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What I’ve Learned from 10 Years with Clinical Depression & Anxiety

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I have put off sharing my experiences with mental illness for a long time because I wanted to be “fixed” before I could feel like I was in a place to be giving advice to others.

I would have felt like a fraud to be telling others how things can get better, when I kept taking the odd step backward with my own mental health.

But here’s the secret: there is no “fixed,” and that is OK.

There isn’t a black and white divide between being happy and struggling through. And that is why we need to learn to give ourselves a break when we stumble or have a bad day, or a bad month. It’s OK.

I have always been a deeply private person; feeling more comfortable listening than holding the attention of others.

However, when I see others suffering, I feel that there is a greater need to do what I can to give those people out there some advice, and a bit of hope. So I’ve decided to put my privacy aside for this article.

That’s why I’d like to share my experiences with mental illness, and the lessons I’ve learned along the way. I hope it helps even one person feel less alone.

It’s thanks to my own support network as well as a much-needed push after reading Navah Hopkins’ great article on Search Engine Journal which inspired me to finally show this post the light of day, after years of going backward and forward between writing out my thoughts only to back out and scrap them.

My Experiences with Clinical Depression & Anxiety

I started experiencing symptoms of severe clinical depression at the age of 15.

As someone well-versed in hiding my emotions, I have always noted the surprise on doctors’ faces — both on the day of my diagnosis and on multiple occasions over the years — when I would come into their office with the most convincing smile and politeness I could muster, and then score in the mid-20s on the NHS Depression in Adults questionnaire.

Alongside depression, my anxiety levels soon started creeping up and I have been struggling with a combination of both for more than 10 years now.

Depression and anxiety are a really difficult combination to deal with.

The depression will sap you of your drive and energy, and the anxiety will be sending you waves of panic because you aren’t being as high-functioning as you could be.

The two are at constant war with one another. You’re sinking further underwater and losing touch with the world around you, but also feeling alarm bells ringing inside at the same time.

This is something I still struggle with, but where I am now compared to where I was 10 years ago is astonishing when I stop and think about it.

I was a very unhappy and lost teenager who couldn’t see a way forward for myself. I felt that applying to university would be dishonest because I didn’t see a future for myself, and I didn’t want to waste anyone’s time as I didn’t think I’d be around long enough to actually go.

I did apply, however. And with a combination of multiple visits to the campus doctor, a course of strong antidepressants and postponing my final year and graduating a year later than expected, I made it through with a degree to show for it.

My journey into technical SEO hasn’t been all smooth sailing, which I have written about previously. However, I have now reached a point in my career where I’m really proud of what I have achieved.

I have developed the internal strength and mental control to face new challenges, such as publishing articles and research pieces to wide audiences, as well as delivering technical talks at digital marketing conferences around the world.

By no means has this journey been easy; my progress has been down to a number of different factors that I have had to work really hard at over the years.

That’s why I’ve decided to put together some of the biggest lessons I’ve learned through my own experiences, in the hope that they can help others.

1. Open up & Talk

This is the first and most important step to getting better.

I know it can be overwhelming to consider letting someone else know how you’re feeling inside, because they’ll never understand and they’ll think you’re ‘crazy,’ right?

Wrong.

The people who matter will want to help you, and they will not judge you or see you any differently.

I’ve been surprised by how understanding people have been when I’ve told them about my illness, whether that’s family members, friends, or even bosses. It can be scary to strike up that conversation, but you’ll feel so much better once it’s out in the open.

Choose a few select people to tell about your illness and build your own support network of people who will be there to check in on you.

This will help to start clearing those feelings of loneliness, and your network will also be able to provide you with the support you need to go and talk to a doctor who will be able to help you plan out your recovery strategy.

2. Practice Mindfulness

This has been one of the most effective methods that have helped me over the years.

I’ve attended a variety of different types of therapy sessions in my time, but something really clicked for me in one particular CBT (Cognitive Behavioural Therapy) session.

