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Data Management

Structured Data: A Beginner’s Guide For Optimizing & Organizing A Website

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Structured Data: A Beginner’s Guide For Optimizing & Organizing A Website

The concept behind structured data may be confusing, here is a guide for you

If you’ve ever wondered how search engines like Google can come up with precise answers for your queries at a moment’s notice or why certain websites are dominantly featured on the search engine results pages, the answer is structured data. As its name suggests, it refers to sets of structured and organized data on a web page. In terms of search engine optimization, it is tagged with certain text groups that enable search engines to understand the information’s context better to deliver more accurate results for the users.

The concept behind structured web data may be confusing. However, its implementation process is neither as tricky nor complicated as it appears. In fact, there’s a bevy of tools that can make the task more straightforward, such as the Structured Data Mark-up Helper and the various testing software of Google. While it’s undoubtedly possible to do everything by hand, using these tools will enable you to ensure accuracy. As a result, it makes your life much easier.

 

Adding structured data on a website

As brilliant as Google is, it’s unable to understand many things by itself. With the use of the proper markup, you’ll help make your content much more apparent to the search engine, potentially elevating your online property’s visibility and click-through rate. With that said, here’s how to add structured data through the Structured Data Mark-up Tool.

1. Open the tool. The first step is to open the Structured Data Mark-up Tool of Google. It would be best if you made sure that the software’s website tab remains open. Select the data type to which you want your HTML markup to be added. Plug the URL of the webpage and click on the option to start tagging.

2. Highlight the page elements. Once the tool completes loading, there should be a web page that appears to the left while the data items are on the right side. Highlight various components of the webpage so you can assign specific data tags like the data published, author, and name. Once you’ve completed assigning and tagging items, click on the create HTML in the upper-right corner.

3. Add a schema markup. When the next screen loads up, the markup for the structured data should appear. While the tool will automatically produce the script as a JSON-LD markup, it’s possible to change it via the drop-down menu. Copy the HTML markup to the webpage’s source code or CMS if you want to publish it.

4. Test it. Open the testing tool Google and enter the URL of the site you’re looking to test. The tool can be used to fix and diagnose issues. Once it does, all that’s left to do is to wait. Google may take a while to recrawl the new HTML, but so long as you maintain the proper standards of data structure, your website will benefit from it.

 

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Conclusion

Structured data can be incredibly useful, especially when you consider that most search engines continue to evolve and improve the way they present and aggregate information. They offer intelligent and enhanced search experiences with a focus on the users, and with structured data, you’ll be able to keep up the pace.

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Data Management

Synthetic Data In Healthcare Industry

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Synthetic Data In Healthcare Industry

Many AI applications in healthcare involve machine learning and deep learning models

Integrating healthcare data among researchers, universities, and firms developing AI solutions has a variety of advantages. However, due to restrictions such as HIPAA, exchanging patient data safely is a significant barrier in the healthcare business. Synthetic data can assist healthcare researchers in creating shareable data and overcoming these limitations.

 

Improves machine learning model accuracy

Many AI applications in healthcare involve machine learning and deep learning models, like patient data analytics, medical imaging, and medication development. It is critical for successful prediction to feed these algorithms with adequate and reliable training patient data. By extending the training dataset size without breaking data privacy requirements, synthetic data increases machine learning or deep learning model accuracy.

 

Enables prediction of rare diseases

Clinical trials with a small number of patients provide erroneous results. Synthetic data can be used to construct control groups for clinical studies including uncommon or recently found diseases for which there is insufficient existing data, allowing for the diagnosis of rare diseases.

This is analogous to the advantage of synthetic data in supporting ML model accuracy, although it can be more obvious in circumstances where data is scarce.

 

Enables collaboration

Collaboration between medical and pharmaceutical organizations can help doctors identify patients faster and improve medication discovery. Synthetic patient data that mimics the features of real patients can help in collaboration.

 

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Provides reproducibility for medical research

It is critical for scientific development to be able to duplicate the outcomes of a research or experiment. Nevertheless, patient data privacy rules can impede clinical research reproducibility. Clinical researchers can guarantee that their outcomes are reproducible by conducting studies on and sharing synthetic patient databases.

 

Problems with using synthetic data

When employed in healthcare, synthetic data can have drawbacks.

For starters, it isn’t as valuable as real data. The integrity of clinical synthetic data is heavily influenced by the training data and the data synthesis method.   The research team discovered that the experimental group could only match the control team’s results with 70% reliability, which may not be suitable in some instances.

Another issue with synthetic clinical data is the possibility of omitting outliers that would otherwise be included in a real dataset. Data-generation neural networks are terrible in generating unusual-but-possible data sets. Furthermore, outliers are frequently more significant than average data points.

While useful for some applications, the transfer of outliers from an “actual data” training set to a synthetic dataset may raise privacy problems. If a neural network passes outliers in the training sample of patient data into synthetic data, these different data points might possibly be used to identify specific patients.

Furthermore, neural network systems that generate synthetic data are susceptible to cyberattacks and must rely on genuine private data. A hacker who gains access to the data-generating system may be able to reverse engineer confidential information. Although some synthetic data systems use severely restricted access to prevent this type of attack, complete prevention is difficult.

 

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Data Management

Sharing Double Encrypted Data Is The Only Way To Protect Privacy

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Sharing Double Encrypted Data Is The Only Way To Protect Privacy

Let’s see how encryption could help us reap the full benefits of big data, and protect us from data privacy fraud

Fully Homomorphic encryption allows us to run analysis on data without ever seeing the contents. It is a form of encryption that permits users to perform computations on its encrypted data without first decrypting it. And it could help us reap the full benefits of big data, from fighting data privacy fraud to catching diseases early. Big data is a collection of data that is huge in volume, yet growing exponentially with time. One of the doctor’s instruments of choice is no scalpel or stethoscope, it is far more powerful than that. This could be life-saving.

