Data Mining - Retrieving Information From Data

Data Mining - Retrieving Information From Data

Data mining definition is the process of retrieving information from data. It has become very important now days because data that is processed is usually kept for future reference and mainly for security purposes in a company. Data transforms is processed into information and it is mostly used in different ways depending on what information one is extracting and from where the person is extracting the information.

It is commonly used in marketing, scientific information and research work, fraud detection and surveillance and many more and most of this work is done using a computer. This definition can come in different terms data snooping, data fishing and data dredging all this refer to data mining but it depends in which department one is. One must know data mining definition so that he can be in a position to make data.

The method of data mining has been there for so many centuries and it is used up to date. There were early methods which were used to identify data mining there are mainly two: regression analysis and bayes theorem. These methods are never used now days because a lot of people have advanced and technology has really changed the entire system.

With the coming up or with the introduction of computers and technology, it becomes very fast and easy to save information. Computers have made work easier and one can be able to expand more knowledge about data crawling and learn on how data is stored and processed through computer science.

Computer science is a course that sharpens one skill and expands more about data crawling and the definition of what data mining means. By studying computer science one can be in a position to know: clustering, support vector machines and decision trees there are some of the units that are found on computer science.

It's all about all this and this knowledge must be applied here. Government institutions, small scale business and supermarkets use data.

The main reason most companies use data mining is because data assist in the collection of information and observations that a company goes through in their daily activity. Such information is very vital in any companies profile and needs to be checked and updated for future reference just in case something happens.

Businesses which use data crawling focus mainly on return of investments, and they are able to know whether they are making a profit or a loss within a very short period. If the company or the business is making a profit they can be in a position to give customers an offer on the product in which they are selling so that the business can be a position to make more profit in an organization, this is very vital in human resource departments it helps in identifying the character traits of a person in terms of job performance.

Most people who use this method believe that is ethically neutral. The way it is being used nowadays raises a lot of questions about security and privacy of its members. Data mining needs good data preparation which can be in a position to uncover different types of information especially those that require privacy.

A very common way in this occurs is through data aggregation.

Data aggregation is when information is retrieved from different sources and is usually put together so that one can be in a position to be analyze one by one and this helps information to be very secure. So if one is collecting data it is vital for one to know the following:

    How will one use the data that he is collecting?
    Who will mine the data and use the data.
    Is the data very secure when am out can someone come and access it.
    How can one update the data when information is needed
    If the computer crashes do I have any backup somewhere.

It is important for one to be very careful with documents which deal with company's personal information so that information cannot easily be manipulated.

source : http://ezinearticles.com/?Data-Mining---Retrieving-Information-From-Data&id=5054887

One of the Main Differences Between Statistical Analysis and Data Mining

One of the Main Differences Between Statistical Analysis and Data Mining

Two methods of analyzing data that are common in both academic and commercial fields are statistical analysis and data mining. While statistical analysis has a long scientific history, data mining is a more recent method of data analysis that has arisen from Computer Science. In this article I want to give an introduction to these methods and outline what I believe is one of the main differences between the two fields of analysis.

Statistical analysis commonly involves an analyst formulating a hypothesis and then testing the validity of this hypothesis by running statistical tests on data that may have been collected for the purpose. For example, if an analyst was studying the relationship between income level and the ability to get a loan, the analyst may hypothesis that there will be a correlation between income level and the amount of credit someone may qualify for.

The analyst could then test this hypothesis with the use of a data set that contains a number of people along with their income levels and the credit available to them. A test could be run that indicates for example that there may be a high degree of confidence that there is indeed a correlation between income and available credit. The main point here is that the analyst has formulated a hypothesis and then used a statistical test along with a data set to provide evidence in support or against that hypothesis.

Data mining is another area of data analysis that has arisen more recently from computer science that has a number of differences to traditional statistical analysis. Firstly, many data mining techniques are designed to be applied to very large data sets, while statistical analysis techniques are often designed to form evidence in support or against a hypothesis from a more limited set of data.

Probably the mist significant difference here, however, is that data mining techniques are not used so much to form confidence in a hypothesis, but rather extract unknown relationships may be present in the data set. This is probably best illustrated with an example. Rather than in the above case where a statistician may form a hypothesis between income levels and an applicants ability to get a loan, in data mining, there is not typically an initial hypothesis. A data mining analyst may have a large data set on loans that have been given to people along with demographic information of these people such as their income level, their age, any existing debts they have and if they have ever defaulted on a loan before.

A data mining technique may then search through this large data set and extract a previously unknown relationship between income levels, peoples existing debt and their ability to get a loan.

While there are quite a few differences between statistical analysis and data mining, I believe this difference is at the heart of the issue. A lot of statistical analysis is about analyzing data to either form confidence for or against a stated hypothesis while data mining is often more about applying an algorithm to a data set to extract previously unforeseen relationships.

Source:http://ezinearticles.com/?One-of-the-Main-Differences-Between-Statistical-Analysis-and-Data-Mining&id=4578250

One of the Main Differences Between Statistical Analysis and Data Mining

One of the Main Differences Between Statistical Analysis and Data Mining

Two methods of analyzing data that are common in both academic and commercial fields are statistical analysis and data mining. While statistical analysis has a long scientific history, data mining is a more recent method of data analysis that has arisen from Computer Science. In this article I want to give an introduction to these methods and outline what I believe is one of the main differences between the two fields of analysis.

Statistical analysis commonly involves an analyst formulating a hypothesis and then testing the validity of this hypothesis by running statistical tests on data that may have been collected for the purpose. For example, if an analyst was studying the relationship between income level and the ability to get a loan, the analyst may hypothesis that there will be a correlation between income level and the amount of credit someone may qualify for.

The analyst could then test this hypothesis with the use of a data set that contains a number of people along with their income levels and the credit available to them. A test could be run that indicates for example that there may be a high degree of confidence that there is indeed a correlation between income and available credit. The main point here is that the analyst has formulated a hypothesis and then used a statistical test along with a data set to provide evidence in support or against that hypothesis.

Data mining is another area of data analysis that has arisen more recently from computer science that has a number of differences to traditional statistical analysis. Firstly, many data mining techniques are designed to be applied to very large data sets, while statistical analysis techniques are often designed to form evidence in support or against a hypothesis from a more limited set of data.

Probably the mist significant difference here, however, is that data mining techniques are not used so much to form confidence in a hypothesis, but rather extract unknown relationships may be present in the data set. This is probably best illustrated with an example. Rather than in the above case where a statistician may form a hypothesis between income levels and an applicants ability to get a loan, in data mining, there is not typically an initial hypothesis. A data mining analyst may have a large data set on loans that have been given to people along with demographic information of these people such as their income level, their age, any existing debts they have and if they have ever defaulted on a loan before.

A data mining technique may then search through this large data set and extract a previously unknown relationship between income levels, peoples existing debt and their ability to get a loan.

While there are quite a few differences between statistical analysis and data mining, I believe this difference is at the heart of the issue. A lot of statistical analysis is about analyzing data to either form confidence for or against a stated hypothesis while data mining is often more about applying an algorithm to a data set to extract previously unforeseen relationships.

