with processes that drive accountability and consistency.
Why Data Visualization is Only the Tip of the IcebergConnor Jordan
The document discusses how data visualization is only the tip of the iceberg when it comes to effectively communicating and using data. While data visualization helps analyze and understand complex data, challenges remain in connecting metrics to goals, accessing outdated reports, and restricting user customization. A more effective approach involves a scorecard system to track results, a accountability system for corrective actions, and a communication system to cascade information. Combined, these elements provide permissions control, cascading scorecards, an action register, and dashboards to give management insights into performance. The software PBL ScoreCard is said to integrate these components for efficient management.
The rise of data - business value and the management imperativesSheriff Shitu
Directing the attention of business managers and strategy executives away from the flood of Big Data marketing unto internal organizational factors important for the success of Data-related initiatives. Such include developing a coherent understanding of the potential of data, assessing the preparedness of the business from a capability perspective, limiting waste by starting small, and understanding the requirements for sustaining these initiatives through strategy, culture, and governance.
The report narrows in on becoming a data-driven company from three dimensions:
• Datafication of internal operations from which useful data can be generated. Such data reveals insights that can be used to save costs or optimize business operations.
• Datafication of external customer engagement and service delivery channels to ensure that sufficient data is generated from which insights about customer behaviour and preferences can be generated.
• Making necessary management changes (data governance, organizational strategy and culture) to nurture and support the adoption of sustainable data-driven initiatives.
Business Intelligence is an umbrella term that combines architectures, applications, and databases. It enables the real-time, interactive access, analysis, and manipulation of information, which provides the business community with easy access to business data. By giving this valuable insight, BI helps decision-makers make more informed decisions and supplies end-users with critical business information on their customers or partners, including information on behaviours and trends.”
The evolution of the Architecture of Enterprises (AKA Enterprise Architecture) Leo Barella
We are in the era of competitive advantage through smart information and analytics. Process automation and leveraging transactional systems is a "thing of the past". To advance organizations need to start designing their architecture leveraging microservices and focus on data management / analytics efficiency.
How to Drive Better Business Insights with Strong Data GovernanceMatt Dillon
Learn why leading CMOs and CEOs are making data governance and data integration a top priority for driving revenue growth and improving profitability.
Your data is one of the most important assets you have as a business but are you taking the necessary steps to manage your data with care?
Are you using your data to improve the customer experience and make better business decisions?
Webinar includes:
- Creating a centre of excellence
- Establishing data standardization
- Developing application management plans
- Release management
- Incorporating data & systems integration strategies
- Data cleansing essentials
Calculating the ROI on investing in data products?
Analytics return $13.01 for every dollar spent, according to Nucleus Research. That’s a 13:1 ROI for you, and for your customers when you offer embedded analytics in your SaaS solution. Check out this guide to learn more about the benefits of buying vs. building, and how GoodData customers like Influitive and Demandbase are achieving upwards of 650% ROI.
Optimize Business Intelligence Efforts With Embedded, Application-Driven Anal...SAP Analytics
Want to discover, procure, and provision business intelligence data more effectively? Forrester Consulting has a suggestion: embedded, application-driven analytics.
2 application aware storage drives business agility & competitive advantageDr. Wilfred Lin (Ph.D.)
The document discusses the rise of application-aware systems and the need for IT transformation. It makes three key points:
1) Business outcomes and innovation are becoming top priorities and KPIs for CIOs, requiring new IT strategies that are aligned with business goals.
2) The "Third Platform" of billions of connected devices, apps, and users is driving immense data growth and creating opportunities for competitive advantage through new technologies.
3) To address the challenges of rapid data growth and application explosion, there is a shift towards more integrated, application-aware infrastructure that is optimized for specific workloads through tighter software-hardware integration.
Real Life, Strategic BI Strategy for your IT Organizationmayamidmore
This document summarizes key aspects of developing a strategic business intelligence (BI) approach, including fitting BI within an overall IT strategy, implementing BI competency centers and standards, and using BI to improve IT performance. It discusses establishing a BI strategy to determine priority business questions and initiatives. The document also provides examples of strategic BI implementations and outlines stages of BI maturity from an initial, siloed approach to an integrated, strategic enabler of business goals.
Assure Creative Projects Deliver Awesome Business Resultsmaryat
Assure creative projects designed in-house, in ad agencies or in design shops deliver awesome business results by understanding the core components of a successful workflow.
