The document discusses business data and the importance of aligning business data with business strategy. It defines business data as data collected and stored by businesses to support operations and decision making. It also discusses common types of business data like customer data, transactions, and social media data. The document emphasizes that a data strategy should be driven by business goals and outlines key elements of an effective data strategy like defining goals, governance, and aligning data initiatives with business objectives.
Becoming a Data-Driven Organization - Aligning Business & Data StrategyDATAVERSITY
More organizations are aspiring to become ‘data driven businesses’. But all too often this aim fails, as business goals and IT & data realities are misaligned, with IT lagging behind rapidly changing business needs. So how do you get the perfect fit where data strategy is driven by and underpins business strategy? This webinar will show you how by de-mystifying the building blocks of a global data strategy and highlighting a number of real world success stories. Topics include:
•How to align data strategy with business motivation and drivers
•Why business & data strategies often become misaligned & the impact
•Defining the core building blocks of a successful data strategy
•The role of business and IT
•Success stories in implementing global data strategies
Chief Data & Analytics Officer Fall Boston - PresentationSrinivasan Sankar
Data Asset Catalog & Metadata Management - Is It a Fad or Is It the Future?
Many have dubbed metadata as “the new black,” but is this accurate?
How to leverage metadata management to streamline data governance and ensure transparency
Improving data quality and ensuring consistency and accuracy of data across various reporting systems
Looking at the flip side: what are the additional training requirements and value-added for the business?
Data Strategy - Executive MBA Class, IE Business SchoolGam Dias
For today's enterprise Data is now very much a corporate asset, vital to delivering products and services efficiently and cost effectively. There are few organizations that can survive without harnessing data in some way.
Viewed as a strategic asset, data can be a source of new internal efficiencies, improved competitive advantage or a source of entirely new products that can be targeted at your existing or new customers.
This slide deck contains the highlights of a one day course on Data Strategy taught as part of the Executive MBA Program at IE Business School in Madrid.
Enterprise Analytics: Serving Big Data Projects for HealthcareDATA360US
Andrew Rosenberg's Presentation on "Enterprise Analytics: Serving Big Data Projects for Healthcare" at DATA 360 Healthcare Informatics Conference - March 5th, 2015
The Chief Data Officer: Tomorrow's Corporate RockstarKatrina Read
The transformative power of data and analytics is being harnessed by organisations around the world to make smarter, quicker and more analytical-driven decisions. At the helm of this transformation is the Chief Data Officer – a strategic leader who employs data and analytics to create tangible business value, and who is rapidly attracting rock star status.
Becoming a Data-Driven Organization - Aligning Business & Data StrategyDATAVERSITY
More organizations are aspiring to become ‘data driven businesses’. But all too often this aim fails, as business goals and IT & data realities are misaligned, with IT lagging behind rapidly changing business needs. So how do you get the perfect fit where data strategy is driven by and underpins business strategy? This webinar will show you how by de-mystifying the building blocks of a global data strategy and highlighting a number of real world success stories. Topics include:
•How to align data strategy with business motivation and drivers
•Why business & data strategies often become misaligned & the impact
•Defining the core building blocks of a successful data strategy
•The role of business and IT
•Success stories in implementing global data strategies
Chief Data & Analytics Officer Fall Boston - PresentationSrinivasan Sankar
Data Asset Catalog & Metadata Management - Is It a Fad or Is It the Future?
Many have dubbed metadata as “the new black,” but is this accurate?
How to leverage metadata management to streamline data governance and ensure transparency
Improving data quality and ensuring consistency and accuracy of data across various reporting systems
Looking at the flip side: what are the additional training requirements and value-added for the business?
Data Strategy - Executive MBA Class, IE Business SchoolGam Dias
For today's enterprise Data is now very much a corporate asset, vital to delivering products and services efficiently and cost effectively. There are few organizations that can survive without harnessing data in some way.
Viewed as a strategic asset, data can be a source of new internal efficiencies, improved competitive advantage or a source of entirely new products that can be targeted at your existing or new customers.
This slide deck contains the highlights of a one day course on Data Strategy taught as part of the Executive MBA Program at IE Business School in Madrid.
