The document discusses a disconnect between IT executives and staff on data strategy and management. While executives understand data's strategic importance, staff who manage data day-to-day have less business focus. This disconnect can hamper an organization's ability to effectively use data. The document also notes business users are taking more control of data initiatives, potentially sidelining IT. Both executives and staff need better communication to align on strategic and operational data issues.
Cracking the Data Conundrum: How Successful Companies Make #BigData OperationalSubrahmanyam KVJ
There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.
So, how can organizations make Big Data operational? There are many factors that go into the making of a successful Big Data implementation. However, the single biggest factor that we observed in our research was that organizations that have a strong operating model stood apart. This operating model has multiple distinct elements, which include, among others, a well-defined organizational structure, systematic implementation plan, and strong leadership support. For instance, success rates for organizations with an analytics business unit are nearly 2.5 times those that have ad-hoc, isolated teams. The report highlights the key factors for successful Big Data implementations.
Analytics is all about course correcting the future. While this starts with accurate predictions of the future, without resultant actions steering the future toward company goals, knowing that future is academic. Successful companies must be grounded in successful data-based prescription. In this webinar, William will present a data maturity model with a focus on how analytic competitors outdo the competition by looking forward to a data-influenced future.
There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.
Cracking the Data Conundrum: How Successful Companies Make #BigData OperationalSubrahmanyam KVJ
There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.
So, how can organizations make Big Data operational? There are many factors that go into the making of a successful Big Data implementation. However, the single biggest factor that we observed in our research was that organizations that have a strong operating model stood apart. This operating model has multiple distinct elements, which include, among others, a well-defined organizational structure, systematic implementation plan, and strong leadership support. For instance, success rates for organizations with an analytics business unit are nearly 2.5 times those that have ad-hoc, isolated teams. The report highlights the key factors for successful Big Data implementations.
Analytics is all about course correcting the future. While this starts with accurate predictions of the future, without resultant actions steering the future toward company goals, knowing that future is academic. Successful companies must be grounded in successful data-based prescription. In this webinar, William will present a data maturity model with a focus on how analytic competitors outdo the competition by looking forward to a data-influenced future.
There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.
Is Your Company Braced Up for handling Big Datahimanshu13jun
Has your company recently launched new product or company is concerned with the poor sales figure or want to reach new prospects and also reduce the existing customers' attrition, then this thought evoking short hand guide is available for you to explore.
FIMA's latest whitepaper evaluates how financial services companies are managing the challenges posed by data quality management. By analyzing which data types and data characteristics businesses are struggling with, it uncovers the true business costs associated with data quality. It will also gauge how data governance programs are maturing and how they are being measured. Finally, it assesses how data is being managed within financial institutions.
Key findings include:
Data quality has never been more important for financial institutions, but most of those companies feel their data is only mediocre: Quality data serves a myriad of central business goals, from risk reduction to increased productivity. Unfortunately, many businesses continue to struggle with data quality, despite the fact that four-fifths of them have it ranked as a top priority.
The top two business functions impacted by poor data quality are regulatory compliance and risk management: Because these concerns tend to be the most important drivers of data quality, many financial institutions see data governance as a “must-do,” rather than a ROI-boosting activity. Furthermore, the vast majority of financial services companies can not quantify the business cost of poor data quality.
Financial institutions vary greatly in the maturity of their data governance programs: Data governance cannot be overlooked – unsurprisingly, businesses with formalized data governance programs reported that their data was higher quality than most other groups.
Data quality management requires close collaboration between business and IT leaders: That collaboration already exists for 83% of respondents in this study, who say that IT and business leaders work together to manage data quality in their organizations. However, the tools these businesses use to manage their data are not all equal, leading to an uneven allocation of resources.
Operationalizing the Buzz: Big Data 2013VMware Tanzu
The 2013 EMA/9sight Big Data research makes a clear case for the maturation of Big Data as a critical approach for innovative companies. This year’s survey went beyond simple questions of strategy, adoption and use to explore why and how companies are utilizing Big Data. This year’s findings show an increased level of Big Data sophistication between 2012 and 2013 respondents. An improved understanding of the “domains of data” drives this increased sophistication and maturity. Highly developed use of
Process-mediated, Machine-generated and Human-sourced information is prevalent throughout this year’s study.
Building an Effective Data Management StrategyHarley Capewell
In June 2013, Experian hosted a Data
Management Summit in London, with over
100 delegates from the public, private and
third sectors. Speakers from Experian
and across the data industry explored the
challenges of developing and implementing
data quality strategies - and how to
overcome them. Read on for more information.
Develop and Implement an Effective Data Management Strategy and Roadmap Info-Tech Research Group
Treat data as an asset and gain a competitive advantage.
Your Challenge
Despite the growing focus on data, many organizations struggle to develop an effective strategy for their data assets. This is due to their intangible nature and varying use across the business.
Data Management is a business process managed by IT. This creates a challenge for IT as it is required to create and manage complex systems of operations that link closely to integral business operations.
Our Advice
Critical Insight
Data Management is not one size fits all. Cut through the noise related to Data Management and create a strategy and process that is right for your organization.