Source:http://ezinearticles.com/?One-of-the-Main-Differences-Between-Statistical-Analysis-and-Data-Mining&id=4578250

Web Data Extraction Services

Web Data Extraction Services

Web Data Extraction from Dynamic Pages includes some of the services that may be acquired through outsourcing. It is possible to siphon information from proven websites through the use of Data Scrapping software. The information is applicable in many areas in business. It is possible to get such solutions as data collection, screen scrapping, email extractor and Web Data Mining services among others from companies providing websites such as Scrappingexpert.com.

Data mining is common as far as outsourcing business is concerned. Many companies are outsource data mining services and companies dealing with these services can earn a lot of money, especially in the growing business regarding outsourcing and general internet business. With web data extraction, you will pull data in a structured organized format. The source of the information will even be from an unstructured or semi-structured source.

In addition, it is possible to pull data which has originally been presented in a variety of formats including PDF, HTML, and test among others. The web data extraction service therefore, provides a diversity regarding the source of information. Large scale organizations have used data extraction services where they get large amounts of data on a daily basis. It is possible for you to get high accuracy of information in an efficient manner and it is also affordable.

Web data extraction services are important when it comes to collection of data and web-based information on the internet. Data collection services are very important as far as consumer research is concerned. Research is turning out to be a very vital thing among companies today. There is need for companies to adopt various strategies that will lead to fast means of data extraction, efficient extraction of data, as well as use of organized formats and flexibility.

In addition, people will prefer software that provides flexibility as far as application is concerned. In addition, there is software that can be customized according to the needs of customers, and these will play an important role in fulfilling diverse customer needs. Companies selling the particular software therefore, need to provide such features that provide excellent customer experience.

It is possible for companies to extract emails and other communications from certain sources as far as they are valid email messages. This will be done without incurring any duplicates. You will extract emails and messages from a variety of formats for the web pages, including HTML files, text files and other formats. It is possible to carry these services in a fast reliable and in an optimal output and hence, the software providing such capability is in high demand. It can help businesses and companies quickly search contacts for the people to be sent email messages.

It is also possible to use software to sort large amount of data and extract information, in an activity termed as data mining. This way, the company will realize reduced costs and saving of time and increasing return on investment. In this practice, the company will carry out Meta data extraction, scanning data, and others as well.

Source: http://ezinearticles.com/?Web-Data-Extraction-Services&id=4733722

Data Mining vs Screen-Scraping

Data Mining vs Screen-Scraping

Data mining isn't screen-scraping. I know that some people in the room may disagree with that statement, but they're actually two almost completely different concepts.

In a nutshell, you might state it this way: screen-scraping allows you to get information, where data mining allows you to analyze information. That's a pretty big simplification, so I'll elaborate a bit.

The term "screen-scraping" comes from the old mainframe terminal days where people worked on computers with green and black screens containing only text. Screen-scraping was used to extract characters from the screens so that they could be analyzed. Fast-forwarding to the web world of today, screen-scraping now most commonly refers to extracting information from web sites. That is, computer programs can "crawl" or "spider" through web sites, pulling out data. People often do this to build things like comparison shopping engines, archive web pages, or simply download text to a spreadsheet so that it can be filtered and analyzed.

Data mining, on the other hand, is defined by Wikipedia as the "practice of automatically searching large stores of data for patterns." In other words, you already have the data, and you're now analyzing it to learn useful things about it. Data mining often involves lots of complex algorithms based on statistical methods. It has nothing to do with how you got the data in the first place. In data mining you only care about analyzing what's already there.

The difficulty is that people who don't know the term "screen-scraping" will try Googling for anything that resembles it. We include a number of these terms on our web site to help such folks; for example, we created pages entitled Text Data Mining, Automated Data Collection, Web Site Data Extraction, and even Web Site Ripper (I suppose "scraping" is sort of like "ripping"). So it presents a bit of a problem-we don't necessarily want to perpetuate a misconception (i.e., screen-scraping = data mining), but we also have to use terminology that people will actually use.

Source: http://ezinearticles.com/?Data-Mining-vs-Screen-Scraping&id=146813

Three Common Methods For Web Data Extraction

Three Common Methods For Web Data Extraction

Probably the most common technique used traditionally to extract data from web pages this is to cook up some regular expressions that match the pieces you want (e.g., URL's and link titles). Our screen-scraper software actually started out as an application written in Perl for this very reason. In addition to regular expressions, you might also use some code written in something like Java or Active Server Pages to parse out larger chunks of text. Using raw regular expressions to pull out the data can be a little intimidating to the uninitiated, and can get a bit messy when a script contains a lot of them. At the same time, if you're already familiar with regular expressions, and your scraping project is relatively small, they can be a great solution.

Other techniques for getting the data out can get very sophisticated as algorithms that make use of artificial intelligence and such are applied to the page. Some programs will actually analyze the semantic content of an HTML page, then intelligently pull out the pieces that are of interest. Still other approaches deal with developing "ontologies", or hierarchical vocabularies intended to represent the content domain.

There are a number of companies (including our own) that offer commercial applications specifically intended to do screen-scraping. The applications vary quite a bit, but for medium to large-sized projects they're often a good solution. Each one will have its own learning curve, so you should plan on taking time to learn the ins and outs of a new application. Especially if you plan on doing a fair amount of screen-scraping it's probably a good idea to at least shop around for a screen-scraping application, as it will likely save you time and money in the long run.

So what's the best approach to data extraction? It really depends on what your needs are, and what resources you have at your disposal. Here are some of the pros and cons of the various approaches, as well as suggestions on when you might use each one:

Raw regular expressions and code

Advantages:

- If you're already familiar with regular expressions and at least one programming language, this can be a quick solution.
- Regular expressions allow for a fair amount of "fuzziness" in the matching such that minor changes to the content won't break them.
- You likely don't need to learn any new languages or tools (again, assuming you're already familiar with regular expressions and a programming language).
- Regular expressions are supported in almost all modern programming languages. Heck, even VBScript has a regular expression engine. It's also nice because the various regular expression implementations don't vary too significantly in their syntax.

Disadvantages:

- They can be complex for those that don't have a lot of experience with them. Learning regular expressions isn't like going from Perl to Java. It's more like going from Perl to XSLT, where you have to wrap your mind around a completely different way of viewing the problem.
- They're often confusing to analyze. Take a look through some of the regular expressions people have created to match something as simple as an email address and you'll see what I mean.
- If the content you're trying to match changes (e.g., they change the web page by adding a new "font" tag) you'll likely need to update your regular expressions to account for the change.
- The data discovery portion of the process (traversing various web pages to get to the page containing the data you want) will still need to be handled, and can get fairly complex if you need to deal with cookies and such.

When to use this approach: You'll most likely use straight regular expressions in screen-scraping when you have a small job you want to get done quickly. Especially if you already know regular expressions, there's no sense in getting into other tools if all you need to do is pull some news headlines off of a site.

Ontologies and artificial intelligence

Advantages:

- You create it once and it can more or less extract the data from any page within the content domain you're targeting.
- The data model is generally built in. For example, if you're extracting data about cars from web sites the extraction engine already knows what the make, model, and price are, so it can easily map them to existing data structures (e.g., insert the data into the correct locations in your database).
- There is relatively little long-term maintenance required. As web sites change you likely will need to do very little to your extraction engine in order to account for the changes.

