Collecting and Managing Data for Small Businesses

Is your company doing well? Are you profitable? How much longer will it be until you are? Where can you cut expenses? What are your most profitable products or services? Which are the least profitable? What the shelf-life of a particular product? How effective is your marketing strategy?

As business leaders, this type of information is the type of information that we need to have on hand in order to make decisions about the company. Planning solely on what is in the bank, or focusing only on one aspect of what makes your company a) successful or b) keeps it out of closing is a very poor way of operating. This is pure tunnel vision.

If you consider, for a moment, two tools that are widely used in the business world - Porter's Five Forces and the SWOT analysis, you'll notice that part of the analysis is based on things that impact the business - are outside of the business's control. As business leaders, you know that strategically, this cannot occur just in exercise, but must exist in the way that you do business. Monitoring, making adjustments and acting must be an ongoing mentality if your goal is to build an extraordinary business. Several recommendations we have made to clients include:

    Understand what questions you want to answer. Here are some samples:

    How do we know when we can purchase a new building or expand capacity

    How do we know how effective our sales people are

    How do we know how effective our marketing and other business development activities are

    Understand what kind of data you need to collect in order to make your decisions. Typically, these are going to be things such as your financials - sales, cost of goods sold, expenses, profit, investments, interest and taxes, your business development activities, manufacturing costs and rates, etc.

    Determine how to collect the data - including what is feasible. Take into consideration how you operate - does it need to be mobile? does the information need to be housed in a cloud?

    Determine how the data needs to be delivered. If you have a ton of data and like to drill down from "high level" analysis down to the details, perhaps you want something more visual. If you like to play with the numbers yourself and run scenarios, perhaps you like to play with the raw data.

•    Decide how much your level of investment. Consider this:

•    No Investment - If you don't get the data to make decisions, the likelihood of success is minimalized.

•    Your Time - If you collect and mine the data yourself, what else could or should you be doing to build or grow the business.

•    Your Resources - You could have a qualified employee collect and mine the data for you.

•    Your Money - You could invest in a software solution - be it customized, off the shelf, or a combination of the two - that could collect the data. We have used and recommend a product called Work, Etc., to centrally house most transaction that occur in the business in order to give us a single data-collection source.

    Research solutions that will work best for your company, considering the factors we pointed out above. You may consider other factors that are specific to your company, such as the ability to sync with certain existing solutions or the ability to be housed on a centrally located or remote server, etc.. May companies look at Open Source solutions such as xTurple or SugarCRM. Despite your choice, consider the total cost of ownership of the solution before investing in it.

    Determine how the solution will be implemented and how training on the software will roll out. This may mean hiring a consultant, going to a class or classes, investing in an online training solution or spending time with tech support for a self-install. Consider the different types of investment.

    Plan to re-enforce the need to use whatever solution is recommended. Change takes time. Habits take time. By providing some structure, you will increase the likelihood of success.

Your company's ability to collect and decipher the data from the activities in and around your company can be the determine factor between a series of successful ventures and a series of hit-or-miss activities. Even the simplest data-collection activities should help you determine your company's path.

Source:http://ezinearticles.com/?Collecting-and-Managing-Data-for-Small-Businesses&id=6923386

Using Bulk SMS Services how the stock advisory companies increasing their sales volume?

BNI's philosophy is "Givers Gain" - members are expected to focus on giving referrals to other members to build relationships and receive referrals in return. Why I made this point here is the BNI is one of the World’s largest referral organization which works via referral networks. Even Stock Advisory firms are also working on the similar kind of model. What they do? They send the list of their targeted audience or prospective customers stating that i.e.) Buy “ABC Ltd Share” BSE Code: XXXX @ Rs. 142-145 for 1 Month Target 380-460 Huge Govt. Orders received buy 2000 shared for 2Lacs Profit in 1 Month. 10 Days Free Trail also Available.

One More Sample – How much did you earn in Stock Market?? Today our client earned 20,500 INR. To join and earn daily in Stock Market.

When the person read the above messages they are tempted to add few more savings to their bank accounts so they show much interest what’s is this and how to invest in a smaller way etc… so the stock advisory firms to ask them to sign up for 10 days free trail where they will get lot of messages from the stock advisory companies like what’s the Buy && Sell and Profit, etc… Especially every day EOD, you will get the report like what’s the profit made by the major customer, what’s the investment, profit, etc… You will also be amazing! With the small amount of money, how the customers are earning more money. You will be showing much interest to have few more earnings. You are start welcoming lot of messages like this i.e.)