Enterprise Analytics: Serving Big Data Projects for HealthcareDATA360US
Andrew Rosenberg's Presentation on "Enterprise Analytics: Serving Big Data Projects for Healthcare" at DATA 360 Healthcare Informatics Conference - March 5th, 2015
The Chief Data Officer: Tomorrow's Corporate RockstarKatrina Read
The transformative power of data and analytics is being harnessed by organisations around the world to make smarter, quicker and more analytical-driven decisions. At the helm of this transformation is the Chief Data Officer – a strategic leader who employs data and analytics to create tangible business value, and who is rapidly attracting rock star status.
Data Analytics: An On-Ramp to a Better Understanding of Your BusinessSkoda Minotti
Data analytics is a hot topic in business today. But is it right for your business? What does it do for you, and most importantly, how do you get started? This executive overview explores the business implications of data analytics, while leaving the technicalities to the side.
Improve IT Security and Compliance with Mainframe Data in SplunkPrecisely
Avoid security blind spots with an enterprise-wide view.
If your organization relies on Splunk as its security nerve center, you can’t afford to leave out your mainframes.
They work with the rest of your IT infrastructure to support critical business applications–and they need to be
viewed in that wider context to address potential security blind spots.
Although the importance of including mainframe data in Splunk is undeniable, many organizations have left it out
because Splunk doesn’t natively support IBM Z® environments. Learn how Precisely Ironstream can help with a
straight-forward, powerful approach for integrating your mainframe security data into Splunk, and making it actionable
once it’s there.
Designed to address more mature programs, this tutorial covers the issues and approaches to sustaining Data Governance and value creation over time, amongst a changing business and personnel environment.
Part of the reason many companies launch a Data Governance program again and again is that over time, it is challenging to maintain the enthusiasm and excitement that accompanies a newly initiated program.
Learn about:
• Typical obstacles to sustainable Data Governance
• Re-energizing your program after a key player (or two) leave and other personnel challenges
• Staying relevant to the company as the business evolves over time
• Understanding the role of metrics and why they are critical
• Leveraging Communication and Stakeholder Management practices to maintain commitment
• Embedding Data Governance into the operations of the company
Data governance involves setting up procedures and regulations to enable the smooth sharing, managing, and availability of data.
The idea is to prevent an overlap of resources. When you have data governance procedures you experience faster decision-making processes while moving data from just a company’s by-product to a critical asset within the organization. Check out this and know how to build a strong Governance framework for your organization
How to Create and Manage a Successful Analytics OrganizationDATAVERSITY
For the last few years, analytics, data science and data management have achieved tremendous exposure on all the media channels. Big Data has become a major topic of discussion, catalyzing attention among the C-Level executives and driving investments and projects inside the enterprise. However, it is really interesting that just a selected group of business has created successful data teams and has mastered the skills to manage it. What we have seen is that most companies still do not know how to create, implement and manage a data and analytics organization. Above all, if data has become an strategic asset and is being considered the new oil for the 21st century economy, what your strategy to handle it ? This webinar will help to bring some concepts and ideas to enlighten your path to create and manage an analytics organization, providing some real life examples on companies which did it.
Thanks to wiki leaks, the NSA and intense regulatory environments, metadata is now a common term. What is the role of the Top Data Job and what are the options to deploy an effective metadata environment. John will explore some new avenues and lessons learned for metadata.
In this webinar, we will discuss:
Metadata Architectures
Metadata Types and Categories
Trends in Metadata Technology
The CDO’s role in the Metadata Function
The Data Driven University - Automating Data Governance and Stewardship in Au...Pieter De Leenheer
Data Governance and Stewardship requires automation of business semantics management at its nucleus, in order to achieve data trust between business and IT communities in the organization. University divisions operate highly autonomously and decentralized, and are often geographically distributed. Hence, they benefit more from an collaborative and agile approach to Data Governance and Stewardship approach that adapts to its nature.
In this lecture, we start by reviewing 'C' in ICT and reflect on the dilemma: what is the most important quality of data being shared: truth or trust? We review the wide spectrum of business semantics. We visit the different phases of growing data pain as an organization expands, and we map each phase on this spectrum of semantics.
Next, we introduce our principles and framework for business semantics management to support Data Governance and Stewardship focusing on the structural (what), processual (how) and organizational (who) components. We illustrate with use cases from Stanford University, George Washington University and Public Science and Innovation Administrations.
In this PPT, We describing the important things about Data Management and Data Governance. The data governance approach provides the right practices and processes that help an enterprise manage its data flows.