Have the business drive your Data Management project.
It all starts and ends with Data Governance. At a minimum, invest in Data Governance initiatives.
Impact and Result
Coordination between IT and the business will create a Data Management strategy that understands and satisfies the data requirements of the business.
Data Management requirements and initiatives will be derived from the following: business goals and strategic plans, current capability assessments, business drivers for data, understanding of market and technology opportunities, and a clear understanding of the business’s drivers regarding data.
Creating a clear Data Management Strategy and developing a roadmap of initiatives will allow IT to create a plan for how to bridge the gap between IT and the business and create a Data Management framework that supports the business’s immediate and long-term data requirements.
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
It’s been three years since the General Data Protection Regulation shook up how organizations manage data security and privacy, ushering in a new focus on Data Governance. But what is the state of Data Governance today?
How has it evolved? What’s its role now? Building on prior research, erwin by Quest and ESG have partnered on a new study about what’s driving the practice of Data Governance, program maturity and current challenges. It also examines the connections to data operations and data protection, which is interesting given the fact that improving data security is now the No. 1 driver of Data Governance, according to this year’s survey respondents.
So please join us for this webinar to learn about the:
Other primary drivers for enterprise Data Governance programs
Most common bottlenecks to program maturity and sustainability
Advantages of aligning Data Governance with the other data disciplines
In a post-COVID world, data has the power to be even more transformative, and 84% of business and technology professionals say it represents the best opportunity to develop a competitive advantage during the next 12 to 24 months. Let’s make sure your organization has the intelligence it needs about both data and data systems to empower stakeholders in the front and back office to do what they need to do.
Creating a Data-Driven Organization, Data Day Texas, January 2016Carl Anderson
What does it mean for an organization to be data-driven? How does an organization get there? Many organizations think that they are data-driven but the reality is that few genuinely are and that we could all do better. In this talk, I cover what it truly means to be data driven. The answer, it turns out, is not to do with the latest tools and technologies (although they can help) but having an appropriate data culture than spans the whole organization, where data is accessible broadly, embedded into operations and processes, and enables effective decision making. In this presentation, I dissect what an effective data-driven culture entails, covering facets such as data leadership, data literacy, and A/B testing, illustrating concepts with examples from different industries as well as personal experience.
Making happier, healthier patients
The link between happiness and health is well documented. More accurately referred to as subjective wellbeing, it’s been demonstrated that a positive outlook is not only the result of good health, but the cause of it.
Is Your Company Braced Up for handling Big Datahimanshu13jun
Has your company recently launched new product or company is concerned with the poor sales figure or want to reach new prospects and also reduce the existing customers' attrition, then this thought evoking short hand guide is available for you to explore.
FIMA's latest whitepaper evaluates how financial services companies are managing the challenges posed by data quality management. By analyzing which data types and data characteristics businesses are struggling with, it uncovers the true business costs associated with data quality. It will also gauge how data governance programs are maturing and how they are being measured. Finally, it assesses how data is being managed within financial institutions.
Key findings include:
Data quality has never been more important for financial institutions, but most of those companies feel their data is only mediocre: Quality data serves a myriad of central business goals, from risk reduction to increased productivity. Unfortunately, many businesses continue to struggle with data quality, despite the fact that four-fifths of them have it ranked as a top priority.
The top two business functions impacted by poor data quality are regulatory compliance and risk management: Because these concerns tend to be the most important drivers of data quality, many financial institutions see data governance as a “must-do,” rather than a ROI-boosting activity. Furthermore, the vast majority of financial services companies can not quantify the business cost of poor data quality.
Financial institutions vary greatly in the maturity of their data governance programs: Data governance cannot be overlooked – unsurprisingly, businesses with formalized data governance programs reported that their data was higher quality than most other groups.
Data quality management requires close collaboration between business and IT leaders: That collaboration already exists for 83% of respondents in this study, who say that IT and business leaders work together to manage data quality in their organizations. However, the tools these businesses use to manage their data are not all equal, leading to an uneven allocation of resources.
Operationalizing the Buzz: Big Data 2013VMware Tanzu
The 2013 EMA/9sight Big Data research makes a clear case for the maturation of Big Data as a critical approach for innovative companies. This year’s survey went beyond simple questions of strategy, adoption and use to explore why and how companies are utilizing Big Data. This year’s findings show an increased level of Big Data sophistication between 2012 and 2013 respondents. An improved understanding of the “domains of data” drives this increased sophistication and maturity. Highly developed use of
Process-mediated, Machine-generated and Human-sourced information is prevalent throughout this year’s study.
Building an Effective Data Management StrategyHarley Capewell
In June 2013, Experian hosted a Data
Management Summit in London, with over
100 delegates from the public, private and
third sectors. Speakers from Experian
and across the data industry explored the
challenges of developing and implementing
data quality strategies - and how to
overcome them. Read on for more information.
Develop and Implement an Effective Data Management Strategy and Roadmap Info-Tech Research Group
Treat data as an asset and gain a competitive advantage.
