How to Protect the Sensitive Data of Your Company with a Secure Web Gateway?

How to Protect the Sensitive Data of Your Company with a Secure Web Gateway?

As an organization, you’re entrusted with a lot of data that you must keep safe. The information might be about your clients, the work of your company, or sensitive data about your employees. 

With data breaches on the rise, it’s getting more and more difficult to protect organizations from data breaches and data leaks. Hackers are getting savvier and have been discovering new ways to breach companies every year. 

What’s more, due to remote work, more companies use cloud technology and online networks that are easier to access remotely. This is understandable, since there are many benefits of the cloud. However, if not protected, they can pose a major flaw in cybersecurity. 

In the past year, almost 45% of the companies in the U.S. have experienced a data breach. We expect this number to get even higher in the coming months. 

Since everyone is tired of hearing about breaches and data leaks, all your employees and clients want to know is if their data is safe and whether they should share it with your company. 

To protect their data and monitor the information that is coming into and going out of their systems, companies have been using Secure Web Gateway. 

What is a Secure Web Gateway, how can it prevent data loss, and how does it ensure that the data you have is used according to compliance policies? 

Read on to find out.

What is a Secure Web Gateway? 

Secure Web Gateway (SWG) is multilayered protection that enforces various company policies and keeps your systems and network safe from possible cyber-attacks and malware. 

SWG works similarly to a firewall that monitors traffic between the user and the internet. The key difference is in the tool’s complexity because it has more features. 

The application of Secure Web Gateway is versatile. You can use it to filter specific URLs, control the use of applications, prevent data loss, work as an antivirus, or inspect HTTPS. 

For example, to determine whether the traffic is unwanted, it considers company policies that govern whether a certain website should be accessible for your employees on company devices or not.

For every company, setting what SWG will do exactly depends on what you need, what is likely to be high risk in your company, and your company’s exact regulations. 

Some organizations use SWG in-house and others on their cloud — depending on what kind of systems you use to operate your company. 

How to Prevent Data Loss with Secure Web Gateway? 

Data can be leaked intentionally or unintentionally, causing you to lose vulnerable information about your customers, employees, and clients.

Secure Web Gateway includes data loss prevention, a feature that makes sure any sensitive information about you, your clients, and your employees aren’t leaked outside of your network.  

To make sure that data isn’t accessible outside of the network, the data loss prevention feature of the Secure Web Gateway monitors all the data that is circulating in your system. It also cross-checks its use with the compliance policy of your company. 

The data loss prevention feature can monitor information that is being shared via email, during web browsing, or data that is shared over various files.

Alerts on whether your information is handled properly are sent to your end-users and they can help your IT team save time and take care of a possible data loss before it even happens.  

Safer Surfing for Your Employees with Secure Web Gateway

Training your less tech-savvy employees that covers the basics of cybersecurity might not be enough. They might learn to recognize websites that are high-risk, but not know how to put training into practice.

What’s more, you can’t control whether they’ll use work devices for private purposes or accidentally access websites that contain malware. While working, they might open a link in a phishing email or click on a website that seems safe, but really isn’t.

One blunder is all it takes, and when it comes to human error, mistakes are unfortunately inevitable.

That’s why it’s important to have tools that block certain traffic to prevent your team from accessing malicious sites. 

Secure Web Gateway can: 

Block access to applications and websites that are marked as potentially malicious Ensures that all the company’s regulations, protocols, and policies are enforced for a person using your devicesDetect if your device already is infected with malware as the virus might find its way to your devices via external disks Provide cybersecurity protection for remote employees that have to connect to your network from their home devices

Websites that are infected with malware can download viruses to work devices. If the malware isn’t removed from your system, it might collect data or monitor the activity of your employees. 

Changing data compliance policies are getting complex to follow and enforce on your systems — especially if the traffic on your network is from different parts of the world.

The shift to remote work has caused a lot of cybersecurity concerns and data loss. Employees connect to your network from their less than secure home devices containing vulnerabilities that hackers can exploit to obtain data.

Complex Networks Require Layered Data Security

As your company grows, it’s getting more and more challenging to track how data in the company is being used and whether all the data compliance policies are being enforced. 

Also, you get more information every day — a lot of it is sensitive and confidential and thus can damage your reputation if it gets out.

To keep the trust that you’ve built with your customers, clients, and employees, you need a layered cybersecurity system. 

For monitoring traffic and data that is in their systems, scalable companies with remote workers have been using Secure Web Gateway. 

They rely on SWG to prevent data loss in their company, enforce compliance policies, limit the use of high-risk web applications, mitigate malware on devices, and inspect traffic that is passing through the gateway.

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7 Data Lineage Tool Tips For Preventing Human Error in Data Processing

7 Data Lineage Tool Tips For Preventing Human Error in Data Processing

Errors in data entry might have serious effects if they are not discovered quickly. Human mistake is the most common cause of data entry errors. Since typical data entry errors may be minimized with the right steps, there are numerous data lineage tool strategies that a corporation can follow. 

The steps organizations can take to reduce mistakes in their firm for a smooth process of business activities will be discussed in this blog.

Make Enough Hires

There should always be enough personnel in a company to handle the workload. There is a limitation to everything, no matter how effective your employee is. The company’s daily data entry needs must be met by a sufficient number of people. 

However, they must be well trained in the preceding areas of data mistakes so that they can perform quickly and accurately. As a result of a thorough audit and the presence of enough staff in each shift, the burden will be equally distributed and business operations will run smoothly.

Verify Your Work a Second Time

When it comes to reducing data input errors, double-checking all data entering works should be the usual operating procedure. It’s an excellent approach to combat human mistakes in data entry. Time-consuming and demanding data entry activities might lead to errors in data entry or the automation of the entire process itself. These are some of the biggest challenges businesses face with handling data.

For smaller data input activities, a more frequent and more thorough double-check can help identify potential problems. When an organization is updating or changing its technology, these checkpoints help make data entry easier.

Use the Most Current Technology

Many companies employ software solutions like ICR (Intelligent Character Recognition), which extracts data and decreases the effort of data entry workers, and OCR (Optical Character Recognition). Because of this, human errors by employees are reduced. 

Other tools, such as automatic error reporting, can be used to verify the accuracy of the data entered into the system. Data entry errors will gradually be reduced by these technologies, and operators will be able to fix the problems as soon as they become aware of them.

