Seventy-Two Big Data Infographics | Big Data Technology | Big Data Questions | Syed Alaudeen

Full list:

1. How The USA Federal Government Thinks Big With Data
2. Are You Ready For The Future of the Internet of Things?
3. How Big Data Centers Impact the Environment
4. A Look Into How Data Centers Actually Work
5. How Big Data Gives Retailers a Competitive Edge and Boosts Growth
6. How We Are Heading Towards a Smart Planet with The Internet of Things
7. How Data Disasters Can Seriously Harm Your Company
8. How Data Mining & Decision Support Systems Can Create A Powerful Marketing Strategy
9. Five Myths Marketers Believe About Big Data
10. The Explosion of the Internet of Things
11. What Are The Trends: A Big Data Survey
12. How The Internet of Things Will Make Our World Smart- Infographic
13. Big Data Analytics Trends for 2014
14. How Big Data Will Improve Decision Making in Your Organisation
15. How M2M Data Will Have a Major Impact by 2020
16. The World’s Most Unusual Data Centers
17. 7 Ways Big Data Could Revolutionize Our Lives by 2020
18. 5 Ways To Become Extinct As Big Data Evolves
19. Why Marketers Should Stop Worrying And Start Loving Big Data
20. In The World Of Digital Storage, Size Does Matter
21. Maximize Online Sales With Product Recommendations
22. Understanding The Various Sources of Big Data
23. The Who, What and Why of Big Data
24. Customers Sharing Their Personal Data Should Be Cared For
25. Is Bad Data A Hazard For Your Customer Experience
26. What The Consumer Really Thinks Of Data Privacy
27. Financial Services Firms Leveraging Big Data
28. A Reality Gap Exists With Big Data Initiatives
29. Smart Cities Turn Big Data Into Insight
30. Using Big Data To Predict Dengue Fever And Malaria Outbreaks
31. Keeping Track Of The People Keeping Track Of You
32. Are European Companies Ready For Big Data?
33. How Are Companies Organising Their Big Data Initiatives
34. 10 Greatest Challenges Preventing Businesses From Capitalizing On Big Data
35. What Will The World Look Like When We Connect The Unconnected
36. What Is The Value Of The Internet Of Things
37. What Are The Real Costs Of A Data Breach
38. How Google Applies Big Data To Know You
39. How To Put Big Data To Work
40. How To Become More Competitive With Big Data
41. How Big Data Can Help To Minimize Attacks On Your Digital Assets
42. How Can Big Data Improve Education
43. A Closer Look Into The Future Big Data Ecosystem
44. Data Lovers vs. Data Haters
45. How The Internet Of Things Will Create A Smart World
46. 8 Industries That Could Benefit From Big Data
47. Five Steps To Data-Driven Marketing
48. Big Data is Big Business in Banking
49. The body as a source of big data
50. Is your data secure?
51. The viability of big data
52. A visualization of the world’s largest data breaches
53. The long road to become a big data scientist
54. The illustrious big data scientist
55. What data do the five largest tech companies collect
56. Getting sales and marketing aligned with big data
57. Big Data in the Supply Chain
58. The promise of big personalization
59. Understanding the growing world of bytes
60. The history of predictive analytics
61. The Big Data Industry Atlas
62. CIOs and Big Data
63. How retailers can deal with big data
64. The importance of Big Data Governance
65. Big Data and the possibilities with Hadoop
66. Big Data brings big benefits, but what are the costs of too much data?
67. Giving employees access to big data has big potential
68. Big Data will transform healthcare
69. Five opportunities to effectively and efficiently analyze big data
70. Challenging the traditional RDBMS Status Quo
71. Big data is Big News
72. Big Data Snapshot

Microsoft Now Officially Owns Nokia's Phone Business

Microsoft's acquisition of Nokia's Devices and Services business is now complete, both companies have announced.

The $7.2 billion deal, originally announced in September 2013, was approved by Nokia shareholders one month later, and has now been approved by governmental regulatory agencies around the world.

SEE ALSO: 5 Futuristic Nokia Patents Microsoft Should Make

“Today we welcome the Nokia Devices and Services business to our family. The mobile capabilities and assets they bring will advance our transformation,” saidMicrosoft CEO Satya Nadella.

Stephen Elop chimed in with a similarly worded open letter. "As Microsoft and Nokia Devices and Services come together as an expanded family, we will unify our passion, dedication and commitment to bringing you the best of what our joint technologies have to offer (...) From today onwards, the possibilities are endless. As now, we’re one," he wrote.

Microsoft's press release seems to confirm a recently leaked memo, which indicated Microsoft would be renaming Nokia to Microsoft Mobile.

"Microsoft refers to Microsoft Corp. and its affiliates, including Microsoft Mobile Oy, a subsidiary of Microsoft. Microsoft Mobile Oy develops, manufactures and distributes Lumia, Asha and Nokia X mobile phones and other devices," writes Microsoft.

Have something to add to this story? Share it in the comments.

