Rifi’s platform, housed in the cloud, empowers data teams to stay ahead of the curve by offering unparalleled visibility into their operations. Sanjay highlights two key features that define Rifi’s offering. Firstly, the platform helps teams manage their budgets effectively, ensuring they don’t exceed allocations for cloud services like Snowflake or BigQuery. This proactive approach to cost management saves money and fosters trust within organizations, as data flows smoothly and reliably.

Secondly, Rifi prioritizes the time of data teams, recognizing that efficiency is crucial for building trust and making informed decisions. Sanjay notes that Rifi has enabled some customers to drastically reduce escalations from data teams to their businesses, a testament to the platform’s ability to streamline operations and increase productivity.

One of Rifi’s standout success stories involves a public company with a $10 billion market cap. Within just three weeks of implementing Rifi’s solution on BigQuery, the company noticed a significant increase in failed jobs, indicating issues with data accessibility. Instead of resorting to the traditional approach of requesting more resources, Rifi’s platform enabled the company to identify the root cause of the problem quickly: certain user and query patterns consuming excessive capacity. By addressing these issues promptly, the company was able to free up nearly a quarter-million dollars worth of capacity, demonstrating the tangible impact of Rifi’s technology on the bottom line.

When asked about Rifi’s approach to innovation, Sanjay emphasizes the company’s commitment to listening to its customers. With clients spanning various industries, including public, healthcare, finance, and startups, Rifi understands the diverse needs and challenges facing data teams today. By staying attuned to customer feedback and continuously iterating on its platform, Rifi ensures that it remains at the forefront of innovation, delivering solutions that meet the dynamic demands of the modern tech landscape.

In conclusion, Rifi’s groundbreaking approach to data observability and rapid issue detection is revolutionizing the way organizations harness and leverage their data. By combining cutting-edge technology with a customer-centric approach, Rifi empowers data teams to navigate the complexities of today’s digital world with confidence and agility.

According to new research by VPN provider SurfShark, the US government makes the most requests for user data from Big Tech companies than any other jurisdiction in the world. The company analyzed data requests to Apple, Google, Meta, and Microsoft by “government agencies of 177 countries between 2013 and 2021.”

The US came in first with 2,451,077 account requests, more than four times the number of Germany, the number two country on the list. In fact, the US made more requests than all of Europe, including the UK, which collectively came in under 2 million.

While the US and EU were responsible for a combined total of 60% of all data requests, the US “made 8 times more requests than the global average (87.9/100k).”

The number of accounts being accessed is also growing, with a five-times increase in requests from 2013 to 2021. The US alone saw a 348% increase during the time frame, and the scope and purpose of the requests are expanding.

“Besides requesting data from technology companies, authorities are now exploring more ways to monitor and tackle crime through online services. For instance, the EU is considering a regulation that would require internet service providers to detect, report, and remove abuse-related content,” says Gabriele Kaveckyte, Privacy Counsel at Surfshark. “On one hand, introducing such new measures could help solve serious criminal cases, but civil society organizations expressed their concerns of encouraging surveillance techniques which may later be used, for example, to track down political rivals.”

The report also sheds light on which companies comply the most versus which ones push back against requests. For all of its privacy-oriented marketing — “what happens on your iPhone stays on your iPhone” — Apple complies with data requests more than any other company, handing it over 82% of the time.

In contrast, Meta complies 72% of the time, and Google does 71% of the time. Microsoft, on the other hand, pushes back the most among Big Tech companies, only handing data over 68% of the time.

The findings may also put a dent in US efforts to ban TikTok and other foreign apps under the guise of protecting user privacy and data.

The CSA is an organization dedicated to helping secure cloud computing. A survey the organization conducted with Netskope found that DLP solutions are a critical component used in cloud security.

Unfortunately, that’s where the good news ends. While companies are relying on DLP systems, nearly a third struggle to use them effectively.

Among the top challenges cited by organizations are management difficulties (29%), too many false positives (19%), the need for manual version upgrades (18%), and deployment complexity (15%).

“DLP solutions are an integral part of organizations’ data security strategy, but leaders are still struggling with this strategy and the implementation of solutions, especially for how complicated legacy and on-prem based solutions are to manage and maintain,” said Naveen Palavalli, Vice President of Products, Netskope. “These findings highlight the need for a comprehensive and easy-to-use cloud delivered data protection solution that integrates into their existing security controls and is a key tenant of their Zero Trust security strategy.”

Cloud security is increasingly in the spotlight as more and more organizations experience data breaches at a time when the cloud is becoming integral to more companies and industries.

The Biden administration has signaled it is preparing to regulate cloud security in an effort to better protect organizations. If the CSA’s findings are any indication, it looks like the industry could use the help.

Python is one of the most popular languages for data processing and analytics, thanks to its ease of use, versatility, and powerful features. Microsoft is now giving users the ability to leverage that power with a Public Preview of Python in Excel, according to a company blog post:

Now you can do advanced data analysis in the familiar Excel environment by accessing Python directly from the Excel ribbon. No set up or installation is required. Using Excel’s built-in connectors and Power Query, you can easily bring external data into Python in Excel workflows.

We’re partnering with Anaconda, a leading enterprise grade Python repository used by tens of millions of data practitioners worldwide. Python in Excel leverages Anaconda Distribution for Python running in Azure, which includes the most popular Python libraries such as pandas for data manipulation, statsmodels for advanced statistical modeling, and Matplotlib and seaborn for data visualization.