Make Data Profiling Available

To ensure that the data in the network is accurate, data profiling is a typical procedure. Data analysis profiling is a useful data lineage tool for discovering errors in data by detecting anomalies in known patterns and other preconditions.

An automated data profiling tool can discover and filter potentially inaccurate values while marking the information for further investigation or assessment. It aids in the identification of erroneous data and its sources.

Streamline the Methodology

Standardizing the data collecting and data input process can go a long way toward ensuring optimal accuracy. The data entry workers will have a better understanding of what to search for and anticipate in each batch of data, as well as the proper protocols to follow, because of the standardization of the process throughout the agency. 

In the long run, this will allow for the entire process to be securely automated reducing the burden and the number of mistakes.

Reduce the Amount of Redundant Information

One of the most common reasons for mistakes is data redundancy. It takes a long time to enter new data into the system. Data entry errors can be reduced by minimizing the number of unnecessary records in the system. 

Reducing data redundancy is made easier by reviewing and modifying forms, data, and documents regularly. Errors will be less likely to be entered into the system if redundant data is removed from it.

Accurately Identifying Inaccuracies

To reduce the likelihood of data input errors, you need first to determine where the inaccurate data is coming from, both outside and internally. Data should be double-checked at each stage of the way using a system that is enabled. 

There should be a double-check and verification of every step, from examining the data migration across various databases to making time-bound modifications. In addition to verifying the source of the data inaccuracies, this protocol will also maintain track of the probable faulty data that causes data entry errors to occur, therefore making it easier to correct them.


Double-checking all data entries should be the usual operating procedure. Data entry errors will gradually be reduced by these data lineage tools. Operators will be able to fix the problems as soon as they become aware of them. The steps organizations can take to reduce mistakes in their firm will be discussed. 

Data analysis profiling is a useful tool for discovering errors in data by detecting anomalies in known patterns and other preconditions. An automated data profiling tool can discover and filter potentially inaccurate values while marking the information for further investigation or assessment. It aids in the identification of erroneous data and its sources.

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Preserving Data Quality is Critical for Leveraging Analytics with Amazon PPC

Preserving Data Quality is Critical for Leveraging Analytics with Amazon PPC

Amazon is without a doubt the largest retailer in the world. More businesses than ever are turning to Amazon to expand their reach. Unfortunately, the Amazon marketplace has become extraordinarily competitive in recent years.

Companies that utilize data analytics to make the most of their business model will have an easier time succeeding with Amazon. One of the best ways to create a profitable business model with Amazon involves using data analytics to optimize your PPC marketing strategy. Amazon used big data to rule the e-commerce sector, so companies using their platform should as well.

Amazon has its own in-house PPC platform, which companies can use to expand their reach and maximize sales. Companies that know how to take advantage of data analytics will have better insights when executing their PPC marketing strategy.

How can companies use data analytics to optimize their PPC marketing strategy with Amazon?

Amazon PPC traffic has extraordinary conversion rates. Marketing experts estimate that the conversion rate is around 9.55%.

However, conversion rates aren’t nearly that high for companies that have just started marketing their products on Amazon with the internal PPC platform. It takes a lot of split-testing and data collection to optimize your strategy to approach these types of conversion rates.

Companies with an in-depth understanding of data analytics will have more successful Amazon PPC marketing strategies. However, it is important to make sure the data is reliable. Data quality is possibly even more important than data quantity, because you can be led astray by relying on data that doesn’t accurately depict the perspective of your customers and the actions they take after clicking on your Amazon PPC ads.

Grow Amz shared some major benefits of using quality data to promote your business with Amazon PPC.

More accurately understand your target customers

One of the most important aspects of marketing is understanding the demographics of your target customer. You need to create an accurate profile of the people that are going to be purchasing your products before you can hope to reach them.

Data analytics makes it a lot easier to research your online customers. You can use analytics driven tools like Quantcast, CrazyEgg and Google Analytics too figure out more about the demographics of people looking for various products and services. If you are selling to customers in a specific region, then you can also use government data, such as the Census website to find out more about the ages, income and other demographic variables of people living in that area.

There is so much data out there that can help you find out more about the people that you intend to sell to. However, is important to make sure that the data is actually accurate. The last thing that you want to do is use data from unreliable sources, because you might tailor your PPC marketing strategy to the wrong buyers.

It is better to get a smaller quantity of data that is highly reliable then compiling data from various consumer resources that might be a lot more questionable. This is going to involve vetting different sources of data before you begin collecting it. You are probably going to get more quality data by relying on resources from the government or respectable market research firms.

Identify the best performing keywords when running PPC campaigns

Any PPC marketing campaign is going to largely depend on the keywords that you focus on. This is just as true for Amazon PPC marketing as it is for Google Ads or Microsoft Ads.

But how do you know which keywords are going to convert the best? The frustrating truth is that you don’t – at least at first. Intuition is very often inaccurate when it comes to marketing. Any seasoned marketer will tell you that their logical preconceptions about what would actually work in real life is different from what customers actually do. This is just as true for targeting keywords as anything else.

This underscores the importance of using data analytics to identify the best keywords for your Amazon PPC marketing strategy. You are going to need to run a variety of split testing campaigns and see which keywords perform the best. If you are diligent about collecting data from your conversions, you will have an easier time accomplishing this.

Again, you are going to have to make sure that you accurately record the keywords customers are searching for. Amazon’s PPC interface should share the right keywords, but you have to make sure they are earmarked properly when adding them into your database.

Identifying the best marketing copy and images

There are a couple of huge advantages of marketing products on Amazon over your own website or other e-commerce platforms. One of the biggest benefits is that conversion rates are around five times higher. Another benefit is that most lending pages are relatively similar. You don’t have to test a ton of different variations of landing pages, because the overall style is relatively uniform across the site.

However, this does not mean that you don’t have to test anything at all. You are still going to have to test different types of copy and images to see which drive the best conversion rates. You don’t want to be negligent with your testing, because you are paying for every click to your sales page when you promote it with PPC. You can quickly burn through your budget without having a great return if you haven’t tested things carefully.

This is another important area where data analytics is going to help. You will be able to use analytics tools to split-test different versions of your sales pages. However, data quality is again going to be very important. You must make sure the right sales page versions are stored in your database. You have to also be careful about ruining your data quality by making intermittent changes along the way that might taint your conversion data.