Disadvantages:

- It's relatively complex to create and work with such an engine. The level of expertise required to even understand an extraction engine that uses artificial intelligence and ontologies is much higher than what is required to deal with regular expressions.
- These types of engines are expensive to build. There are commercial offerings that will give you the basis for doing this type of data extraction, but you still need to configure them to work with the specific content domain you're targeting.
- You still have to deal with the data discovery portion of the process, which may not fit as well with this approach (meaning you may have to create an entirely separate engine to handle data discovery). Data discovery is the process of crawling web sites such that you arrive at the pages where you want to extract data.

When to use this approach: Typically you'll only get into ontologies and artificial intelligence when you're planning on extracting information from a very large number of sources. It also makes sense to do this when the data you're trying to extract is in a very unstructured format (e.g., newspaper classified ads). In cases where the data is very structured (meaning there are clear labels identifying the various data fields), it may make more sense to go with regular expressions or a screen-scraping application.

Screen-scraping software

Advantages:

- Abstracts most of the complicated stuff away. You can do some pretty sophisticated things in most screen-scraping applications without knowing anything about regular expressions, HTTP, or cookies.
- Dramatically reduces the amount of time required to set up a site to be scraped. Once you learn a particular screen-scraping application the amount of time it requires to scrape sites vs. other methods is significantly lowered.
- Support from a commercial company. If you run into trouble while using a commercial screen-scraping application, chances are there are support forums and help lines where you can get assistance.

Disadvantages:

- The learning curve. Each screen-scraping application has its own way of going about things. This may imply learning a new scripting language in addition to familiarizing yourself with how the core application works.
- A potential cost. Most ready-to-go screen-scraping applications are commercial, so you'll likely be paying in dollars as well as time for this solution.
- A proprietary approach. Any time you use a proprietary application to solve a computing problem (and proprietary is obviously a matter of degree) you're locking yourself into using that approach. This may or may not be a big deal, but you should at least consider how well the application you're using will integrate with other software applications you currently have. For example, once the screen-scraping application has extracted the data how easy is it for you to get to that data from your own code?

When to use this approach: Screen-scraping applications vary widely in their ease-of-use, price, and suitability to tackle a broad range of scenarios. Chances are, though, that if you don't mind paying a bit, you can save yourself a significant amount of time by using one. If you're doing a quick scrape of a single page you can use just about any language with regular expressions. If you want to extract data from hundreds of web sites that are all formatted differently you're probably better off investing in a complex system that uses ontologies and/or artificial intelligence. For just about everything else, though, you may want to consider investing in an application specifically designed for screen-scraping.

As an aside, I thought I should also mention a recent project we've been involved with that has actually required a hybrid approach of two of the aforementioned methods. We're currently working on a project that deals with extracting newspaper classified ads. The data in classifieds is about as unstructured as you can get. For example, in a real estate ad the term "number of bedrooms" can be written about 25 different ways. The data extraction portion of the process is one that lends itself well to an ontologies-based approach, which is what we've done. However, we still had to handle the data discovery portion. We decided to use screen-scraper for that, and it's handling it just great. The basic process is that screen-scraper traverses the various pages of the site, pulling out raw chunks of data that constitute the classified ads. These ads then get passed to code we've written that uses ontologies in order to extract out the individual pieces we're after. Once the data has been extracted we then insert it into a database.

source: http://ezinearticles.com/?Three-Common-Methods-For-Web-Data-Extraction&id=165416

Assuring Scraping Success with Proxy Data Scraping

Assuring Scraping Success with Proxy Data Scraping


Have you ever heard of "Data Scraping?" Data Scraping is the process of collecting useful data that has been placed in

the public domain of the internet (private areas too if conditions are met) and storing it in databases or spreadsheets

for later use in various applications. Data Scraping technology is not new and many a successful businessman has

made his fortune by taking advantage of data scraping technology.

Sometimes website owners may not derive much pleasure from automated harvesting of their data. Webmasters

have learned to disallow web scrapers access to their websites by using tools or methods that block certain ip

addresses from retrieving website content. Data scrapers are left with the choice to either target a different website,

or to move the harvesting script from computer to computer using a different IP address each time and extract as

much data as possible until all of the scraper's computers are eventually blocked.

Thankfully there is a modern solution to this problem. Proxy Data Scraping technology solves the problem by using

proxy IP addresses. Every time your data scraping program executes an extraction from a website, the website thinks

it is coming from a different IP address. To the website owner, proxy data scraping simply looks like a short period of

increased traffic from all around the world. They have very limited and tedious ways of blocking such a script but

more importantly -- most of the time, they simply won't know they are being scraped.

You may now be asking yourself, "Where can I get Proxy Data Scraping Technology for my project?" The "do-it-

yourself" solution is, rather unfortunately, not simple at all. Setting up a proxy data scraping network takes a lot of

time and requires that you either own a bunch of IP addresses and suitable servers to be used as proxies, not to

mention the IT guru you need to get everything configured properly. You could consider renting proxy servers from

select hosting providers, but that option tends to be quite pricey but arguably better than the alternative: dangerous

and unreliable (but free) public proxy servers.

There are literally thousands of free proxy servers located around the globe that are simple enough to use. The trick

however is finding them. Many sites list hundreds of servers, but locating one that is working, open, and supports the

type of protocols you need can be a lesson in persistence, trial, and error. However if you do succeed in discovering a

pool of working public proxies, there are still inherent dangers of using them. First off, you don't know who the server

belongs to or what activities are going on elsewhere on the server. Sending sensitive requests or data through a public

proxy is a bad idea. It is fairly easy for a proxy server to capture any information you send through it or that it sends

back to you. If you choose the public proxy method, make sure you never send any transaction through that might

compromise you or anyone else in case disreputable people are made aware of the data.

A less risky scenario for proxy data scraping is to rent a rotating proxy connection that cycles through a large number

of private IP addresses. There are several of these companies available that claim to delete all web traffic logs which

allows you to anonymously harvest the web with minimal threat of reprisal. Companies such as offer large scale

anonymous proxy solutions, but often carry a fairly hefty setup fee to get you going.

Source:http://ezinearticles.com/?Assuring-Scraping-Success-with-Proxy-Data-Scraping&id=248993

How Xpath Plays Vital Role In Web Scraping Part 2

How Xpath Plays Vital Role In Web Scraping Part 2

Here is a piece of content on  Xpaths which is the follow up of How Xpath Plays Vital Role In Web Scraping

Let’s dive into a real-world example of scraping amazon website for getting information about deals of the day. Deals of the day in amazon can be found at this URL. So navigate to the amazon (deals of the day) in Firefox and find the XPath selectors. Right click on the deal you like and select “Inspect Element with Firebug”:

If you observe the image below keenly, there you can find the source of the image(deal) and the name of the deal in src, alt attribute’s respectively.

So now let’s write a generic XPath which gathers the name and image source of the product(deal).

  //img[@role=”img”]/@src  ## for image source
  //img[@role=”img”]/@alt   ## for product name

In this post, I’ll show you some tips we found valuable when using XPath in the trenches.

If you have an interest in Python and web scraping, you may have already played with the nice requests library to get the content of pages from the Web. Maybe you have toyed around using Scrapy selector or lxml to make the content extraction easier. Well, now I’m going to show you some tips I found valuable when using XPath in the trenches and we are going to use both lxml and Scrapy selector for HTML parsing.

Avoid using expressions which contains(.//text(), ‘search text’) in your XPath conditions. Use contains(., ‘search text’) instead.

