Learn about real MRO Material Master Data Cleansing project success stories. Discover the significant benefits and cost savings that IMA Data Cleansing has deliver to manufacturing and asset intensive organizations worldwide.
Data cleansing presentation by Digital Bucket CompanyHumayun Qureshi
Digital Bucket Company junior consultant's presentation on Data Cleansing. A key service of Digital Bucket Company is helping organisations to have organised and clean data sets. Please watch and for further information please contact www.digitalbucketcompany.com
Data-Ed Online: Approaching Data QualityDATAVERSITY
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, the delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This, in turn, allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Learning Objectives:
Help you understand foundational Data Quality concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBoK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
This document discusses the growing importance of data centers in supporting the digital economy. It notes that global investment in data centers grew 8% to $151.3 billion in 2012-2013, with Asia Pacific growing 6.8%. Data centers are crucial hubs where large amounts of data are collected, stored, and analyzed. However, challenges for data centers include coping with higher data volumes and workloads, ensuring high availability and reliability, and securely handling growing amounts of sensitive business data and information. Looking ahead, issues involve building flexible storage infrastructure and aligning data center operations with business needs.
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, the delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This, in turn, allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
DataEd Slides: Data Management Best PracticesDATAVERSITY
It is clear that Data Management best practices exist, and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes — permitting organizations the opportunity to benefit from the best of both. It also permits organizations to understand:
• Their current Data Management practices
• Strengths that should be leveraged
• Remediation opportunities
Data-Ed: A Framework for no sql and HadoopData Blueprint
Big Data and NoSQL continue to make headlines everywhere. However, most of what has been written about these topics is focused on the hardware, services, and scale out. But what about a Big Data and NoSQL Strategy, one that supports your business strategy? Virtually every major organization thinking about these data platforms is faced with the challenge of figuring out the appropriate approach and the requirements. This presentation will provide guidance on how to think about and establish realistic Big Data management plans and expectations. We will introduce a framework for evaluating the various choices when it comes to implementing and succeeding with Big Data/NoSQL and show how to demonstrate a sample use case.
DataEd Slides: Exorcising the Seven Deadly Data SinsDATAVERSITY
The difficulty of implementing a new data strategy often goes under-appreciated, particularly the multi-faceted procedural challenges that need to be met while doing so. Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data managers, but beyond that there are several cultural and structural barriers common to virtually all organizations that must be eliminated in order to facilitate effective management of data. This webinar will discuss these barriers – the titular “Seven Deadly Data Sins” – and in the process will also:
• Elaborate upon the three critical factors that lead to strategy failure
• Demonstrate a two-stage Data Strategy implementation process
• Explore the sources and rationales behind the “Seven Deadly Data Sins,” and recommend solutions
How you can gain rapid insights and create more flexibility by capturing and storing data from a variety of sources and structures into a NoSQL database.
Data cleansing presentation by Digital Bucket CompanyHumayun Qureshi
Digital Bucket Company junior consultant's presentation on Data Cleansing. A key service of Digital Bucket Company is helping organisations to have organised and clean data sets. Please watch and for further information please contact www.digitalbucketcompany.com
Data-Ed Online: Approaching Data QualityDATAVERSITY
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, the delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This, in turn, allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Learning Objectives:
Help you understand foundational Data Quality concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBoK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
This document discusses the growing importance of data centers in supporting the digital economy. It notes that global investment in data centers grew 8% to $151.3 billion in 2012-2013, with Asia Pacific growing 6.8%. Data centers are crucial hubs where large amounts of data are collected, stored, and analyzed. However, challenges for data centers include coping with higher data volumes and workloads, ensuring high availability and reliability, and securely handling growing amounts of sensitive business data and information. Looking ahead, issues involve building flexible storage infrastructure and aligning data center operations with business needs.
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, the delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This, in turn, allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
DataEd Slides: Data Management Best PracticesDATAVERSITY
It is clear that Data Management best practices exist, and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes — permitting organizations the opportunity to benefit from the best of both. It also permits organizations to understand:
• Their current Data Management practices
• Strengths that should be leveraged
• Remediation opportunities
Data-Ed: A Framework for no sql and HadoopData Blueprint
Big Data and NoSQL continue to make headlines everywhere. However, most of what has been written about these topics is focused on the hardware, services, and scale out. But what about a Big Data and NoSQL Strategy, one that supports your business strategy? Virtually every major organization thinking about these data platforms is faced with the challenge of figuring out the appropriate approach and the requirements. This presentation will provide guidance on how to think about and establish realistic Big Data management plans and expectations. We will introduce a framework for evaluating the various choices when it comes to implementing and succeeding with Big Data/NoSQL and show how to demonstrate a sample use case.
DataEd Slides: Exorcising the Seven Deadly Data SinsDATAVERSITY
The difficulty of implementing a new data strategy often goes under-appreciated, particularly the multi-faceted procedural challenges that need to be met while doing so. Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data managers, but beyond that there are several cultural and structural barriers common to virtually all organizations that must be eliminated in order to facilitate effective management of data. This webinar will discuss these barriers – the titular “Seven Deadly Data Sins” – and in the process will also:
• Elaborate upon the three critical factors that lead to strategy failure
• Demonstrate a two-stage Data Strategy implementation process
• Explore the sources and rationales behind the “Seven Deadly Data Sins,” and recommend solutions
How you can gain rapid insights and create more flexibility by capturing and storing data from a variety of sources and structures into a NoSQL database.
This document provides tips and recommendations for implementing a successful 1:1 initiative based on the experiences of Rowan-Salisbury School System. Key recommendations include: securing buy-in from leadership; focusing on learning over devices; researching best practices; networking with other districts; addressing infrastructure, funding, deployment logistics; developing comprehensive professional development and support for educators; and celebrating milestones. Common pitfalls to avoid include underestimating preparation needs, forgetting to update policies, and not establishing ongoing support.