Unlocking Success in the 3 Stages of Master Data ManagementPerficient, Inc.
Master data management (MDM) comprises the processes, governance, policies, standards and tools that define and manage critical data. MDM is used to conduct strategic initiatives such as customer 360, product excellence and operational efficiency.
The quality of enterprise Information depends on the master data, so getting it right should be a high priority. This webinar will highlight key factors needed for success in each of the three stages of the MDM journey:
Planning
Implementation
Steady state
We review each stage in detail and provide insight into planning and collaborative activities. In this slideshare you will learn:
Best practices, tips and techniques for a successful MDM program
Top considerations for business case building, architecture and going live
How to support the overall program after launching your MDM program
Alignment: Office of the Chief Data Officer & BCBS 239Craig Milroy
Alignment: Office of the Chief Data Officer & BCBS 239. Alignment overview between OCDO framework and Principles for Effective Risk Data Aggregation and Risk Reporting.
Are you your company’s chief data officer? Given the scarcity of the official role, it’s likely that you’re not — at least in title. But that doesn't mean that you shouldn't operate like one. Do you approach data leadership as a C-level executive or a senior data head? Is your team’s output strategic or just operational? In this interactive keynote, one of the Windy City’s foremost data leaders will lead an interactive discussion on what it takes to lead like a chief, what it looks like, and how to get there and get it done.
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
Business impact without data governanceJohn Bao Vuu
Presentation on common business issues and challenges in organizations that do not have formal data governance practices. Data management on the whole has evolved over the years, but data governance is still one of the greatest constraints in strategic transformation and operational effectiveness.
1. What is Data Governance?
2. Business Impact without Data Governance
3. Benefits of Data Governance
4. Implementing Data Governance
Data-Ed Webinar: Data Governance StrategiesDATAVERSITY
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Takeaways:
Understanding why data governance can be tricky for most organizations
Steps for improving data governance within your organization
Guiding principles & lessons learned
Understanding foundational data governance concepts based on the DAMA DMBOK
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Check out more webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Data Analytics: An On-Ramp to a Better Understanding of Your BusinessSkoda Minotti
Data analytics is a hot topic in business today. But is it right for your business? What does it do for you, and most importantly, how do you get started? This executive overview explores the business implications of data analytics, while leaving the technicalities to the side.
Improve IT Security and Compliance with Mainframe Data in SplunkPrecisely
Avoid security blind spots with an enterprise-wide view.
If your organization relies on Splunk as its security nerve center, you can’t afford to leave out your mainframes.
They work with the rest of your IT infrastructure to support critical business applications–and they need to be
viewed in that wider context to address potential security blind spots.
Although the importance of including mainframe data in Splunk is undeniable, many organizations have left it out
because Splunk doesn’t natively support IBM Z® environments. Learn how Precisely Ironstream can help with a
straight-forward, powerful approach for integrating your mainframe security data into Splunk, and making it actionable
once it’s there.
Designed to address more mature programs, this tutorial covers the issues and approaches to sustaining Data Governance and value creation over time, amongst a changing business and personnel environment.
Part of the reason many companies launch a Data Governance program again and again is that over time, it is challenging to maintain the enthusiasm and excitement that accompanies a newly initiated program.
Learn about:
• Typical obstacles to sustainable Data Governance
• Re-energizing your program after a key player (or two) leave and other personnel challenges
• Staying relevant to the company as the business evolves over time
• Understanding the role of metrics and why they are critical
• Leveraging Communication and Stakeholder Management practices to maintain commitment
• Embedding Data Governance into the operations of the company
Data governance involves setting up procedures and regulations to enable the smooth sharing, managing, and availability of data.
The idea is to prevent an overlap of resources. When you have data governance procedures you experience faster decision-making processes while moving data from just a company’s by-product to a critical asset within the organization. Check out this and know how to build a strong Governance framework for your organization
How to Create and Manage a Successful Analytics OrganizationDATAVERSITY
For the last few years, analytics, data science and data management have achieved tremendous exposure on all the media channels. Big Data has become a major topic of discussion, catalyzing attention among the C-Level executives and driving investments and projects inside the enterprise. However, it is really interesting that just a selected group of business has created successful data teams and has mastered the skills to manage it. What we have seen is that most companies still do not know how to create, implement and manage a data and analytics organization. Above all, if data has become an strategic asset and is being considered the new oil for the 21st century economy, what your strategy to handle it ? This webinar will help to bring some concepts and ideas to enlighten your path to create and manage an analytics organization, providing some real life examples on companies which did it.