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AI is Creating Powerful New Online Time Tracking Tools

AI is Creating Powerful New Online Time Tracking Tools

Artificial intelligence technology has been instrumental in driving many important changes in our daily lives. We use a ton of online tools and mobile apps that rely heavily on AI technology.

How important has AI been in transforming mobile apps and online tools? One study from Gartner found that it increased 270% between 2015 and 2019.

Online time tracking apps are among those that use AI technology to improve the customer experience and offer the best service. Tools like TimesheetKiller and use sophisticated AI algorithms to provide the best time tracking services. was considered one of the leading breakthroughs in AI-driven productivity tools, but there are now a number of others on the market that are at least as good.

Here are some of the reasons that AI technology is essential for time tracking tools:

These tools have AI algorithms can analyze your work patterns and schedules to identify regular commitments. They can give you the option to block out a specific time each week for these commitments. The more complex ones have AI algorithms that can identify less regular appointments, such as those that take place every other month.They are using artificial intelligence to get a better sense of the time commitment for each project or appointment. They can let you know how much time it is likely to last based on previous data points, which allows you to see how much time you have left to allocate for other issues.AI technology makes it easier to improve the general interface of the app, so you can enjoy a better user experience.These tools also use AI to streamline reporting, which can also be used to improve billing for clients. We discussed this in our post 5 Massively Important AI Benefits in Time Tracking Applications.

AI technology has really changed the state of time-tracking apps. You will want to be aware of the apps on the market and the AI features that they provide.

Best online time tracker apps that use AI

Time tracking apps refer to the software that helps record the time spent on a particular website, project, and application. Time tracking software helps individuals and businesses record the timing and computer activities. These apps are designed to make work more productive and within your financial limits by using sophisticated AI algorithms. AI technology has largely helped eliminate the need for cumbersome timesheets that most other time tracking services used to require.

There is an element of accuracy with manual time tracking software, which is getting better as AI algorithms become more adapt at understanding these functions. Manual time entries often lead to time theft which hampers the project budget and deadlines. Online time tracking apps that use the latest AI algorithms bring immense benefits to society by reducing expenses, helping with invoices, improving workflow, and avoiding distractions.

Here are some of the best time tracking apps that use AI technology.


This app is ideal for large business organizations. This app comes with 4 pricing plans –

Basic plan with $17per month.Standard plan with $26each month.Pro plan with $39 per month.Enterprise plan.

It also gives a free trial. offers you all in one solution, consisting of a time tracking module. You can easily track time anytime and anywhere with accurate time frames. You get to know what your employees are working on and on what.

AI technology has really made the app very impressive. Some of the AI features are as follows: provides data-driven decisions which help you to develop flexible reports.It helps you to integrate with useful and favorite tools if yours.It offers insights into the time spent on each task and project.It offers a mobile application to help you in tracking time anytime and anywhere you want.This software comes with sophisticated and smart features that help you in setting smart reminders, and you get quickly notified.According to its website, it uses artitificial intelligence to help make decisions based on context, which reduces wasted time and boosts productivity. is one of the best time tracking apps that uses AI technology.

#2) Buddy Punch

Buddy Punch

This software is best suited for both small and large businesses. It comes with two price plans-

Time and attendance along with schedule plan for $35 each month.Time and attendance plan for $25 every month.

Apart from the pricing plans, you also get annual and monthly billing options and a 30days free trial feature.

This AI employee time tracking tool has great functionalities such as vacation tracking, GPS tracking, and set a reminder. In addition to using sophisticated AI features, it also stores data on the cloud. This software is a web-based solution that offers a customizable interface. You get help from Buddy Punch in employee management, scheduling, and monitoring. This software comes with the ability to the automatic split working hours into double-time, overtime, and regular time. The AI-based features of this software include-

Unique QR codes and facial recognition options for logins.It helps in setting unique overtime rules based on per employee.It comes with many features like tracking time, GPS tracking for job codes, and project. Schedule employee time off with its built-in calendar.There is an automatic brake feature that helps in creating and assigning a rule to any employee. 

#3) Time Doctor

This app is best suited for remote teams. It comes with a single pricing plan for $9.99 each month and a free trial option for 14days. This employee tracking software features monitoring apps, websites, and screenshots. It follows both automatic and manual tracking. Other options include webcam shots, attendance tracking, and GPS tracking. There are the following features AI helps bring to this app:

Integration with many management platforms.Screenshot monitoring facility.It helps track time and breaks the time spent on tasks, clients, and projects.Client access with no extra cost.

#4) Homebase

This app is suitable for both teams and individuals. There are four pricing plans: A free basic plan, an essential plan with $16 each month, Plus plan with $40 per month, and an Enterprise plan with $80 per month. A free trial option comes for 14days. This time tracking can be used for any device. Some options included in this app are Team Messaging, Manager Training, Chats, Email support, and Payroll expert. Following are the best features of this app-.

Enables managers in editing time.Offers GPS Based clock in such off-site workers can also use it.This software doesn’t require you to remember passwords and usernames by enabling PIN-based entry.

#5) TSheets

This app can be used by enterprises, freelancers, and even small businesses. There are three pricing plans –

A free plan for self-employed ones.Small businesses plan for $4 each month.Enterprise plan for $4 each month.

It also offers a free trial option. With TimeSheets, you can easily track time. Other sophisticated options include PIN-based entry, Alerts, Mobile App, and GPS tracking. Both teams and individuals can use TSheets. Some of the ways that this app uses AI to offer better service to customers include:

Offers reminders and alerts.PIN-based entry.GPS time tracking tool.Employee scheduling feature.Easy tracking the employee paid time off.

#6) ClickTime

ClickTime is another app that uses complex AI algorithms to offer better service to customers. It is a great time management tool for any organization that wants to leverage the power of artificial intelligence.

There are four pricing plans with this app. A starter plan with $9 per person each month. A team plan with $12 per person each month, a premier plan of $24 per person each month, and an enterprise plan. A free trial option is also available for both team and starter plans. This online app provides you with project visibility within your budget. ClickTime is also a customizable and perfect solution for consultants, universities, agencies, IT architects, etc.