Your Challenge
Despite the growing focus on data, many organizations struggle to develop an effective strategy for their data assets. This is due to their intangible nature and varying use across the business.
Data Management is a business process managed by IT. This creates a challenge for IT as it is required to create and manage complex systems of operations that link closely to integral business operations.
Our Advice
Critical Insight
Data Management is not one size fits all. Cut through the noise related to Data Management and create a strategy and process that is right for your organization.
Have the business drive your Data Management project.
It all starts and ends with Data Governance. At a minimum, invest in Data Governance initiatives.
Impact and Result
Coordination between IT and the business will create a Data Management strategy that understands and satisfies the data requirements of the business.
Data Management requirements and initiatives will be derived from the following: business goals and strategic plans, current capability assessments, business drivers for data, understanding of market and technology opportunities, and a clear understanding of the business’s drivers regarding data.
Creating a clear Data Management Strategy and developing a roadmap of initiatives will allow IT to create a plan for how to bridge the gap between IT and the business and create a Data Management framework that supports the business’s immediate and long-term data requirements.
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
It’s been three years since the General Data Protection Regulation shook up how organizations manage data security and privacy, ushering in a new focus on Data Governance. But what is the state of Data Governance today?
How has it evolved? What’s its role now? Building on prior research, erwin by Quest and ESG have partnered on a new study about what’s driving the practice of Data Governance, program maturity and current challenges. It also examines the connections to data operations and data protection, which is interesting given the fact that improving data security is now the No. 1 driver of Data Governance, according to this year’s survey respondents.
So please join us for this webinar to learn about the:
Other primary drivers for enterprise Data Governance programs
Most common bottlenecks to program maturity and sustainability
Advantages of aligning Data Governance with the other data disciplines
In a post-COVID world, data has the power to be even more transformative, and 84% of business and technology professionals say it represents the best opportunity to develop a competitive advantage during the next 12 to 24 months. Let’s make sure your organization has the intelligence it needs about both data and data systems to empower stakeholders in the front and back office to do what they need to do.
Creating a Data-Driven Organization, Data Day Texas, January 2016Carl Anderson
What does it mean for an organization to be data-driven? How does an organization get there? Many organizations think that they are data-driven but the reality is that few genuinely are and that we could all do better. In this talk, I cover what it truly means to be data driven. The answer, it turns out, is not to do with the latest tools and technologies (although they can help) but having an appropriate data culture than spans the whole organization, where data is accessible broadly, embedded into operations and processes, and enables effective decision making. In this presentation, I dissect what an effective data-driven culture entails, covering facets such as data leadership, data literacy, and A/B testing, illustrating concepts with examples from different industries as well as personal experience.
Making happier, healthier patients
The link between happiness and health is well documented. More accurately referred to as subjective wellbeing, it’s been demonstrated that a positive outlook is not only the result of good health, but the cause of it.
ما هو الباك لينك و أفضل الطرق للحصول عليهWesam Helmy
كثيراً ما يصلنى أسئلة على حسابى فى مواقع التواصل الإجتماعى أو على البريد الإلكترونى عن الباك لينك و ماهيته و كيفية الحصول عليه بطرق مجانية بدون دفع بعض الأموال للحصول عليه من مواقع الخدمات المُصغرة، لذلك قررت أن أوضح بعض التعاريف المبسطة للباك لينك و توضيح كيفية الحصول عليه بطرق مجانية شرعية و مفيدة لموقعك.
http://www.profitsyouthsociety.com/
A presentation given by Dave Hunt, HAVAS LYNX EU CEO at Digipharm in October 2012. Dave discusses the benefits of gamification to encourage user engagement and goal completion within healthcare.
Ngomik.com is a Distribution Platform (web & mobile) which Brings You Enjoyable Digital Comics.
Ngomik.com is one of startups graduate from Ideabox Batch 1 Accelerator Program and has obtained business valuation more than $ 1 million (more than IDR 11 billion) and will soon obtain external funding from Japanese investors.
Download Ngomik App at Google Play Store: https://play.google.com/store/apps/details?id=com.ngomik
Speaking digital: The key to global healthcare communications Havas Lynx Group
For patients, carers and professionals, wherever they are in the world, digital technology is inherent in their everyday lives. Digital is, so to speak, a global language. The success of Facebook, Google & Apple, have succeeded where armies, politics and religion have failed, in uniting disparate populations. Individual expectations have been aligned across the globe, and whilst context may change, the user experience need not.
The pharma approach to global communications is based on traditional ideas and borders, can a fresh approach aligned to the new world of communications represent a critical competitive advantage?
Folic acid is used for preventing and treating low blood levels of folic acid (folic acid deficiency), as well as its complications, including “tired blood” (anemia) and the inability of the bowel to absorb nutrients properly. Folic acid is also used for other conditions commonly associated with folic acid deficiency, including ulcerative colitis, liver disease, alcoholism, and kidney dialysis.
Biotin acts as a coenzyme in the metabolism of fats and carbohydrates, the breakdown of proteins to urea, and the conversion of amino acids from protein into blood sugar for energy. You should eat at least 30 micrograms a day.