Early Warning Signs of IT Project Failure -- The Deadly Dozen and the Four Ho...Leon Kappelman
The document discusses early warning signs of IT project failure. It identifies the top 12 early warning signs, called the "Deadly Dozen", which are grouped into people and process factors. The people factors center around five groups: top management, project management, project team members, subject matter experts, and stakeholders. The process factors center around five key project management processes. Addressing these early warning signs is important as the cost of fixing issues rises over time and human nature can exacerbate problems. Process, tools, and best practices can help mitigate risks.
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
DataEd Slides: Data Strategy — Plans Are Useless, but Planning Is InvaluableDATAVERSITY
Too often, I hear the question, “Can you help me with our Data Strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component — the Data Strategy itself. A more useful request is, “Can you help me apply data strategically?” Yes, at early maturity phases, the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (much less perfect) Data Strategy on the first attempt is generally not productive — particularly giving the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” Refocus on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. This approach can also contribute to three primary organizational data goals. Learn how improving the following will help in ways never imagined:
• Your organization’s data
• The way your people use data
• The way your people use data to achieve your organizational strategy
Data is your sole non-depletable, non-degradable, durable strategic asset, and it is pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (Data Strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
• A cohesive argument for why Data Strategy is necessary for effective Data Governance
• An overview of prerequisites for effective strategic use of Data Strategy, as well as common pitfalls
• A repeatable process for identifying and removing data constraints
• The importance of balancing business operation and innovation
Data Management and Data Governance are the same thing! Aren’t they? Most people would say that this line of thinking is absurd – or even worse. There is NO WAY that they are the same thing. Or are they?
Join Bob Seiner and Anthony Algmin for a lively, interactive, and entertaining discussion targeted at providing attendees ways to consider relating these two disciplines. You’ve never attended a session like this.
In this session, Bob and Anthony will discuss:
- The similarities between Data Management and Data Governance
- The differences between the two
- How to use Data Management to sell Data Governance … and the other way around
- Deciding if the two disciplines are the same … or different
The first step towards understanding data assets impact on your organization is understanding what those assets mean for each other. Metadata – literally, data about data – is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices and enable you to combine practices into sophisticated techniques supporting larger and more complex business initiatives. Program learning objectives include:
• Understanding how to leverage metadata practices in support of business strategy
• Discussing foundational metadata concepts
• Exploring guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
This document discusses developing an effective data strategy. It begins by introducing the speakers and defining their roles. It then discusses how regulations and opportunities presented by data have led to the emergence of the Chief Data Officer role. The responsibilities of the CDO include centralizing data, evangelizing its use, and facilitating stakeholders. Effective data strategies start by understanding business objectives and linking them to specific tactics enabled by technology. The strategies should define priorities, roadmaps, and remain flexible over time. Hiring an experienced CDO can help organizations optimize processes and gain new insights from their data.
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords such as “big data,” “NoSQL,” “data scientist,” and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, Data Modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important are the data models driving the engineering and architecture activities o
Successfully Kickstarting Data Governance's Social Dynamics: Define, Collabor...Stijn (Stan) Christiaens
The document discusses data governance and outlines several key points:
1) Data governance is about bringing business and IT together to govern data as a key enterprise asset and ensure there is a common understanding of what data means.
2) Existing tools and approaches are insufficient for handling today's data complexity, and semantic technology can help by clarifying the meaning of data elements.
3) Effective data governance requires a combination of technology, organizational structure, methodology, and culture to define roles and processes for validating and reconciling data across stakeholders.
SIM IT Trends Study 2013 - SIMposium SessionLeon Kappelman
Since 1980 the Society for Information Management (SIM) has conducted a survey of its senior IT executive members to gauge trends within the IT industry. SIM's members are among the most accomplished and innovative leaders in IT, so their responses help to benchmark various areas such as major management issues, largest and most worrisome IT investments, sourcing, CIO roles, staffing, spending, and salaries. SIM's IT Trends Study is widely recognized as one of the most representative barometers of the information technology industry. More information at http://www.simnet.org/?ITTrendsStudy.
View the companion webinar at: http://embt.co/1L8V6dI
Some claim that, in the age of Big Data, data modeling is less important or even not needed. However, with the increased complexity of the data landscape, it is actually more important to incorporate data modeling in order to understand the nature of the data and how they are interrelated. In order to do this effectively, the way that we do data modeling needs to adapt to this complex environment.
One of the key data modeling issues is how to foster collaboration between new groups, such as data scientists, and traditional data management groups. There are often different paradigms, and yet it is critical to have a common understanding of data and semantics between different parts of an organization. In this presentation, Len Silverston will discuss:
+ How Big Data has changed our landscape and affected data modeling
+ How to conduct data modeling in a more ‘agile’ way for Big Data environments
+ How we can collaborate effectively within an organization, even with differing perspectives
About the Presenter:
Len Silverston is a best-selling author, consultant, and a fun and top rated speaker in the field of data modeling, data governance, as well as human behavior in the data management industry, where he has pioneered new approaches to effectively tackle enterprise data management. He has helped many organizations world-wide to integrate their data, systems and even their people. He is well known for his work on "Universal Data Models", which are described in The Data Model Resource Book series (Volumes 1, 2, and 3).
A Peek @ Trends'15 - SIMposium'14 FINAL 2postLeon Kappelman
The document summarizes findings from the 2015 SIM IT Trends Study, which surveyed over 1000 senior IT executives. Key findings include:
1) Organizations are undergoing profound changes in how they focus technology spending, deliver IT, and structure IT departments and leadership roles.
2) IT budgets are changing slightly, with a projected 1.9% increase in 2014 and 0.9% increase in 2015 on average. Spending is shifting from hardware to cloud and business services.
3) IT organization structures continue trending away from centralized models, with 71% now having decentralized, federated or hybrid structures.
DataOps - The Foundation for Your Agile Data ArchitectureDATAVERSITY
Achieving agility in data and analytics is hard. It’s no secret that most data organizations struggle to deliver the on-demand data products that their business customers demand. Recently, there has been much hype around new design patterns that promise to deliver this much sought-after agility.