The AI features include the following-

Offers real-time insight and data for various projects.Using the mobile app, you take also take the receipt picture.Offers sophisticated features for executives, managers, and employees.Mobile timesheet.Allows you to track time on your laptop or phone.

#7) DeskTime

This online time tracking software is automated with AI and comes with 2pricing plans –

Lite plan is designed for individuals and comes for free.Pro plan, which may cost you $7.

This online software gives a complete overview of how the team is working. You can use this software both on mobile phones as well. As on desktops. It supports operating systems like Linux, Mac, and Windows. The features of DeskTime include the following-

This software provides auto screenshots with artificial intelligence.Allows you to download, send and customize CSV reports through AI-driven AI.It will offer insights into websites and apps used by employees.

#8) Paymo

This is good for freelancers and small and large businesses. There are two pricing plans: the business and the small office plans. It is built with functionality that captures work incomplete details. It offers a desktop widget, web timer, and mobile apps. The timesheet settings of Paymo are also customizable.

AI Helps with Time Tracking Tools

AI technology has helped improve productivity and offered many other benefits to organizations around the world, such as improved scalability. One of the best applications of AI is with time tracking tools. The aforementioned tools use AI to help keep track of time and provide better reports.

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Why Machine Learning Can Lead to the Perfect Web Design

Why Machine Learning Can Lead to the Perfect Web Design

Machine learning technology is becoming a more important aspect of modern marketing. One of the biggest reasons for this is that digital marketing is playing a huge role in marketing strategies for most companies. Companies are expected to spend $460 billion on digital marketing this year. Machine learning technology is a very important element of digital marketing.

One of the most valuable applications of machine learning technology is with web design. Web developers can use machine learning technology to create a better user experience, lower the cost of design, provide greater functionality and ensure resources are used efficiently.

Importance of Using Machine Learning in the Web Design Process

The first contact point that potential customers usually have with a brand or company for a modern business is its website. Therefore, it makes perfect sense for many entrepreneurs today to ensure that their online properties can communicate what they’re about through user-friendly and professional designs from the likes of digital web development Oxford agencies such as Xist2, especially those who are based in the same vicinity.

There are a lot of ways to improve the quality of web designs. A number of web development tools use machine learning. Here are some examples:

The Wix Website Builder incorporates AI algorithms that can automate many aspects of the design process. It has a machine learning interface that continually offers more relevant design features as more people use the platform. Firedrop is a web development tool that uses machine learning to offer chatbot services to visitors. Adobe Sensei also uses machine learning to automate many aspects of the design process. It can identify different patterns with AI algorithms and incorporate them into the design so a human designer doesn’t need to.

The list of benefits AI offers to web developers is virtually endless. Many variables make up web design, including but not necessarily limited to layout, graphics, content, conversion rate, and search engine optimization. Machine learning can help with all of this.

While it’s undoubtedly a critical factor of an organization’s promotional efforts, it’s also a part of the overall digital advertising plan. Therefore, it should remain consistent in purpose, feel, and look with other marketing efforts. And with the consideration of every facet of the company’s digital marketing campaign, a solid web design can serve as the core of your efforts and help you achieve your objectives.

You will need to know how to use machine learning to achieve these objectives. Here are some benefits of using AI to improve the design process.

Machine learning can help create an excellent user experience

Websites that fail to load, are straining on the eyes, or have confusing navigation are more likely to frustrate users and turn them off instead of reel them in. Similarly, those who cannot get to the desired information quickly will leave and search for what they need elsewhere. This is where excellent user experience comes into play. By ensuring that your site is visually appealing, is easy to browse through, and doesn’t slow down, you will not only be able to attract more people to your site, but you’ll also keep them more engaged with your content.

There are a lot of ways that machine learning can improve the user experience. It can evaluate analytics information on users and provide meaningful changes. Machine learning can also allow for automation of certain tasks like testing different background colors. You can also use certain tools like chatbots that rely heavily on AI.

Machine learning can ensure a branding strategy is executed consistently

Let’s face it — first impressions last. They count more than they’re given credit for, so ask yourself how the website of your business stacks up. The feel and look of the online domain should always remain consistent with other marketing content and communicate the brand’s message properly. Some of the things you’ll want to consider are the following:


Machine learning technology can help achieve a consistent feel for your visitors. AI algorithms will be able to simulate the experience of different users on various devices and browsers to ensure a consistent experience through responsive elements.

Machine learning helps with SEO and advertising

Many businesses overhaul their websites to ensure that they appear much higher on the search results for their targeted keywords and improve their inbound web traffic. They can achieve it through search engine optimization, from the use of the right keywords to the creation of quality content. In addition, mobile optimization, faster loading speeds, and link-building practices can all help draw in more users and increase your conversion rate.

Anyone who runs PPC ad campaigns understands the value of a landing page. For this reason, the transition to your landing page from your ad needs to be as seamless as possible. But, more importantly, it must guide or encourage visitors in taking actions that will benefit your business. If it isn’t, visitors could experience a disconnect between the elements.

Machine learning technology is very important for all of these aspects of your marketing. You can automate many of the tasks that are necessary for search engine marketing, such as analyzing keyword data and identify linkbuilding opportunities.

Machine Learning is Fundamental to Web Design

No one can deny the importance of web design in today’s digital age. If you want to get the most out of it, you must use machine learning technology in your web design to make sure that you consider both the user experience and your marketing plans and tie their various components together. Doing so will reel in your audience, keep them engaged, and entice them to come back.

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Automatic data risk management for BigQuery using DLP

Automatic data risk management for BigQuery using DLP

Protecting sensitive data and preventing unintended data exposure is critical for businesses. However, many organizations lack the tools to stay on top of where sensitive data resides across their enterprise. It’s particularly concerning when sensitive data shows up in unexpected places – for example, in logs that  services generate, when customers inadvertently send it in a customer support chat, or when managing unstructured analytical workloads. This is where Automatic Data Loss Prevention (DLP) for BigQuery can help.

Data discovery and classification is often implemented as a manual, on-demand process, and as a result  happens less frequently than many organizations would like. With a large amount of data being created on the fly, a more modern, proactive approach is to build discovery and classification into existing data analytics tools. By making it automatic, you can ensure that a key way to surface risk happens continuously – an example of Google Cloud’s invisible security strategy. Automatic DLP is a fully-managed service that continuously scans data across your entire organization to give you general awareness of what data you have, and specific visibility into where sensitive data is stored and processed. This awareness is a critical first step in protecting and governing your data and acts as a key control to help improve your security, privacy, and compliance posture.