Here is why: the expression .//text() yields a collection of text elements — a node-set(collection of nodes).and when a node-set is converted to a string, which happens when it is passed as argument to a string function like contains() or starts-with(), results in the text for the first element only.

from scrapy import Selector
html_code = “””<a href=”#”>Click here to go to the <strong>Next Page</strong></a>”””
sel = Selector(text=html_code)
xp = lambda x: sel.xpath(x).extract()           # Let’s type this only once
print xp(‘//a//text()’)                                       # Take a peek at the node-set
[u’Click here to go to the ‘, u’Next Page’]   # output of above command
print xp(‘string(//a//text())’)                           # convert it to a string
  [u’Click here to go to the ‘]                           # output of the above command

Let’s do the above one by using lxml then you can implement XPath by both lxml or Scrapy selector as XPath expression is same for both methods.

lxml code:

from lxml import html
html_code = “””<a href=”#”>Click here to go to the <strong>Next Page</strong></a>””” # Parse the text into a tree
parsed_body = html.fromstring(html_code)  # Perform xpaths on the tree
print parsed_body(‘//a//text()’)                      # take a peek at the node-set
[u’Click here to go to the ‘, u’Next Page’]   # output
print parsed_body(‘string(//a//text())’)              # convert it to a string
[u’Click here to go to the ‘]                    # output

A node converted to a string, however, puts together the text of itself plus of all its descendants:

>>> xp(‘//a[1]’)  # selects the first a node
[u'<a href=”#”>Click here to go to the <strong>Next Page</strong></a>’]

>>> xp(‘string(//a[1])’)  # converts it to string
[u’Click here to go to the Next Page’]

Beware of the difference between //node[1] and (//node)[1]//node[1] selects all the nodes occurring first under their respective parents and (//node)[1] selects all the nodes in the document, and then gets only the first of them.

from scrapy import Selector

html_code = “””<ul class=”list”>
<li>1</li>
<li>2</li>
<li>3</li>
</ul>

<ul class=”list”>
<li>4</li>
<li>5</li>
<li>6</li>
</ul>”””

sel = Selector(text=html_code)
xp = lambda x: sel.xpath(x).extract()

xp(“//li[1]”) # get all first LI elements under whatever it is its parent

[u'<li>1</li>’, u'<li>4</li>’]

xp(“(//li)[1]”) # get the first LI element in the whole document

[u'<li>1</li>’]

xp(“//ul/li[1]”)  # get all first LI elements under an UL parent

[u'<li>1</li>’, u'<li>4</li>’]

xp(“(//ul/li)[1]”) # get the first LI element under an UL parent in the document

[u'<li>1</li>’]

Also,

//a[starts-with(@href, ‘#’)][1] gets a collection of the local anchors that occur first under their respective parents and (//a[starts-with(@href, ‘#’)])[1] gets the first local anchor in the document.

When selecting by class, be as specific as necessary.

If you want to select elements by a CSS class, the XPath way to do the same job is the rather verbose:

*[contains(concat(‘ ‘, normalize-space(@class), ‘ ‘), ‘ someclass ‘)]

Let’s cook up some examples:

>>> sel = Selector(text='<p class=”content-author”>Someone</p><p class=”content text-wrap”>Some content</p>’)

>>> xp = lambda x: sel.xpath(x).extract()

BAD: because there are multiple classes in the attribute

>>> xp(“//*[@class=’content’]”)

[]

BAD: gets more content than we need

 >>> xp(“//*[contains(@class,’content’)]”)

     [u'<p class=”content-author”>Someone</p>’,
     u'<p class=”content text-wrap”>Some content</p>’]

GOOD:

>>> xp(“//*[contains(concat(‘ ‘, normalize-space(@class), ‘ ‘), ‘ content ‘)]”)
[u'<p class=”content text-wrap”>Some content</p>’]

And many times, you can just use a CSS selector instead, and even combine the two of them if needed:

ALSO GOOD:

>>> sel.css(“.content”).extract()
[u'<p class=”content text-wrap”>Some content</p>’]

>>> sel.css(‘.content’).xpath(‘@class’).extract()
[u’content text-wrap’]

Learn to use all the different axes.

It is handy to know how to use the axes, you can follow through these examples.

In particular, you should note that following and following-sibling are not the same thing, this is a common source of confusion. The same goes for preceding and preceding-sibling, and also ancestor and parent.

Useful trick to get text content

Here is another XPath trick that you may use to get the interesting text contents: 

//*[not(self::script or self::style)]/text()[normalize-space(.)]

This excludes the content from the script and style tags and also skip whitespace-only text nodes.

Tools & Libraries Used:

Firefox
Firefox inspect element with firebug
Scrapy : 1.1.1
Python : 2.7.12
Requests : 2.11.0

 Have questions? Comment below. Please share if you found this helpful.

Source: http://blog.datahut.co/how-xpath-plays-vital-role-in-web-scraping-part-2/

Why Outsourcing Data Mining Services?

Why Outsourcing Data Mining Services?

Are huge volumes of raw data waiting to be converted into information that you can use? Your organization's hunt for valuable information ends with valuable data mining, which can help to bring more accuracy and clarity in decision making process.

Nowadays world is information hungry and with Internet offering flexible communication, there is remarkable flow of data. It is significant to make the data available in a readily workable format where it can be of great help to your business. Then filtered data is of considerable use to the organization and efficient this services to increase profits, smooth work flow and ameliorating overall risks.

Data mining is a process that engages sorting through vast amounts of data and seeking out the pertinent information. Most of the instance data mining is conducted by professional, business organizations and financial analysts, although there are many growing fields that are finding the benefits of using in their business.

Data mining is helpful in every decision to make it quick and feasible. The information obtained by it is used for several applications for decision-making relating to direct marketing, e-commerce, customer relationship management, healthcare, scientific tests, telecommunications, financial services and utilities.

Data mining services include:

•     Congregation data from websites into excel database
•     Searching & collecting contact information from websites
•     Using software to extract data from websites
•     Extracting and summarizing stories from news sources
•     Gathering information about competitors business

In this globalization era, handling your important data is becoming a headache for many business verticals. Then outsourcing is profitable option for your business. Since all projects are customized to suit the exact needs of the customer, huge savings in terms of time, money and infrastructure can be realized.

Advantages of Outsourcing Data Mining Services:

•     Skilled and qualified technical staff who are proficient in English
•     Improved technology scalability
•     Advanced infrastructure resources
•     Quick turnaround time
•     Cost-effective prices
•     Secure Network systems to ensure data safety
•     Increased market coverage

Outsourcing will help you to focus on your core business operations and thus improve overall productivity. So data mining outsourcing is become wise choice for business. Outsourcing of this services helps businesses to manage their data effectively, which in turn enable them to achieve higher profits.

Source: http://ezinearticles.com/?Why-Outsourcing-Data-Mining-Services?&id=3066061

Outsource Data Mining Services to Offshore Data Entry Company

Outsource Data Mining Services to Offshore Data Entry Company

Companies in India offer complete solution services for all type of data mining services.

Data Mining Services and Web research services offered, help businesses get critical information for their analysis and marketing campaigns. As this process requires professionals with good knowledge in internet research or online research, customers can take advantage of outsourcing their Data Mining, Data extraction and Data Collection services to utilize resources at a very competitive price.

