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https://docs.microsoft.com/en-us/power-bi/sample-human-resources
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Presented by Abhinav Saxena - Practice Executive, Microsoft Dynamics 365, DXC Eclipse.
If you are an Accounting professional looking for a career change, please look no further. We are going to recommend the best alternative career to leverage on your Accounting knowledge and start earning more than what you have been earning now in your profession. - See more at: http://www.sapficotraining.com/
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This presentation talks about how SAP S/4HANA can empower finance to strategically guide your business evolution via instant insights, intuitive user experience, and a flexible non-disruptive platform.
S/4 HANA conversion functional value propositionVignesh Bhatt
Many consultants have a lot of questions and find it difficult to convince the clients regarding Advantages of HANA over ECC and it's Business Value. This document will probably clear a lot of doubts. I have created this document based on module wise covering a wide array of topics including Logistics, Procurement, Finance & Controlling and Master Data, mentioning advantages of HANA over ECC and highlighting it's business values. Hope this helps.
The world of Microsoft Dynamics 365: a new day in your life with DynamicsDXC Eclipse
Microsoft Dynamics 365: Continue Your Transformation Journey.
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Today, Dynamics 365 delivers more than ever before. It offers the opportunity to take your workforce beyond the standard features of ERP and CRM with a new way of working that will transform your operations.
Presented by Abhinav Saxena - Practice Executive, Microsoft Dynamics 365, DXC Eclipse.
If you are an Accounting professional looking for a career change, please look no further. We are going to recommend the best alternative career to leverage on your Accounting knowledge and start earning more than what you have been earning now in your profession. - See more at: http://www.sapficotraining.com/
Business Intelligence made easy! This is the first part of a two-part presentation I prepared for one of our customers to help them understand what Business Intelligence is and what can it do...
S/4 HANA Editions
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- private, managed cloud
- private cloud
- public cloud
- TCO reductions by using SUSE Linux
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Ramco ERP on Cloud - The Best Cloud Computing Solution Worldwide Ramco Systems
Visit: http://www.ramco.com/erp-suite/index.aspx
Why Cloud ERP Software?
Ramco ERP on Cloud gives you incredible cost benefits with no investment. It is the most preferred choice for businesses across the globe as it encompasses in-built intelligence and business acumen for your industry.
Go- through in detail about Ramco ERP on Cloud.
This presentation will help you understand the basic building blocks of Business Intelligence. Learn how decisions are triggered, the complete decision process and who makes decisions in the corporate world.
More importantly, understand core components of a Business Intelligence architecture such as a data warehouse, data mining, OLAP (Online analytical procession) , OLTP (Online Transaction Processing) and data reporting. Each component plays an integral part which enables today's managers and decision makers collect, analyze and interpret data to make it actionable for decision making.
Business intelligence has become an integral part that needs to be incorporated to ensure business survival. It is a tool that helps analyze historical data and forecast future so that your are always one step ahead in your business.
Please feel free to like, share and comment as you please!
Types of database processing,OLTP VS Data Warehouses(OLAP), Subject-oriented
Integrated
Time-variant
Non-volatile,
Functionalities of Data Warehouse,Roll-Up(Consolidation),
Drill-down,
Slicing,
Dicing,
Pivot,
KDD Process,Application of Data Mining
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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.
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M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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.
1. Business Intelligence
(BI)
Lecturer: PhD Taras V. Panchenko
Associate Professor
@ Theory and Technology for Programming Chair
@ Cybernetics Faculty
@ National Taras Shevchenko University of Kyiv
2. Introduction
Computers are useless.
They can only give you answers.
Pablo Picasso
… Meaning that asking questions and true
creativity are things that computers aren't
capable of yet.
3. BI – Def(s)
Business intelligence (BI) is the ability to apprehend the
interrelationships of presented facts in such a way as to
guide action towards a desired goal.
Hans Peter Luhn, IBM, 1958
BI is the transformation of raw data into meaningful and
useful information for business analysis purposes.
Wikipedia
4. BI – Def(s)
Business intelligence (BI) BI is the transformation of raw
data into meaningful and useful information for business
analysis purposes.
• BI can handle enormous amounts of unstructured data
to help identify, develop and otherwise create new
strategic business opportunities
• BI allows for the easy interpretation of volumes of data
• Identifying new opportunities and implementing an
effective strategy can provide a competitive market
advantage and long-term stability
Wikipedia
5. BI – Def(s)
Business intelligence (BI) is an umbrella term that
includes the applications, infrastructure and tools, and
best practices that enable access to and analysis of
information to improve and optimize decisions and
performance.
Gartner
BI is a set of methodologies, processes, architectures,
and technologies that leverage the output of information
management processes for analysis, reporting,
performance management, and information delivery.
Research coverage includes executive dashboards as well
as query and reporting tools.
Forrester
6. The Problem
• Try to model and analyze activity of the bank
– Develop the model: clients, accounts, currency,
transactions, …
– Possible questions to system/model from analyst
• Performance (analytical query speed)
• Dynamic reports & ad-hoc analysis
• or: analyze sales of products by regions in time
• Is RDBMS the best solution?