Cracking the Data Conundrum: How Successful Companies Make #BigData OperationalCapgemini
There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.
So, how can organizations make Big Data operational? There are many factors that go into the making of a successful Big Data implementation. However, the single biggest factor that we observed in our research was that organizations that have a strong operating model stood apart. This operating model has multiple distinct elements, which include, among others, a well-defined organizational structure, systematic implementation plan, and strong leadership support. For instance, success rates for organizations with an analytics business unit are nearly 2.5 times those that have ad-hoc, isolated teams. The report highlights the key factors for successful Big Data implementations.
Infographic | Quality of Data & Cost of Bad Data | Sapience AnalyticsSapience Analytics
As the quality of data becomes more and more crucial to the success of an organization, the cost of bad data goes staggeringly high.
Read this Infographic and understand the dependence of organizations on data in terms of:
Importance of data
Quality of data
Cost of bad data
Reasons for bad data quality
Driving A Data-Centric Culture: The Leadership ChallengePlatfora
Embracing data as a corporate asset—and a source of competitive advantage—is not just a “good idea” that companies should consider. Such adoption will help determine the winners and losers across multiple markets and industries in the future.
In the last couple of years, corporate focus has shifted: first, from investing in the right technology and tools; then to acquiring the right talent and skills; and now to building the right organizational culture that can realize the business value of powerful big-data analytic tools.
Most organizations today are still focused on putting in place the right technology and talent, but others have evolved further and are working toward fostering a data-centric corporate culture.
As businesses generate and manage vast amounts of data, companies have more opportunities to gather data, incorporate insights into business strategy and continuously expand access to data across the organisation. Doing so effectively—leveraging data for strategic objectives—is often easier said
than done, however. This report, Transforming data into action: the business outlook for data governance, explores the business contributions of data governance at organisations globally and across industries, the challenges faced in creating useful data governance policies and the opportunities to improve such programmes.
We conducted a groundbreaking survey of the UK’s data and business professionals to get a snapshot of the state of the world of data, uncover some of the issues facing the industry and get a sense of the changes on the horizon. The results were enlightening, and in some cases, very surprising.
Find out:
Why nearly a third of IT Directors feel their organisation uses data poorly
What the hybrid data manager of the future will look like
Why understanding customer behaviour remains the holy grail for so many
We conducted a ground-breaking survey of the UK’s data and business professionals to get a snapshot of the state of the world of data, uncover some of the issues facing the industry and get a sense of the changes on the horizon. The results were enlightening, and in some cases, very surprising.
We conducted a survey of the UK's data and business professionals to get a snapshot of the state of the world of data, uncover some of the issues facing the industry and get a sense of the changes on the horizon. The results were enlightening, and in some cases, very surprising.
This whitepaper aims to assist Chief Data Officers in promoting a data-driven culture at their
organization, helping them lead the enterprise on a digital transformation journey backed by
analytical insights.
Data Science Leaders Outlook In India 2019: By AIM & SimplilearnRicha Bhatia
In its fifth year, our Data Science Leaders Outlook in India 2019 in collaboration with Simplilearn takes stock of the analytics landscape in India and how enterprises have moved up the analytics maturity index. What was once viewed as a competitive advantage is now powering the core operations and helping companies launch entirely new business models. Analytics and Data Science has changed the dynamics of the industry, spawning a winner-takes-all market.
Data-Ed Online Webinar: Monetizing Data ManagementDATAVERSITY
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
Takeaways:
Learn to think about data differently, in terms of how it can drive organizational needs. Data is not an IT solution but an information solution.
Take a broad view to ensure data sharing across organizational silos
Start small and go for quick wins: Build momentum and support
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
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.
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.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...
Research Data Drives Profit
1. But IT exec-staff disconnect
threatens data’s impact on the
bottom line
Research: Data
Drives Profit
2. 2
Table of Contents
1 2 3 4 5
chapter chapter chapter chapter chapter
6 7 8 9
chapter chapter chapter chapter
Introduction Data-Fueled Enterprises
Are Successful
Enterprises
When IT Execs and Staff
Don’t See Eye to Eye
IT Risks Getting
Sidelined on Data
Initiatives
Are Executives Out of
Touch on Day-To-Day
Data Issues?
Out of Sync Data
Aspirations Can Hurt
Business
Needed: Consensus on
What Is Being
Measured to Determine
Data Strategy Success
Data Needs To Be
Clean, Safe, Connected
Conclusion
4. 4
Introduction Numbers rarely lie, and those we’ve gathered recently
show that businesses using data strategically are gaining
competitive advantage over those that don’t.
Enterprises with a less-sophisticated grasp of the strategic importance
of data are experiencing lower revenue growth and reduced
competitiveness, according to “The Data Directive,” an Economist
Intelligence Unit study sponsored by systems integrator Wipro and
published last year. When we investigated further—commissioning our
own global survey of 210 IT professionals—we found a lack of business
knowledge and skills among data professionals has created a disconnect
between IT executives and rank-and-file data professionals in many
organizations. And that disconnect results in a less well-executed data
strategy, which in turn impedes the competitive responsiveness of the
business.