In October of last year, we announced the public preview for Automatic DLP for BigQuery. Since the announcement, our customers have already scanned and processed both structured and unstructured BigQuery data at multi-petabyte scale to identify where sensitive data resides and gain visibility into their data risk. That’s why we are happy to announce that Automatic DLP is now Generally Available. As part of the release we’ve also added several new features to make it even easier to understand your data and to make use of the insights in more Cloud workflows. These features include:

Premade Data Studio dashboards to give you more advanced summary, reporting, and investigation tools that you can customize to your business needs.

Easy to understand dashboards give a quick overview of data in BQ

Finer grained controls to adjust frequency and conditions for when data is profiled or reprofiled, including the ability to enable certain subsets of your data to be scanned more frequently, less frequently, or skipped from profiling.

Granular settings for how often data is scanned

Automatic sync of DLP profiler insights and risk scores for each table into Chronicle, our Security Analytics platform. We aim to build synergy across our security portfolio, and with this integration we allow analysts using Chronicle to gain immediate insight into if the BQ data involved in a potential incident is of high value or not. This can significantly help to enhance threat detections, prioritizations, and security investigations. For example, if Chronicle detects several attacks, knowing if one is targeting highly sensitive data will help you prioritize, investigate, and remediate the most urgent threats first.

Deep native integration into Chronicle helps speed up detection and response

Managing data risk with data classification

Examples of sensitive data elements that typically need special attention are credit cards, medical information, Social Security numbers, government issued IDs, addresses, full names, and account credentials. Automatic DLP leverages machine learning and provides more than 150 predefined detectors to help discover, classify, and govern this sensitive data, allowing you to make sure the right protections are in place. 

Once you have visibility into your sensitive data, there are many options to help remediate issues or reduce your overall data risk. For example, you can use IAM to restrict access to datasets or tables or leverage BigQuery Policy Tags to set fine-grained access policies at the column level. Our Cloud DLP platform also provides a set of tools to run on-demand deep and exhaustive inspections of data or can help you obfuscate, mask, or tokenize data to reduce overall data risk. This capability is particularly important if you’re using data for analytics and machine learning, since that sensitive data must be handled appropriately to ensure your users’ privacy and compliance with privacy regulations.

How to get started

Automatic DLP can be turned on for your entire organization, selected organization folders, or individual projects. To learn more about these new capabilities or to get started today, open the Cloud DLP page in the Cloud Console and check out our documentation.

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Cloud Data Loss Prevention is now automatic!

Google Cloud DLP is now automatic and can help you gain visibility into sensitive data across your entire BigQuery footprint.

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BigQuery Omni innovations enhance customer experience to combine data with cross cloud analytics

BigQuery Omni innovations enhance customer experience to combine data with cross cloud analytics

IT leaders pick different clouds for many reasons, but the rest of the company shouldn’t be left to navigate the complexity of those decisions.  For data analysts, that complexity is most immediately felt when navigating between data silos. Google Cloud has invested deeply in helping customers break down these barriers inherent in a disparate data stack.  Back in October 2021, we launched BigQuery Omni to help data analysts access and query data across the barriers of multi cloud environments. We are continuing to double down in cross-cloud analytics: a seamless approach to view, combine, and analyze data across-clouds with a single pane of glass.  

Earlier this year, one of BigQuery Omni’s early adopters, L’Oreal, discussed the merits of a cross-cloud analytics to maximize their data platform.  We know that enterprises need to analyze data without needing to move or copy any data.  We also know that enterprises sometimes need to move small amounts of data between clouds to leverage unique cloud capabilities.  A full cross-cloud analytics solution offers the best of both worlds: analyzing data where it is and flexibility to replicate data when necessary. 

Last week, we launched BigQuery Omni cross-cloud transfer to help customers with combining data across clouds.  From a single-pane-of-glass, data analysts, scientists, and engineers, can load data from AWS and Azure to BigQuery without any data pipelines. Because it is all managed in SQL, it is accessible among all levels of an organization.  We have designed this feature to provide three core benefits:

Usability: With one single-pane-of-glass, users tell BigQuery to filter and move data between clouds without any context-switching

Security: With a federated identity model, users don’t have to share or store credentials between cloud providers to access and copy their data

Latency: With data movement managed by BigQuery’s high-performance storage API, users can effortlessly move just the relevant data without having to wait for complex pipes

A core use case that we have heard from customers is to combine point of sales (PoS) data from AWS/Azure with Google Analytics data and create a consolidated purchase prediction model. Here’s a demo of that:

As you saw in the demo, a data analyst can drive end-to-end workflows across clouds. They can transform data using BigQuery Omni, they can load data using cross-cloud transfer, and they can train an ML model all in SQL. This empowers them to drive real business impact by providing the ability to:  

Improve training data by de-deuplicating users across datasets

Improve accuracy of marketing segmentation models

Improve Return on Ads Spend and save potentially millions for enterprise campaigns

But we’re not stopping there, we will continue to build upon this experience by providing more BigQuery native tools for our customers to assist with smart data movement.  Over time, our cross-cloud data movement will be built on pipeless pipelines:  A cross-cloud lakehouse without the fuss. 

Get involved with the preview and start participating in our development process by submitting this short form.

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Picture this: How the U.S. Forest Service uses Google Cloud tools to analyze a changing planet

Picture this: How the U.S. Forest Service uses Google Cloud tools to analyze a changing planet

For 117 years, the U.S. Department of Agriculture’s Forest Service has been a steward of America’s forests, grasslands, and waterways. It directly manages 193 million acres and supports sustainable management on a total of 500 million acres of private, state, and tribal lands. Its impact reaches far beyond even that, offering its research and learning freely to the world.

At Google, we’re big admirers of the Forest Service’s mission. So we were thrilled to learn in 2011 that its scientists were using Google Earth Engine, our planetary-scale platform for Earth Science data and analysis, to aid its research, understanding, and effectiveness. In the years since, Google has worked with the Forest Service to meet its unique requirements for visual information about the planet. Using both historical and current data, the Forest Service built new products, workflows, and tools that help more effectively and sustainably manage our natural resources. The Forest Service also uses Earth Engine and Google Cloud to study the effects of climate change, forest fires, insects and disease, helping them create new insights and strategies.