In this webinar, Chris Bergh, CEO and Head Chef of DataKitchen will cut through the noise and describe several elegant and effective data architecture design patterns that deliver low errors, rapid development, and high levels of collaboration. He’ll cover:
• DataOps, Data Mesh, Functional Design, and Hub & Spoke design patterns;
• Where Data Fabric fits into your architecture;
• How different patterns can work together to maximize agility; and
• How a DataOps platform serves as the foundational superstructure for your agile architecture.
Both digital and traditional businesses are constantly evolving, and the need to move fast is a pervasive reality. Delivering what customers want and need goes beyond the creation of delivery channels. In fact, it relies on the company’s ability to produce, consume, organise, understand, curate, and distribute data.
In this presentation, Dan Aragao and Simon Hope provide a glimpse of the journey ThoughtWorks and REA are currently undergoing to create a truly data-centric, cutting-edge digital business.
Data Prep - A Key Ingredient for Cloud-based AnalyticsDATAVERSITY
Data for analytics comes in many forms, from many sources. This data holds invaluable insights for business, but currently business intelligence teams are spending as much as 80 percent of their time preparing and cleansing this data, rather than analyzing it. The challenge for today's BI and data science teams is to make this data preparation phase more efficient, so they can combine data from multiple sources - on premise and in the cloud - and shape it to be fully optimized for analytics. This webinar will demonstrate how new cloud applications and services can enable an ecosystem where data preparation, movement and analytics are seamless, for both the technical and non technical user within the enterprise.
A Year in Review - Building a Comprehensive Data Management ProgramDataWorks Summit
This document discusses Microsoft Research's efforts to build a centralized data management and processing platform. It provides an overview of big data and its importance to Microsoft. It outlines the vision, principles, goals, and architecture of the platform, which includes Hadoop, GPUs, HPC resources, Azure, and access to datasets like MNIST and Bing data. The platform aims to support research through centralized, compliant data storage and a flexible processing system. It also discusses ensuring data privacy, security, and ethical use of data on the platform.
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
You had a strategy. You were executing it. You were then side-swiped by COVID, spending countless cycles blocking and tackling. It is now time to step back onto your path.
CCG is holding a workshop to help you update your roadmap and get your team back on track and review how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
Enterprise Data Webinar World Series: Leading the Data Asset Management Team ...DATAVERSITY
This document discusses the roles of Chief Data Officer (CDO) and top data job in an organization. It explores what qualifications and traits are needed for these roles and how the CDO organization fits within the larger organizational structure. The key responsibilities of a CDO include developing the enterprise data strategy, owning data governance and architecture, and managing legal and compliance issues. For the top data role to be effective, it requires a blend of technical skills, business knowledge, and strong relationship and communication abilities.
In that session we will discuss about Data Governance, mainly around that fantastic platform Power BI (but also around on-prem concerns).
How to avoid dataset-hell ? What are the best practices for sharing queries ? Who is the famous Data Steward and what is its role in a department or in the whole company ? How do you choose the right person ?
Keywords : Power Query, Data Management Gateway, Power BI Admin Center, Datastewardship, SharePoint 2013, eDiscovery
Level 200
MAR-BAL INCORPORATEDQUARTER OF A MILLION DOLLARS IN SAVINGAlleneMcclendon878
MAR-BAL INCORPORATED
QUARTER OF A MILLION
DOLLARS IN SAVINGS
Case Study
WHAT WOULD YOU DO IF YOU SUDDENLY FOUND
5,000 EXTRA MACHINE HOURS?
Mar-Bal, Inc. is a one-source solution provider
of thermoset composite products. From design and
formulation to compounding, molding and finishing,
Mar-Bal is a privately-held manufacturing company
with 350 employees producing state-of-the-art products
across four facilities in North America. Over the last few
years, Mar-Bal had been experiencing challenges in its
day-today operations. Mar-Bal’s customized (AS400-
based) ERP software was outdated, preventing the
necessary increase in manufacturing activity required to
keep Mar-Bal competitive in today’s global economy. As
each year passed, it became more expensive to operate
the old ERP system due to the specialized support
required to maintain and upgrade it.
Among many of the old system’s pain points was the
absence of a strong Electronic Data Interchange (EDI)
program required by Mar-Bal’s customers. Inbound EDI
with the old system was limited to a time-consuming
interface with customer portals to retrieve customer
product demand and outbound EDI capability was
nonexistent. The old system lacked the essential services
(such as the automatic transmission of advanced shipping
notices and electronic invoices) necessary to conduct
business with key clients.
Inventory control with the old system was also sorely
deficient. The inability to scan inventory from the shop
floor was making inventory management a manual
and extremely time-consuming process that included
redundant data entry and unavoidable data entry errors.
As Mar-Bal expanded and increased manufacturing
activity, its operations needed to be more agile, but
employees were instead spending an average of six hours
a day verifying inventory. The lack of inventory visibility
also required a full physical inventory to be performed
once per month that included machine downtime,
wasting valuable production time while all employees
manually checked inventory levels.
In addition, consigned inventory was also difficult to
manage with the old system. It did not keep track of
what was on hand at the customer site and allowed no
way to add or reduce vendor managed inventory (VMI)
based on shipments and consumption.
To round it all out, the old ERP system contained very
limited reporting tools and forecasting abilities and no
way to easily segregate the separate plants’ costs and
sales. Month-end activities required nearly two weeks
to wrap up. Internal system communication and access
to information was also a problem, resulting in poor
customer service as sales people on the road could not
access necessary order status information, resulting in
longer response times to customers.
AN EXTENSIVE ERP SOFTWARE SEARCH BEGINS
Mar-Bal had two options: pour more money into its
outdated system and attempt to manually streamline
processes in its manufacturing syst ...