… for Multidimensional model …
8. Problem of Relational Database Model
• Most notably lacking has been the ability to
consolidate, view, and analyze data according
to multiple dimensions, in ways that make
sense to one or more specific enterprise
analysts at any given point in time. This
requirement is called “multidimensional data
analysis.”
E.F. Codd
9. Limitations: lack of … analytics
• Until recently, the end-user products that had
been developed as front-ends to the relational
DBMS provided very straightforward simplistic
functionality. The query/report writers and
spreadsheets have been extremely limited in the
ways in which data (having already been
retrieved from the DBMS) can be aggregated,
summarized, consolidated, summed, viewed, and
analyzed.
E.F. Codd
10. BI (or – partially – OLAP)
• Is the solution
• The only one “point of truth”
– Contains all information about business
(… or any subject area) in one place
• Gives analytical & reporting means
– Speed (performance)
– Flexibility (many instruments)
11. BI is about
• Decision Support Systems
• Business Analytics
• Complex & Comprehensive, Intelligent
Reporting
• Multidimensional Analysis (real-time)
• “OLTP -> OLAP” – is the part of strategy
– OLAP is the core of BI
13. ETL = OLTP OLAP
• OnLine Transaction Processing System
– accounting of transactions
(E)xtract
(T)ransform
(L)oad
• OnLine Analytical Processing System
– gives analytical, intelligence (to transactional data)
14. OLAP (~Def.)
• Is an approach to answering multi-dimensional
analytical queries swiftly
• Technology for information processing for
quick answering on multidimensional
analytical queries
• Allows consolidation and analysis of data in a
multidimensional space
• Is not stand-alone!
– but based on OLTP data
15. OLAP Applications
• Business reporting
– Sales
– Marketing
– Management etc.
• Financial Reporting
• Budgeting
• Forecasting
• Planning
• Business Process Management
… in any business (any subject area)
16. The Difference
• OLTP: Operations
– RDBMS
• Large number of
short transactions
• 3NF, ER-model
– ACID
Atomicity, Consistency,
Isolation, Durability
– Business Process
– Online, real-time info
• OLAP: Information
– Multidimensional
• Complex queries
involve aggregations
• Sparse n-dim. spaces
– Aggregates
precalculated
– Analytical Data
Warehouse
– Large historical data
storage
17. Transaction vs. Analytical Approach
Transaction Systems Analytical Systems
Technology OLTP OLAP
Data visualization Grid (Table) Pivot Table
End-user visual querying QBE Cube browsing
(drill-down, slice & dice)
Query language SQL MDX + XMLA
18. OLTP vs. OLAP
OLTP System – Online Transaction Processing
(Operational System)
OLAP System – Online Analytical Processing
(Data Warehouse)
Source of
data
Operational data; OLTPs are the original source of
the data.
Consolidation data; OLAP data comes from the various
OLTP Databases
Purpose of
data
To control and run fundamental business tasks
To help with planning, problem solving, and decision
support
What the
data
Reveals a snapshot of ongoing business
processes
Multi-dimensional views of various kinds of business
activities
Inserts and
Updates
Short and fast inserts and updates initiated by
end users
Periodic long-running batch jobs refresh the data
Queries
Relatively standardized and simple queries
Returning relatively few records
Often complex queries involving aggregations
Processing
Speed
Typically very fast
Depends on the amount of data involved; batch data
refreshes and complex queries may take many hours;
query speed can be improved by creating indexes
Space
Require-ments
Can be relatively small if historical data is
archived
Larger due to the existence of aggregation structures and
history data; requires more indexes than OLTP
Database
Design
Highly normalized with many tables
Typically de-normalized with fewer tables; use of star
and/or snowflake schemas
Backup and
Recovery
Backup religiously; operational data is critical to
run the business, data loss is likely to entail
significant monetary loss and legal liability
Instead of regular backups, some environments may
consider simply reloading the OLTP data as a recovery
method
19. BI includes
• ETL procedure (= Extract – Transform – Load)
– often: Data Warehouse, via Data Marts
• OLAP Multidimensional Storage & Engine
– Ad-hoc questions & multi-purpose querying
• Reporting
– flexible, interactive, dynamic, effective, …
• Data Mining
– clustering, associations, trends (time analysis),
predictions, …
20. BI core is OLAP
(engine & storage)
• Multidimensional (hyper-)cube:
21. OLAP Concepts
• Flexible Information Synthesis
• Multiple Data Dimensions /
/ Consolidation Paths
(i.e. Multidimensional Conceptual View)
38. BI Introduction
• Business Intelligence enhances business (or
any other area) vision and understanding
• OLAP is a core of BI
• BI includes
– ETL (OLTP Data Warehouse with Data Marts
OLAP)
– OLAP Multidimensional Storage & Engine
– Reporting (multi-purpose, comprehensive)
– Data Mining (clustering, associations, trends (time
analysis), predictions, …)