Reference From Linked in:

http://mashable.com/2014/04/25/nokia-devices-is-now-officially-a-part-of-microsoft/

During this year birthday Celebration Spend a valuable time with Orphan Childrens | Syed Alaudeen

Google Algorithm Update Status | Syed Alaudeen | Digital Marketing Consultant

In 2005 Google published its “Web Authoring Statistics” report, which provided a unique insight into how a large search engine views the Web at the very basic HTML level.

In August 2009 Matt Cutts invited Webmasters to help test a new indexing technology that Google had dubbed Caffeine. The SEO community immediately fell to rampant speculation about how Caffeine would affect rankings (in fact, the only effect was unintentional).

By February 2010 even I had fallen prey to Caffeine Speculationitis. On February 25, 2010 Matt McGee confirmed that Google had not yet implemented the Caffeine technology on more than 1 data center (at this time, in April 2013, there are only 13 Google Data Centers around the world).

On June 8, 2010 Google announced the completion of rolling out its Caffeine indexing technology. Caffeine gave Google the ability to index more of the Web at a faster rate than ever before. This larger, faster indexing technology invariably changed search results because all the newly discovered content was changing the search engine’s frame of reference for millions of queries.

On November 11, 2010 Matt Cutts said that Google might use as many as 50 variations for some of its 200+ ranking signals, a point that Danny Sullivan used to extrapolate a potential 10,000 “signals” Google might use in its algorithm.

On February 24, 2011 Google announced the release of its first Panda algorithm iteration into the index.

On March 2, 2011 Google asked Webmasters to share URLs of sites they believed should not have been downgraded by Panda. The discussion went on for many months and the thread is more than 1000 posts long. Google engineers occasionally confirmed throughout 2011 that they were still watching the discussion and collecting more information.The next day Wired published an interview with Amit Singhal and Matt Cutts (see below).

On May 6, 2011 Amit Singhal published 23 questions that drew much criticism from frustrated Web marketers. The angry mobs did not understand the context in which the questions should be used.

On June 21, 2011 Danny Sullivan suggested that Panda may be a ranking factor more than just a filter (a view that I and others had also come to hold by that time, but Danny was the first to suggest this publicly).

In mid-March 2013 Google announced that the Panda algorithm had been “incorporated into our indexing process”, meaning it was now essentially running on autopilot. Between February 24, 2011 and March 15, 2013 there were more than 20 confirmed and suspected “iterations” of the Panda algorithm that changed the search results for millions of queries.

Google Analytics Segmentations Using User Defined Tracking | Custom variable user defined variable tutorials | Web analytics Consultant Chennai

The Google Analytics’ user defined report allows analyst to compare visitors from segments that you have defined. I will go through several types of segmentations that you could possibly set. You can define these segments by calling a line of code/function in your web page. So every time a page with the code is requested, a custom value is captured and stored in the user defined variable.
The main approach to execute this is to simply apply a code like this:

pageTracker._setVar(“test_value”);
As an example, one of the sites I work on has English and Japanese sections. Each section will have a value to identify its section, defined as “/viewed/english” or “/viewed/japanese”.
Once you have these segmentations in place, you’ll be able to see how users are behaving differently in each section.

Visitor Type Segmentation
This is a powerful segmentation to get an actionable insight to who is behaving differently. Let’s say you have a form entry that leads to a confirmation page. Assuming the form has a field with values “Engineer”, “Project Manager” and “Director”, and when the form is completed, one these values will be stored into user defined variable.

At certain point, you may learn that Directors are likely to complete the form and convert. This will tell you something about your visitor that you didn’t know.

Landing Page Segmentation

You may have custom landing pages to serve different campaigns or promotions. Identifying the landing pages through user defined report would be a powerful method to analyze effective landing pages or even its campaign effectiveness.
(Example 1) Say you have two different direct mails with different slogan or description. You can have two different friendly URLs set in each of the direct mail. Direct mail A would have a landing page A, and landing page B for direct mail B. When traffic and performance for landing page B performed better than landing page A, that could mean that the direct mail B’s strategy was more effective than version A.
(Example 2) You can have one campaign with a landing page, but a page can receive traffic from other various sources. Setting two different landing pages and segmenting it through user defined variables can show you which landing page is more effective while testing various traffic sources.

A/B Test
Above examples can speak as an example for A/B test. Another example to perform A/B test is testing different call to actions (CTA). Say you have two links; one with an image link and other as a simple text link.
You may distinguish these different CTAs (or possibly link type, position, etc.), and store it into user defined report to assess which criteria performed better.

Referrer Segmentation
Google analytics has a sophisticated campaign tracking method. However, you can choose to use user defined report to parse certain attribute within URLs and apply it to the report. One possible example of such application would be an existing links with identifier in the URL (not compliant to Google’s campaign tracking), where the links are located in two different sites.
Site abc.com with a link “yoursite.com?source=123″
Site xyz.com with a link “yoursite.com?source=456″
Your landing page could parse these source codes and allocate proper value to a segment. Therefore you should be able to assess the referrers’ performance and its effectiveness.

Google Webmaster Tools Index Status | Google Webmaster Tools Tutorial | Syed Alaudeen | Digital Marketing Consultant Chennai

Google web master tool will now let you know how many pages of your website are currently included in Google’s index. The new feature can be found by visiting the Health menu. Once there, you’ll find a graph with a year’s worth of data
.