Image 1*

Besides gaining newfound depths of insight, the Forest Service has also sped up its research dramatically, enabling everyone to do more. Using Google Cloud and Earth Engine, the Forest Service reduced the time it took to analyze 10 years worth of land-cover changes from three months to just one hour, using just 100 lines of code. The agency built new models for coping with change, then mapped these changes over time, in its Landscape Change Monitoring System (LCMS) project. 

Emergency responders can now work better on new threats that arise after wildfire, hurricanes, and other natural disasters. Forest health specialists can detect and monitor the impacts of invasive insects, diseases, and drought. More Forest Service personnel can use new tools and products within Earth Engine, thanks to numerous training and outreach sessions within the Forest Service.

Image 2*

Researchers elsewhere also benefited when the Forest Service created new toolkits, and posted them to GitHub for public use. For example, there’s geeViz, a repository of Google Earth Engine Python code modules useful for general data processing, analysis, and visualization. 

This is only the start. Recently, the Forest Service started using Google Cloud’s processing and analysis tools for projects like California’s Wildfire and Forest Resilience Action Plan. Forest Service researchers also use Google Cloud to better understand ecological conditions across landscapes in projects like Fuelcast, which provides actionable intelligence for rangeland managers, fire specialists, and growers, and the Scenario Investment Planning Platform for modeling local and national land management scenarios.

Image 3*

The Forest Service is a pioneer in building technology to help us better understand and care for our planet. With more frequent imaging, rich satellite data sets, and sophisticated database and computation systems, we can view and model the Earth as a large-scale dynamic system. 

We are honored and excited to respond to the unique set of requirements of the scientists, engineers, rangers, and firefighters of the USFS, and look forward to years of learning about — and better caring for — our most precious resources. 

*Image 1: The USDA Forest Service (USFS) Geospatial Technology and Applications Center (GTAC) uses science-based remote sensing methods to characterize vegetation and soil condition after wildland fire events. The results are used to facilitate emergency assessments to support hazard mitigation, to inform post-fire restoration planning, and to support the monitoring of national fire policy effectiveness. GTAC currently conducts these mapping efforts using long-established geospatial workflows. However, GTAC has adapted its post-fire mapping and assessment workflows to work within Google Earth Engine (GEE) to accommodate the needs of other users in the USFS. The spatially and temporally comprehensive coverage of moderate resolution multispectral data sources (e.g., Landsat, Sentinel 2) and analytical power provided by GEE allows users to create geospatial burn severity products quickly and easily. Box 1 shows a pre-fire Sentinel-2 false color composite image. Box 2 shows a post-fire Sentinel-2 false color composite image with the fire scar apparent in reddish brown. Box 3 shows a differenced Normalized Burn Ratio (dNBR) image showing the change between the pre- and post-fire images in Boxes 1 and 2. Box 4 shows a thresholded dNBR image of the burned area with four classes of burn severity (unburned to high severity), which is the final output delivered to forest managers.

*Image 2: Leveraging Google Earth Engine (GEE), the USDA Forest Service (USFS) Geospatial Technology and Applications Center (GTAC) and USFS Region 8, developed the Tree Structure Damage Impact Predictive (TreeS-DIP) modeling approach to predict wind damage to trees resulting from large hurricane events and produce spatial products across the landscape. TreeS-DIP results become available within 48 hours following landfall of a large storm event to allow allocation of ground resources to the field for strategic planning and management. Boxes 1 and 3 above show TreeS-DIP modeled outputs with varying data inputs and parameters. Box 2 shows changes in greenness (Normalized Burn Ratio; NBR) that was measured with GEE during the recovery from Hurricane Ida and is shown as a visual comparison to the rapidly available products from TreeS-DIP.

*Image 3: Severe drought conditions across the American West prompted concern about the health and status of pinyon-juniper woodlands, a vast and unique ecosystem. In a cooperative project between the USDA Forest Service (USFS) Geospatial Technology and Applications Center (GTAC) and Forest Health Protection (FHP), Google Earth Engine (GEE) was used to map pinyon pine and juniper mortality across 10 Western US States. The outputs are now being used to plan for future work including on-the-ground efforts, high-resolution imagery acquisitions, aerial surveys, in-depth mortality modeling, and planning for 2022 field season work.

Box 1 contains remote sensing change detection outputs (in white) generated with GEE, showing pinyon-juniper decline across the Southwestern US. Box 2 shows NAIP imagery from 2017 with, with box 3 showing NAIP imagery from 2021. NAIP imagery from these years shows trees changing from healthy and green in 2017 to brown and dying in 2021. In addition, box 2 and box 3 show change detection outputs from Box 1 for a location outside of Flagstaff, AZ converted to polygons (in white). The polygon in box 2 is displayed as a dashed line to serve as a reference, while the solid line in box 3 shows the measured change in 2021. Converting rasters to polygons allows the data to be easily used on tablet computers, as well as the ability to add information and photographs from field visits.

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Top 5 Takeaways from Data Cloud Summit ‘22

Top 5 Takeaways from Data Cloud Summit ‘22

To compete in a fast moving, transforming, and increasingly digital world, every team, business, process and individual needs to level up the way they think about data. Which is why this year at our Data Cloud Summit 2022, we saw record turnout, both in volume and diversity of attendance. Our thanks go out to all the customers, partners, and data community who made it such a great success!

Did you miss out on the live sessions? Not to worry – all the content is now available on demand

Here are the five biggest areas to catch up on from Data Cloud Summit 2022:

#1: Product announcements to level up your data skills

Data is no longer solely the realm of the analyst. Every team, customer and partner needs to be able to interact with the data they need to achieve their goals. To help them do so, we announced 15 new products, capabilities and initiatives that help remove limits for our users. Here are some highlights:

BigLake allows companies to unify data warehouses and lakes to analyze data without worrying about the underlying storage format or system. 
Spanner change streams tracks Spanner inserts, updates, deletes, and streams the changes in real-time across the entire Spanner database so that users will always have access to the latest data. 
Cloud SQL Insights for MySQL helps developers quickly understand and resolve database performance issues for MySQL
Vertex AI Workbench delivers a single interface for data and ML systems. 