1 2 3 4 5 6 7 8 9Contents
5. 5
Introduction
Informatica’s survey of IT professionals—C-level executives as well as
data professionals—explored attitudes and viewpoints on data around
the world and across a mix of industries. Business agility, competitive
advantage, and customer responsiveness were globally agreed to be
the top benefits of a well-executed data strategy.
1 2 3 4 5 6 7 8 9
In response to the question, "To what extent do you
agree with the following statements, where 1 means
'completely disagree' and 10 means 'completely
agree'?" Chart shows percent of respondents
indicating 9 or 10.
Data Attitudes
Contents
0% 20% 40% 60%
Our data management is good enough to satisfy our current needs
The increasing complexity of managing data is becoming overwhelming
The breadth of current technologies are challenging to manage
Our organization tries to achieve "a single source of truth" when it comes to data quality and management
Ever increasing volumes of data strain our team's capabilities
Data is the responsibility of IT
Our priority is to leverage data for business processes
Data can be utilized to empower internal users
We regularly consult with our business leaders on our data management strategies
Our data strategy focuses on business functionality
Mastering business data improves business collaboration across our enterprise
Improving the ability to merge numerous sources of data improves the predictive capabilities of our business
Data is managed as a strategic asset
Improving our data management strategy allows me to be more responsive to our customer
An effective data strategy can be a competitive advantage for companies
Business agility is enhanced when data is leveraged
Non-Executive Executive
6. 6
How important is it to have such a strategy? Very, but that’s
just a piece of the story, as “The Data Directive”1
shows:
• Ninety-seven percent of C-level executives consider data as
a strategic priority.
• Yet there’s a discrepancy between what enterprises aspire
to, and what their data reality is. Only 12 percent of
executives believe they are “highly” effective” at using data
strategically.
• Most enterprises also suspect they’re trailing behind their
competitors in their strategic use of data. Only 15 percent of
CEOs consider themselves “significantly above average” in
their use of data for their markets.
Bottom Line: Few companies have come to
grips with how to use data effectively, even
as they rush to collect more of it.
1 2 3 4 5 6 7 8 9
of C-level executives consider data as
a strategic priority
97%
believe they are “highly” effective” at
using data strategically
12%
of CEOs consider themselves
“significantly above average” in their use
of data for their markets
15%
C-Level Perceptions of Data
"The Data Directive"1
Introduction
Contents
7. 7
In this ebook, we’ll look closely at research that
shows the growing importance of data to
organizations. Specifically, we’ll show how
businesses that use data strategically—we call
them Data-Fueled Enterprises—perform better
financially. Most important, we’ll examine how
and why the disconnects between IT executives
and staff can put the brakes on this. Finally, we
prescribe four critical ways that IT executives and
staff can align to get the most out of their data
and optimize chances for success.
1 2 3 4 5 6 7 8 9Contents 7
9. 9
81%
57%
Using Data Effectively
High-growth firms were found to use data effectively
far more likely than no-growth firms
High-growth
firms
Low-growth
firms
Using Data Effectively:
Good for Bottom Line
Smart use of data equals higher revenue
growth over extended periods of time
A closer look at “The Data Directive”
2
report delivers more
good news for data junkies: it found that “high-growth”
firms—categorized as such based on their EBITDA
performance over the past three years—were far more likely
than “no-growth” firms to use data effectively (81 percent
compared to 57 percent). Or, to look at it another way,
high-growth firms were also far less likely to be ineffective at
using data to drive strategic decisions (4 percent compared
to 17 percent).
Data-Fueled Enterprises Are Successful Enterprises
1 2 3 4 5 6 7 8 9
"The Data Directive“2
Contents
10. 10
92%
35%
Data-Fueled Executives
High-growth firms provide their senior executives with new data
and information to support their roles and decisions
And when most other attributes of their strategic decision-
making processes are the same, these high-performing,
data-fueled firms are more likely to use the results of the data
they have. They also:
• Almost universally provide their senior executives with new
data and information to support their roles and decisions
(92 percent, versus just 35 percent of ineffective firms)
• Are 12 times more likely to consider their strategic planning
and decision-making data-driven
• Put their CEO in charge on data-related initiatives within the
business, ahead of the CIO
As a final point, nearly twice as many no-growth firms admit
to collecting large volumes of data but not consistently
maximizing its usage (38 percent versus 20 percent of high-
growth firms).
1 2 3 4 5 6 7 8 9
Data-Fueled Enterprises Are Successful Enterprises
"The Data Directive“3
High-growth
firms
Low-growth
firms
Contents
11. 11
Chapter 3
When IT Execs and Staff Don’t
See Eye to Eye
1 2 3 4 5 6 7 8 9Contents 11
12. 12
Business Users of Data?
What Business Users?
Although IT executives get the importance of
working closely with business users on data-
related initiatives, rank-and-file IT workers who
manage the data day-to-day don’t—yet.