The document discusses how supply chain optimization can help companies improve growth, sustainability, and agility. It provides examples of how companies like JBS and Papyrus have used supply chain optimization software from AIMMS to increase margins, improve adherence to plans, and transition to more efficient regional supply chain networks. The document promotes AIMMS software as a way to build customized optimization apps, empower planning teams, and simulate scenarios to make better strategic, tactical, and operational decisions.
This document provides tips and recommendations for implementing a successful 1:1 initiative based on the experiences of Rowan-Salisbury School System. Key recommendations include: securing buy-in from leadership; focusing on learning over devices; researching best practices; networking with other districts; addressing infrastructure, funding, deployment logistics; developing comprehensive professional development and support for educators; and celebrating milestones. Common pitfalls to avoid include underestimating preparation needs, forgetting to update policies, and not establishing ongoing support.
Early Warning Signs of IT Project Failure -- The Deadly Dozen and the Four Ho...Leon Kappelman
The document discusses early warning signs of IT project failure. It identifies the top 12 early warning signs, called the "Deadly Dozen", which are grouped into people and process factors. The people factors center around five groups: top management, project management, project team members, subject matter experts, and stakeholders. The process factors center around five key project management processes. Addressing these early warning signs is important as the cost of fixing issues rises over time and human nature can exacerbate problems. Process, tools, and best practices can help mitigate risks.
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
DataEd Slides: Data Strategy — Plans Are Useless, but Planning Is InvaluableDATAVERSITY
Too often, I hear the question, “Can you help me with our Data Strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component — the Data Strategy itself. A more useful request is, “Can you help me apply data strategically?” Yes, at early maturity phases, the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (much less perfect) Data Strategy on the first attempt is generally not productive — particularly giving the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” Refocus on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. This approach can also contribute to three primary organizational data goals. Learn how improving the following will help in ways never imagined:
• Your organization’s data
• The way your people use data
• The way your people use data to achieve your organizational strategy
Data is your sole non-depletable, non-degradable, durable strategic asset, and it is pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (Data Strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
• A cohesive argument for why Data Strategy is necessary for effective Data Governance
• An overview of prerequisites for effective strategic use of Data Strategy, as well as common pitfalls
• A repeatable process for identifying and removing data constraints
• The importance of balancing business operation and innovation
Data Management and Data Governance are the same thing! Aren’t they? Most people would say that this line of thinking is absurd – or even worse. There is NO WAY that they are the same thing. Or are they?
Join Bob Seiner and Anthony Algmin for a lively, interactive, and entertaining discussion targeted at providing attendees ways to consider relating these two disciplines. You’ve never attended a session like this.
In this session, Bob and Anthony will discuss:
- The similarities between Data Management and Data Governance
- The differences between the two
- How to use Data Management to sell Data Governance … and the other way around
- Deciding if the two disciplines are the same … or different
The first step towards understanding data assets impact on your organization is understanding what those assets mean for each other. Metadata – literally, data about data – is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices and enable you to combine practices into sophisticated techniques supporting larger and more complex business initiatives. Program learning objectives include:
• Understanding how to leverage metadata practices in support of business strategy
• Discussing foundational metadata concepts
• Exploring guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
This document discusses developing an effective data strategy. It begins by introducing the speakers and defining their roles. It then discusses how regulations and opportunities presented by data have led to the emergence of the Chief Data Officer role. The responsibilities of the CDO include centralizing data, evangelizing its use, and facilitating stakeholders. Effective data strategies start by understanding business objectives and linking them to specific tactics enabled by technology. The strategies should define priorities, roadmaps, and remain flexible over time. Hiring an experienced CDO can help organizations optimize processes and gain new insights from their data.
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords such as “big data,” “NoSQL,” “data scientist,” and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, Data Modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important are the data models driving the engineering and architecture activities o
Successfully Kickstarting Data Governance's Social Dynamics: Define, Collabor...Stijn (Stan) Christiaens
The document discusses data governance and outlines several key points:
1) Data governance is about bringing business and IT together to govern data as a key enterprise asset and ensure there is a common understanding of what data means.
2) Existing tools and approaches are insufficient for handling today's data complexity, and semantic technology can help by clarifying the meaning of data elements.
3) Effective data governance requires a combination of technology, organizational structure, methodology, and culture to define roles and processes for validating and reconciling data across stakeholders.
SIM IT Trends Study 2013 - SIMposium SessionLeon Kappelman
Since 1980 the Society for Information Management (SIM) has conducted a survey of its senior IT executive members to gauge trends within the IT industry. SIM's members are among the most accomplished and innovative leaders in IT, so their responses help to benchmark various areas such as major management issues, largest and most worrisome IT investments, sourcing, CIO roles, staffing, spending, and salaries. SIM's IT Trends Study is widely recognized as one of the most representative barometers of the information technology industry. More information at http://www.simnet.org/?ITTrendsStudy.
View the companion webinar at: http://embt.co/1L8V6dI
Some claim that, in the age of Big Data, data modeling is less important or even not needed. However, with the increased complexity of the data landscape, it is actually more important to incorporate data modeling in order to understand the nature of the data and how they are interrelated. In order to do this effectively, the way that we do data modeling needs to adapt to this complex environment.
One of the key data modeling issues is how to foster collaboration between new groups, such as data scientists, and traditional data management groups. There are often different paradigms, and yet it is critical to have a common understanding of data and semantics between different parts of an organization. In this presentation, Len Silverston will discuss:
+ How Big Data has changed our landscape and affected data modeling
+ How to conduct data modeling in a more ‘agile’ way for Big Data environments
+ How we can collaborate effectively within an organization, even with differing perspectives
About the Presenter:
Len Silverston is a best-selling author, consultant, and a fun and top rated speaker in the field of data modeling, data governance, as well as human behavior in the data management industry, where he has pioneered new approaches to effectively tackle enterprise data management. He has helped many organizations world-wide to integrate their data, systems and even their people. He is well known for his work on "Universal Data Models", which are described in The Data Model Resource Book series (Volumes 1, 2, and 3).