Note: Of course the stock advisory company whatever gives you the statement like X is making so much of profit that’s 100% correct. There is one thing you forget to realize, the X is spending his dedicated day ONLY for STOCK’s business. NOT as Part time he is earning HUGE PROFIT.

Here it’s NOT the question of earning the profit or loss. It’s one of the Marketing Strategy how the Stock Advisory Companies are converting the business very nicely with their targeted customer. Most of the time, they are ONLY using Bulk SMS Services as Marketing Strategy to promote their business. They do use Promotional & Transactional Bulk SMS Services. To convert anyone into business they do periodic Promotional Bulk SMS to them regarding the service and daily gain by different customer. Once if the customer is signed for FREE TRAIL then start using Transactional Bulk SMS Services to send the market updates, market call, staring, mid day, closing status, exit rate, etc…

Source:http://blogs.siliconindia.com/bulksmscompany/Business/Using-Bulk-SMS-Services-how-the-stock-advisory-companies-increasing-their-sales-volume-bid-fP9gkbI282792976.html

Data scraping tool for non-coding journalists launches

A tool which helps non-coding journalists scrape data from websites has launched in public beta today.

Import.io lets you extract data from any website into a spreadsheet simply by mousing over a few rows of information.

Until now import.io, which we reported on back in April, has been available in private developer preview and has been Windows only. It is now also available for Mac and is open to all.

Although import.io plans to charge for some services at a later date, there will always be a free option.

The London-based start-up is trying to solve the problem of the fact that there is "lots of data on the web, but it's difficult to get at", Andrew Fogg, founder of import.io, said in a webinar last week.

Those with the know-how can write a scraper or use an API to get at data, Fogg said. "But imagine if you could turn any website into a spreadsheet or API."

Uses for journalists

Journalists can find stories in data. For example, if I wanted to do a story on the type of journalism jobs being advertised and the salaries offered, I could research this by looking at various websites which advertise journalism jobs.

If I were to gather the data from four different jobs boards and enter the information manually into a spreadsheet it would take would take hours if not days; if I were to write a screen scraper for each of the sites it would require knowledge and would probably take a couple of hours. Using import.io I can create a single dataset from multiple sources in a few minutes.

I can then search and sort the dataset and find out different facts, such as how many unpaid internships are advertised, or how many editors are currently being sought.

How it works

When you download the import.io application you see a web browser. This browser allows you to enter a URL for any site you want to scrape data from.

To take the example of the jobs board, this is structured data, with the job role, description and salaries displayed.

The first step is to set up 'connectors' and to do this you need to teach the system where the data is on the page. This is done by hitting a 'record' button on the right of the browser window and mousing over a few examples, in this case advertised jobs. You then click 'train rows'.

It takes between two and five examples to teach import.io where all of the rows are, Fogg explained in the webinar.

The next step is to declare the type of data and add column names. For example there may be columns for 'job title', 'job description' and 'salary'. Data is then extracted into the table below the browser window.

Data from different websites can then be "mixed" into a single searchable database.

In the example used in the webinar, Fogg demonstrated how import.io could take data relating to rucksacks for sale on a shopping website. The tool can learn the "extraction pattern", Fogg explained, and apply that to to another product. So rather than mousing over the different rows of sleeping bags advertised, for example, import.io was automatically able to detect where the price and product details were on the page as it had learnt the structure from how the rucksacks were organised. The really smart bit is that the data from all products can then be automatically scraped and pulled into the spreadsheet. You can then search 'shoes' and find the data has already been pulled into your database.

When a site changes its code a screen scraper would become ineffective. Import.io has a "resilience to change", Fogg said. It runs tests twice a day and users get notified of any changes and can retrain a connector.

It is worth noting that a site that has been scraped will be able to detect that import.io has extracted the data as it will appear in the source site's web logs.

Case studies

A few organisations have already used import.io for data extraction. Fogg outlined three.

    British Red Cross

The British Red Cross wanted to create an iPhone app with data from the NHS Choices website. The NHS wanted the charity to use the data but the health site does not have an API.

By using import.io, data was scraped from the NHS site. The app is now in the iTunes store and users can use it to enter a postcode to find hospital information based on the data from the NHS site.

"It allowed them to build an API for a website where there wasn't one," Fogg said.

    Hewlett Packard

Fogg explained that Hewlett Packard wanted to monitor the prices of its laptops on retailers' websites.

They used import.io to scrape the data from the various sites and were able monitor the prices at which the laptops were being sold in real-time.

