The document discusses how companies can leverage "free data", or information that is automatically collected during regular business operations but not fully utilized. It provides examples of how analyzing patterns in existing data from sales, mail volume, and inventory can help optimize staffing, forecasting, and product distribution. The key message is that companies invest heavily in automation yet often fail to mine the valuable data already being captured for additional uses that could streamline operations and reduce costs. Fully leveraging free data requires looking at processes with a new perspective to discover overlooked opportunities.
This document discusses the importance of data quality and provides tips for ensuring high quality data. It notes that while data can be very useful, it is only valuable if it is clean and structured. When extracting large amounts of data, it recommends developing extractors, combining extractors, and automating the extraction process. For scaling operations, having processes to clean, validate, and maintain data quality is crucial. The document offers suggestions for writing effective XPaths and regex expressions to extract the right data. It also stresses the importance of measuring data quality through completeness, coverage, and detecting anomalies both during and after the extraction process.
This document discusses the discovery of a potential new stellar stream in the Milky Way galaxy. Analysis of data from the RAVE survey identified a large overdensity of stars in the region of l = 240 – 270 deg, b = 10 – 30 deg. Stars in this region have outliers in metallicity and radial velocity compared to surrounding stars. When proper motions are plotted for these stars, they are distinct from other stars in the center of the graph. Further work is being done to verify if this substructure is truly a new discovery or part of known structure.
La rhinoplastie est une intervention courante de chirurgie esthétique qui doit faire l'objet d'une décision réfléchie de la part du patient désireux de refaire son nez - Le point du vue du Dr Séchaud, spécialiste de la rhinoplastie sur l'approche déontologique nécessaire à adopter pour que la rhinoplastie donne un résultat satisfaisant
This document discusses the importance of data quality and provides tips for ensuring high quality data. It notes that while data can be very useful, it is only valuable if it is clean and structured. When extracting large amounts of data, it recommends developing extractors, combining extractors, and automating the extraction process. For scaling operations, having processes to clean, validate, and maintain data quality is crucial. The document offers suggestions for writing effective XPaths and regex expressions to extract the right data. It also stresses the importance of measuring data quality through completeness, coverage, and detecting anomalies both during and after the extraction process.
This document discusses the discovery of a potential new stellar stream in the Milky Way galaxy. Analysis of data from the RAVE survey identified a large overdensity of stars in the region of l = 240 – 270 deg, b = 10 – 30 deg. Stars in this region have outliers in metallicity and radial velocity compared to surrounding stars. When proper motions are plotted for these stars, they are distinct from other stars in the center of the graph. Further work is being done to verify if this substructure is truly a new discovery or part of known structure.
La rhinoplastie est une intervention courante de chirurgie esthétique qui doit faire l'objet d'une décision réfléchie de la part du patient désireux de refaire son nez - Le point du vue du Dr Séchaud, spécialiste de la rhinoplastie sur l'approche déontologique nécessaire à adopter pour que la rhinoplastie donne un résultat satisfaisant
The document lists 10 things that make companies unproductive: 1) not providing staff with adequate tools to do their work, 2) storing written information that is only on paper, 3) inefficiently stocking and picking items from warehouses, 4) manually collecting data instead of digitally, 5) re-keying electronic information from other sources, 6) repetitive data entry into different systems, 7) using spreadsheets to report data stored in databases, 8) delivering consistent customer information by phone instead of online, 9) creating unused reports, and 10) not filtering spam from emails.
An Insight Into the Differences Between Data Mining and Machine LearningAndrew Leo
Data mining is a cross-disciplinary field that utilizes machine learning along with other techniques for discovering the properties of a dataset. The latter is a subset of data science that focuses on designing algorithms that can learn from data and make predictions accordingly. Thus, data mining uses machine learning but not vice versa.
Read here the Originally Posted blog: https://www.damcogroup.com/blogs/data-mining-vs-machine-learning-understanding-key-differences
#datamining
#dataminingservices
#webdatamining
#dataminingfirms
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Gerenral insurance Accounts IT and Investmentvijayk23x
The document provides an overview of topics that may be covered in accounting, IT and investment exams, including:
1. The exam questions will be split between investment, IT, accounting standards and ratios, and preparation of financial accounts.
2. IT topics include storage units, network types, protocols, programming languages, databases, data warehousing concepts like data marts, operational data stores, and dimensional modeling techniques like star and snowflake schemas.