In the time of recession every company is very careful about cost. So companies are now trying to find ways to cut down cost and outsourcing is good option for reducing cost. It is essential for each size of business from small size to large size organization. Data entry is most famous work among all outsourcing work. To meet high quality and precise data entry demands most corporate firms prefer to outsource data entry services to offshore countries like India.

In India there are number of companies which offer high quality data entry work at cheapest rate. Outsourcing data mining work is the crucial requirement of all rapidly growing Companies who want to focus on their core areas and want to control their cost.

Why outsource your data entry requirements?

Easy and fast communication: Flexibility in communication method is provided where they will be ready to talk with you at your convenient time, as per demand of work dedicated resource or whole team will be assigned to drive the project.

Quality with high level of Accuracy: Experienced companies handling a variety of data-entry projects develop whole new type of quality process for maintaining best quality at work.

Turn Around Time: Capability to deliver fast turnaround time as per project requirements to meet up your project deadline, dedicated staff(s) can work 24/7 with high level of accuracy.

Affordable Rate: Services provided at affordable rates in the industry. For minimizing cost, customization of each and every aspect of the system is undertaken for efficiently handling work.

Outsourcing Service Providers are outsourcing companies providing business process outsourcing services specializing in data mining services and data entry services. Team of highly skilled and efficient people, with a singular focus on data processing, data mining and data entry outsourcing services catering to data entry projects of a varied nature and type.

Why outsource data mining services?

360 degree Data Processing Operations
Free Pilots Before You Hire
Years of Data Entry and Processing Experience
Domain Expertise in Multiple Industries
Best Outsourcing Prices in Industry
Highly Scalable Business Infrastructure
24X7 Round The Clock Services

The expertise management and teams have delivered millions of processed data and records to customers from USA, Canada, UK and other European Countries and Australia.

Outsourcing companies specialize in data entry operations and guarantee highest quality & on time delivery at the least expensive prices.

Herat Patel, CEO at 3Alpha Dataentry Services possess over 15+ years of experience in providing data related services outsourced to India.

Visit our Facebook Data Entry profile for comments & reviews.

Our services helps to convert any kind of  hard copy sources, our data mining services helps to collect business contacts, customer contact, product specifications etc., from different web sources. We promise to deliver the best quality work and help you excel in your business by focusing on your core business activities. Outsource data mining services to India and take the advantage of outsourcing and save cost.

Source: http://ezinearticles.com/?Outsource-Data-Mining-Services-to-Offshore-Data-Entry-Company&id=4027029

Web Scraping with Python: A Beginner’s Guide

Web Scraping with Python: A Beginner’s Guide

In the Big Data world, Web Scraping or Data extraction services are the primary requisites for Big Data Analytics. Pulling up data from the web has become almost inevitable for companies to stay in business. Next question that comes up is how to go about web scraping as a beginner.

Data can be extracted or scraped from a web source using a number of methods. Popular websites like Google, Facebook, or Twitter offer APIs to view and extract the available data in a structured manner.  This prevents the use of other methods that may not be preferred by the API provider. However, the demand to scrape a website arises when the information is not readily offered by the website. Python, an open source programming language is often used for Web Scraping due to its simple and rich ecosystem. It contains a library called “BeautifulSoup” which carries on this task. Let’s take a deeper look into web scraping using python.

Setting up a Python Environment:

To carry out web scraping using Python, you will first have to install the Python Environment, which enables to run code written in the python language. The libraries perform data scraping;

Beautiful Soup is a convenient-to-use python library. It is one of the finest tools for extracting information from a webpage. Professionals can scrape information from web pages in the form of tables, lists, or paragraphs. Urllib2 is another library that can be used in combination with the BeautifulSoup library for fetching the web pages. Filters can be added to extract specific information from web pages. Urllib2 is a Python module that can fetch URLs.

For MAC OSX :

To install Python libraries on MAC OSX, users need to open a terminal win and type in the following commands, single command at a time:

sudoeasy_install pip

pip install BeautifulSoup4

pip install lxml

For Windows 7 & 8 users:

Windows 7 & 8 users need to ensure that the python environment gets installed first. Once, the environment is installed, open the command prompt and find the way to root C:/ directory and type in the following commands:

easy_install BeautifulSoup4

easy_installlxml

Once the libraries are installed, it is time to write data scraping code.

Running Python:

Data scraping must be done for a distinct objective such as to scrape current stock of a retail store. First, a web browser is required to navigate the website that contains this data. After identifying the table, right click anywhere on it and then select inspect element from the dropdown menu list. This will cause a window to pop-up on the bottom or side of your screen displaying the website’s html code. The rankings appear in a table. You might need to scan through the HTML data until you find the line of code that highlights the table on the webpage.

Python offers some other alternatives for HTML scraping apart from BeautifulSoup. They include:

    Scrapy
    Scrapemark
    Mechanize

 Web scraping converts unstructured data from HTML code into structured form such as tabular data in an Excel worksheet. Web scraping can be done in many ways ranging from the use of Google Docs to programming languages. For people who do not have any programming knowledge or technical competencies, it is possible to acquire web data by using web scraping services that provide ready to use data from websites of your preference.

HTML Tags:

To perform web scraping, users must have a sound knowledge of HTML tags. It might help a lot to know that HTML links are defined using anchor tag i.e. <a> tag, “<a href=“http://…”>The link needs to be here </a>”. An HTML list comprises <ul> (unordered) and <ol> (ordered) list. The item of list starts with <li>.

HTML tables are defined with<Table>, row as <tr> and columns are divided into data as <td>;

    <!DOCTYPE html> : A HTML document starts with a document type declaration
    The main part of the HTML document in unformatted, plain text is defined by <body> and </body> tags
    The headings in HTML are defined using the heading tags from <h1> to <h5>
    Paragraphs are defined with the <p> tag in HTML
    An entire HTML document is contained between <html> and </html>

Using BeautifulSoup in Scraping:

While scraping a webpage using BeautifulSoup, the main concern is to identify the final objective. For instance, if you would like to extract a list from webpage, a step wise approach is required:

    First and foremost step is to import the required libraries:

 #import the library used to query a website

import urllib2

#specify the url wiki = “https://”

#Query the website and return the html to the variable ‘page’

page = urllib2.urlopen(wiki)

#import the Beautiful soup functions to parse the data returned from the website

from bs4 import BeautifulSoup

#Parse the html in the ‘page’ variable, and store it in Beautiful Soup format

soup = BeautifulSoup(page)

    Use function “prettify” to visualize nested structure of HTML page
    Working with Soup tags:

Soup<tag> is used for returning content between opening and closing tag including tag.

    In[30]:soup.title

 Out[30]:<title>List of Presidents in India till 2010 – Wikipedia, the free encyclopedia</title>

    soup.<tag>.string: Return string within given tag
    In [38]:soup.title.string
    Out[38]:u ‘List of Presidents in India and Brazil till 2010 in India – Wikipedia, the free encyclopedia’
    Find all the links within page’s <a> tags: Tag a link using tag “<a>”. So, go with option soup.a and it should return the links available in the web page. Let’s do it.
    In [40]:soup.a

Out[40]:<a id=”top”></a>

    Find the right table:

As a table to pull up information about Presidents in India and Brazil till 2010 is being searched for, identifying the right table first is important. Here’s a command to scrape information enclosed in all table tags.

all_tables= soup.find_all(‘table’)

Identify the right table by using attribute “class” of table needs to filter the right table. Thereafter, inspect the class name by right clicking on the required table of web page as follows:

    Inspect element
    Copy the class name or find the class name of right table from the last command’s output.

 right_table=soup.find(‘table’, class_=’wikitable sortable plainrowheaders’)

right_table

That’s how we can identify the right table.