I was given a thought sheet, which is a piece of paper with different columns where you write down your worry or negative thought, and then fill in the following details:

Thought-Sheet

At face value, I thought this was a basic method that wouldn’t be useful. I initially thought I would humor the therapist by trying it out.

However, the first time I filled it out I was able to see how unwell I really was and how unhealthy my existing mental processes were.

At that point, I had gone through 23 years of my life by letting my mind run completely wild with negative thoughts, never once pulling them up and questioning their validity.

But once I could write them down on paper and see a blank column for “facts that support the unhelpful thought” and a full column for “facts that provide evidence against the unhelpful thought,” everything changed.

Writing down your thoughts is step one, the next step is to practice this process internally, which is also known as mindfulness.

Restructuring the way you think is a challenge, but it’s essential to be able to live a happier, healthier life.

It can feel really alien at first to monitor your own brain for negative thoughts and then work through them one at a time, but it does work and starts to become a normal part of the way you think.

It takes practice and perseverance. Trust me.

Thinking about your own thoughts can feel exhausting at first. Training your mind in this way can feel like exercising a muscle that you’ve never used before.

But over time it gets easier, and the process becomes more immediate. When negative thoughts crop up you’ll be able to cut right into your internal dialogue with questions such as:

  • “Is there any evidence that I’m doing a bad job?”
  • “What about the time that x complimented me on my performance?”

3. Find an Exercise Routine That Works for You

When I’m feeling down, the last thing I want to do is leave the house, let alone physically exert myself.

But I always feel so much better once I’ve forced myself to go for a run or attend a gym class. It helps you push the reset button on your brain and return to the rest of your day with a calmer mind.

I’m someone who is really motivated by seeing progression, so signing up to a gym near my house has been great for me.

There are some classes that run through similar combinations of moves each week, and I’m able to see myself progressing as certain moves get easier for me each session, which gives me a real sense of accomplishment.

Push yourself to get out of the house a few times a week and go for a walk, a run, or a swim, anything. Just go and do something that can get your endorphins flowing. You’ll thank yourself for it once you’ve finished.

4. Give Yourself a Break

Don’t beat yourself up for having a tough time. This is where having anxiety can be a real problem when you have a low mood, as it fills you with overwhelming thoughts that you don’t have time to feel sad and that you’re letting yourself and everyone else around you down for not being at your best.

First of all, stop and breathe.

Whenever I feel like this, I practice a simple breathing exercise. Three slow, deep breaths. That’s it. This always works wonders for me and helps me reset.

And-Breathe

Secondly, this is perfectly normal. Every single one of us struggles from time to time.

Recognize when you’re feeling overwhelmed and just take a step back and practice some self-care until you feel like yourself again.

Take some time to rest as well as doing some of the things you enjoy. I know that depression can rob you of your ability to enjoy things, but taking a break to rest first really helps to open me up to more positive feelings.

A “self-care” day will look different for everyone.

For example, my ideal rest day would probably involve a lie in, playing video games, a yoga session, a bubble bath, and a walk in nature where I can pet some dogs.

If I’ve had a tough day, my partner will take me out on a walk to our local park to look at dogs. It’s simple but it works every time!

5. Give Back

Nothing can pull me out of a dark place like seeing someone else hurting. Helping someone else with their problems can transport you away from your own darkness.

Make a note to check in with the people you care about every now and again and see if there’s any way you can support them.

We all like to brush things off and say that everything is fine, so instigating a genuine conversation with someone about how they’re feeling can be incredibly impactful. It’s important that we all take the time to acknowledge and check in with each other on a deeper level.

As well as helping people in your immediate network, think about other ways in which you can use your own experiences to give advice and support to others struggling with mental illness. It might be starting a blog on mental health, or raising money for charities like Mind or Samaritans, for example.

As painful as it can be to suffer from a mental illness, I believe that it gives you the superpower of enhanced empathy.

You know what it’s like to hurt, so you have a heightened sense of the pain in others and wanting to help them feel better so they don’t have to experience the kinds of things that you yourself have felt.