Thanks to wiki leaks, the NSA and intense regulatory environments, metadata is now a common term. What is the role of the Top Data Job and what are the options to deploy an effective metadata environment. John will explore some new avenues and lessons learned for metadata.
In this webinar, we will discuss:
Metadata Architectures
Metadata Types and Categories
Trends in Metadata Technology
The CDO’s role in the Metadata Function
The Data Driven University - Automating Data Governance and Stewardship in Au...Pieter De Leenheer
Data Governance and Stewardship requires automation of business semantics management at its nucleus, in order to achieve data trust between business and IT communities in the organization. University divisions operate highly autonomously and decentralized, and are often geographically distributed. Hence, they benefit more from an collaborative and agile approach to Data Governance and Stewardship approach that adapts to its nature.
In this lecture, we start by reviewing 'C' in ICT and reflect on the dilemma: what is the most important quality of data being shared: truth or trust? We review the wide spectrum of business semantics. We visit the different phases of growing data pain as an organization expands, and we map each phase on this spectrum of semantics.
Next, we introduce our principles and framework for business semantics management to support Data Governance and Stewardship focusing on the structural (what), processual (how) and organizational (who) components. We illustrate with use cases from Stanford University, George Washington University and Public Science and Innovation Administrations.
In this PPT, We describing the important things about Data Management and Data Governance. The data governance approach provides the right practices and processes that help an enterprise manage its data flows.
Unlocking Success in the 3 Stages of Master Data ManagementPerficient, Inc.
Master data management (MDM) comprises the processes, governance, policies, standards and tools that define and manage critical data. MDM is used to conduct strategic initiatives such as customer 360, product excellence and operational efficiency.
The quality of enterprise Information depends on the master data, so getting it right should be a high priority. This webinar will highlight key factors needed for success in each of the three stages of the MDM journey:
Planning
Implementation
Steady state
We review each stage in detail and provide insight into planning and collaborative activities. In this slideshare you will learn:
Best practices, tips and techniques for a successful MDM program
Top considerations for business case building, architecture and going live
How to support the overall program after launching your MDM program
Alignment: Office of the Chief Data Officer & BCBS 239Craig Milroy
Alignment: Office of the Chief Data Officer & BCBS 239. Alignment overview between OCDO framework and Principles for Effective Risk Data Aggregation and Risk Reporting.
Are you your company’s chief data officer? Given the scarcity of the official role, it’s likely that you’re not — at least in title. But that doesn't mean that you shouldn't operate like one. Do you approach data leadership as a C-level executive or a senior data head? Is your team’s output strategic or just operational? In this interactive keynote, one of the Windy City’s foremost data leaders will lead an interactive discussion on what it takes to lead like a chief, what it looks like, and how to get there and get it done.
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
Business impact without data governanceJohn Bao Vuu
Presentation on common business issues and challenges in organizations that do not have formal data governance practices. Data management on the whole has evolved over the years, but data governance is still one of the greatest constraints in strategic transformation and operational effectiveness.
1. What is Data Governance?
2. Business Impact without Data Governance
3. Benefits of Data Governance
4. Implementing Data Governance
Data-Ed Webinar: Data Governance StrategiesDATAVERSITY
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Takeaways:
Understanding why data governance can be tricky for most organizations
Steps for improving data governance within your organization
Guiding principles & lessons learned
Understanding foundational data governance concepts based on the DAMA DMBOK
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Check out more webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
how to successfully implement a data analytics solution.pdfbasilmph
The adoption of data analytics in business has demonstrated a transformative power in modern entrepreneurship. By analyzing vast reservoirs of data, businesses can make informed decisions, optimize operations and predict trends, thus fueling growth.
Data-Driven Dynamics Leveraging Analytics for Business GrowthBryce Tychsen
Explore the dynamic landscape of data-driven growth and learn how analytics can propel businesses to success. Discover strategies, tools, and best practices for harnessing data insights to drive growth and innovation.
Stop the madness - Never doubt the quality of BI again using Data GovernanceMary Levins, PMP
Does this sound familiar? "Are you sure those numbers are right?" "Why are your numbers different than theirs?"
We've all heard it and had that gut wrenching feeling of doubt that comes with uncertainty around the quality of the numbers.