A Peek @ Trends'15 - SIMposium'14 FINAL 2postLeon Kappelman
The document summarizes findings from the 2015 SIM IT Trends Study, which surveyed over 1000 senior IT executives. Key findings include:
1) Organizations are undergoing profound changes in how they focus technology spending, deliver IT, and structure IT departments and leadership roles.
2) IT budgets are changing slightly, with a projected 1.9% increase in 2014 and 0.9% increase in 2015 on average. Spending is shifting from hardware to cloud and business services.
3) IT organization structures continue trending away from centralized models, with 71% now having decentralized, federated or hybrid structures.
DataOps - The Foundation for Your Agile Data ArchitectureDATAVERSITY
Achieving agility in data and analytics is hard. It’s no secret that most data organizations struggle to deliver the on-demand data products that their business customers demand. Recently, there has been much hype around new design patterns that promise to deliver this much sought-after agility.
In this webinar, Chris Bergh, CEO and Head Chef of DataKitchen will cut through the noise and describe several elegant and effective data architecture design patterns that deliver low errors, rapid development, and high levels of collaboration. He’ll cover:
• DataOps, Data Mesh, Functional Design, and Hub & Spoke design patterns;
• Where Data Fabric fits into your architecture;
• How different patterns can work together to maximize agility; and
• How a DataOps platform serves as the foundational superstructure for your agile architecture.
Both digital and traditional businesses are constantly evolving, and the need to move fast is a pervasive reality. Delivering what customers want and need goes beyond the creation of delivery channels. In fact, it relies on the company’s ability to produce, consume, organise, understand, curate, and distribute data.
In this presentation, Dan Aragao and Simon Hope provide a glimpse of the journey ThoughtWorks and REA are currently undergoing to create a truly data-centric, cutting-edge digital business.
Data Prep - A Key Ingredient for Cloud-based AnalyticsDATAVERSITY
Data for analytics comes in many forms, from many sources. This data holds invaluable insights for business, but currently business intelligence teams are spending as much as 80 percent of their time preparing and cleansing this data, rather than analyzing it. The challenge for today's BI and data science teams is to make this data preparation phase more efficient, so they can combine data from multiple sources - on premise and in the cloud - and shape it to be fully optimized for analytics. This webinar will demonstrate how new cloud applications and services can enable an ecosystem where data preparation, movement and analytics are seamless, for both the technical and non technical user within the enterprise.
A Year in Review - Building a Comprehensive Data Management ProgramDataWorks Summit
This document discusses Microsoft Research's efforts to build a centralized data management and processing platform. It provides an overview of big data and its importance to Microsoft. It outlines the vision, principles, goals, and architecture of the platform, which includes Hadoop, GPUs, HPC resources, Azure, and access to datasets like MNIST and Bing data. The platform aims to support research through centralized, compliant data storage and a flexible processing system. It also discusses ensuring data privacy, security, and ethical use of data on the platform.
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
You had a strategy. You were executing it. You were then side-swiped by COVID, spending countless cycles blocking and tackling. It is now time to step back onto your path.
CCG is holding a workshop to help you update your roadmap and get your team back on track and review how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
Enterprise Data Webinar World Series: Leading the Data Asset Management Team ...DATAVERSITY
This document discusses the roles of Chief Data Officer (CDO) and top data job in an organization. It explores what qualifications and traits are needed for these roles and how the CDO organization fits within the larger organizational structure. The key responsibilities of a CDO include developing the enterprise data strategy, owning data governance and architecture, and managing legal and compliance issues. For the top data role to be effective, it requires a blend of technical skills, business knowledge, and strong relationship and communication abilities.
In that session we will discuss about Data Governance, mainly around that fantastic platform Power BI (but also around on-prem concerns).
How to avoid dataset-hell ? What are the best practices for sharing queries ? Who is the famous Data Steward and what is its role in a department or in the whole company ? How do you choose the right person ?
Keywords : Power Query, Data Management Gateway, Power BI Admin Center, Datastewardship, SharePoint 2013, eDiscovery
Level 200
MAR-BAL INCORPORATEDQUARTER OF A MILLION DOLLARS IN SAVINGAlleneMcclendon878
MAR-BAL INCORPORATED
QUARTER OF A MILLION
DOLLARS IN SAVINGS
Case Study
WHAT WOULD YOU DO IF YOU SUDDENLY FOUND
5,000 EXTRA MACHINE HOURS?
Mar-Bal, Inc. is a one-source solution provider
of thermoset composite products. From design and
formulation to compounding, molding and finishing,
Mar-Bal is a privately-held manufacturing company
with 350 employees producing state-of-the-art products
across four facilities in North America. Over the last few
years, Mar-Bal had been experiencing challenges in its
day-today operations. Mar-Bal’s customized (AS400-
based) ERP software was outdated, preventing the
necessary increase in manufacturing activity required to
keep Mar-Bal competitive in today’s global economy. As
each year passed, it became more expensive to operate
the old ERP system due to the specialized support
required to maintain and upgrade it.
Among many of the old system’s pain points was the
absence of a strong Electronic Data Interchange (EDI)
program required by Mar-Bal’s customers. Inbound EDI
with the old system was limited to a time-consuming
interface with customer portals to retrieve customer
product demand and outbound EDI capability was
nonexistent. The old system lacked the essential services
(such as the automatic transmission of advanced shipping
notices and electronic invoices) necessary to conduct
business with key clients.
Inventory control with the old system was also sorely
deficient. The inability to scan inventory from the shop
floor was making inventory management a manual
and extremely time-consuming process that included
redundant data entry and unavoidable data entry errors.
As Mar-Bal expanded and increased manufacturing
activity, its operations needed to be more agile, but
employees were instead spending an average of six hours
a day verifying inventory. The lack of inventory visibility
also required a full physical inventory to be performed
once per month that included machine downtime,
wasting valuable production time while all employees
manually checked inventory levels.