Embrace your powers and share them with the people around you.

In Summary

I’ve made a lot of progress with managing my mental health up until this point, but I’m not completely “fixed,” as this is an unhealthy idea that isn’t realistic.

Instead of striving for a place of perfection, we need to keep working toward being more understanding and forgiving of ourselves over time.

This post originally was published on Medium, and has been republished with permission of the author.

More Resources:


Image Credits

Screenshot taken by author, August 2019
In-Post Image: Max van den Oetelaar/Unsplash



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Video: Chris Boggs on experience in the SEM industry

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Chris Boggs has been doing the SEO and SEM thing since 2000 — yes, for over 20 years. Boggs does both SEO and PPC and has worked at both large agencies and smaller agencies and in-house at both large companies and small companies. He has been on his own, running his own agency named Web Traffic Advisors, since 2014.

Boggs is a former US Marine and credits a lot of his success in the industry to what he learned from his service. He also credits his previous jobs and bosses with his success. We spoke about that and also chatted about some of the earlier days in SEM.

Our conversation went into some technical SEO topics and PPC topics as well. I hope you enjoy learning about Chris Boggs, he is a good man.

I started this vlog series recently, and if you want to sign up to be interviewed, you can fill out this form on Search Engine Roundtable. You can also subscribe to my YouTube channel by clicking here.


About The Author

Barry Schwartz a Contributing Editor to Search Engine Land and a member of the programming team for SMX events. He owns RustyBrick, a NY based web consulting firm. He also runs Search Engine Roundtable, a popular search blog on very advanced SEM topics. Barry’s personal blog is named Cartoon Barry and he can be followed on Twitter here.



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Leverage Python and Google Cloud to extract meaningful SEO insights from server log data

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For my first post on Search Engine Land, I’ll start by quoting Ian Lurie:

Log file analysis is a lost art. But it can save your SEO butt!

Wise words.

However, getting the data we need out of server log files is usually laborious:

  • Gigantic log files require robust data ingestion pipelines, a reliable cloud storage infrastructure, and a solid querying system
  • Meticulous data modeling is also needed in order to convert cryptic, raw logs data into legible bits, suitable for exploratory data analysis and visualization

In the first post of this two-part series, I will show you how to easily scale your analyses to larger datasets, and extract meaningful SEO insights from your server logs.

All of that with just a pinch of Python and a hint of Google Cloud!

Here’s our detailed plan of action:

#1 – I’ll start by giving you a bit of context:

  • What are log files and why they matter for SEO
  • How to get hold of them
  • Why Python alone doesn’t always cut it when it comes to server log analysis

#2 – We’ll then set things up:

  • Create a Google Cloud Platform account
  • Create a Google Cloud Storage bucket to store our log files
  • Use the Command-Line to convert our files to a compliant format for querying
  • Transfer our files to Google Cloud Storage, manually and programmatically

#3 – Lastly, we’ll get into the nitty-gritty of Pythoning – we will:

  • Query our log files with Bigquery, inside Colab!
  • Build a data model that makes our raw logs more legible 
  • Create categorical columns that will enhance our analyses further down the line
  • Filter and export our results to .csv

In part two of this series (available later this year), we’ll discuss more advanced data modeling techniques in Python to assess:

  • Bot crawl volume
  • Crawl budget waste
  • Duplicate URL crawling

I’ll also show you how to aggregate and join log data to Search Console data, and create interactive visualizations with Plotly Dash!

Excited? Let’s get cracking!

System requirements

We will use Google Colab in this article. No specific requirements or backward compatibility issues here, as Google Colab sits in the cloud.

Downloadable files

  • The Colab notebook can be accessed here 
  • The log files can be downloaded on Github – 4 sample files of 20 MB each, spanning 4 days (1 day per file)

Be assured that the notebook has been tested with several million rows at lightning speed and without any hurdles!

Preamble: What are log files?