Despite the clear evidence of the link between effective
data usage and the bottom line in “The Data Directive”
report, IT workers fell well behind IT executives’ grasp of
how data impacts the business in the Informatica research
study.
Moreover, IT staff were significantly less inclined to work
with business users. Informatica’s research shows just
17.2% of non-executive respondents indicate they
“regularly consult with business leaders on data
management strategies.” That’s compared with 55% of
executive respondents.
1 2 3 4 5 6 7 8 9
When IT Execs and Staff Don’t See Eye to Eye
IT Executive-IT Staff Disconnect
In response to the question, “To what extent do you agree with the
following statements, where 1 means ‘completely disagree’ and 10
means ‘completely agree’?“ Chart shows percent of respondents
indicating 9 or 10.
63.1%
61.3%
55.0%
41.4%
42.4%
17.2%
0% 20% 40% 60% 80%
Business agility is enhanced when
data is leveraged
Improving our data management
strategy allows me to be more
responsive to our customer
We regularly consult with our
business leaders on our data
management strategies
Non-Exec Exec
Contents
13. 13
That the people who actually administer the data lack a
business-centric perspective clearly has ramifications for
organizations’ ability to use data effectively overall.
Other research backs this up. Although “data use” was the
top strategic priority of CIOs (72 percent) for the more than
700 CIO respondents to CIO’s annual “State of the CIO”
survey in 2014, nearly half of them (47 percent) said they
were frustrated pushing their IT staff to be more business-
oriented and customer-facing.
4
The top strategic priority among CIOs was “data use” in
CIO Magazine’s 2014 “State of the CIO4” study, but
nearly half expressed frustration pushing staff to be more
business- and customer-focused.
72%
47%
1 2 3 4 5 6 7 8 9
When IT Execs and Staff Don’t See Eye to Eye
Contents
14. 141 2 3 4 5 6 7 8 9
Interestingly, even though IT executives and staff
weren’t aligned on the importance of issues like
consulting business leaders, customer
responsiveness, and business agility, they were
likely to agree on the generic statement that an
effective data strategy is a competitive advantage.
And they agree that data can be utilized to
empower internal employees. The majority of both
populations don’t agree that data management
techniques are good enough to satisfy their
organizations’ current needs. Otherwise, having
their noses to the data grindstone made them
relatively indifferent to the business effects of data,
according to the Informatica study.
Contents 14
15. 15
This suggests that organizations would be well
served to educate IT workers on the specific
business impact of what they do. This also suggests
that Data-Fueled Enterprises not only agree that an
effective data strategy can be a competitive
advantage—but agree on what an effective data
strategy looks like in tangible terms--for example,
that it should be linked to specific business KPIs
(key performance indicators).
63.1%
23.4%
49.5%
55.6%
16.2%
44.4%
0% 20% 40% 60% 80%
An effective data strategy can be a
competitive advantage for companies
Our data management is good enough
to satisfy our current needs
Data can be utilized to empower
internal users
Non-Exec Exec
1 2 3 4 5 6 7 8 9
When IT Execs and Staff Don’t See Eye to Eye
Where IT Executives and Staff Agree
In response to the question, “To what extent do you agree with the
following statements, where 1 means ‘completely disagree’ and 10
means ‘completely agree’?“ Chart shows percent of respondents
indicating 9 or 10.
Contents
16. 161 2 3 4 5 6 7 8 9
Chapter 4
IT Risks Getting Sidelined
on Data Initiatives
Contents 16
17. 17
Changing attitudes toward data and business might
be an imperative for IT survival: according to CIO
Magazine’s 2014 State of the CIO survey
5
, 28
percent of CIOs say the CIO role at their enterprise
is being “sidelined” and 52 percent say the CIO’s
future will be one focused solely on managing
contractors and service providers.
Given these attitudes, it’s not surprising that only 25
percent of the CIOs from the CIO survey believe that
their IT organizations are perceived by colleagues as
true business peers capable of being “game
changers” to their businesses.
Another body of research, from Enterprise
Management Associates
6
, shows that information
consumers (users) of data projects are moving from
data scientists and other technical personnel to
employees with business backgrounds.
With user-friendly next-generation analytics and
data management tools, today, nearly 50
percent of users of data projects have business
backgrounds—most predominantly, line-of-
business executives and business analysts.
1 2 3 4 5 6 7 8 9
Control of Data Moving
to the Business
Next-generation analytics and data
management tools mean that business users
are seizing control of data initiatives—
potentially leaving IT in the dust
IT Risks Getting Sidelined on Data Initiatives
Contents
18. 18
With user-friendly next-generation analytics and data
management tools, today, nearly 50 percent of users
of data projects have business backgrounds--most
predominantly, line-of-business executives and business
analysts.
What’s more, nearly 50 percent of all data projects are
sponsored by business units such as finance, marketing,
and sales. Only 20 percent of data projects are sponsored
directly by the CIO7.
But this shift is causing challenges, since 81 percent of
enterprises indicate data projects developed without IT
involvement create problems8.
Clearly, the business-IT disconnect on data has far-reaching
effects. Also clearly: Data-Fueled Enterprises know how to
balance IT input with business involvement in data projects.