Business intelligence (BI) software tools integrate customer data to help managers ascertain business prospects and increase operational efficiency. BI tools comprise features like data mining, searching, querying, OLAP, dashboards, and reporting to plan strategic business decisions. Cloud, on-premises, and SaaS are common deployment methods for BI software. Standard BI tools should accommodate multiple users, provide insightful reports, adapt to existing systems/data, and ensure accurate, up-to-date customer information. BI software solutions from vendors like MicroStrategy and Tableau introduce novel features and help managers make critical decisions around cost reduction, opportunities, resource allocation, and performance management.
Power bi implementation for finance services firmsaddendanalytics
Addend Analytics is a Microsoft Power BI-partner based in Mumbai, India. Apart from being authorized for Power BI implementations, Addend has successfully executed Power BI projects for 100+ clients across sectors like financial services, Banking, Insurance, Retail, Sales, Manufacturing, Real estate, Logistics, and Healthcare in countries like the US, Europe, Australia, and India. Companies partnering with us save their valuable time and efforts of searching and managing resources while saving hugely on the development costs and hence, most of the small and medium enterprises in North America prefer Addend to be their Power BI implementation partner.
These slides contain general advice for considering an ALM tooling solution. This includes management of requirements, tests and defects. It is a draft release.
Customer analytics. Turn big data into big valueJosep Arroyo
BIRT Analytics is a customer analytics solution that allows companies to gain valuable insights from big data. It integrates data from multiple sources, analyzes large volumes of data, and provides clear and granular customer information. Tools allow users to explore data, identify patterns, profile customers, and forecast trends. Advanced analytics help optimize marketing, identify cross-sell opportunities, and understand customer behavior. The solution aims to help companies understand customer needs and adapt strategies based on real customer data.
How to Build Data Governance Programs That Last: A Business-First ApproachPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves governance leaders and data stewards having to continually make the case for data governance to secure business adoption.
In this introductory session, we will share the core components of a business-first data governance approach that promotes organizational adoption, lays the foundation for data integrity, and consistently delivers business value for the long term.
The software development process is complete for computer project analysis, and it is important to the evaluation of the random project. These practice guidelines are for those who manage big-data and big-data analytics projects or are responsible for the use of data analytics solutions. They are also intended for business leaders and program leaders that are responsible for developing agency capability in the area of big data and big data analytics .
For those agencies currently not using big data or big data analytics, this document may assist strategic planners, business teams and data analysts to consider the value of big data to the current and future programs.
This document is also of relevance to those in industry, research and academia who can work as partners with government on big data analytics projects.
Technical APS personnel who manage big data and/or do big data analytics are invited to join the Data Analytics Centre of Excellence Community of Practice to share information of technical aspects of big data and big data analytics, including achieving best practice with modeling and related requirements. To join the community, send an email to the Data Analytics Centre of Excellence
This document discusses 3 trends driving the adoption of AI into everyday enterprise use in 2022 and beyond. The first trend is that business users are starting to deliver more value with AI than data scientists alone. This is enabled by citizen data science programs that upskill analysts and business people to work directly with data and build AI models. The second trend is the convergence of automation, business intelligence, and AI into a single practice. The third trend is that over 50% of machine learning projects that organizations want to deploy are making it into production.
Emagia Master Class 3 | Integrated Order-to-Cash (OTC) Transformation for Glo...emagia
Integrated Order-to-Cash (OTC) Transformation for Global Shared Service Organizations. Emagia Master Class 3. Automated consolidated receivables – in total and by customer from multiple ERP’s
https://www.emagia.com/master-class/
5 Steps To Measure ROI On Your Data Science Initiatives - WebinarGramener
1. Measuring ROI from data science initiatives is challenging for many organizations as the outcomes are often not clearly defined, quantified, or attributed to the initiatives. Breaking the chain from data to insights to actions to outcomes is common.
2. A framework is presented for quantifying the value of data science initiatives using 5 steps - define success metrics, measure the metrics, attribute outcomes to causal factors, calculate net costs and benefits to determine breakeven, and benchmark results.
3. The framework is applied to a case study of a beverage manufacturer that used analytics to optimize plant costs. Key metrics like cost savings, employee productivity, and process efficiency were defined and attribution methods like A/B testing were used
NLB is a technology, analytics and advisory services company founded in 2007 with over 1500 resources in the US and Canada. It helps clients innovate and improve processes through best practices from Fortune 1000 companies. NLB provides services including analytics, process re-engineering, staff augmentation, and vendor environment optimization to help clients reduce costs and realize measurable business impacts. It takes an end-to-end approach to predictive analytics from identifying patterns to developing prescriptive actions and embedding insights into client systems and culture.