3. Key concepts in machine learning, deep learning, big data, data lakes and artificial intelligence are also defined.
The success of an organization increasingly depends on their ability to draw conclusions regarding the various types of data available. Staying ahead of competitors requires many times to identify a trend, problem or opportunity microseconds before anyone else. That's why organizations must be able to analyze this information if they want to find insights that will help them to identify new opportunities underlying this phenomenon.
People are spontaneously uploading large amounts of information on the internet and this represents a great opportunity for companies to segment according to their behavior and not only socio-demographic factors. Companies store transactional information from their customers by making them fill in forms but the challenge for brands is to enrich these databases with information describing their customer’s behavior and daily habits. This info can be obtained through the online conversation and can be processed, crossed and enriched with many other types of information through different models based on Big Data. Following this procedure, we can complement the information we already have from our customers without having to ask them directly and therefor providing more value-added proposals to clients from a brand perspective.
Using the same technology with the right platform and the correct tactic, companies can achieve more ambitious goals that provide valuable information for the brand, which in turn could also enrich the customer’s experience, improving the customer journey for all types of clients.
less
How Accounts Payable Automation and Agility Drive Long-Term Business Producti...Dana Gardner
The document discusses how optimizing and automating accounts payable (AP) functions through intelligent automation can provide businesses several benefits. It can improve control over cash flow, payables, and financial situational awareness. This allows for better management during times of economic uncertainty. Automating AP processes can increase productivity, reduce processing times, and unlock billions in potential working capital benefits. It also enables skills shifts toward roles requiring more data analytics and strategic thinking to capitalize on insights from invoice data. Companies implementing AP automation solutions have seen over 40% reductions in invoice processing costs and gains in touchless invoice processing.
Matt Wynn, developer and watchdog reporter at the Omaha World-Herald, offers tips on the following in this handout for Lincoln, Nebraska, NewsTrain on April 9, 2016:
--Why learn about data journalism?
--How to get started in data journalism
--Where to find data sets
--Examples of data sets for government, education, criminal justice, health, sports and other beats
--Where to learn more about data journalism
--Things he wishes someone had told him about data journalism
It accompanies his presentation, "Data-Driven Enterprise off Your Beat." NewsTrain is a training initiative of Associated Press Media Editors (APME). More info: http://bit.ly/NewsTrain
Jer Thorp's work focuses on adding meaning and narrative to large amounts of data to help people understand and control the information around them. There are six ways to make data more human: use human insight to frame problems, remember that more data is not always better and can find false correlations, account for human biases and self-deception in data, understand that context is important, embrace that data can help abandon stereotypes, and realize that stories told by robots lack human emotion. These insights are relevant for managers in India because collecting and storing vast amounts of personal data overseas risks privacy violations and data access by foreign governments or companies that could affect a nation's policies.
The document discusses the rise of big data and how organizations can leverage it. It defines big data as data that cannot be analyzed with traditional tools due to its large volume, velocity, and variety. It describes how technological advances have led to more data being generated and collected from a variety of sources. The document advocates that organizations must find ways to analyze all this data to gain valuable insights that can improve decision making, customer experiences, and business strategies. It provides several examples of how companies in different industries have successfully used big data analytics.
Big Data has recently gained relevance because companies are realizing what it can do for them and that it is a gold mine for finding competitive advantages. Proximity’s Juan Manuel Ramírez, Director of Strategy and...
The document discusses how big data is enabling new opportunities for companies to better understand customer behavior and make more informed decisions. It defines big data as information that cannot be analyzed with traditional tools due to its large volume, velocity, and variety. Examples are provided of how companies in various industries like retail, healthcare, and transportation are using big data analytics to improve operations, prevent fraud, and personalize customer experiences. The importance of accessibility and technologies like Hadoop for making big data solutions more widely available is also covered.
Business Intelligence provides the tools and practices for users accessing information needed for decision-making.
Most people obtain information about their business by reading reports.
Comparative Study of Improved Association Rules Mining Based On Shopping SystemEswar Publications
Data mining is a process of discovering fascinating designs, new instructions and information from large amount of sales facts in transactional and interpersonal catalogs. The main purpose of this function is to find frequent patterns, associations and relationship between various database using different Algorithms. Association rule mining (ARM) is used to improve decisions making in the applications. ARM became essential in an information and decision-overloaded world. They changed the way users make decisions, and helped their creators to increase revenue at the same time. Bringing ARM to a broader audience is essential in order to popularize them beyond the limits of scientific research and high technology entrepreneurship. It will be able to expand and apply effective
marketing strategies and in disease identification frequent patterns are generated to discover the frequently occur
diseases in a definite area. The conclusion in all applications is some kind of association rules (AR) that are useful for efficient decision making.