Enterprises indicating
problems with projects that
are developed without IT
81%
30%
50%
20%
1 2 3 4 5 6 7 8 9
CIO
Business units
Business is Sponsoring Data
Projects7 …
… But Leaving Out IT Can Lead
to Problems8
IT Risks Getting Sidelined on Data Initiatives
Contents
19. 191 2 3 4 5 6 7 8 9
Chapter 5
Are IT Executives Out of Touch on
Day-To-Day Data Issues?
Contents 19
20. 20
33.3%
28.8%
45.0%
47.7%
44.1%
45.9%
23.2%
17.2%
31.3%
32.3%
26.3%
16.2%
0% 20% 40% 60%
The breadth of current technologies
are challenging to manage
The increasing complexity of
managing data is becoming
overwhelming
Ever increasing volumes of data
strain our team's capabilities
Our priority is to leverage data for
business processes
Our organization tries to achieve "a
simgle source of truth" when it comes
to data quality and management
Data is the responsibility of IT
Non-Exec Exec
1 2 3 4 5 6 7 8 9
Are Expectations About
Data Management
Realistic?
Although both IT executives and workers
strongly agree that data can be leveraged
to empower employees and help customers,
they disagree on a number of key
operational issues.
Just as IT workers fail to fully grasp the business
significance of data, IT executives may have blinders on
when it comes to what is—and isn’t—important to IT data
professionals, according to Informatica’s own research.
Disagreement on Key Operational Issues
In response to the question, “To what extent do you agree with these
statements, where 1 means ‘completely disagree’ and 10 means
‘completely agree’?“ Chart shows percent respondents indicating
9 or 10.
Are IT Executives Out of Touch on Day-To-Day Data Issues?
Contents
21. 21
Most significantly, execs and IT workers disagree on things that are
closer to the day-to-day duties of the data professional. This implies that
executives, although seeing the strategic importance of data to the
business more clearly—and understanding the imperative to align with
the business more closely—may not understand the operational issues
facing the data workers in the trenches.
Interestingly enough, executives were far more likely to say that the
technologies were difficult to manage, and that the complexity of
managing data was overwhelming, than staff members who actually
worked with the technologies and the data.
This suggests that information exchanges can flow both ways: IT
executives and workers need to listen to each other to come to consensus
on issues both strategic and pragmatic.
Executives, although seeing
the strategic importance of
data to the business more
clearly—and understanding
the imperative to align with
the business more closely—
may not understand the
operational issues facing
the data workers in the
trenches.
1 2 3 4 5 6 7 8 9
Are IT Executives Out of Touch on Day-To-Day Data Issues?
Contents
22. 221 2 3 4 5 6 7 8 9
Chapter 6
Out of Sync Data Aspirations
Can Hurt Business
Contents 22
23. 23
Right Data, Right Time, Right Way
Every application, every process and every person is
smarter when the right data is used at the right time. To
unleash your organization’s full potential in this data-
centric world, it is critical to think differently about your
data:
• Data can no longer be defined by its source or
application. Data needs to be managed as an
interconnected ecosystem spanning all applications,
processes, computing platforms, devices, users, and
use cases.
• Your data technology landscape will never again be
a static standardized architecture, but rather will be
constantly changing and adapting to incorporate new
technologies or applications;
• With the consumerization of IT, companies are sitting
on an ever-growing pool of data and technology
skills, in both IT as well as the business, that need to
be harnessed for the combined good of the company.
1 2 3 4 5 6 7 8 9
Out of Sync Data Aspirations Can Hurt Business
Lack of Common
Data Vision
Stresses disconnect on business attitudes
between IT execs and employees.
In the Informatica survey, when asked if they agreed with or
would embrace a strategy that included the statement to the
right, more executives responded “yes” than lower-ranking
IT employees, again stressing the disconnect on business
attitudes between senior IT and IT employees.
Do you agree or disagree with the statement in the yellow
box to the right? What’s your opinion? Read our blog “Does
Your IT Organization Have a Common Data Vision” to share
your thoughts.
Contents
24. 24
This again suggests that IT employees need
to adopt a more strategic view of what they
do if they want to push their organizations
to be Data-Fueled Enterprises.58.7%
46.9%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
Executives Non-Executive
1 2 3 4 5 6 7 8 9
Out of Sync Data Aspirations Can Hurt Business
Agreement with Data Strategy Statement
Contents
25. 251 2 3 4 5 6 7 8 9
Chapter 7
Needed: Consensus on What Is
Being Measured to Determine
Data Strategy Success
Contents 25
26. 26
0% 20% 40% 60% 80% 100%
Data quality
IT productivity
ROI on data management
New product or process innovation
Customer satisfaction / retention
Cost savings
Data governance capability
Project on-time / on-budget
Budget controls
Service level agreements
Agility / speed to market
Revenue generation
Business user productivity
Non-Exec Exec
1 2 3 4 5 6 7 8 9
Discord Around
Definition of Data
Strategy Success
Execs focus on productivity,
governance, and innovation; non-
execs emphasize SLAs and on-time
projects.