Connected Sheets for Looker and the ability to access Looker data models within Data Studio combine the best of both worlds of BI, giving you centralized, governed reporting where you need it, without inhibiting open-ended exploration and analysis.

More product news announced at Data Cloud Summit can be found here.

#2: Customers to learn from

Customers are at the heart of everything we do, and that was evident at the Data Cloud Summit. Wayfair, Walmart, Vodafone, ING Group, Forbes, Mayo Clinic, Deutsche Bank, Exabeam and PayPal all spoke about their use of Google’s Data Cloud to accelerate data-driven transformation. Check out some of their sessions to learn more:

Unify your data for limitless innovation, featuring Wayfair and Vodafone

Unlocking innovation with limitless data, featuring Exabeam

Spotlight: Database strategy and product roadmap, featuring Paypal

We also heard from you directly! Here are some great quotes from the post-event survey:

“This is the first time that I have been exposed to some of these products. I am a Google Analytics, Data Studio, Search Console, Ads and YouTube customer…so this is all very interesting to me. I’m excited to learn about BigQuery and try it out.”

“The speakers are very knowledgeable, but I appreciate the diversity in speakers at these cloud insights.”

“Great experience because of the content and the way that it is presented.”

“This is definitely useful to new Google Admin Managers like I am.”

“This was a great overview of everything new in such a short time!”

#3: Partners to deliver the best customer experiences

Our partner ecosystem is critical to delivering the best experience possible for our customers. With more than 700 partners powering their applications using Google Cloud, we are continuously investing in the ecosystem. At Data Cloud Summit, we announced a new Data Cloud Alliance, along with the founding partners Accenture, Confluent, Databricks, Dataiku, Deloitte, Elastic, Fivetran, MongoDB, Neo4j, Redis, and Starburst, to make data more portable and accessible across disparate business systems, platforms, and environments—with a goal of ensuring that access to data is never a barrier to digital transformation. In addition, we announced a new Database Migration Program to accelerate your move to managed database services. Many of these partners delivered sessions of their own at Data Cloud Summit 2022:

Accelerate Enterprise AI adoption by 25-100x, featuring C3 AI

Rise of the Data Lakehouse in Google Cloud, featuring Databricks

The Connected Consumer Experience in Healthcare and Retail, featuring Deloitte

Investigate and prevent application exploits with the Elasticsearch platform on Google Cloud

#4: Product demos to elevate your product knowledge

Experts from Google Cloud delivered demos giving a hands-on look at a few of the latest innovations in Google’s Data Cloud:

Cross-cloud analytics and visualization with BigQuery Omni and Looker, with Maire Newton and Vidya Shanmugam

Build interactive applications that delight customers with Google’s data cloud, with Leigha Jarett and Gabe Weiss

Build a data mesh on Google Cloud with Dataplex, with Prajakta Damie and Diptiman Raichaudhuri

Additional demos are available here on-demand.

#5: Resources to dig into

If you want to go even deeper than the Summit sessions themselves, we’ve put together a great list of resources and videos of on-demand contentto help you apply these innovations in your own organization. Here are some of the highlights:

Guide to Google Cloud Databases (PDF)

How does Pokémon Go scale to millions of requests? (video)

MLOps in BigQuery ML using Vertex AI (video)

Database Engineer Learning Path (course)

Machine Learning Engineer Learning Path (course)

BI and Analytics with Looker (course)

Thanks again for joining us at this year’s Data Cloud Summit. Join us again at Applied ML Summit June 9, 2022!

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MLOps in BigQuery ML with Vertex AI Model Registry

MLOps in BigQuery ML with Vertex AI Model Registry

Without a central place to manage models, those responsible for operationalizing ML models have no way of knowing the overall status of trained models and data. This lack of manageability can impact the review and release process of models into production, which often requires offline reviews with many stakeholders. 

Earlier this week we announced Vertex AI Model Registry, a central repository to manage and govern the lifecycle of your ML models. Model Registry organizes your model artifacts by version, making it easy for data scientists to share models and application developers to deploy them. It’s designed to work with any type of model and deployment target, whether that’s through BigQuery, Vertex AI, custom deployments on GCP or even out of the cloud. 

In this blog, we’ll dive into how Model Registry works with BigQuery ML, showcasing the features that allow you to register, version, and easily deploy your BigQuery ML Models to Vertex AI: 

Registering BigQuery ML models with Vertex AI Model Registry

1. With Vertex AI Model Registry, you can now see and manage all your ML models (AutoML, custom-trained, and BigQuery ML) in the same place
2. You can register BigQuery ML models to Vertex AI Model Registry when creating your model using SQL

Model versioning with Vertex AI Model Registry

3. Model versioning is now available on Vertex AI Model Registry, including for BigQuery ML models

Easier deployment of BigQuery ML models to Vertex AI endpoints

4. From Vertex AI Model Registry, you can deploy BigQuery ML models to Vertex endpoints directly 

Let’s dive deeper into each of these new and exciting capabilities.

Registering models with Vertex AI Model Registry

View and manage all your ML models in the one place

You can now see all your ML models within Vertex AI Model Registry, making it easier for your organization to manage and deploy models. This includes models built with BigQuery ML, AutoML, and custom trained models.

Full documentation on Vertex AI Model Registry here: Vertex AI Model Registry | Google Cloud. (Click to enlarge)

Registering BigQuery ML models to Vertex AI Model Registry

Let’s go over some common questions you might have:

How do you register a BigQuery ML to Vertex AI Model Registry?

Using the CREATE MODEL syntax, now you can add in an optional model_registry=”vertex_ai” field to register the model to Model Registry when the model has finished training. You can also specify a Vertex AI model ID to register to, otherwise it will register it as a new model in Model Registry using the BigQuery ML model id. You can also specify any custom tags to help you label your model, such as “staging”, “production”.