 

Encryption could solve data privacy problems:

The relationships between genetic markers and diseases require an awful lot of data, more than anyone hospital has on its own. Most hospitals could pool their information, but it isn’t so simple. Because putting together a large enough data set is more often than not, the limiting factor. Genetic data contains all sorts of sensitive details about people that could lead to embarrassment, discrimination or worse. Genetic data is personal data relating to inherited or acquired genetic characteristics of a natural person acquired through DNA or RNA analysis.

Microcosm of one of the world’s biggest technological problems. It is the human race or human nature seen as an epitome. The inability to safely share data hampers progress in all kinds of other spheres too, from detecting financial crime to responding to disasters and governing nations effectively. But present new technology of encryption is making out data without anyone ever actually seeing it. This could help end the data privacy problem

Data could do good for all of us too. Data is a currency that helps make the modern world go around. That way, more people can look at it and conduct analysis of data, potentially drawing out unforeseen conclusions. That data has to be kept somewhere, which often means on a cloud storage server, owned by an external company.

There is an obligation to keep data privacy, not just because it is the right thing to do, but because of stringent privacy laws, such as the European Union’s General Data Protection Regulation. Differential privacy is the way of maintaining people’s privacy. Still, differential privacy has its limits. It only provides statistical patterns and can’t flag up specific records.

In 1978, devised a theoretical way of making the equivalent of a secure glovebox to protect data called a homomorphism, it is a mathematical idea. It has the ability to map data from one form to another without changing its underlying structure.

Homomorphic encryption schemes allow to carry out a restricted set of operations, for instance only additions or multiplications. One approach to homomorphic encryption at the time involved an idea called lattice cryptography. This encrypts ordinary numbers by mapping them onto a grid with many more dimensions. A privacy technology start-up is achieving significantly faster speeds by helping customers better structure their data and tailoring tools to their problems.

 

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Artificial Intelligence

What Is A Customer Data Platform And How To Use It?

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What Is A Customer Data Platform And How To Use It?

A Customer Data Platform (CDP) is a piece of AI programming that joins information from various apparatuses.

What is a Customer Data Platform (CDP)?

A Customer Data Platform (CDP) is a piece of AI programming that joins information from various apparatuses to make a solitary concentrated client data set containing information on all touch focuses and associations with your item or administration. That CDP data protection base can then be portioned in an almost unending number of ways, making it more customized showcasing efforts analysis by protecting data.

Understanding what CDP programming does, the most effective way to make sense of this is as a visual demonstration. Say an organization is attempting to get a superior comprehension of their clients. Their CDP would be utilized to gather information from contact platforms like Facebook, the organization’s site, email, and some other spot a client could collaborate with the organization. The CDP will gather those data of interest, combine them into an effectively justifiable brought together client profile, and afterward make that profile usable to different frameworks that could require it — like the Facebook promotions stage.

That cycle permits the organization to utilize division to more readily figure out their crowd and make more customized showcasing efforts. The organization could undoubtedly make a publicizing crowd in light of every individual who has visited a particular page on their site and furthermore the organization’s live talk include. Or on the other hand, they could rapidly fragment and view information on location guests who’ve deserted their trucks.

That is one of the manners in which Drift can make customized showcasing efforts. They utilize Segment’s Personas item to assist with three assignments:

Personality goal – Unifies client history across gadgets and channels into a solitary client view for every client.

Quality and crowd building – Synthesizes information into attributes and crowds for every client, including which clients have shown expectations and how that coordinates into generally speaking record movement.

Actuation – Pushes their client and record-level crowds to various instruments in their stack to coordinate customized, continuous outbound informing.

How to Use a Customer Data Platform?

A large number of CDP vendors on the market can be overwhelming. When choosing a vendor, it can be helpful to consider the list of use cases you hope to accomplish with a help of CDP. While it’s important to have high-level goals (improve the customer experience, foster loyalty), you also need to know how a CDP can help you achieve those goals through lower-level use cases.

1. Online to Offline Connection

Merge online and offline activities in order to create an accurate customer profile. Identify customers from online activities when they enter a brick-and-mortar store.

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2. Customer Segmentation & Personalization

Segment customers according to their behaviour (RFM, LTV prediction) and subsequently deliver a personalized, omnichannel experience throughout the entire customer lifecycle.

3. Predictive Customer Scoring

Enrich your customer profiles with predictive data (probability of purchase, churn, visit, email open).

4. Smart Behavioral Retargeting & Lookalike Advertising

Integration with Facebook Ads, Google Ads, Analytics & Double-click enables you to leverage insights from Exponea to run powerful acquisition & retention (lookalike) campaigns outside of your website.

5. Product Recommendations

Create and use different recommendation models such as “similar products” or “customers also bought” and deliver the best shopping experience to drive engagement, increase brand loyalty, and sell, up-sell, or cross-sell your products or services.

6. Conversion Rate Optimization & A/B Testing

Quickly transform the appearance of your pages. Use our smart website overlays (pop-ups), or send cart abandonment emails to increase your ROI. Create different designs and determine which variant performs better with the automation.

7. Omni-Channel Automation

Guide your customers through their entire lifecycle with personalized and timely messages sent to their preferred channel, significantly enhancing your opportunities to both acquire and keep a loyal customers.

8. Email Deliverability Enhancement

Increase email open rates. Thanks to an AI-powered algorithm, you can determine the ideal distribution time for each user based on their email opening habits, and reach them at this optimal hour.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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