    Extract the information to DataFrame: There is a need to iterate through each row (tr) and then assign each element of tr (td) to a variable and add it to a list. Let’s analyse the Table’s HTML structure of the table. (extract information for table heading <th>)

To access value of each element, there is a need to use “find(text=True)” option with each element.  Finally, there is data in dataframe.

There are various other ways to scrape data using “BeautifulSoup” that reduce manual efforts to collect data from web pages. Code written in BeautifulSoup is considered to be more robust than the regular expressions. The web scraping method we discussed use “BeautifulSoup” and “urllib2” libraries in Python. That was a brief beginner’s guide to start using Python for web scraping.

Source: https://www.promptcloud.com/blog/web-scraping-python-guide

How to use Web Content Extractor(WCE) as Email Scraper?

How to use Web Content Extractor(WCE) as Email Scraper?

Web Content Extractor is a great web scraping software developed by Newprosoft Team. The software has easy to use project wizard to create a scraping configuration and scrape data from websites.

One day I came to see the Visual Email Extractor which is also product of Newprosoft and similar to Web Content Extractor but it’s primary use is to scrape email addresses by crawling websites you feed to the scraper. I had noticed that with the little modification in Web Content Extractor project configuration you can use it same as Visual Email Extractor to extract email addresses.

In this post I will show you what configuration makes the Web Content Extractor to extract email addresses. I still recommend Visual Email Extractor as it has lot more features then extracting email using WCE.

Here are the configuration that makes WCE to Extract Emails.

Step 1 : Open Web Content Extractor and Create New Project and Click on Next.

Step 2:  Under Crawling Rules -> Advanced Rules Tab do the following settings

Crawling Level 1 Settings

Follow Links if link text equals:
*contact*; *feedback*; *support*; *about*

for 'Follow Links if link text equals' text box enter following values:
contact; feedback; support; about

for 'Do not Follow links if URL contains' text box enter following values:

google.; yahoo.; bing; msn.; altavista.; myspace.com; youtube.com; googleusercontent.com; =http; .jpg; .gif; .png; .bmp; .exe; .zip; .pdf;

Set 'Maximum Crawling Deapth' to 2

set 'Crawling Order' to Deapth First Crawling

Tick mark below below check boxes:

->Follow all internal links

  Crawling Level 2  Settings

set 'Follow links if link text equals' to below value

*contact*; *feedback*; *support*; *about*

set 'Follow links if url contains' text box to below value

contact; feedback; support; about

set 'DO NOT follow links if url contains' text box to below value

=http

Step 3 After doing above settings now click on Next  -> in Extraction Pattern window -> Click on Define ->  in Web Page Address (URL) give any URL where email is given.  and click on  + sign right of Date Fields to define scraping pattern.

Now inside HTML Structure selects HTML check box or Body check box which means for each page it will take whole page content to parse data.

Now last settings to extract emails from page using regular expression based email extraction function.  Open Predefined Script window and select ‘Extract_Email_Addresses‘ and click on OK. and if you have used page that contains email then in Script Result’ you will be able to see the harvested email.

Hope this will help you to use your Web Content Extractor as a Email Scraper.. Share your view in comment.

Source: http://webdata-scraping.com/use-web-content-extractor-as-email-scraper/

Powerful Web Scraping Software – Content Grabber Review

Powerful Web Scraping Software – Content Grabber Review

There are many web scraping software and cloud based web scraping services available in the market for extracting data from the websites. They vary widely in cost and features. In this article, I am going to introduce one such advanced web scraping tool “Content Grabber”, which is widely used and the best web scraping software in the market.

Content Grabber is used for web extraction, web scraping and web automation. It can extract content from complex websites and export it as structured data in a variety of formats like Excel Spreadsheets, XML, CSV and databases. Content Grabber can also extract data from highly dynamic websites. It can extract from AJAX-enabled websites, submit forms repeatedly to cover all possible input values, and manage website logins.

Content Grabber is designed to be reliable, scalable and customizable. It is specifically designed for users with a critical reliance on web scraping and web data extraction. It also enables you to make standalone web scraping agents which you can market and sell as your own royalty free web scraping software.

Applications of Content Grabber:

The following are the few applications of Content Grabber:

•     Data aggregation – for example news aggregation.
•     Competitive pricing and monitoring e.g. monitor dealers for price compliance.
•     Financial and Market Research e.g. Make proactive buying and selling decisions by continuously receiving corporate operational data.
•     Content Integration i.e. integration of data from various sources at one place.
•     Business Directory Scraping – for example: yellow pages scraping, yelp scraping, superpages scraping etc.
•     Extracting company data from yellow pages for scraping common data fields like Business Name, Address, Telephone, Fax, Email, Website and Category of Business.
•     Extracting eBay auction data like: eBay Product Name, Store Information, Buy it Now prices, Product Price, List Price, Seller Price and many more.
•     Extracting Amazon product data: Information such as Product title, cost, description, details, availability, shipping info, ASIN, rating, rank, etc can be extracted.

Content Grabber Features:

The following section highlights some of the key features of Content Grabber:

1. Point and Click Interface

The Content Grabber editor has an easy to use point and click interface that provides easy point and click configuration. One simply needs to click on web elements to configure website navigation and content capture.

2. Easy to Use

The Content Grabber point and click interface is so simple to use that it can easily be used by beginners and non-programmers. There is certain built in facilities that automatically detect and configure all commands. It will automatically create a list of links, lists of content, manage pagination, handle web pages, download or upload files and capture any action you perform on a web page. You can also manually configure the agent commands, so Content Grabber gives you both simplicity and control.

3. Reliable and Scalable

Content Grabber’s powerful features like testing and debugging, solid error handling and error recovery, allows agent to run in the most difficult scenarios. It easily handles and scrapes dynamic websites built with JavaScript and AJAX. Content Grabber’s Intelligent agents don’t break with most site structure changes. These features enable us to build reliable web scraping agents. There are various configurations and performance tuning options that makes Content Grabber scalable. You can build as many web scraping agents as you want with Content Grabber.

4. High Performance

Multi-threading is used to increase the performance in Content Grabber. Content Grabber uses optimized web browsers. It uses static browsers for static web pages and dynamic browsers for dynamic web pages. It has an ultra-fast HTML5 parser for ultra-fast web scraping. One can use many web browsers concurrently to boost performance.

5. Debugging, Logging and Error Handling

Content Grabber has robust support for debugging, error handling and logging. Using a debugger, you can test and debug the web scraping agents which helps you to build reliable and error free web scraping solutions because most of the issues are addressed at design time. Content Grabber allows agent logging with three detail levels: Log URLs, Log raw HTML, Log to database or file. Logs can be useful to identify problems that occurred during execution of a web scraping agent. Content Grabber supports automatic error handling and custom error handling through scripting. Error status reports can also be mailed to administrators.

6. Scripting

Content Grabber comes with a built in script editor with IntelliSense that one can use in case of some unusual requirements or to fine tune some process. Scripting can be used to control agent behaviour, content transformation, customize data export and delivery and to generate data inputs for agent.