Here’s an example of using CREATE MODEL with model_registry=’vertex_ai’:

code_block[StructValue([(u’code’, u”CREATE OR REPLACE MODEL `bqml_tutorial.my_penguins_model`rnOPTIONSrn (model_type=’linear_reg’,rn input_label_cols=[‘body_mass_g’],rn model_registry=’vertex_ai’,rn vertex_ai_model_version_aliases=[‘linear_reg’, ‘experimental’]rn ) ASrnSELECTrn *rnFROMrn `bigquery-public-data.ml_datasets.penguins`rnWHERErn body_mass_g IS NOT NULL”), (u’language’, u”)])]

Full documentation here: Managing models with Vertex AI | BigQuery ML | Google Cloud

Note: If you see an error indicating Access Denied: BigQuery BigQuery: Permission ‘aiplatform.models.upload’ denied on resource, you may first need to follow the instructions here to set the correct permissions. This is temporary. In a future release, you won’t need to explicitly set these permissions before registering BigQuery ML models with Vertex AI Model Registry. 

After training is complete, the BigQuery ML model (my_penguins_model) now shows up in Vertex AI Model Registry:

Click to enlarge

Clicking on the model lets me inspect the model with more details, including the model version and aliases:

Click to enlarge

You might have a few questions at this point:

Do all BigQuery ML models get automatically registered to Vertex AI Model Registry? 

No, BigQuery ML models do not get automatically registered to Model Registry unless the user wants them to. As data scientists iterate and experiment through different models, they might want to only register a subset of models to the Model Registry. So users of BigQuery ML can pick and choose which models they explicitly want to register to the Vertex AI Model Registry using model_registry=”vertex_ai” in the CREATE MODEL query. All models created using BigQuery ML will still be viewable within BigQuery, regardless of whether or not they have been registered to Vertex AI Model Registry.

Can any BigQuery ML model be registered to Vertex AI Model Registry?

Not all of them, currently. BigQuery ML has many supported model types, and built-in models as well as imported TensorFlow models can be registered to the Vertex AI Model Registry (full documentation). 

Can you delete BigQuery ML models directly from Vertex AI Model Registry?

Currently, no you cannot. The only way to delete BigQuery ML models is from BigQuery ML. If you delete a BigQuery ML model, it will automatically be removed from Vertex AI Model Registry. More information on deleting BigQuery ML models can be found in the documentation.

Model versioning with Vertex AI Model Registry

Model versioning is now available on Vertex AI Model Registry, including for BigQuery ML

Users can now keep track of model versions on Vertex AI Model Registry, including BigQuery ML models. Model versioning allows you to create multiple versions of the same model. With model versioning, you can organize your models in a way that helps you navigate and understand which changes had what effect on the models. With Vertex AI Model Registry you can view your models and all of their versions in a single view.

So when you register an initial BigQuery ML model to Model Registry, and then register a second version to the same model_id, you will see two versions on Model Registry.

For example, after training the initial model my_penguins_model, you can train another model version and register it to Vertex AI Model Registry, using the same vertex_ai_model_id, and adding any aliases you’d like:

code_block[StructValue([(u’code’, u”CREATE MODEL `bqml_tutorial.my_penguins_model_2`rnOPTIONSrn (model_type=’linear_reg’,rn input_label_cols=[‘body_mass_g’],rn model_registry=’vertex_ai’,rn vertex_ai_model_id=’my_penguins_model’,rn vertex_ai_model_version_aliases=[‘ready_for_staging’]rn ) ASrnSELECTrn *rnFROMrn `bigquery-public-data.ml_datasets.penguins`rnWHERErn body_mass_g IS NOT NULL”), (u’language’, u”)])]

Looking at the model details in the Vertex AI Model Registry allows me to see a new version of the model:

Full documentation on model versioning here: Model versioning with Vertex AI Model Registry | Google Cloud. (Click to enlarge)

Easier deployment of BigQuery ML models to Vertex AI endpoints

Why might you consider deploying BigQuery ML models to a Vertex AI endpoint? Today, BigQuery ML is great for batch predictions on large datasets. However, BigQuery ML is unsuitable for situations requiring online predictions, which typically involve low-latency and high-query-per-second inference. In other situations, sometimes data scientists and ML engineers may prefer to use a REST endpoint to serve predictions, rather than use SQL queries for model inference. To solve for either scenario, users can now more easily deploy their BigQuery ML models to a Vertex AI endpoint.

Deploy BigQuery ML models to Vertex endpoints directly from Vertex AI Model Registry

Once a BigQuery ML model is registered on Vertex AI Model Registry, you can now easily deploy the model to an endpoint in just a few clicks from the Model Registry interface. 

You can select to “Deploy to endpoint”:

Click to enlarge

Then you can select a name and compute resources to use for your Vertex endpoint:

Click to enlarge

Make an online prediction request to the Vertex endpoint

With a BQML model successfully deployed to an endpoint, you can now make online prediction requests. You’ll need to make sure your prediction request is following the correct input format. Here’s an example of what a prediction request (with new test data) as a JSON file might look like:


code_block[StructValue([(u’code’, u'{“instances”: [{“species”: “Adelie Penguin (Pygoscelis adeliae)”, rn “island”: “Dream”, rn “culmen_length_mm”: 36.6, rn “culmen_depth_mm”: 18.4, rn “flipper_length_mm”: 184.0, rn “sex”: “FEMALE”}]}’), (u’language’, u”)])]

Then, you can make an online prediction request (documentation):

code_block[StructValue([(u’code’, u’ENDPOINT_ID=”MY-ENDPOINT-ID”rnPROJECT_ID=”MY-PROJECT-ID”rnINPUT_DATA_FILE=”prediction_request.json”rncurl \rn-X POST \rn-H “Authorization: Bearer $(gcloud auth print-access-token)” \rn-H “Content-Type: application/json” \rn${PROJECT_ID}/locations/us-central1/endpoints/${ENDPOINT_ID}:predict \rn-d “@${INPUT_DATA_FILE}”‘), (u’language’, u”)])]

Note: If you’re using an imported TensorFlow model from BigQuery ML, you will need to use a raw prediction request instead.


With these new integrations between BigQuery ML and Vertex AI Model Registry, you will be able to keep track of your models, version your models, and deploy with greater ease than before. Happy modeling!

Want to learn more?

Learn more about Vertex AI Model Registry

Learn more about BigQuery ML with Vertex AI Model Registry

Learn more about BigQuery ML and try out a tutorial

Learn more about Vertex AI and deploying private endpoints or traffic splitting

Read about using BigQuery and BigQuery ML operators in a Vertex AI Pipeline

Special thanks to Abhinav Khushraj, Henry Tappen, Ivan Nardini, Shana Matthews, Sarah Dugan, Katie O’Leary for their contributions to this blogpost.

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