    Recruitment site

A US recruitment firm wanted to set up a system so that when any job vacancy appeared on a competitor's website, they could extract the details and push that into their Salesforce software. The initial solution was to write scrapers, Fogg said, but this was costly and in the end they gave up. Instead they used import.io to scrape the sites and collate the data.


Source: http://www.journalism.co.uk/news/data-scraping-tool-for-non-coding-journalists-launches/s2/a554002/

ScraperWiki lets anyone scrape Twitter data without coding

The Obama administration’s open data mandate announced on Thursday was made all the better by the unveiling of the new ScraperWiki service on Friday. If you’re not familiar with ScraperWiki, it’s a web-scraping service that has been around for a while but has primarily focused on users with some coding chops or data journalists willing to pay to have someone scrape data sets for them. Its new service, though, currently in beta, also makes it possible for anyone to scrape Twitter to create a custom data set without having to write a single line of code.

Taken alone, ScraperWiki isn’t that big of a deal, but it’s part of a huge revolution that has been called the democratization of data. More data is becoming available all the time — whether from the government, corportations or even our own lives — only it’s not of much use unless you’re able to do something with it. ScraperWiki is now one of a growing list of tools dedicated to helping everyone, not just expert data analysts or coders, analyze — and, in its case, generate — the data that matters to them.

After noticing a particularly large numbers of tweets in my stream about flight delays yesterday, I thought I’d test out ScraperWiki’s new Twitter search function by gathering a bunch of tweets directed to @United. The results — from 1,697 tweets dating back to May 3 — are pretty fun to play with, if not that surprising. (Also, I have no idea how far back the tweet search will go or how long it will take using the free account, which is limited to 30 minutes of compute time a day. I just stopped at some point so I could start digging in.)

First things first, I ran my query. Here’s what the data looks like viewed in a table in the ScraperWiki app.

Next, it’s a matter of analyzing it. ScraperWiki lets you view it in a table (like above), export it to Excel or query it using SQL, and will also summarize it for you. This being Twitter data, the natural thing to do seemed to be analyzing it for sentiment. One simple way to do this right inside the ScraperWiki table is to search for a particular term that might suggest joy or anger. I chose a certain four-letter word that begins with f.

Surprisingly, I only found eight instances. Here’s my favorite: “Your Customer Service is better than a hooker. I paid a bunch of money and you’re still…” (You probably get the idea.)

But if you read my “data for dummies” post from January, you know that we mere mortals have tools at our disposal for dealing with text data in a more refined way. IBM’s Many Eyes service won’t let me score tweets for sentiment, but I can get a pretty good idea overall by looking at how words are used. For this job, though, a simple word cloud won’t work, even after filtering out common words, @united and other obvious terms. Think of how “thanks” can be used sarcastically and you can see why.

Using the customized word tree, you can see that “thanks” sometimes means “thanks.” Other times, not so much. I know it’s easy to dwell on the negative, but consider this: “worst” had 28 hits while “best” had 15. One of those was referring to Tito’s vodka and at least three were referring to skyline views. (Click here to access it and search by whatever word you want.)

Here’s a phrase net filtering the results by phrases where the word “for” connects two words.

Anyhow, this was just a fast, simple and fairly crude example of what ScraperWiki now allows users to do, and how that resulting data can be combined with other tools to analyze and visualize it. Obviously, it’s more powerful if you can code, but new tools are supposedly on the way (remember, this is just a beta version) that should make it easier to scrape data from even more sources.

In the long term, though, services like ScraperWiki should become a lot more valuable as tools for helping us generate and analyze data rather than just believe what we’re told. Want to improve your small business, put your life in context or perhaps just write the best book report your teacher has ever seen? It’s getting easier every day.


Source: http://gigaom.com/2013/05/10/scraperwiki-lets-anyone-scrape-twitter-data-without-coding/

What is data scraping and how can I stop it?

Data scraping (also called web scraping) is the process of extracting information from websites. Data scraping focuses on transforming unstructured website content (usually HTML) into structured data which can be stored in a database or spreadsheet.

The way data is scraped from a website is similar to that used by search bots – human web browsing is simulated by using programs (bots) which extract (scrape) the data from a website.

Unfortunately, there is no efficient way to fully protect your website from data scraping. This is so because data scraping programs (also called data scrapers or web scrapers) obtain the same information as your regular web visitors.

Even if you block the IP address of a data scraper, this will not prevent it from accessing your website. Most data scraping bots use large IP address pools and automatically switch the IP address in case one IP gets blocked. And if you block too many IPs, you will most probably block many of your legitimate visitors.

One of the best ways to protect globally accessible data on a website is through copyright protection. This way you can legally protect the intellectual ownership of your website content.