Business Intelligence - Why Data Visualization is Only the Tip of the IcebergPaul Campbell
Clear concise access to data is critical in today's rapidly changing business environment. However, there are pitfalls in reviewing data trends only. This presentation contains thoughts on how to complement your data visualization with processes that drive accountability and consistency.
Why Data Visualization is Only the Tip of the IcebergConnor Jordan
The document discusses how data visualization is only the tip of the iceberg when it comes to effectively communicating and using data. While data visualization helps analyze and understand complex data, challenges remain in connecting metrics to goals, accessing outdated reports, and restricting user customization. A more effective approach involves a scorecard system to track results, a accountability system for corrective actions, and a communication system to cascade information. Combined, these elements provide permissions control, cascading scorecards, an action register, and dashboards to give management insights into performance. The software PBL ScoreCard is said to integrate these components for efficient management.
The rise of data - business value and the management imperativesSheriff Shitu
Directing the attention of business managers and strategy executives away from the flood of Big Data marketing unto internal organizational factors important for the success of Data-related initiatives. Such include developing a coherent understanding of the potential of data, assessing the preparedness of the business from a capability perspective, limiting waste by starting small, and understanding the requirements for sustaining these initiatives through strategy, culture, and governance.
The report narrows in on becoming a data-driven company from three dimensions:
• Datafication of internal operations from which useful data can be generated. Such data reveals insights that can be used to save costs or optimize business operations.
• Datafication of external customer engagement and service delivery channels to ensure that sufficient data is generated from which insights about customer behaviour and preferences can be generated.
• Making necessary management changes (data governance, organizational strategy and culture) to nurture and support the adoption of sustainable data-driven initiatives.
Business Intelligence is an umbrella term that combines architectures, applications, and databases. It enables the real-time, interactive access, analysis, and manipulation of information, which provides the business community with easy access to business data. By giving this valuable insight, BI helps decision-makers make more informed decisions and supplies end-users with critical business information on their customers or partners, including information on behaviours and trends.”
The evolution of the Architecture of Enterprises (AKA Enterprise Architecture) Leo Barella
We are in the era of competitive advantage through smart information and analytics. Process automation and leveraging transactional systems is a "thing of the past". To advance organizations need to start designing their architecture leveraging microservices and focus on data management / analytics efficiency.
How to Drive Better Business Insights with Strong Data GovernanceMatt Dillon
Learn why leading CMOs and CEOs are making data governance and data integration a top priority for driving revenue growth and improving profitability.
Your data is one of the most important assets you have as a business but are you taking the necessary steps to manage your data with care?
Are you using your data to improve the customer experience and make better business decisions?
Webinar includes:
- Creating a centre of excellence
- Establishing data standardization
- Developing application management plans
- Release management
- Incorporating data & systems integration strategies
- Data cleansing essentials
Calculating the ROI on investing in data products?
Analytics return $13.01 for every dollar spent, according to Nucleus Research. That’s a 13:1 ROI for you, and for your customers when you offer embedded analytics in your SaaS solution. Check out this guide to learn more about the benefits of buying vs. building, and how GoodData customers like Influitive and Demandbase are achieving upwards of 650% ROI.
Optimize Business Intelligence Efforts With Embedded, Application-Driven Anal...SAP Analytics
Want to discover, procure, and provision business intelligence data more effectively? Forrester Consulting has a suggestion: embedded, application-driven analytics.
2 application aware storage drives business agility & competitive advantageDr. Wilfred Lin (Ph.D.)
The document discusses the rise of application-aware systems and the need for IT transformation. It makes three key points:
1) Business outcomes and innovation are becoming top priorities and KPIs for CIOs, requiring new IT strategies that are aligned with business goals.
2) The "Third Platform" of billions of connected devices, apps, and users is driving immense data growth and creating opportunities for competitive advantage through new technologies.
3) To address the challenges of rapid data growth and application explosion, there is a shift towards more integrated, application-aware infrastructure that is optimized for specific workloads through tighter software-hardware integration.