When asked what key performance indicators
(KPIs) they used to measure the success of data
strategies, IT executives and staff workers were
again in discord. Although data quality is key
for both groups, execs focus on productivity,
governance, and innovation, while non-execs
put a relatively greater emphasis on SLAs and
project timeliness.
KPIs for Data Strategy Success
When asked “Which of the following metrics or KPIs does your
organization currently use to measure success of your data
management and data integration strategy?”
Needed: Consensus on What Is Being Measured to Determine Data Strategy Success
Contents
27. 271 2 3 4 5 6 7 8 9
Chapter 8
Data Needs To Be Clean,
Safe, Connected
Contents 27
28. 28
The statistics on data are rather dismal:9
of the average enterprise database
is inaccurate25%
of companies surveyed had an overall
data health scale of “unreliable”64%
of companies have “risky”
phone contact records80%
1 2 3 4 5 6 7 8 9
At Last, Agreement:
Data Must Be Safe
That data is safe (trusted, secure,
compliant, and failsafe) is seen as
the single most important attribute
by both IT executives and workers.
According to the Informatica survey, IT
executives and non-executives alike agree
above all that data must be clean, safe, and
connected.
However, “safe” is named most critical by a
slight margin.
Data Needs To Be Clean, Safe, Connected
Contents
29. 29
Data Priorities
In answer to the question, “How would you rank the following
statements in terms of its relevance to your organization with regard to
data management and integration?” Sum of top 1st and 2nd choices
shown.
1 2 3 4 5 6 7 8 9
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
Clean Safe Connected Predictive Reusable Changeable
Executive Non-Executive
Clean
Data needs to be clean
(e.g. accurate, “single
source of truth,“ centrally
mastered, standardized)
Safe
Data needs to be safe,
(e.g. data movement is fully
trusted, secure, compliant
and failsafe)
Connected
Data needs to be connected
across systems, technologies,
devices, and users in a
relevant and actionable way
Predictive
Data can be used in a
proactive way, allowing for
predictive analytics and
applications.
Reusable
Data infrastructure needs to
leverage and reuse existing
skills and technical artifacts to
increase agility and
productivity.
Changeable
Data infrastructure has to
be architected to handle
frequent change.
Data Needs To Be Clean, Safe, Connected
Contents
30. 30
Where executives and non-executives disagree is in the
predictive aspect of data. Executives are far more likely to
say that data should be used proactively for predictive
analytics and applications than IT staff workers. And,
perhaps not surprisingly, IT workers rate the ability to have
a data infrastructure capable of supporting frequent
changes much higher than executives.
What’s clear is that in a Data-Fueled
Enterprise, both things should be true: data
should be used proactively, and the data
infrastructure must be able to shift agilely
to mirror changes in markets and the
business.
1 2 3 4 5 6 7 8 9Contents 30
32. 32
Four Imperatives for
Getting the Most
Out of Your Data
How to Enable the Data-Fueled
Enterprise?
If the Data-Fueled Enterprise supports better financial
performance, what practices might you put in place to enable
it? We can think of at least four:
Best Practice No. 1: Assess the current health of your data to
establish a baseline for data quality. Ongoing improvements are
measured against this baseline.
Best Practice No. 2: Establish and promote joint IT and business
ownership on projects. Hire people with business analysis experience
onto the IT team so that IT can speak the business’s language.
Best Practice No. 3: Shift toward a model where business self-service
is enabled by IT, who can ensure policies are adhered to, while enabling
the business to act more quickly on their own (with guard rails).
Best Practice No. 4: If your enterprise architecture is still anchored
around business applications, consider re-orienting your architecture to
anchor on the data. After all, apps come and go. Your data lives on.
1 2 3 4 5 6 7 8 9
If you’re interested in the concept of a Data-Fueled
Enterprise, you might also benefit from reading
Forrester’s “How to Make a Business Case for a Data
Investment.” Download it here. Or contact us here.
Conclusion
Contents
33. 33
Sources 1 - The data directive: How data is driving corporate strategy—and what still lies
ahead, Economist Intelligence Unit, commissioned by Wipro, April 2013.
http://www.economistinsights.com/analysis/data-directive
2 – Ibid
3 – Ibid
4 - State of the CIO Survey 2014, CIO Magazine, February 2014.
http://www.cio.com/article/744601/State_of_the_CIO_2014_The_Great_Schism.
5 – Ibid
6 - Operationalizing the Buzz: Big Data 2013: An Enterprise Management Associates®
(EMA™) and 9sight Consulting Research Report.
http://www.9sight.com/BigData_2013_Survey.pdf
7 – Ibid
8 – State of the CIO Survey 2014, CIO Magazine, February 2014.
http://www.cio.com/article/744601/State_of_the_CIO_2014_The_Great_Schism
9 – 2013 NetProspex Marketing Data Benchmark Report.
http://content.netprospex.com/marketing-data-benchmark-report
1 2 3 4 5 6 7 8 9
Conclusion
Published July 2014 IN18_0514_2651
Contents