Another way to protect your site content is to password protect it. This way your website data will be available only to people who can authenticate with the correct username and password.


Source: http://kb.siteground.com/what_is_data_scraping_and_how_can_i_stop_it/

A Guide to Web Scraping Tools

This post is ghost written by one of my bloggers, I outsource 95% of my life.  The author’s views below are entirely his or her own and may not reflect the views of  Gareth James aka SEO Doctor

Web Scrapers are tools designed to extract / gather data in a website via crawling engine usually made in Java, Python, Ruby and other programming languages.Web Scrapers are also called as Web Data Extractor, Data Harvester , Crawler and so on which most of them are web-based or can be installed in local desktops.

Its main purpose is to enable webmasters, bloggers, journalist and virtual assistants to harvest data from a certain website whether text, numbers, contact details and images in a structured way which cannot be done easily thru manual copy and paste method. Typically, it transforms the unstructured data on the web, from HTML format into a structured data stored in a local database or spreadsheet or automates web human browsing.


Source: http://www.garethjames.net/a-guide-to-web-scrapping-tools/

What you need to know about web scraping: How to understand, identify, and sometimes stop

This is a gust article by Rami Essaid, co-founder and CEO of Distil Networks.

Here’s the thing about web scraping in the travel industry: everyone knows it exists but few know the details.

Details like how does web scraping happen and how will I know? Is web scraping just part of doing business online, or can it be stopped? And lastly, if web scraping can be stopped, should it always be stopped?

These questions and the challenge of web scraping are relevant to every player in the travel industry. Travel suppliers, OTAs and meta search sites are all being scraped. We have the data to prove it; over 30% of travel industry website visitors are web scrapers.

Google Analytics, and most other analytics tools do not automatically remove web scraper traffic, also called “bot” traffic, from your reports – so how would you know this non-human and potentially harmful traffic exists? You have to look for it.

This is a good time to note that I am CEO of a bot-blocking company called Distil Networks, and we serve the travel industry as well as digital publishers and eCommerce sites to protect against web scraping and data theft – we’re on a mission to make the web more secure.

So I am admittedly biased, but will do my best to provide an educational account of what we’ve learned to be true about web scraping in travel – and why this is an issue every travel company should at the very least be knowledgeable about.

Overall, I see an alarming lack of awareness around the prevalence of web scraping and bots in travel, and I see confusion around what to do about it. As we talk this through I’ll explain what these “bots” are, how to find them and how to manage them to better protect and leverage your travel business.

What are bots, web scrapers and site indexers? Which are good and which are bad?

The jargon around web scraping is confusing – bots, web scrapers, data extractors, price scrapers, site indexers and more – what’s the difference? Allow me to quickly clarify.

–> Bots: This is a general term that refers to non-human traffic, or robot traffic that is computer generated. Bots are essentially a line of code or a program that is created to perform specific tasks on a large scale.  Bots can include web scrapers, site indexers and fraud bots. Bots can be good or bad.

–> Web Scraper: (web harvesting or web data extraction) is a computer software technique of extracting information from websites (source, Wikipedia). Web scrapers are usually bad.

If your travel website is being scraped, it is most likely your competitors are collecting competitive intelligence on your prices. Some companies are even built to scrape and report on competitive price as a service. This is difficult to prove, but based on a recent Distil Networks study, prices seem to be main target.You can see more details of the study and infographic here.

One case study is Ryanair. They have been particularly unhappy about web scraping and won a lawsuit against a German company in 2008, incorporated Captcha in 2011 to stop new scrapers, and when Captcha wasn’t totally effective and Cheaptickets was still scraping, they took to the courts once again.

So Ryanair is doing what seems to be a consistent job of fending off web scrapers – at least after the scraping is performed. Unfortunately, the amount of time and energy that goes into identifying and stopping web scraping after the fact is very high, and usually this means the damage has been done.

This type of web scraping is bad because:

    Your competition is likely collecting your price data for competitive intelligence.
    Other travel companies are collecting your flights for resale without your consent.
    Identifying this type of web scraping requires a lot of time and energy, and stopping them generally requires a lot more.

Web scrapers are sometimes good

Sometimes a web scraper is a potential partner in disguise.

Meta search sites like Hipmunk sometimes get their start by scraping travel site data. Once they have enough data and enough traffic to be valuable they go to suppliers and OTAs with a partnership agreement. I’m naming Hipmunk because the Company is one of the few to fess up to site scraping, and one of the few who claim to have quickly stopped scraping when asked.