In addition, consigned inventory was also difficult to
manage with the old system. It did not keep track of
what was on hand at the customer site and allowed no
way to add or reduce vendor managed inventory (VMI)
based on shipments and consumption.
To round it all out, the old ERP system contained very
limited reporting tools and forecasting abilities and no
way to easily segregate the separate plants’ costs and
sales. Month-end activities required nearly two weeks
to wrap up. Internal system communication and access
to information was also a problem, resulting in poor
customer service as sales people on the road could not
access necessary order status information, resulting in
longer response times to customers.
AN EXTENSIVE ERP SOFTWARE SEARCH BEGINS
Mar-Bal had two options: pour more money into its
outdated system and attempt to manually streamline
processes in its manufacturing syst ...
The document discusses how supply chain optimization can help companies improve growth, sustainability, and agility. It provides examples of how companies like JBS and Papyrus have used supply chain optimization software from AIMMS to increase margins, improve adherence to plans, and transition to more efficient regional supply chain networks. The document promotes AIMMS software as a way to build customized optimization apps, empower planning teams, and simulate scenarios to make better strategic, tactical, and operational decisions.
Tiki Tar Industries India Limited is India's largest private sector bitumen company with operations in 9 locations since 1964. The company provides various products and services related to roads, runways, waterproofing, thermal insulation, paints, coatings, and anti-corrosion. To sustain growth in the competitive market, Tiki Tar adopted Microsoft Dynamics Navision 2009 ERP software to integrate business functions like manufacturing, distribution, and finance. The ERP solution improved workflow efficiency, reduced costs, and improved customer satisfaction through on-time deliveries and quality. Key benefits included establishing uniform processes, reducing inventory costs through better planning, and improving decision making with compiled data.
Tiki Tar Industries India Limited is India's largest private sector bitumen company with operations in 9 locations since 1964. The company provides various products and services related to roads, runways, waterproofing, thermal insulation, paints, coatings, and anti-corrosion. To sustain growth in the competitive market, Tiki Tar adopted Microsoft Dynamics Navision 2009 ERP system from Robosoft to integrate business functions and gain operational efficiencies. Key benefits included reduced data entry, improved workflow, lower inventory costs, faster collections, and improved decision making through real-time information. Client testimonials praised the rapid 8-10 week implementation and 80% reduction in data entry efforts.
Leonard Munyua, CIO at Simba Corporation - Legacy modernisation and adequate ...Global Business Events
Leonard Munyua, CIO of Simba Corporation, discussed the company's legacy modernization efforts and ERP implementation. Simba Corporation has five businesses running on different legacy applications and systems. To reduce costs and complexity, the company consolidated its operations onto a single ERP system. This involved replacing some legacy applications that were limited and integrating other applications. Simba evaluated ERP solutions from two vendors over one year before selecting and implementing a new system to improve operations across its businesses. The modernization aims to maximize the value of existing IT investments while reducing costs and increasing business agility.
Aspen Consulting is an IT solutions provider that has been an IBM Premier Business Partner since 1996. To expand its offerings to deliver comprehensive data management solutions, Aspen began offering IBM's Tivoli storage management solutions. This has increased Aspen's revenue through new clients and repeat business from existing clients, such as completing over 80 Tivoli Storage Manager deployments in three years. Aspen expects future growth by continuing to provide integrated solutions from IBM to meet evolving customer needs.
Enterprise resource planning (ERP) systems integrate core business functions like manufacturing, distribution, accounting, and human resources in a single system. ERP experienced rapid growth in the 1990s as companies replaced legacy systems. While ERP implementation at Cadbury improved efficiency, a failed implementation at Hershey cost the company $150 million due to order fulfillment issues. Lessons from failures show the importance of adequate testing and phased implementations.
ARC Forum Highlights RPM as an Enabler to Optimized Business PerformanceARC Advisory Group
ARC Forum Highlights RPM as an Enabler to Optimized Business Performance
Executives from all over the world gathered to discuss Real Time Performance
Management (RPM) at last week’s ARC forum in Boston,
Massachusetts. RPM drives enterprises toward an environment that supports
innovation and creates new value, not just reduced costs, as dynamic
performance targets reflect current corporate objectives
to ensure actions taken by employees
optimize plant performance. Implementing RPM
requires management commitment along with a
culture change as this philosophy requires tearing
down the silo mentality in order for all stakeholders
to benefit. Above all, this new culture
needs accurate information to instill confidence
and promote proper application as everyone is
now a decision maker.
Economic necessity is driving the adoption
of RPM as an integral part of today’s
corporate philosophy. Real time costing
underlies many of the concepts that seek
optimal performance while adapting to
changing market conditions. An effective
RPM implementation makes every worker
a business manager by aligning
everyone’s actions to the overall business
strategy.
SourceGas implemented SAP Workforce Scheduling and Optimization by ClickSoftware to automate its work order and dispatch processes across four states. It previously relied on manual dispatching of 500,000 annual work orders to 500 technicians. SourceGas took ownership of the implementation to reduce costs, ensure the system met its unique needs, and allow for customization. After a year-long implementation process, the new system automates work order scheduling, maintains the existing timesheet process, and improves routing efficiency while adding safety features like audio alerts.
Cimlogic TrakSYS Customer Case study - APS GroupClaire Healey
APS Group is a marketing solutions provider that was seeking to measure and improve the efficiency of its manufacturing equipment as part of a LEAN manufacturing journey. Cimlogic implemented its TrakSYS performance management software at APS, allowing operators to track downtime in real-time. This provided visibility into key performance metrics like OEE. Within 4 months of implementation, APS saw a 22% improvement in OEE and was better able to reduce waste and optimize equipment usage. The TrakSYS solution gave APS the data-driven insights it needed to advance its LEAN initiatives.
Understand how a leading textiles organization achieved effective control and...Zensar Technologies Ltd.