Real Life, Strategic BI Strategy for your IT Organizationmayamidmore
This document summarizes key aspects of developing a strategic business intelligence (BI) approach, including fitting BI within an overall IT strategy, implementing BI competency centers and standards, and using BI to improve IT performance. It discusses establishing a BI strategy to determine priority business questions and initiatives. The document also provides examples of strategic BI implementations and outlines stages of BI maturity from an initial, siloed approach to an integrated, strategic enabler of business goals.
Assure Creative Projects Deliver Awesome Business Resultsmaryat
Assure creative projects designed in-house, in ad agencies or in design shops deliver awesome business results by understanding the core components of a successful workflow.
Business intelligence (BI) software tools integrate customer data to help managers ascertain business prospects and increase operational efficiency. BI tools comprise features like data mining, searching, querying, OLAP, dashboards, and reporting to plan strategic business decisions. Cloud, on-premises, and SaaS are common deployment methods for BI software. Standard BI tools should accommodate multiple users, provide insightful reports, adapt to existing systems/data, and ensure accurate, up-to-date customer information. BI software solutions from vendors like MicroStrategy and Tableau introduce novel features and help managers make critical decisions around cost reduction, opportunities, resource allocation, and performance management.
Power bi implementation for finance services firmsaddendanalytics
Addend Analytics is a Microsoft Power BI-partner based in Mumbai, India. Apart from being authorized for Power BI implementations, Addend has successfully executed Power BI projects for 100+ clients across sectors like financial services, Banking, Insurance, Retail, Sales, Manufacturing, Real estate, Logistics, and Healthcare in countries like the US, Europe, Australia, and India. Companies partnering with us save their valuable time and efforts of searching and managing resources while saving hugely on the development costs and hence, most of the small and medium enterprises in North America prefer Addend to be their Power BI implementation partner.
These slides contain general advice for considering an ALM tooling solution. This includes management of requirements, tests and defects. It is a draft release.
Customer analytics. Turn big data into big valueJosep Arroyo
BIRT Analytics is a customer analytics solution that allows companies to gain valuable insights from big data. It integrates data from multiple sources, analyzes large volumes of data, and provides clear and granular customer information. Tools allow users to explore data, identify patterns, profile customers, and forecast trends. Advanced analytics help optimize marketing, identify cross-sell opportunities, and understand customer behavior. The solution aims to help companies understand customer needs and adapt strategies based on real customer data.
How to Build Data Governance Programs That Last: A Business-First ApproachPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves governance leaders and data stewards having to continually make the case for data governance to secure business adoption.
In this introductory session, we will share the core components of a business-first data governance approach that promotes organizational adoption, lays the foundation for data integrity, and consistently delivers business value for the long term.
The software development process is complete for computer project analysis, and it is important to the evaluation of the random project. These practice guidelines are for those who manage big-data and big-data analytics projects or are responsible for the use of data analytics solutions. They are also intended for business leaders and program leaders that are responsible for developing agency capability in the area of big data and big data analytics .
For those agencies currently not using big data or big data analytics, this document may assist strategic planners, business teams and data analysts to consider the value of big data to the current and future programs.
This document is also of relevance to those in industry, research and academia who can work as partners with government on big data analytics projects.
Technical APS personnel who manage big data and/or do big data analytics are invited to join the Data Analytics Centre of Excellence Community of Practice to share information of technical aspects of big data and big data analytics, including achieving best practice with modeling and related requirements. To join the community, send an email to the Data Analytics Centre of Excellence
This document discusses 3 trends driving the adoption of AI into everyday enterprise use in 2022 and beyond. The first trend is that business users are starting to deliver more value with AI than data scientists alone. This is enabled by citizen data science programs that upskill analysts and business people to work directly with data and build AI models. The second trend is the convergence of automation, business intelligence, and AI into a single practice. The third trend is that over 50% of machine learning projects that organizations want to deploy are making it into production.
Booz Allen Hamilton uses its Cloud Analytics Reference Architecture to build technology infrastructures that can withstand the weight of massive datasets – and deliver the deep insights organizations need to drive innovation.
Maximize the Value of Your Data: Neo4j Graph Data PlatformNeo4j
In this 60-minute conversation with IDC, we will highlight the momentum and reasons why a graph data platform is a breakthrough solution for businesses in need of a flexible data model.