While I don’t want to babble too much about what log files are, why they can be invaluable for SEO, etc. (heck, there are many great articles on the topic already!), here’s a bit of context.

A server log file records every request made to your web server for content.

Every. Single. One.

In their rawest forms, logs are indecipherable, e.g. here are a few raw lines from an Apache webserver:

Daunting, isn’t it?

Raw logs must be “cleansed” in order to be analyzed; that’s where data modeling kicks in. But more on that later.

Whereas the structure of a log file mainly depends on the server (Apache, Nginx, IIS etc…), it has evergreen attributes:

  • Server IP
  • Date/Time (also called timestamp)
  • Method (GET or POST)
  • URI
  • HTTP status code
  • User-agent

Additional attributes can usually be included, such as:

  • Referrer: the URL that ‘linked’ the user to your site
  • Redirected URL, when a redirect occurs
  • Size of the file sent (in bytes)
  • Time taken: the time it takes for a request to be processed and its response to be sent

Why are log files important for SEO?

If you don’t know why they matter, read this. Time spent wisely!

Accessing your log files

If you’re not sure where to start, the best is to ask your (client’s) Web Developer/DevOps if they can grant you access to raw server logs via FTP, ideally without any filtering applied.

Here are the general guidelines to find and manage log data on the three most popular servers:

We’ll use raw Apache files in this project.

Why Pandas alone is not enough when it comes to log analysis

Pandas (an open-source data manipulation tool built with Python) is pretty ubiquitous in data science.

It’s a must to slice and dice tabular data structures, and the mammal works like a charm when the data fits in memory!

That is, a few gigabytes. But not terabytes.

Parallel computing aside (e.g. Dask, PySpark), a database is usually a better solution for big data tasks that do not fit in memory. With a database, we can work with datasets that consume terabytes of disk space. Everything can be queried (via SQL), accessed, and updated in a breeze!

In this post, we’ll query our raw log data programmatically in Python via Google BigQuery. It’s easy to use, affordable and lightning-fast – even on terabytes of data!

The Python/BigQuery combo also allows you to query files stored on Google Cloud Storage. Sweet!

If Google is a nay-nay for you and you wish to try alternatives, Amazon and Microsoft also offer cloud data warehouses. They integrate well with Python too:

Amazon:

Microsoft:

Create a GCP account and set-up Cloud Storage

Both Google Cloud Storage and BigQuery are part of Google Cloud Platform (GCP), Google’s suite of cloud computing services.

GCP is not free, but you can try it for a year with $300 credits, with access to all products. Pretty cool.

Note that once the trial expires, Google Cloud Free Tier will still give you access to most Google Cloud resources, free of charge. With 5 GB of storage per month, it’s usually enough if you want to experiment with small datasets, work on proof of concepts, etc…

Believe me, there are many. Great. Things. To. Try!

You can sign-up for a free trial here.

Once you have completed sign-up, a new project will be automatically created with a random, and rather exotic, name – e.g. mine was “learned-spider-266010“!

Create our first bucket to store our log files

In Google Cloud Storage, files are stored in “buckets”. They will contain our log files.

To create your first bucket, go to storage > browser > create bucket:

The bucket name has to be unique. I’ve aptly named mine ‘seo_server_logs’!

We then need to choose where and how to store our log data:

  • #1 Location type – ‘Region’ is usually good enough.
  • #2 Location – As I’m based in the UK, I’ve selected ‘Europe-West2’. Select your nearest location
  • #3 Click on ‘continue’

Default storage class: I’ve had good results with ‘nearline‘. It is cheaper than standard, and the data is retrieved quickly enough:

Access to objects: “Uniform” is fine:

Finally, in the “advanced settings” block, select:

  • #1 – Google-managed key
  • #2 – No retention policy
  • #3 – No need to add a label for now

When you’re done, click “‘create.”

You’ve created your first bucket! Time to upload our log data.

Adding log files to your Cloud Storage bucket

You can upload as many files as you wish, whenever you want to!