Stop the madness! Presented in Dunwoody on April 18 by industry leading expert Mary Levins who discusseses what it takes to successfully take control of your data using the Data Governance Framework. This framework is proven to improve the quality of your BI solutions.
Mary is the founder of Sierra Creek Consulting
Building a Data Strategy Your C-Suite Will SupportReid Colson
Being a data leader in any industry is an advantage that creates measurable financial benefits. Many studies have shown this – I’ve seen them from Bain, McKinsey, MIT and more. Since most firms are measured on profit, getting good at making data driven decisions is a key to being competitive. You can't get there without a plan. That is where a data strategy comes in.
In speaking with ~300 firms who indicated that their organizations were effective in using data and analytics, McKinsey found that construction of a data strategy was the number one contributing factor to their success. Being good at using data to drive decisions creates a meaningful profit advantage and those who are leaders indicated that the number one driver of their success was their data strategy.
This presentation will cover what a data strategy is, how to construct one, and how to get buy in from your executive team. The author is a former Fortune 500 Chief Data Officer and has held senior data roles at Capital One and Markel.
Here are a few helpful links for your data journey:
Free Data Investment ROI Template:
https://www.udig.com/digging-in/roi-calculator-for-it-projects/
Real world data use cases:
https://www.udig.com/our-work/?category=data
Contact Me:
https://www.udig.com/contact/
Presentation to introduce information governance. This should be used in conjunction with the paper I published on my website. A full information governance methodology, with research included from the foremost authorities on data governance.
Top Data Collection Tips to Help You Unlock Your Business PotentialAndrew Leo
A systematic process of gathering observations or measurements, data collection plays a vital role in enhancing customer experience and promoting informed decision-making. Data can be collected through multiple sources such as feedback, surveys, online tracking, social media monitoring, etc.
Read here the inspired blog: https://www.damcogroup.com/blogs/top-data-collection-tips-to-help-you-unlock-your-business-potential
#datacollectionservices
#webdatacollection
#datacollectionplatforms
#datacollectioncompany
#datacollectioncompanies
The Business Value of Metadata for Data GovernanceRoland Bullivant
In today’s digital economy, data drives the core processes that deliver profitability and growth - from marketing, to finance, to sales, supply chain, and more. It is also likely that for many large organizations much of their key data is retained in application packages from SAP, Oracle, Microsoft, Salesforce and others. In order to ensure that their foundational data infrastructure runs smoothly, most organizations have adopted a data governance initiative. These typically focus on the people and processes around managing data and information. Without an actionable link to the physical systems that run key business processes, however, governance programs can often lack the ‘teeth’ to effectively implement business change.
Metadata management is a process that can link business processes and drivers with the technical applications that support them. This makes data governance actionable and relevant in today’s fast-paced and results-driven business environment. One of the challenges facing data governance teams however, is the variety in format, accessibility and complexity of metadata across the organization’s systems.
5 Steps to Transform into a Data-Driven Organization - Ganes Kesari - Gramen...Ganes Kesari
This session was presented on May 27th, 2021, in a Webinar organized by Gramener.
https://info.gramener.com/5-steps-to-transform-into-data-driven-organization
Session Details:
Today, organizations struggle to get value from data despite significant investments. Did you know that there's one factor that influences the outcomes of all your data initiatives?
This webinar will highlight how an organization's data maturity influences its performance. It will show how you can assess your data maturity and plan the five steps for data-driven business transformation.
Pain points we would be discussing:
Most organizations stagnate midway in their data journey.
Gartner says that over 87% of organizations in the industry are at lower levels of data maturity (levels 1 and 2 on a scale of 5).
Just doing more data science projects will not improve your capabilities or outcomes. The fact is that the top challenges reported by CDOs fall into five common areas.
This webinar will show what they are and how you can tackle them.
Who should attend
- Executives, Chief Data/Analytics Officers, Technology leaders, Business heads, Managers
What Will You Learn?
- What is data science maturity, and why does it matter?
- How do you assess data science maturity and limitations of the assessment?
- How can data science maturity help your organization level up (explained with an example)?