Please join Mohit Sagar, Group Managing Director of CIO Network, as he hosts the conversation with Dr. Christopher Lee Marshall, Associate VP at IDC, and Nik Vora, Vice President of APAC at Neo4. During this very exciting discussion, you'll discover the insights and knowledge unlocked with the graph data platform.
Big data refers to massive amounts of structured and unstructured data that is difficult to process using traditional databases. It is characterized by volume, variety, velocity, and veracity. Major sources of big data include social media posts, videos uploaded, app downloads, searches, and tweets. Trends in big data include increased use of sensors, tools for non-data scientists, in-memory databases, NoSQL databases, Hadoop, cloud storage, machine learning, and self-service analytics. Big data has applications in banking, media, healthcare, energy, manufacturing, education, and transportation for tasks like fraud detection, personalized experiences, reducing costs, predictive maintenance, measuring teacher effectiveness, and traffic control.
This document provides summaries of trends in IT, including cloud computing, business analytics, artificial intelligence and machine learning, and database management systems. It discusses how cloud computing allows users to access computing resources over the internet rather than owning hardware. It also explains how business analytics uses data and modeling to help businesses make decisions, and how artificial intelligence and machine learning use algorithms to enable machines to learn from data and mimic human behavior. Finally, it defines a database management system as software that interfaces with databases and allows users to organize, access, and manage data.
This document provides a summary of 19 vendor briefings from the 2016 Strata Conference in NYC. It includes 3-sentence summaries of presentations by Alation, AllSight, Alpine Data, Basho Technologies, Cambridge Semantics, Continuum Analytics, Dataiku, Dell EMC, GigaSpaces, Logtrust, MapR Technologies, Rocana, and SAP. Each summary highlights the vendor's solution, how it addresses key challenges identified in DEJ research, and a relevant quote from the presentation.
How Analytics Has Changed in the Last 10 Years (and How It’s Staye.docxpooleavelina
How Analytics Has Changed in the Last 10 Years (and How It’s Stayed the Same)
· Thomas H. Davenport
June 22, 2017
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Ten years ago, Jeanne Harris and I published the book Competing on Analytics, and we’ve just finished updating it for publication in September. One major reason for the update is that analytical technology has changed dramatically over the last decade; the sections we wrote on those topics have become woefully out of date. So revising our book offered us a chance to take stock of 10 years of change in analytics.
Of course, not everything is different. Some technologies from a decade ago are still in broad use, and I’ll describe them here too. There has been even more stability in analytical leadership, change management, and culture, and in many cases those remain the toughest problems to address. But we’re here to talk about technology. Here’s a brief summary of what’s changed in the past decade.
The last decade, of course, was the era of big data. New data sources such as online clickstreams required a variety of new hardware offerings on premise and in the cloud, primarily involving distributed computing — spreading analytical calculations across multiple commodity servers — or specialized data appliances. Such machines often analyze data “in memory,” which can dramatically accelerate times-to-answer. Cloud-based analytics made it possible for organizations to acquire massive amounts of computing power for short periods at low cost. Even small businesses could get in on the act, and big companies began using these tools not just for big data but also for traditional small, structured data.
Insight Center
· Putting Data to Work
Analytics are critical to companies’ performance.
Along with the hardware advances, the need to store and process big data in new ways led to a whole constellation of open source software, such as Hadoop and scripting languages. Hadoop is used to store and do basic processing on big data, and it’s typically more than an order of magnitude cheaper than a data warehouse for similar volumes of data. Today many organizations are employing Hadoop-based data lakes to store different types of data in their original formats until they need to be structured and analyzed.
Since much of big data is relatively unstructured, data scientists created ways to make it structured and ready for statistical analysis, with new (and old) scripting languages like Pig, Hive, and Python. More-specialized open source tools, such as Spark for streaming data and R for statistics, have also gained substantial popularity. The process of acquiring and using open source software is a major change in itself for established busines ...
The document summarizes predictions from experts at the International Institute for Analytics (IIA) regarding analytics in 2012. Some key predictions include:
- Big data analytics will change the technology landscape in 2012 as organizations leverage new sources of data.
- Cloud-based predictive analytics will see rapid growth driven by positive results from early adopters.
- Analytics applications will evolve to integrate business processes, predictive modeling, data warehousing, and social collaboration.
- Privacy concerns will be a major factor in how big data analytics evolves given increased scrutiny of data collection and usage.
- Demand for analytics talent will continue rising but at a slower rate than previous years.