The simplest way is to drag and drop your files to Cloud Storage’s Web UI, as shown below:

Yet, if you really wanted to get serious about log analysis, I’d strongly suggest automating the data ingestion process!

Here are a few things you can try:

  • Cron jobs can be set up between FTP servers and Cloud Storage infrastructures: 
  • FTP managers like Cyberduck also offer automatic transfers to storage systems, too
  • More data ingestion tips here (AppEngine, JSON API etc.)

A quick note on file formats

The sample files uploaded in Github have already been converted to .csv for you.

Bear in mind that you may have to convert your own log files to a compliant file format for SQL querying. Bigquery accepts .csv or .parquet. 

Files can easily be bulk-converted to another format via the command line. You can access the command line as follows on Windows:

  • Open the Windows Start menu
  • Type “command” in the search bar
  • Select “Command Prompt” from the search results
  • I’ve not tried this on a Mac, but I believe the CLI is located in the Utilities folder

Once opened, navigate to the folder containing the files you want to convert via this command:

CD 'path/to/folder’

Simply replace path/to/folder with your path.

Then, type the command below to convert e.g. .log files to .csv:

for file in *.log; do mv "$file" "$(basename "$file" .*0).csv"; done

Note that you may need to enable Windows Subsystem for Linux to use this Bash command.

Now that our log files are in, and in the right format, it’s time to start Pythoning!

Unleash the Python

Do I still need to present Python?!

According to Stack Overflow, Python is now the fastest-growing major programming language. It’s also getting incredibly popular in the SEO sphere, thanks to Python preachers like Hamlet or JR.

You can run Python on your local computer via Jupyter notebook or an IDE, or even in the cloud via Google Colab. We’ll use Google Colab in this article.

Remember, the notebook is here, and the code snippets are pasted below, along with explanations.

Import libraries + GCP authentication

We’ll start by running the cell below:

It imports the Python libraries we need and redirects you to an authentication screen.

There you’ll have to choose the Google account linked to your GCP project.

Connect to Google Cloud Storage (GCS) and BigQuery

There’s quite a bit of info to add in order to connect our Python notebook to GCS & BigQuery. Besides, filling in that info manually can be tedious!

Fortunately, Google Colab’s forms make it easy to parameterize our code and save time.

The forms in this notebook have been pre-populated for you. No need to do anything, although I do suggest you amend the code to suit your needs.

Here’s how to create your own form: Go to Insert > add form field > then fill in the details below:

When you change an element in the form, its corresponding values will magically change in the code!

Fill in ‘project ID’ and ‘bucket location’

In our first form, you’ll need to add two variables:

  • Your GCP PROJECT_ID (mine is ‘learned-spider-266010′)
  • Your bucket location:
    • To find it, in GCP go to storage > browser > check location in table
    • Mine is ‘europe-west2′

Here’s the code snippet for that form:

Fill in ‘bucket name’ and ‘file/folder path’:

In the second form, we’ll need to fill in two more variables:

The bucket name:

  • To find it, in GCP go to: storage > browser > then check its ‘name’ in the table
  • I’ve aptly called it ‘apache_seo_logs’!

The file path:

  • You can use a wildcard to query several files – Very nice!
  • E.g. with the wildcarded path ‘Loggy*’, Bigquery would query these three files at once:
    • Loggy01.csv
    • Loggy02.csv
    • Loggy03.csv
  • Bigquery also creates a temporary table for that matter (more on that below)

Here’s the code for the form:

Connect Python to Google Cloud Storage and BigQuery

In the third form, you need to give a name to your BigQuery table – I’ve called mine ‘log_sample’. Note that this temporary table won’t be created in your Bigquery account.

Okay, so now things are getting really exciting, as we can start querying our dataset via SQL *without* leaving our notebook – How cool is that?!

As log data is still in its raw form, querying it is somehow limited. However, we can apply basic SQL filtering that will speed up Pandas operations later on.