7. Unlimited Web Scraping Agents

Content Grabber allows building an unlimited number of Self-Contained Web Scraping Agents. Self-Contained agents are a standalone executable that can be run independently, branded as your own and distributed royalty free. Content Grabber provides an easy to use and effective GUI to manage all the agents. One can view status and logs of all the agents or run and schedule the agents in one centralized location.

8. Automation

Require data on a schedule? Weekly? Everyday? Each hour? Content Grabber allows automating and publishing extracted data. Configure Content Grabber by telling what data you want once, and then schedule it to run automatically.

And much more

There are too many features that Content Grabber provides, but here are a few more that may be useful and interest you.

•     Schedule agents
•     Manage proxies
•     Custom notification criteria and messages
•     Email notifications
•     Handle websites logins
•     Capture Screenshots of web elements or entire web page or save as PDF.
•     Capture hidden content on web page.
•     Crawl entire website
•     Input data from almost any data source.
•     Auto scroll to load dynamic data
•     Handle complex JAVASCRIPT and AJAX actions
•     XPATH support
•     Convert Images to Text
•     CAPTCHA handling
•     Extract data from non-HTML documents like PDF and Word Documents
•     Multi-threading and multiple web browsers
•     Run agent from command line.

The above features come with the Professional edition license. Content Grabber’s Premium edition license is available with the following extra features:

1. Visual Studio 2013 integration

One can integrate Content Grabber to Visual Studio and take advantages of extra powerful script editing, debugging, and unit testing.

2. Remove Content Grabber branding

One can remove Content Grabber branding from the Content Grabber agents and distribute the executable.

3. Custom Design Templates

One can customize the Content Grabber agent user interface design with custom HTML templates – e.g. add your own company branding.

4. Royalty free distribution

One can distribute the Content Grabber agent to anybody without paying royalty fees and can run agents from the command line anywhere.

5. Programming Interface

Programming interfaces like Desktop API, Web API and windows service for building and editing agents.

6. Custom Web Scraping Application Development:

Content Grabber provides API and Visual Studio Integration which developer can use to build custom web scraping applications. It provides full control of the user interface and export functionality. One can develop both Desktop as well as Web based custom web scraping applications using the Content Grabber programming interface. It is a great tool and provides opportunity for developers to build general web scraping applications and sell those to generate revenue.

Are you looking for web scraping services? Do you need any assistance related to Content Grabber? We can probably help you to achieve your scraping-based project goals. We would be more than happy to hear from you.

Source: http://webdata-scraping.com/powerful-web-scraping-software-content-grabber/

How to use Social Media Scraping to be your Competitors’ Nightmare

How to use Social Media Scraping to be your Competitors’ Nightmare

Big data and competitive intelligence have been in the limelight for quite some time now. The almost magical power of big data to help a company make just the right decisions have been talked about a lot. When it comes to big data, the kind of benefits that a business can get totally depends upon the sources they acquire it from. Social media is one of the best sources from where you can get data that helps your business in a multitude of ways. Now that every business is deep rooted on the internet, social media data becomes all the more relevant and crucial. Here is how you can use data scraped from social media sites to get an edge in the competition.

Keeping watch on your competitors

Social media is the best place to watch your competitors’ activity and take counter initiatives to keep up or take over them. If you want to know what your competitors are up to, a social media scraping setup for scraping the posts that mention your competitors’ brand/product names can do the trick. This can also be used to learn a thing or two from their activities on social media so that you can take respective measures to stay ahead of them. For example, you could know if your competitor is running a special promotional offer at the moment and come up with something better than theirs to keep up. This can do wonders if you are in a highly competitive industry like Ecommerce where the competition is intense. If you are not using some help from web scraping technology to keep a close watch on your competitors, you could easily get left over in this fast-paced business scene.

Solving customer issues at the earliest

Customers are vocal about their experience with different products and services on social media sites these days. If you have a customer whose issue was left unsolved, there is a good chance that he/she will take it to the social media to vent the frustration. Watching out for such instances and giving them prompt support should be something you should do if you want to retain these customers and stop them from ruining your brand’s image. By scraping social media sites for posts that mention your product/service, you can easily find out if there are such grievances from customers. This can make sure to an extent that you don’t let unhappy customers stay that way, which eventually hurts your business in the long run. Customers can make or break your company, so using social media scraping to serve the customers better can help you succeed eventually.

Sentiment analysis

Social media data can play a good job at helping you understand user sentiments. With the help of social media scraping, a business can get the big picture about general perception of their brand by their users. This can go a long way since this level of feedback can help you fix unnoticed issues with your company and service quickly. By rectifying them, you can make your brand more appealing to the customers. Sentiment analysis will provide you with the opportunity to transform your business into how customers want it to be. Social media scraping is the one and only way to have access to this user sentiment data which can help you optimize your business for the customers.

Web crawling for social media data

When social media data possess so much value to businesses, it makes sense to look for efficient ways to gather and use this data. Manually scrolling through millions of tweets doesn’t make sense, this is why you should use social media scraping to aggregate the relevant data for your business. Besides, web scraping technologies make it possible to handle huge amounts of data with ease. Since the size of data is huge when it comes to business related requirements, web scraping is the only scalable solution worth considering. To make things even simpler, there are reliable web scraping solutions that offer social media scraping services for brand monitoring.

Bottom line

Since social media has become an integral part of online businesses, the data available on these sites possess immense value to companies in every industry. Social media scraping can be used for brand monitoring and gaining competitive intelligence that can be used to optimize your business model for maximum effectiveness. This will in turn make your company stand out from the competition and the added advantage of insights gained from social media data will help you to take over your competitors.

Source: https://www.promptcloud.com/blog/social-media-scraping-for-competitive-intelligence

Business Intelligence & Data Warehousing in a Business Perspective

Business Intelligence & Data Warehousing in a Business Perspective

Business Intelligence

Business Intelligence has become a very important activity in the business arena irrespective of the domain due to the fact that managers need to analyze comprehensively in order to face the challenges.

Data sourcing, data analysing, extracting the correct information for a given criteria, assessing the risks and finally supporting the decision making process are the main components of BI.

In a business perspective, core stakeholders need to be well aware of all the above stages and be crystal clear on expectations. The person, who is being assigned with the role of Business Analyst (BA) for the BI initiative either from the BI solution providers' side or the company itself, needs to take the full responsibility on assuring that all the above steps are correctly being carried out, in a way that it would ultimately give the business the expected leverage. The management, who will be the users of the BI solution, and the business stakeholders, need to communicate with the BA correctly and elaborately on their expectations and help him throughout the process.

Data sourcing is an initial yet crucial step that would have a direct impact on the system where extracting information from multiple sources of data has to be carried out. The data may be on text documents such as memos, reports, email messages, and it may be on the formats such as photographs, images, sounds, and they can be on more computer oriented sources like databases, formatted tables, web pages and URL lists. The key to data sourcing is to obtain the information in electronic form. Therefore, typically scanners, digital cameras, database queries, web searches, computer file access etc, would play significant roles. In a business perspective, emphasis should be placed on the identification of the correct relevant data sources, the granularity of the data to be extracted, possibility of data being extracted from identified sources and the confirmation that only correct and accurate data is extracted and passed on to the data analysis stage of the BI process.