How Slice uses a combination of machine learning, domain experts, crowdsourcing, and outsourcing to create continuously improving system to categorize products
Best data science training Institute: Kellytechnologies is the best data science training Institutes in Hyderabad.Providing greate data science training by realtime faculty in hyderabad.
3 Mitos de Big Data revelados
Uno. Sobre el tamaño de datos: el verdadero valor está en cómo utilizamos los datos, no la cantidad de datos que tenemos.
Dos. Todas las personas necesitan acceder a la información de manera fácil y rápida. Para resolver esta necesidad requerimos alguien especializado en datos (un Data Scientist)
Tres. Existen lo que se denomina "framework de software" especiales como Hadoop. Es un sistema bueno, pero generalmente necesitamos unir información de fuentes dispares que se encuentra dispersa.
Converting Big Data To Smart Data | The Step-By-Step Guide!Kavika Roy
1. The document discusses how to convert big data into smart data through machine learning and artificial intelligence techniques. It involves filtering big data through criteria like timeframes and media channels to create more focused data streams.
2. Analytics are then used to derive insights from the filtered data by identifying themes, influential actors, emotions, and other patterns. This process of filtering and analyzing turns large amounts of raw data into actionable business intelligence.
3. The final stage is integrating smart data with other internal and external data sources through APIs and data sharing to develop a comprehensive view of customers and business operations. This full conversion process extracts strategic lessons from big data to guide decision-making.
Suburbia, Alternative Data Expert (FinTech), asked me to design their sales booklet. This is the outcome. The booklet was meant for their stakeholders.
The document lists 10 things that make companies unproductive: 1) not providing staff with adequate tools to do their work, 2) storing written information that is only on paper, 3) inefficiently stocking and picking items from warehouses, 4) manually collecting data instead of digitally, 5) re-keying electronic information from other sources, 6) repetitive data entry into different systems, 7) using spreadsheets to report data stored in databases, 8) delivering consistent customer information by phone instead of online, 9) creating unused reports, and 10) not filtering spam from emails.
An Insight Into the Differences Between Data Mining and Machine LearningAndrew Leo
Data mining is a cross-disciplinary field that utilizes machine learning along with other techniques for discovering the properties of a dataset. The latter is a subset of data science that focuses on designing algorithms that can learn from data and make predictions accordingly. Thus, data mining uses machine learning but not vice versa.
Read here the Originally Posted blog: https://www.damcogroup.com/blogs/data-mining-vs-machine-learning-understanding-key-differences
#datamining
#dataminingservices
#webdatamining
#dataminingfirms
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Gerenral insurance Accounts IT and Investmentvijayk23x
The document provides an overview of topics that may be covered in accounting, IT and investment exams, including:
1. The exam questions will be split between investment, IT, accounting standards and ratios, and preparation of financial accounts.
2. IT topics include storage units, network types, protocols, programming languages, databases, data warehousing concepts like data marts, operational data stores, and dimensional modeling techniques like star and snowflake schemas.
3. Key concepts in machine learning, deep learning, big data, data lakes and artificial intelligence are also defined.
The success of an organization increasingly depends on their ability to draw conclusions regarding the various types of data available. Staying ahead of competitors requires many times to identify a trend, problem or opportunity microseconds before anyone else. That's why organizations must be able to analyze this information if they want to find insights that will help them to identify new opportunities underlying this phenomenon.
People are spontaneously uploading large amounts of information on the internet and this represents a great opportunity for companies to segment according to their behavior and not only socio-demographic factors. Companies store transactional information from their customers by making them fill in forms but the challenge for brands is to enrich these databases with information describing their customer’s behavior and daily habits. This info can be obtained through the online conversation and can be processed, crossed and enriched with many other types of information through different models based on Big Data. Following this procedure, we can complement the information we already have from our customers without having to ask them directly and therefor providing more value-added proposals to clients from a brand perspective.
Using the same technology with the right platform and the correct tactic, companies can achieve more ambitious goals that provide valuable information for the brand, which in turn could also enrich the customer’s experience, improving the customer journey for all types of clients.
less
How Accounts Payable Automation and Agility Drive Long-Term Business Producti...Dana Gardner
The document discusses how optimizing and automating accounts payable (AP) functions through intelligent automation can provide businesses several benefits. It can improve control over cash flow, payables, and financial situational awareness. This allows for better management during times of economic uncertainty. Automating AP processes can increase productivity, reduce processing times, and unlock billions in potential working capital benefits. It also enables skills shifts toward roles requiring more data analytics and strategic thinking to capitalize on insights from invoice data. Companies implementing AP automation solutions have seen over 40% reductions in invoice processing costs and gains in touchless invoice processing.