I have created 2 SQL queries in this form:

  • “SQL_1st_Filter” to filter any text
  • “SQL_Useragent_Filter” to select your User-Agent, via a drop-down

Feel free to check the underlying code and tweak these two queries to your needs.

If your SQL trivia is a bit rusty, here’s a good refresher from Kaggle!

Code for that form:

Converting the list output to a Pandas Dataframe

The output generated by BigQuery is a two-dimensional list (also called ‘list of lists’). We’ll need to convert it to a Pandas Dataframe via this code:

Done! We now have a Dataframe that can be wrangled in Pandas!

Data cleansing time, the Pandas way!

Time to make these cryptic logs a bit more presentable by:

  • Splitting each element
  • Creating a column for each element

Split IP addresses

Split dates and times

We now need to convert the date column from string to a “Date time” object, via the Pandas to_datetime() method:

Doing so will allow us to perform time-series operations such as:

  • Slicing specific date ranges 
  • Resampling time series for different time periods (e.g. from day to month)
  • Computing rolling statistics, such as a rolling average

The Pandas/Numpy combo is really powerful when it comes to time series manipulation, check out all you can do here!

More split operations below:

Split domains

Split methods (Get, Post etc…)

Split URLs

Split HTTP Protocols

Split status codes

Split ‘time taken’

Split referral URLs

Split User Agents

Split redirected URLs (when existing)

Reorder columns

Time to check our masterpiece:

Well done! With just a few lines of code, you converted a set of cryptic logs to a structured Dataframe, ready for exploratory data analysis.

Let’s add a few more extras.

Create categorical columns

These categorical columns will come handy for data analysis or visualization tasks. We’ll create two, paving the way for your own experiments!

Create an HTTP codes class column

Create a search engine bots category column

As you can see, our new columns httpCodeClass and SEBotClass have been created:

Spotting ‘spoofed’ search engine bots

We still need to tackle one crucial step for SEO: verify that IP addresses are genuinely from Googlebots.

All credit due to the great Tyler Reardon for this bit! Tyler has created  searchtools.io, a clever tool that checks IP addresses and returns ‘fake’ Googlebot ones, based on a reverse DNS lookup.

We’ve simply integrated that script into the notebook – code snippet below:

Running the cell above will create a new column called ‘isRealGbot?:

Note that the script is still in its early days, so please consider the following caveats:

  • You may get errors when checking a huge amount of IP addresses. If so, just bypass the cell
  • Only Googlebots are checked currently

Tyler and I are working on the script to improve it, so keep an eye on Twitter for future enhancements!

Filter the Dataframe before final export

If you wish to further refine the table before exporting to .csv, here’s your chance to filter out status codes you don’t need and refine timescales.

Some common use cases:

  • You have 12 months’ worth of log data stored in the cloud, but only want to review the last 2 weeks
  • You’ve had a recent website migration and want to check all the redirects (301s, 302s, etc.) and their redirect locations
  • You want to check all 4XX response codes

Filter by date 

Refine start and end dates via this form:

Filter by status codes

Check status codes distribution before filtering:

Code:

Then filter HTTP status codes via this form:

Related code:

Export to .csv 

Our last step is to export our Dataframe to a .csv file. Give it a name via the export form:

Code for that last form:

Pat on the back if you’ve followed till here! You’ve achieved so much over the course of this article!

I cannot wait to take it to the next level in my next column, with more advanced data modeling/visualization techniques!

I’d like to thank the following people:

  • Tyler Reardon, who’s helped me to integrate his anti-spoofing tool into this notebook!
  • Paul Adams from Octamis and my dear compatriot Olivier Papon for their expert advice
  • Last but not least, Kudos to Hamlet Batista or JR Oakes – Thanks guys for being so inspirational to the SEO community!

Please reach me out on Twitter if questions, or if you need further assistance. Any feedback (including pull requests! :)) is also greatly appreciated!

Happy Pythoning!