Federated data organizations in public sector face more challenges today than ever before. As discovered via research performed by North Highland Consulting, these are the top issues you are most likely experiencing:
• Knowing what data is available to support programs and other business functions
• Data is more difficult to access
• Without insight into the lineage of data, it is risky to use as the basis for critical decisions
• Analyzing data and extracting insights to influence outcomes is difficult at best
The solution to solving these challenges lies in creating a holistic enterprise data governance program and enforcing the program with a full-featured enterprise data management platform. Kreig Fields, Principle, Public Sector Data and Analytics, from North Highland Consulting and Rob Karel, Vice President, Product Strategy and Product Marketing, MDM from Informatica will walk through a pragmatic, “How To” approach, full of useful information on how you can improve your agency’s data governance initiatives.
Learn how to kick start your data governance intiatives and how an enterprise data management platform can help you:
• Innovate and expose hidden opportunities
• Break down data access barriers and ensure data is trusted
• Provide actionable information at the speed of business
In today's competitive market, many organizations are unaware of the quantity of poor-quality data in their systems. Some organizations assume that their data is of adequate quality, although they have conducted no metrical or statistical analysis to support the assumption. Others know that their performance is hampered by poor-quality data, but they cannot measure the problem.
ERP and Related Technologies
Business Processing Reengineering(BPR), Data Warehousing, Data Mining, On-line Analytical Processing(OLAP), Supply Chain Management (SCM),
Customer Relationship Management(CRM), Electronic Data Interchange (EDI)
Similar to Business Data Alignment-همراستاییِ دادهها با اهداف سازمانی (20)
Data Scienceis an interdisciplinary field about processes and systems to extractknowledgeor insights fromdata, which is a continuation of some of the data analysis fields such as statistics,data mining, andpredictive analytics, similar toKnowledge Discovery in Databases(KDD).
مهم است بدانید اولین قدم به سمت، تغییر داده و سیستمها به شکل دیجیتال آن است
ذخیره داده بر سرورها، ابر یا ابزارهاي
ذخیره محلی براي خلق یک سیستمی که کاربرد دیجیتالی آن داده - منابع ارزشمندي براي کسب وکار، توسعهدهندگان و کارآفرینان-
فراهم میکند حیاتی است....
مشتري بخش كاملي از زنجيره تأمين است.
شامل جابجايي كالا از تأمين كنندگان به سازندگان و توزيع كنندگان است. همچنين شامل جابجايي اطلاعات، پول و محصول در هر دو جهت است.
دقيق تر خواهد بود اگر از اصلاح ”شبكه تأمين“ يا ”وب تأمين“ استفاده شود.
يك زنجيره تأمين عموما شامل تأمين كنندگان، سازندگان، توزيع كنندگان، خرده فروشان و مشتريان است.
البته در برخي از موارد تمامي مراحل وجود ندارد.
از 1950 که رايانه در تحليل و ذخيره سازي داده ها مورد استفاده و بهره برداری قرار گرفت، تا سال 1970 حجم داده ها در پايگاه داده ها دو برابر گردید. با پیشرفت فن آوري اطلاعات هر سال 1990 هر دو سال يکبار حجم داده ها، دو برابر شد. (tan94)
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
2. Business Data
Alignment
طاهری مهدی سید دکتر
مرکزی کتابخانه رییس و علمی هیئت عضو
طباطبائی عالمه دانشگاه
http://www.smtaheri.ir
taherismster@gmail.com
3. What is business data?
• is data that is captured and stored by a business as a digital
asset that may support strategy, decision making and day-to-
day operations.
• This includes source data that a business collects and
data that has been processed such as calculated metrics and
forecasts.
• Business data can be stored in databases that are machine-
readable or represented as information intended for human
consumption such as a user interface, document or report.
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4. The following are common types of
business data.
Audit Trail Customer Data
Dark Data Knowledge
Machine Data Market Research
Master Data Metadata
Metrics Product Catalog
Qualitative Data Quantitative Data
Reference Data Transactional Data
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5. The following are common examples of
business data
Leads & Opportunities
• Lists of potential customers.
Customer
• Customer details such as name and address.
Transactions
• Records of commercial transactions such as customer purchases.
Interactions
• Records of interactions with customers and other stakeholders such as investors, employees
and the media. For example, records of visits to your website.
Social Media
• Data regarding your target markets or reputation that is collected from social media sources.
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6. Business Data Repository (BDR)
• A centralized storage facility, such as a proxy server
or file server, where business transactions, contact
information, files and other data is kept. Also called
“Business Data Archive”.
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7. Business data management
• Business data management is an essential
activity in all types of companies. The four
basic steps in business data management:
Data creation, data storage, data processing,
and data analysis.