Matt Wynn, developer and watchdog reporter at the Omaha World-Herald, offers tips on the following in this handout for Lincoln, Nebraska, NewsTrain on April 9, 2016:
--Why learn about data journalism?
--How to get started in data journalism
--Where to find data sets
--Examples of data sets for government, education, criminal justice, health, sports and other beats
--Where to learn more about data journalism
--Things he wishes someone had told him about data journalism
It accompanies his presentation, "Data-Driven Enterprise off Your Beat." NewsTrain is a training initiative of Associated Press Media Editors (APME). More info: http://bit.ly/NewsTrain
Jer Thorp's work focuses on adding meaning and narrative to large amounts of data to help people understand and control the information around them. There are six ways to make data more human: use human insight to frame problems, remember that more data is not always better and can find false correlations, account for human biases and self-deception in data, understand that context is important, embrace that data can help abandon stereotypes, and realize that stories told by robots lack human emotion. These insights are relevant for managers in India because collecting and storing vast amounts of personal data overseas risks privacy violations and data access by foreign governments or companies that could affect a nation's policies.
The document discusses the rise of big data and how organizations can leverage it. It defines big data as data that cannot be analyzed with traditional tools due to its large volume, velocity, and variety. It describes how technological advances have led to more data being generated and collected from a variety of sources. The document advocates that organizations must find ways to analyze all this data to gain valuable insights that can improve decision making, customer experiences, and business strategies. It provides several examples of how companies in different industries have successfully used big data analytics.
Big Data has recently gained relevance because companies are realizing what it can do for them and that it is a gold mine for finding competitive advantages. Proximity’s Juan Manuel Ramírez, Director of Strategy and...
The document discusses how big data is enabling new opportunities for companies to better understand customer behavior and make more informed decisions. It defines big data as information that cannot be analyzed with traditional tools due to its large volume, velocity, and variety. Examples are provided of how companies in various industries like retail, healthcare, and transportation are using big data analytics to improve operations, prevent fraud, and personalize customer experiences. The importance of accessibility and technologies like Hadoop for making big data solutions more widely available is also covered.
Business Intelligence provides the tools and practices for users accessing information needed for decision-making.
Most people obtain information about their business by reading reports.
Comparative Study of Improved Association Rules Mining Based On Shopping SystemEswar Publications
Data mining is a process of discovering fascinating designs, new instructions and information from large amount of sales facts in transactional and interpersonal catalogs. The main purpose of this function is to find frequent patterns, associations and relationship between various database using different Algorithms. Association rule mining (ARM) is used to improve decisions making in the applications. ARM became essential in an information and decision-overloaded world. They changed the way users make decisions, and helped their creators to increase revenue at the same time. Bringing ARM to a broader audience is essential in order to popularize them beyond the limits of scientific research and high technology entrepreneurship. It will be able to expand and apply effective
marketing strategies and in disease identification frequent patterns are generated to discover the frequently occur
diseases in a definite area. The conclusion in all applications is some kind of association rules (AR) that are useful for efficient decision making.
How Slice uses a combination of machine learning, domain experts, crowdsourcing, and outsourcing to create continuously improving system to categorize products
Best data science training Institute: Kellytechnologies is the best data science training Institutes in Hyderabad.Providing greate data science training by realtime faculty in hyderabad.
3 Mitos de Big Data revelados
Uno. Sobre el tamaño de datos: el verdadero valor está en cómo utilizamos los datos, no la cantidad de datos que tenemos.
Dos. Todas las personas necesitan acceder a la información de manera fácil y rápida. Para resolver esta necesidad requerimos alguien especializado en datos (un Data Scientist)
Tres. Existen lo que se denomina "framework de software" especiales como Hadoop. Es un sistema bueno, pero generalmente necesitamos unir información de fuentes dispares que se encuentra dispersa.
Converting Big Data To Smart Data | The Step-By-Step Guide!Kavika Roy
1. The document discusses how to convert big data into smart data through machine learning and artificial intelligence techniques. It involves filtering big data through criteria like timeframes and media channels to create more focused data streams.
2. Analytics are then used to derive insights from the filtered data by identifying themes, influential actors, emotions, and other patterns. This process of filtering and analyzing turns large amounts of raw data into actionable business intelligence.
3. The final stage is integrating smart data with other internal and external data sources through APIs and data sharing to develop a comprehensive view of customers and business operations. This full conversion process extracts strategic lessons from big data to guide decision-making.