I’d wager that Hipmunk and others use(d) web scraping because it’s easy, and getting a decision maker at a major travel supplier on the phone is not easy, and finding legitimate channels to acquire supplier data is most definitely not easy.

I’m not saying you should allow this type of site scraping – you shouldn’t. But you should acknowledge the opportunity and create a proper channel for data sharing. And when you send your cease and desist notices to tell scrapers to stop their dirty work, also consider including a note for potential partners and indicate proper channels to request data access.

–> Site Indexer: Good.

Google, Bing and other search sites send site indexer bots all over the web to scour and prioritize content. You want to ensure your strategy includes site indexer access. Bing has long indexed travel suppliers and provided inventory links directly in search results, and recently Google has followed suit.

–> Fraud Bot: Always bad.

Fraud bots look for vulnerabilities and take advantage of your systems; these are the pesky and expensive hackers that game websites by falsely filling in forms, clicking ads, and looking for other vulnerabilities on your site. Reviews sections are a common attack vector for these types of bots.

How to identify and block bad bots and web scrapers

Now that you know the difference between good and bad web scrapers and bots, how do you identify them and how do you stop the bad ones? The first thing to do is incorporate bot-identification into your website security program. There are a number of ways to do this.

In-house

When building an in house solution, it is important to understand that fighting off bots is an arms race. Every day web scraping technology evolves and new bots are written. To have an effective solution, you need a dynamic strategy that is always adapting.

When considering in-house solutions, here are a few common tactics:

    CAPTCHAs – Completely Automated Public Turing Tests to Tell Computers and Humans Apart (CAPTCHA), exist to ensure that user input has not been generated by a computer. This has been the most common method deployed because it is simple to integrate and can be effective, at least at first. The problem is that Captcha’s can be beaten with a little workand more importantly, they are a nuisance to end usersthat can lead to a loss of business.

    Rate Limiting- Advanced scraping utilities are very adept at mimicking normal browsing behavior but most hastily written scripts are not. Bots will follow links and make web requests at a much more frequent, and consistent, rate than normal human users. Limiting IP’s that make several requests per second would be able to catch basic bot behavior.
    IP Blacklists - Subscribing to lists of known botnets & anonymous proxies and uploading them to your firewall access control list will give you a baseline of protection. A good number of scrapers employ botnets and Tor nodes to hide their true location and identity. Always maintain an active blacklist that contains the IP addresses of known scrapers and botnets as well as Tor nodes.

    Add-on Modules – Many companies already own hardware that offers some layer of security. Now, many of those hardware providers are also offering additional modules to try and combat bot attacks. As many companies move more of their services off premise, leveraging cloud hosting and CDN providers, the market share for this type of solution is shrinking.

    It is also important to note that these types of solutions are a good baseline but should not be expected to stop all bots. After all, this is not the core competency of the hardware you are buying, but a mere plugin.

Some example providers are:

    Impreva SecureSphere- Imperva offers Web Application Firewalls, or WAF’s. This is an appliance that applies a set of rules to an HTTP connection. Generally, these rules cover common attacks such as Cross-site Scripting (XSS) and SQL Injection. By customizing the rules to your application, many attacks can be identified and blocked. The effort to perform this customization can be significant and needs to be maintained as the application is modified.

    F5 – ASM – F5 offers many modules on their BigIP load balancers, one of which is the ASM. This module adds WAF functionality directly into the load balancer. Additionally, F5 has added policy-based web application security protection.

Software-as-a-service

There are website security software options that include, and sometimes specialize in web scraping protection. This type of solution, from my perspective, is the most effective path.

The SaaS model allows someone else to manage the problem for you and respond with more efficiency even as new threats evolve.  Again, I’m admittedly biased as I co-founded Distil Networks.

When shopping for a SaaS solution to protect against web scraping, you should consider some of the following factors:

    Does the provider update new threats and rules in real time?
    How does the solution block suspected non-human visitors?
    Which types of proactive blocking techniques, such as code injections, does the provider deploy?
    Which of the reactive techniques, such as rate limiting, are used?
    Does the solution look at all of your traffic or a snapshot?
    Can the solution block bots before they reach your infrastructure – and your data?
    What kind of latency does this solution introduce?

I hope you now have a clearer understanding of web scraping and why it has become so prevalent in travel, and even more important, what you should do to protect and leverage these occurrences.

NB: This is a gust article by Rami Essaid, co-founder and CEO of Distil Networks.

NB2: Locked binder image courtesy Shutterstock.


Source: http://www.tnooz.com/article/what-you-need-to-know-about-web-scraping-how-to-understand-identify-and-sometimes-stop/