Case Study - Spentex Industries Limited wanted to have more effective control and enhanced
efficiency, but its many islands of business software stood in the way. The firm
replaced them all with the SAP® Business All-in-One for Mill Products solution,
which enabled it to consolidate and integrate operations across the board, save
greatly on IT costs, and react more nimbly to business challenges.
Kraft Foods Group implemented Worksoft Certify to automate testing of its business processes in order to reduce defects and keep up with frequent software releases. It was able to increase automated test coverage from 50% to 80% by focusing on automating core end-to-end processes. This significantly reduced testing time and effort while improving quality. Kraft now has more time for innovation and is working to automate testing of mobile applications as well.
The document provides a summary of Prithwiraj Dutta's professional experience and qualifications. He has over 12 years of experience leading SAP implementation projects, primarily in ABAP development and technical consulting. His areas of expertise include Materials Management, Sales and Distribution, Finance, Treasury, and Warehouse Management. He has worked on various SAP modules and releases, and has experience managing teams of 15-35 members to deliver projects on time and on budget.
Storage Resource Optimization Delivers “Best Fit” Resources for Your Applicat...Capgemini
As excessive capacity sits underutilized on high-end storage, and as consumption moves toward expense-based operations, organizations continually seek cost-optimized resource-as-a-service offerings.
Discover how new Capgemini and EMC Storage Resource Management Services best match storage and networking resources to specific business and technical requirements.
Storage Resource Optimization ascends to the next level in determining application best-fit solutions at the best price.
First presented at EMC World 2015.
IBM Enterprise Content Management Solutions -Making your industry our businessGanesh Rajapur
IBM® Enterprise Content Management (ECM) solutions
help organizations unlock the value of their content and
transform business processes to enhance productivity, reduce
risk, cut costs and increase revenue. Today, organizations
from a wide range of industries use IBM ECM solutions to
facilitate and automate information access, help ensure
regulatory compliance, enable knowledge workers to make
better decisions and increase competitiveness in new and
innovative ways. Organizations also use IBM ECM solutions
to create a single view of their customers, providing better
insight and customer service. The specifics for each industry
may vary, but IBM ECM solutions help organizations of all
sizes make better, smarter decisions
Success and Failure Examples of ERP ImplementationSunidhi Kumari
The document discusses an ERP implementation success case study of Pantaloons Fashion & Retail Limited which implemented SAP retail solutions to integrate operations and saw benefits. It also covers an ERP implementation failure case study of Overstock.com which rushed implementation of an Oracle system in 6 months, encountered major issues, and had to restate financials. The document also discusses how CRM can be used as an ERP module and its applications and benefits for customer management in the apparel industry.
1) The document discusses Capgemini's UNLIMITED application performance solution, which uses the SAP HANA in-memory platform to improve the speed of custom applications by up to 30 times without changing any code.
2) It explains how traditional approaches to improving application performance through hardware additions and code changes increase costs over time. UNLIMITED aims to boost speed and handle more data through moving applications to SAP HANA.
3) The solution provides an initial analysis of an application to assess improvement potential through benchmarking on SAP HANA, with results typically showing significantly faster performance and reduced database size and costs.
Case 4. 2 Summit electric lights up with a new erp systemniz73
- Summit Electric Supply Co. is a wholesale distributor of electrical equipment and supplies. It obtains goods from manufacturers and sells to contractors. As the middleman, it must handle high transaction volumes and swift inventory turnover.
- Summit's old information systems from the 1980s could no longer keep up with its growth. The systems had limited capabilities and caused delays.
- Summit implemented a new ERP system using SAP to improve operational efficiency. The system allowed for more frequent inventory updates and better inventory management at job sites. It also enhanced business intelligence and chargeback processing.
Norampac, a corrugated packaging plant, was manually managing inventory of nearly 15,000 spare parts with many shortages. In 2010, it began using IMAFS inventory optimization software integrated with its Guide TI maintenance system. This increased the availability of maintenance items from 60% to 95% while only increasing inventory 5%. Simulation showed inventory could be reduced 30% while maintaining service levels. The project involved validating data, classifying items, establishing service targets, and calculating dynamic min-max levels to streamline replenishment.
Similar to MRO Material Master Data Cleansing Case Studies (20)
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Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
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GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
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UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
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Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
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“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
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Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
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Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
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Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
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Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
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2. I.M.A. Ltd.
www.imaltd.com!
CASE STUDIES
MRO Material Master Data Cleansing Projects
§ PepsiCo Bottling North America
§ Global Manufacturing and Engineering Company
§ Tembec Pulp & Paper
CASE STUDIES
3. I.M.A. Ltd.
www.imaltd.com!
PepsiCo Bottling North America
In today's business climate, companies are continuously striving to reduce costs and improve efficiency. PepsiCo, home of some of the
world’s most recognized and respected brands, realized that in order to maximize profits within its bottling division it would need to apply
industry best practices to take advantage of major cost savings within its maintenance storerooms. The process would involve many
phases but none of which could be achieved without first having the item master cleansed by experts who understood PepsiCo’s needs.
After searching the industry, IMA was selected as the best opportunity for PepsiCo to realize targeted savings and achieve the best results.
During the project, IMA cleansed the entire PepsiCo database, which included 63 North American beverage facilities, and created a
corporate item master containing accurate data which provided PepsiCo with the confidence to meet its other financial objectives. After the
identification of all duplicate items, obsolete materials and insufficient descriptions, the new corporate item master contained more data
than ever but with 36% fewer items, resulting in a far more efficient dataset for PepsiCo employees to work with. In addition, IMA also
assigned new corporate part numbers for each unique item allowing for the identification of duplicate items across all PepsiCo divisions, by
direct duplicate as well as fit, form and function.