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8. Enterprise data management
• (EDM) is an organization's ability to effectively
create, integrate, disseminate and manage
data for all enterprise applications, processes
and entities requiring timely and accurate data
delivery.
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9. Data management strategy
• Is the process of planning or creating
strategies/plans for handling the data created,
stored, managed and processed by an
organization.
• It is an IT governance process that aims to create
and implement a well-planned approach in
managing an organization’s data assets.
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10. The key objective behind data management strategy is to
develop a business strategy that ensures that data is:
• Stored, consumed and processed in a manner required
by the organization
• Controlled, monitored, assured and protected using
data governance and security processes and policies
• Stored, categorized and standardized using defined and
known data classification and quality frameworks
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11. Business data analysis
• Aims to evaluate whether business data are aligned with
an organization’s goals or not.
• Business data analysis includes the activities to help
managers make strategic decisions, achieve major goals and
solve complex problems, by collecting, analyzing and
reporting the most useful information relevant to
managers' needs. Information could be about the causes of
the current situation, the most likely trends to occur, and
what should be done as a result.
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12. Business data alignment
• More organizations are aspiring to become ‘data
driven businesses’. But all too often this aim fails, as
business goals and IT & data realities are
misaligned, with IT lagging behind rapidly changing
business needs.
• So how do you get the perfect fit where data
strategy is driven by and underpins business
strategy?
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13. The main ideas are:
• How to align data strategy with business
motivation and drivers
• Why business & data strategies often become
misaligned & the impact
• Defining the core building blocks of a successful data
strategy
• The role of business and IT
• Success stories in implementing global data strategies
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14. Data strategy
• Data Strategy describes a “set of choices and decisions that together, chart
a high-level course of action to achieve high-level goals.” This includes
business plans to use information to a competitive advantage and support
enterprise goals.
• A Data Strategy requires an understanding of the data needs inherent in
the Business Strategy:
• “It’s the opportunity to take your existing product line and market it
better, develop it better, use it to improve customer service, or to get a
360-degree understanding of your customer. Data Strategy is driven by
your organization’s overall Business Strategy and business model.
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15. OR
• A data strategy is a common reference of methods, services,
architectures, usage patterns and procedures for acquiring,
integrating, storing, securing, managing, monitoring, analyzing,
consuming and operationalizing data.
• It is, in effect, a checklist for developing a roadmap toward the
digital transformation journey that companies are actively pursuing
as part of their modernization efforts.
• This includes clarifying the target vision and practical guidance for
achieving that vision, with clearly articulated success criteria and key
performance indicators that can be used to evaluate and rationalize
all subsequent data initiatives.
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16. A Well-Developed Data Strategy has:
• A strong Data Management vision;
• A strong business case/reason;
• “Guiding principles, values, and management perspectives.”;
• Well-considered goals for the data assets under management;
• Metrics and measurements of success;
• Short-term and long-term program objectives;
• Suitably designed and understood roles and responsibilities;
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17. Businesses Develop a Data Strategy to:
• Manage torrents of data that are critical to a company’s
success;
• Think of the future and trends and how to best
manage them;
• Drive innovation and establish a data culture;
• Support the re-imaging of decision making in an
organization – at all levels;
• To develop a sustainable competitive advantage given the
volume, depth and accessibility of digital data.
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18. Four common drivers
• Though the impetus for creating a data strategy
can vary from one organization to the next, there
are four common drivers:
• Unification of business and IT perspectives.
– In this way companies can adopt a “business-
led/technology-enabled” approach for not only
internal operations but also vendor and partner
collaborations.
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19. • Enterprise-wide alignment of vision and
guidance on leveraging data as an asset;
• Definition of key metrics and success criteria
across the enterprise:
– The data strategy defines “success” and
“quality,” thus reinforcing consistency for how
initiatives are measured, evaluated and tracked
across all levels of interacting organizations;
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20. • Reduction of technology debt. A data strategy
takes the current state of the enterprise data
environments and operations into account and
provides guidance for applying innovation with
minimal disruption to ongoing business
operations.
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21. Organizations should include eight
components in their data strategy:
1. Semantics
2. Goals/vision and rationalization
3. Strategic principles
4. Current-state documentation
5. Governance model
6. Data management guidance
7. Reference architecture
8. Sample and starter solution library
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