This year’s SMX Advanced will feature a brand-new SEO for Developers track with highly-technical sessions – many in live-coding format – focused on using code libraries and architecture models to develop applications that improve SEO. SMX Advanced will be held June 8-10 in Seattle. Register today.


Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.


About The Author

Charly Wargnier is a seasoned digital marketing consultant based in the UK, leaning on over a decade of in-the-trenches SEO, BI and Data engineering experience. Charly has worked both in-house and agency-side, primarily for large enterprises in Retail and Fashion, and on a wide range of fronts including complex technical SEO issues, site performance, data pipelining and visualization frameworks. When he isn’t working, he enjoys coding for good and spending quality time with his family – cooking, listening to Jazz music and playing chess, in no particular order!



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Microsoft Office hits pause on forcing Bing search in Chrome, Firefox

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Microsoft recently announced a new “extension” as part of an update to its Office 365 ProPlus software that forcibly changes company-wide Chrome and Firefox search engine defaults to Bing search, automatically, from what is likely set to Google. After considerable backlash, the company is reversing course, a bit.

In a predatory fashion, the extension automatically seeks out, through the network and local device file systems, installations of independent browsers (Chrome and Firefox were mentioned) in order to edit configuration files outside its own software ecosystem.

A compromise

In a halfhearted reversal, Microsoft will compromise with modifications that comply more with administrators’ wishes to make the extension optional. This will result in a timeline delay, as well. Rather than automatically changing default search engines for Chrome and Firefox to Bing, administrators are now required to opt-in for it to do so, and actions will initially be limited to only Active Directory joined devices.

This means, at first, the extension won’t act like a worm that traverses the whole network looking for vulnerable computers — until sometime “in the future.”

In the future we will add specific settings to govern the deployment of the extension to unmanaged devices. 

Microsoft

It’s still troubling Microsoft plans to do this but is understandable when considering what is often done in tandem with an organization’s rules. IT infrastructure setup and maintenance require super-user levels of control over software installation and configuration settings.

The problem is when organizations are less restrictive, allowing users to install Chrome and Firefox rather than limit them to using Microsoft Edge or past versions of IE. Browser applications get very personalized when authenticated with Google and/or Firefox Accounts for services such as Google search.

No matter how convenient the ability to search for docs and refs from shared drives and Microsoft applications via Chrome and Firefox default search is, users of those browsers should be able to do that through company resources and manage search defaults on their own.

Security implications

In more restrictive organizations, like those that require secure access to sensitive information by authenticated staff, having “overlord” control over networked machines is a vital component of IT systems operations. In those cases, it is commonplace to disallow software installations in the first place.

It stands to reason security incidents can increase when browser search with Microsoft in Bing accesses network resources. Administrators have to take care when considering such applications. They certainly didn’t ask for the features the new extension provides and rightly view the move as one of pure marketing.

It’s when users are allowed to install programs that policy and operations should be less impinging. Automatically changing default search settings to Bing while only providing last-minute instructions for administrators who must take action to prevent the extension from executing was a very poor way to introduce a controversial procedure in Office 365 setup.

Why we care

Ironically, ink from the press about the backlash gave the search capability of Microsoft in Bing a spotlight that the extension may not have received otherwise. Microsoft should not resort to leveraging its Office 365 install base to switch user-defined search defaults from a desired choice to Bing in order to unfairly compete. It demonstrates how much it would like to take search market share away from Google. Bing integrated with Microsoft search competes fairly well with its unique results from network resources, something Google can only emulate with its own suite of interoperable services appearing in search results.


About The Author

Detlef Johnson is the SEO for Developers Expert for Search Engine Land and SMX. He is also a member of the programming team for SMX events and writes the SEO for Developers series on Search Engine Land. Detlef is one of the original group of pioneering webmasters who established the professional SEO field more than 20 years ago. Since then he has worked for major search engine technology providers, managed programming and marketing teams for Chicago Tribune, and consulted for numerous entities including Fortune 500 companies. Detlef has a strong understanding of Technical SEO and a passion for Web programming.



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