Using this new and improved item master, IMA was instrumental in implementing the Inventory Reduction program by identifying all
potential excess inventoried items, by plant and region. Other phases of the cost reduction program that would not be possible without first
utilizing IMA data cleansing services include:
1) Vendor consolidation
2) Part number consolidation
3) Implementation of an aftermarket supplier program
4) Creation of a virtual warehouse for bottling locations to share big ticket items
5) Supplier buy back program
In order to maintain the integrity of the new corporate item master, PepsiCo recently contracted IMA to manage all new items being added
or modified through the Catalog Management service. PepsiCo chose to use the Turnkey option, allowing IMA direct access into its INFOR
system to ensure all data is up to date, consistently standardized and always reliable.
CASE STUDIESCASE STUDIES
4. I.M.A. Ltd.
www.imaltd.com!
Global Manufacturing & Industrial Engineering Company
In 2012, IMA was selected as the service provider to implement a Global SAP Material Master Cleansing project, which involved a strategic
rollout schedule to create one common corporate material master across one instance of SAP. Prior to project commencement, IMA
worked closely with the customer to establish a dedicated implementation team, comprehensive project plan and corporate standard
operating procedure. The customer's project team consisted of a project champion, as well as, maintenance, purchasing, procurement, IT,
and finance representatives from various global regions. The IMA project team included a Global Account Manager, Project Manager,
cleansing team, and multiple IT resources. The input from all project team members during the initial planning phase ensured consistency
and common agreement throughout the organization as the decisions made during this time were critical to the success of the project. The
finalized Standard Operating Procedure, which was tailored specifically to the internal business processes and objectives of the customer,
outlined the Noun-Modifier Naming Convention, Abbreviations, Cleansing Policies, and SAP Formatting template. In this particular case,
the customer opted to utilize a slightly customized version of the IMA Standard Noun-Modifier Dictionary and Abbreviation Listing. In
parallel with the customer's SAP go-live schedule, IMA began cleansing, standardizing, enhancing, de-duplicating, and formatting legacy
data by region, according to the pre-defined project standards. Upon completion of each region, IMA delivered the load-ready file and
moved on to the next region in the rollout schedule. While cleansing each individual site/region, IMA continuously cross-referenced for
duplication across the previously cleansed data, and compiled the records to build a single corporate (client level) material master, which
currently houses over 300,000 cleansed ERSA records.
In order to maintain ongoing integrity and consistency of the cleansed material master, IMA's uManage web-based data governance
solution was implemented at each site in parallel with the SAP rollout schedule. Utilizing the unlimited user subscription, the customer
assigned multiple requesters and a single approver to each site. All requests, including new item creations, modifications, extensions, and
suspensions, are now submitted through the uManage portal for review and approval, before being cleansed by a dedicated IMA Data
Governance Specialist. Upon completion, item requests are returned in an SAP load-ready file to a corporate Downloader, who is
responsible for loading the items into the live SAP system. Today, this customer continues to expand its global SAP Conversion and Data
Cleansing project, while managing all new item creations and modifications through the uManage Data Governance solution.
As a result of IMA's ongoing Data Cleansing and Governance services, the customer has gained significant maintenance
efficiency and cost savings through duplicate elimination, improved search ability, detailed reporting, and enterprise visibility.
CASE STUDIES
5. I.M.A. Ltd.
www.imaltd.com!
Tembec Pulp & Paper
Tim Brown, IT Director for Tembec’s Pulp and Paper Groups, was first introduced to IMA at an IndusWorld conference. Discussions
regarding our Catalog Management service were of particular interest to him at that time. He knew Tembec needed to improve its
management of stores inventory, but managing the mills’ Stores catalogs across the corporation had always presented a challenge. It
seemed that IMA could provide them with an affordable solution to their problem.
At Tembec, each of their eleven Pulp and Paper sites operate autonomously, with responsibility for managing their own inventory. The
decision to implement a new CMMS system company wide brought their corporate catalog issues to the forefront. And creating a corporate
catalog of consistent inventory data was something IMA could do for Tembec.
Tembec created a cross-functional team, including members from: Maintenance, Purchasing, IT and Stores. Together we tailored the data
to Tembec’s internal needs. On a site-by-site basis, data was cleaned, standardized and enhanced, then amalgamated into the Corporate
database prior to “go-live” with the new system. All this, while keeping many of the local site naming nuances intact.
“IMA played an important role assisting Tembec in standardizing and improving business processes, systems and naming
nomenclature related to maintenance, stores inventory, and capital spare part stocking activities.”
To date, six of the eleven sites are on the new system. At each site, duplicates and overstock items have been identified. The payback was
evident almost immediately. Identified savings accumulated to a total of roughly $500,000 per site within the first three to six months after
implementation. Additionally, the standardization of the naming nomenclature has set the stage for many more e-commerce cost reduction
opportunities. The real value is realized corporately, enabled by the on-going Catalog Management service. Now Tembec is able to
leverage purchasing, share spares, consolidate suppliers, reduce inventory levels, use down excess stock across the corporation and
create regional and national purchasing programs.
“IMA has worked with Tembec to design and implement a process whereby rapid easy access to a single view of stores
inventory is available. We can now identify opportunities to reduce stores inventory levels as well as report on vendor
performance across all of our mills."
Today, IMA assumes all responsibility for the day-to-day additions/changes/deletions to the Tembec Corporate Catalog. In addition, it also
manages the daily operations for Tembec’s Corporate Vendor file.
CASE STUDIES
CASE STUDIES
6. I.M.A. Ltd.
www.imaltd.com!
CONTACT US
CONTACT US
Rob Hoffer
Global Account Manager!
m. 1 (519) 402-8902!
e. rob.hoffer@imaltd.com!
Jocelyn Facciotti
Marketing Manager!
m. 1 (519) 688-3805!
e. jocelyn.facciotti@imaltd.com!
Peter Hancox
Project Manager!
m. 1 (519) 688-3805!
e. peter.hancox@imaltd.com!
IMA Ltd. Head Office
500 Hwy #3 !
Tillsonburg, ON Canada N4G 4G8!
t. 1 (519) 688-3805!
f. 1 (519) 688-3807!
e. info@imaltd.com!
!
www.imaltd.com
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