Suburbia, Alternative Data Expert (FinTech), asked me to design their sales booklet. This is the outcome. The booklet was meant for their stakeholders.
1. WHO SAID NOTHING IN THIS WORLD IS FREE?
The Discovery of Free Data
What would you do if you suddenly learned you were sitting on a gold mine? Well, start
digging. Odds are, you are rich in resources you probably didn’t even know you had. We call it
“free data” – a gold mine of information.
An abundance of data is captured in an automated format through the overall workflow process
each and every day. However, only a portion of these facts and figures are used for the
immediate task at hand. The rest is typically discarded. This extra information – or free data – is
actually quite valuable and could be put to use in other capacities that would streamline your
operations and save a great deal of money.
You are already collecting information for specific purposes. Now you must look deeper and
ask yourself how you can mine that data and use it elsewhere. We’ve noticed that there is free
data circulating in virtually every industry. You just need an eye to find it.
Information Mining
When is the last time you were at the airport, number 194 of the 200 people in line at your gate
– while one lone counter clerk worked to check everyone in during the ten minutes remaining
before your flight? Had that airline made use of its free data, you could have been relaxing in
the lounge with plenty of time to spare. The airline’s computer is full of information – including
how many tickets were sold. Knowing what time most travelers would arrive for their flight,
management could have easily planned to have more workers behind the counter. Free data.
How about the cash register at the fast food restaurant that logs in sales information? If this data
is saved and analyzed, the manager may notice a pattern. Maybe every Tuesday at 3:30 p.m., the
store experiences a rush. Perhaps that’s the time kids get out of school and come in for their
daily dose of French fries. (You see, the register will also have recorded information as to what
customers are buying.) Now the restaurant can forecast and schedule additional employees for
that afternoon shift. And they will be sure to have plenty of ketchup on hand to go with those
fries. Free data.
A producer of snack foods in Dallas is a prime example of a company that took advantage of
free data and made it work to their advantage. In the 1980s, this company’s trucks would retrieve
unsold items from stores and give storeowners credit for the merchandise. The information
collected on receivables was stored on a computer. One day, someone in the snack food
company’s marketing department took note that data was being gathered on which items were
and were not selling. In the meantime, raw materials – corn and potatoes – were sitting on the
railroad track, waiting to be processed. A quick analysis of this data told snack food company
personnel whether or not potato chips were selling in West Texas and if corn chips were stronger
in the southern part of the state. The Dallas plant then knew what to make and where to ship it.
Free data.
2. 2
Taking off the Blinders
What kind of free data is floating around in your operation? You, like those in many
companies, may be pressed from an operations standpoint to reduce costs and improve profit
margins. People are investing hundreds of thousands (if not millions) of dollars in automation …
but using it with blinders on. Technology is being left on the table.
Take the example of incoming mail. Perhaps you spent a quarter of a million dollars on a
machine that sorts your mail to individual PO boxes. Great. But did you know that other valuable
information is being captured simultaneously? You can also determine your volume and the
pattern of mail by day of week or by hour. All that data is contained within your machine. Now
you can take this knowledge and start forecasting. If you know that every Monday at 8:00 a.m.,
you get 30% of your volume, you can build a staffing schedule. Free data puts you in control of
your workflow environment instead of reacting to it.
Too many companies are wasting time, energy and money on manual processes, when needed
information has already been electronically captured. A mailroom clerk, for example, will sit and
manually log the trays of mail, when the machine has already counted every envelope. Or what
about the employee who re-keys information into a computer for billing purposes? The data was
already in electronic format. It just came off the computer. But it is being entered again. Billing
may not have been the end goal when this particular automation system was purchased, so no
one thinks to mine that information. But it’s there.
Understandably, managers are focused on accomplishing their particular job – getting from
Point A to Point B in the shortest amount of time with the least amount of problems. However,
there may be a number of side paths that can lead to answers for another operation or help in a
particular area of the processing stream.
A Different Perspective
Free data is often discovered when somebody simply looks at a process from a different
perspective. They see that while information is being gathered for one purpose, it can be
effectively used for another. The zoo generates a lot of manure. But some individuals have
turned it into a business enterprise for fertilizer. Again, free data.
Our challenge is to explore the ways in which you can make more use of the data that already is
out there … right at your fingertips. Don’t let this valuable resource slip through your hands.
Grasp hold of it.
Look to see if you have information available that can be used in a different way than originally
intended. The more you train yourself to view your operation with new eyes, the more instinctive
it will become. And you will be the richer for it.