Extreme data growth, changing data types and distribution of data set almost impossible requests to existing traditional data bases. Data base systems are growing more and more complex without actually providing better usage models or increasing business value. Fast and simple access to data of any type and in any location is paramount for extracting real value from data. New data base systems come with shift in fundamental design and possibilities and still provide well know and established mechanisms for data access and integration.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
Data Analytics has become a powerful tool to drive corporates and businesses. check out this 6 Reasons to Use Data Analytics. Visit: https://www.raybiztech.com/blog/data-analytics/6-reasons-to-use-data-analytics
This document discusses big data and its challenges related to the Internet of Things (IoT). It first defines big data and explains how the aggregation of data from many IoT systems can lead to big data. It then discusses some key challenges of big data, including issues with data volume, velocity, variety, and veracity. Specific challenges for big data from IoT systems are also reviewed, such as authentication, security, and uncertainty of data. Finally, the document outlines some potential solutions to big data challenges, such as using MapReduce for heterogeneous data, data cleaning techniques for inconsistencies, and cloud-based security platforms for IoT devices.
Business intelligence (BI) focuses on analyzing past business data to provide insights for decision-making, while data science aims to predict future trends through analyzing patterns in data using machine learning and other advanced analytics techniques. BI uses structured data to identify strengths and weaknesses, while data science can leverage both structured and unstructured data to develop forecasts. Data science requires stronger technical skills than BI and can manage more dynamic data sources.
The document is a seminar report submitted by Nikita Sanjay Rajbhoj to Prof. Ashwini Jadhav at G.S. Moze College of Engineering. It discusses big data, including its definition, characteristics, architecture, technologies, and applications. The report includes an abstract, introduction, and sections on definition, characteristics, architecture, technologies, and applications of big data. It also includes references, acknowledgements, and certificates.
Practical analytics john enoch white paperJohn Enoch
This document discusses using data analytics to provide value to businesses. It recommends starting with smaller, more manageable data sets and business intelligence (BI) projects that have clear goals and can yield quick wins, like analyzing travel costs. While big data holds promise, the author advises focusing first on consolidating existing data that is stuck in silos and using BI to improve processes and save costs in areas employees already know need improvement. Starting small builds skills for larger initiatives and ensures analytics provides practical benefits.
This document provides an overview of predictive analytics and its growing importance. It discusses how advances in technologies like cloud computing and the internet of things are enabling businesses to gather and analyze vast amounts of data. While descriptive and diagnostic analytics describe what happened in the past, predictive analytics uses statistical techniques to create models that forecast future outcomes. The document outlines several key drivers that are pushing predictive analytics towards mainstream adoption over the next few years, including easier-to-use tools, open source software, innovation from startups, and the availability of cloud-based solutions. It concludes that the combination of big data and predictive analytics will continue to accelerate innovation across industries.
The Cloud Analytics Reference Architecture: Harnessing Big Data to Solve Comp...Booz Allen Hamilton
The document discusses Booz Allen's Cloud Analytics Reference Architecture, an innovative approach to implementing big data analytics. It removes constraints of traditional systems by integrating and analyzing all available data from multiple sources. At the core are systems that can handle petabytes of data at reasonable cost while allowing analytics to run at large scales quickly. The goal is to leverage machines to do 80% of routine work while enabling human insights through analysis and creativity.
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Let me tell you what we see.
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https://www.meetup.com/unstructured-data-meetup-new-york/
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Watch the video recording at https://youtu.be/5vjwGfPN9lw
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Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
2. Data and analytics leaders should make mission-
critical investments to accelerate their ability to
predict, transform, and respond based on these
ten trends.
3. The open, containerized analysis architecture improves
the assemblability of analysis capabilities . The assembled
data analysis architecture uses components from
multiple data, analysis, and artificial intelligence solutions
to quickly build flexible, user-friendly smart applications,
helping data and analysis leaders connect insights with
actions.
As the data center shifts to the cloud , assembled data
and analysis architecture will become a more agile way
to build analysis applications through the cloud market
and low-code and no-code solutions.
TREND 1 : SMARTER, RESPONSIBLE,
SCALABLE AI (SMARTER,
RESPONSIBLE, SCALABLE AI)
TREND 2: ASSEMBLED DATA AND
ANALYSIS ARCHITECTURE (COMPOSABLE
DATA AND ANALYTICS)
The influence of artificial intelligence (AI) and
machine learning (ML) is increasing, so companies
must use new technologies to obtain AI solutions
that are smarter, less demanding on data, more
ethically responsible, and more resilient. Enterprises
can accelerate time to value and increase business
impact by deploying smarter, more responsible, and
scalable AI, using learning algorithms and
interpretable systems.