Business oriented stake holders guided by the BA need to put in lot of thought during the analyzing stage as well, which is the second phase. Synthesizing useful knowledge from collections of data should be done in an analytical way using the in-depth business knowledge whilst estimating current trends, integrating and summarizing disparate information, validating models of understanding, and predicting missing information or future trends. This process of data analysis is also called data mining or knowledge discovery. Probability theory, statistical analysis methods, operational research and artificial intelligence are the tools to be used within this stage. It is not expected that business oriented stake holders (including the BA) are experts of all the above theoretical concepts and application methodologies, but they need to be able to guide the relevant resources in order to achieve the ultimate expectations of BI, which they know best.

Identifying relevant criteria, conditions and parameters of report generation is solely based on business requirements, which need to be well communicated by the users and correctly captured by the BA. Ultimately, correct decision support will be facilitated through the BI initiative and it aims to provide warnings on important events, such as takeovers, market changes, and poor staff performance, so that preventative steps could be taken. It seeks to help analyze and make better business decisions, to improve sales or customer satisfaction or staff morale. It presents the information that manager's need, as and when they need it.

In a business sense, BI should go several steps forward bypassing the mere conventional reporting, which should explain "what has happened?" through baseline metrics. The value addition will be higher if it can produce descriptive metrics, which will explain "why has it happened?" and the value added to the business will be much higher if predictive metrics could be provided to explain "what will happen?" Therefore, when providing a BI solution, it is important to think in these additional value adding lines.

Data warehousing

In the context of BI, data warehousing (DW) is also a critical resource to be implemented to maximize the effectiveness of the BI process. BI and DW are two terminologies that go in line. It has come to a level where a true BI system is ineffective without a powerful DW, in order to understand the reality behind this statement, it's important to have an insight in to what DW really is.

A data warehouse is one large data store for the business in concern which has integrated, time variant, non volatile collection of data in support of management's decision making process. It will mainly have transactional data which would facilitate effective querying, analyzing and report generation, which in turn would give the management the required level of information for the decision making.

The reasons to have BI together with DW

At this point, it should be made clear why a BI tool is more effective with a powerful DW. To query, analyze and generate worthy reports, the systems should have information available. Importantly, transactional information such as sales data, human resources data etc. are available normally in different applications of the enterprise, which would obviously be physically held in different databases. Therefore, data is not at one particular place, hence making it very difficult to generate intelligent information.

The level of reports expected today, are not merely independent for each department, but managers today want to analyze data and relationships across the enterprise so that their BI process is effective. Therefore, having data coming from all the sources to one location in the form of a data warehouse is crucial for the success of the BI initiative. In a business viewpoint, this message should be passed and sold to the managements of enterprises so that they understand the value of the investment. Once invested, its gains could be achieved over several years, in turn marking a high ROI.

Investment costs for a DW in the short term may look quite high, but it's important to re-iterate that the gains are much higher and it will span over many years to come. It also reduces future development cost since with the DW any requested report or view could be easily facilitated. However, it is important to find the right business sponsor for the project. He or she needs to communicate regularly with executives to ensure that they understand the value of what's being built. Business sponsors need to be decisive, take an enterprise-wide perspective and have the authority to enforce their decisions.

Process

Implementation of a DW itself overlaps with some phases of the above explained BI process and it's important to note that in a process standpoint, DW falls in to the first few phases of the entire BI initiative. Gaining highly valuable information out of DW is the latter part of the BI process. This can be done in many ways. DW can be used as the data repository of application servers that run decision support systems, management Information Systems, Expert systems etc., through them, intelligent information could be achieved.

But one of the latest strategies is to build cubes out of the DW and allow users to analyze data in multiple dimensions, and also provide with powerful analytical supporting such as drill down information in to granular levels. Cube is a concept that is different to the traditional relational 2-dimensional tabular view, and it has multiple dimensions, allowing a manager to analyze data based on multiple factors, and not just two factors. On the other hand, it allows the user to select whatever the dimension he wish to choose for analyzing purposes and not be limited by one fixed view of data, which is called as slice & dice in DW terminology.

BI for a serious enterprise is not just a phase of a computerization process, but it is one of the major strategies behind the entire organizational drivers. Therefore management should sit down and build up a BI strategy for the company and identify the information they require in each business direction within the enterprise. Given this, BA needs to analyze the organizational data sources in order to build up the most effective DW which would help the strategized BI process.

High level Ideas on Implementation

At the heart of the data warehousing process is the extract, transform, and load (ETL) process. Implementation of this merely is a technical concern but it's a business concern to make sure it is designed in such a way that it ultimately helps to satisfy the business requirements. This process is responsible for connecting to and extracting data from one or more transactional systems (source systems), transforming it according to the business rules defined through the business objectives, and loading it into the all important data model. It is at this point where data quality should be gained. Of the many responsibilities of the data warehouse, the ETL process represents a significant portion of all the moving parts of the warehousing process.

Creation of a powerful DW depends on the correctness of data modeling, which is the responsibility of the database architect of the project, but BA needs to play a pivotal role providing him with correct data sources, data requirements and most importantly business dimensions. Business Dimensional modeling is a special method used for DW projects and this normally should be carried out by the BA and from there onwards technical experts should take up the work. Dimensions are perspectives specific to a business that could be used for analysis purposes. As an example, for a sales database, the dimensions could include Product, Time, Store, etc. Obviously these dimensions differ from one business to another and hence for each DW initiative those dimensions should be correctly identified and that could be very well done by a person who has experience in the DW domain and understands the business as well, making it apparent that DW BA is the person responsible.

Each of the identified dimensions would be turned in to a dimension table at the implementation phase, and the objective of the above explained ETL process is to fill up these dimension tables, which in turn will be taken to the level of the DW after performing some more database activities based on a strong underlying data model. Implementation details are not important for a business stakeholder but being aware of high level process to this level is important so that they are also on the same pitch as that of the developers and can confirm that developers are actually doing what they are supposed to do and would ultimately deliver what they are supposed to deliver.

Security is also vital in this regard, since this entire effort deals with highly sensitive information and identification of access right to specific people to specific information should be correctly identified and captured at the requirements analysis stage.

Advantages

There are so many advantages of BI system. More presentation of analytics directly to the customer or supply chain partner will be possible. Customer scores, customer campaigns and new product bundles can all be produced from analytic structures resulting in high customer retention and creation of unique products. More collaboration within information can be achieved from effective BI. Rather than middle managers getting great reports and making their own areas look good, information will be conveyed into other functions and rapidly shared to create collaborative decisions increasing the efficiency and accuracy. The return on human capital will be greatly increased.

Managers at all levels will save their time on data analysis, and hence saving money for the enterprise, as the time of managers is equal to money in a financial perspective. Since powerful BI would enable monitoring internal processes of the enterprises more closely and allow making them more efficient, the overall success of the organization would automatically grow. All these would help to derive a high ROI on BI together with a strong DW. It is a common experience to notice very high ROI figures on such implementations, and it is also important to note that there are many non-measurable gains whilst we consider most of the measurable gains for the ROI calculation. However, at a stage where it is intended to take the management buy-in for the BI initiative, it's important to convert all the non measurable gains in to monitory values as much as possible, for example, saving of managers time can be converted in to a monitory value using his compensation.

The author has knowledge in both Business and IT. Started career as a Software Engineer and moved to work in the business analysis area of a premier US based software company.

Source: http://ezinearticles.com/?Business-Intelligence-and-Data-Warehousing-in-a-Business-Perspective&id=35640