The Basic tab show the cumulative total of all URLs on your site crawled by Google, though not all crawled URLs get indexed.
If you need to go deeper to identify potential crawling or indexing issues, you can check out the Advanced tab to see how many pages Google:

• Has crawled.
• Knows about but can't crawl because it is blocked by your robots.txt file.
• Has not included in its search results, either because they are "substantially similar" to other pages or they have been redirected to another URL.

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Google analytics Filters | Google analyticsFilter Type | Syed alaudeen | Online marketing consultant chennai

Attention all Google Analytics users around the world: you don’t have to be an expert in regular expressions to use filters. Why? Because this post will help you, that’s why!

No long and drawn-out lead-in to the story this time – here are 5 filters that you can create for your Google Analytics profile(s) that will tidy up your data and make you a happier analyst.

1. Excluding your own traffic from reports

Why: Chances are that your own visits to your own web site aren’t racking up that many visits and page views. Nonetheless, you can still permanently remove your own traffic statistics from appearing in your Google Analytics profile(s).

How: First, grab your IP address from whatismyip.com (or, ask an IT person). If you have administrative access to your account, click on your account’s name, then click on your web property’s name. Next, click on the filters sub-tab (within the profiles tab), click on “Add Filter“, and do the following:

Method: Create New Filter
Filter Name: Exclude my IP Address
Filter Type: Custom Filter >> Exclude
Filter Field: Visitor IP Address
Filter Pattern: ^192\.168\.25\.25$
Case Sensitive: No

Replace the IP address in the example above with your own IP address, but leave the ^, the $, and the three \ symbols (just replace the numbers). Click Save, and you’re done!

2. Lowercasing your hostnames
Why: A hostname is a domain that has sent you visitor data. In other words, a hostname is a URL where your Google Analytics tracking code is present and has at least sent you 1 visit during the selected date-range that you’re looking at. If you ever toggle your report dimension by hostname, or switch the viewing table to show hostnames, you could see mixed cases (upper and lower), which leads to many different variations of your same domain name appearing. That also means you need to work on your SEO re-directs – but that’s something for another time.
How: Go through the same steps as you did in the last filter to get to the filter creation screen. Once there, do this:

Method: Create New Filter
Filter Name: Lowercase Hostnames
Filter Type: Custom Filter >> Lowercase
Filter Field: Hostname

Click Save, and you’re done! You can also create additional lowercase filters to do the same thing to other pieces of data that may look unsightly (one of them might be the Request URI filter field, which represents everything after the .com part of your URL).

3. Search for long, bulky page name; Replace with short, clean page name.
Why: Page names can get long and bulky. There’s probably an important page in your top ten that’s just an eye-sore. How about we shorten it and clean it up some?
How: Follow these filter creation steps – but remember to change the page names to your own, as the following is just an example:

Method: Create New Filter
Filter Name: Search & Replace: Long page with “/john.php”
Filter Type: Custom Filter >> Search and Replace
Filter Field: Request URI
Search String: /your-very-long-and-bulky-page.php?id=1234567
Replace String: /john.php
Case Sensitive: No

4. Add the visitor’s browser to the visitor’s operating system
Why: Why not? Google Analytics lets you create some powerful, advanced filters that let you do something cool (and efficient) like adding the visitor’s browser to the operating system that they’re using. This way, you can see a visitor’s browser along side a visitor’s operating system, without having to apply a secondary dimension (saving your secondary dimension option for something else).
How: Here’s how you do it:

Method: Create New Filter
Filter Name: Operating System + Browser Platform
Filter Type: Custom Filter >> Advanced
Field A -> Extract A: Visitor Operating System Platform -> (.*)
Field B -> Extract B: Visitor Browser Program -> (.*)
Output To -> Constructor: Visitor Operating System Platform -> $A1 – $B1
Field A Required: Yes
Field B Required: No
Override Output Field: Yes
Case Sensitive: No

For Field A and Field B, choose the filter field as described, and then in the blank form field, type in (.*) as shown.

5. Include your domain (and, ONLY your domain!)
Why: Unfortunately, server caching and having your tracking code outright stolen and placed on someone else’s web site is something that we sometimes have to deal with. So, from time to time, you must write a filter that will prohibit the collection of data from every domain except for your own web site.
How: Create your include filter like this:

Method: Create New Filter
Filter Name: Include my domain
Filter Type: Custom Filter >> Include
Filter Field: Hostname
Filter Pattern: mywebsite\.com$
Case Sensitive: No

Click Save to stop the nefarious ones from sending you irrelevant data!

We could write about filters until the next Presidential election, because there is just so much on the topic, and, so many different things that you can do with filters. Even though you can copy the steps outlined in the above 5 filters directly, I still urge you to use caution. Filters are sensitive, temperamental, and must be precise, to say the very least. A poorly-created filter can cause permanent damage, so tread lightly.

What about you? What filters do you like to use? What problems are you experiencing? We’d love to hear your thoughts below!