4. TREND 3: DATA FABRIC IS THE
FOUNDATION
TREND 4: FROM BIG TO SMALL AND WIDE
DATA
With the increase in digitization and the diminishing
of constraints on data consumers, data and analysis
leaders are increasingly using data weaving to help
solve the increasing diversity, dispersion, scale and
complexity of corporate data assets. problem.
Data weaving uses analytical techniques to maintain
monitoring of the data pipeline. Data weaving
supports the design, deployment and utilization of
different data through continuous analysis of data
assets, reducing integration time by 30%,
deployment time by 30%, and maintenance time by
70%.
The extreme business changes triggered by the new crown
epidemic have made machine learning and artificial intelligence
models based on large amounts of historical data less reliable.
At the same time, human and artificial intelligence decision-
making has become more complex and strict, and data and
analysis leaders must have more data in order to better
understand the situation.
Therefore, data and analysis leaders should choose analysis
techniques that can make more effective use of existing data.
Data and analysis leaders rely on “wide” data for the analysis
and collaboration of various “small” and “large”, unstructured
and structured data sources, and also rely on “small” data —
that is, the amount of data required Few, but still able to
provide practical insight analysis techniques.
5. TREND 5: XOPS TREND 6: ENGINEERING DECISION
INTELLIGENCE
The goal of XOps (data, machine learning, models,
and platforms) is to use DevOps best practices to
achieve efficiency and economies of scale, while
ensuring reliability, reusability, and repeatability, while
reducing the duplication of technology and
processes and achieving automation.
The reason most analytics and AI projects fail is to
treat business as an afterthought. If data and
analysis leaders use XOps to implement large-scale
business, they will be able to achieve the
reproducibility, traceability, integrity, and integration
of analysis and AI assets.
Engineering decision-making intelligence is not only
suitable for single decision-making, but also for
continuous decision-making. This technology can group
decision-making into business processes and even for
emerging decision-making networks. With the increasing
degree of automation and enhancement of decision-
making, engineering decision-making provides data and
analysis leaders with opportunities to make decision-
making more accurate, repeatable, transparent and
traceable.
6. TREND 7: DATA AND ANALYTICS AS A
CORE BUSINESS FUNCTION (DATA AND
ANALYTICS AS A CORE BUSINESS
FUNCTION)
TREND 8: GRAPH RELATES EVERYTHING
Data and analysis activities are no longer a secondary
activity, but transformed into a core business
function. In this case, data and analysis have become
shared business assets consistent with business
results, and due to better collaboration between the
central and federal data and analysis teams, the
problem of data and analysis silos is easily solved.
Graph technology has become the basis of many modern
data and analysis capabilities, and can discover the
relationship between people, places, things, events, and
locations in different data assets. Data and analysis leaders
rely on graph technology to quickly answer complex
business questions that need to be answered after
understanding the situation and understanding the nature
of the connections and advantages between multiple
entities.
By 2025, the proportion of graph technology in data and
analysis innovation will rise from 10% in 2021 to 80%. The
technology will facilitate rapid decision-making throughout
the enterprise organization.
7. TREND 9: THE RISE OF THE
AUGMENTED CONSUMER (THE RISE OF
THE AUGMENTED CONSUMER)
TREND 10: DATA AND ANALYTICS AT THE
EDGE
Today, most business users are using predefined
dashboards and manual data exploration, which can
lead to wrong conclusions and flawed decisions and
actions. Pre-defined dashboards will gradually be
replaced by automated, conversational, mobile and
dynamically generated insights, and these insights
are customized according to user needs and
delivered to the user when they need to consume the
data.
“This will enable information data consumers, that is,
enhanced data consumers, to use analytical
techniques, so that they can gain insights and
knowledge that only analysts and citizen data
experts can have.”
Data, analytics, and other technologies that support them
are moving to the edge computing environment,
constantly approaching real-world assets and beyond
the scope of IT.
Data and analytics leaders can use this trend to achieve
greater data management flexibility, speed, governance,
and resilience. From supporting real-time event analysis
to realizing autonomous behavior of “things”, various
types of use cases are stimulating people’s interest in
edge data and analysis capabilities.