The feature is already gaining fans among customers.

“The ability to run Python in Excel simplifies McKinney’s reporting workflows. We used to manipulate data structures, filter, and aggregate data in a Jupyter Notebook, and build visuals in Excel,” said Greg Barnes, McKinney Executive Director of Data and Analytics.  “Now we can manage the entire workflow in Excel. This is going to make Excel that much more powerful and make Python more accessible across the organization. Python support is the most exciting update for Excel in my career!”

When Salesforce introduced Genie, it touted the platform’s ability to process customer data and provide insights in real-time:

“When milliseconds matter most, your healthcare provider can deliver proactive guidance and care recommendations with access to your real-time patient data,” the company wrote shortly after its release.

Unfortunately, according to Wirth’s lawsuit, “it was all a lie.” The lawsuit alleges that much of Genie’s supposedly “real-time” functionality didn’t live up to the hype and “in fact many of its processes took several hours.”

According to Business Insider, Wirth raised concerns about the platform’s performance, which he says were ignored.

“Plaintiff reasonably believed that publicly claiming the CDP operated in ‘real-time’ without actually having (or even intending to have in the near future) such a capability would be fraudulent, and likely violate numerous provisions of Federal law relating to fraud against shareholders, as well as rules and regulations of the Securities and Exchange Commission,” the lawsuit stated.

Wirth’s lawsuit claims that Lidiane Jones, now Slack CEO, engaged in a “deceitful campaign to diminish” Wirth’s reputation within the company, in response to his concerns. When he took his concerns to CTO Parker Harris, Wirth was fired within hours.

With’s lawsuit alleges “whistleblower retaliation” and seeks monetary damages.

Microsoft is rolling out ChatGPT across multiple products and has no objection to its own employees using the tech. However, the company wants to make sure no sensitive information is shared with the AI.

“Please don’t send sensitive data to an OpenAI endpoint, as they may use it for training future models,” a senior engineer wrote in an internal post that was reviewed by Business Insider.

The memo demonstrates one of the biggest challenges moving forward with large language model AIs, namely controlling what information it has access to, and how that information will be used if it is shared.

ChatGPT is a conversational AI that learns from its interactions and what people type into it. As such, it’s not surprising that Microsoft wants to make sure no sensitive information is shared with it, since the AI could then end up using that information in its responses to users.

“Human beings sign NDAs and consequently have incentives to be careful in how they share information. But large language models such as ChatGPT do not have the ability to reason about such issues, at least by default,” Vincent Conitzer, Carnegie Mellon University computer science professor and director of its AI lab, told Insider.

Microsoft’s caution is one other companies would do well to imitate.

Google is calling the new service “Looker (Google Cloud core)” — no one ever accused engineers of being good at naming things — and comes in Standard and Enterprise editions, as well as a dedicated Embed edition.

Integrating Looker into Google Cloud console brings a number of benefits, according to the company:

Looker (Google Cloud core) offers organizations a fresh, consistent real-time view of their business data, and extends the benefits that a commissioned study by Forrester Consulting on behalf of Google – “The Total Economic Impact Of Google BigQuery and Looker” (April 2023) says leads to an ROI of greater than 200%, while bringing the offering closer to Google Cloud’s array of leading products. This new offering builds upon the semantic modeling and data exploration capabilities Looker has been known for over the last decade and adds expanded security options, Google Cloud integrations, and instance management features.

Being part of Google Cloud console also means that businesses can test drive Google’s business intelligence solutions at no cost for 30 days.

Microsoft Azure is the second-largest cloud computing platform behind AWS. Microsoft clearly wants to improve its data center offerings, and sees Fungible as a way to achieve that.

“Fungible’s technologies help enable high-performance, scalable, disaggregated, scaled-out datacenter infrastructure with reliability and security,” writes Girish Bablani, Corporate Vice President, Azure Core.

“The Fungible team will join Microsoft’s datacenter infrastructure engineering teams and will focus on delivering multiple DPU solutions, network innovation and hardware systems advancements.”

Microsoft sees Fungible as a long-term investment that will help it differentiate its offerings.

“Today’s announcement further signals Microsoft’s commitment to long-term differentiated investments in our datacenter infrastructure, which enhances our broad range of technologies and offerings including offloading, improving latency, increasing datacenter server density, optimizing energy efficiency and reducing costs,” Bablani adds.

No financial terms of the acquisition were revealed.

Facebook and Meta have come under growing criticism for the impact they have on young ones. Unfortunately, young people represent an important market, meaning that Facebook and other social media companies are strongly incentivized to profit from young people’s activities.

The FTC wants to put a stop to it, and is proposing new changes to the 2020 privacy order between the agency and Facebook.

“Facebook has repeatedly violated its privacy promises,” said Samuel Levine, Director of the FTC’s Bureau of Consumer Protection. “The company’s recklessness has put young users at risk, and Facebook needs to answer for its failures.”

The agency’s new proposals would prohibit Facebook from monetizing youth data in any way:

As part of the proposed changes, Meta, which changed its name from Facebook in October 2021, would be prohibited from profiting from data it collects, including through its virtual reality products, from users under the age of 18. It would also be subject to other expanded limitations, including in its use of facial recognition technology, and required to provide additional protections for users.

The FTC outlined five changes to the 2020 order, changes that would impact all of Facebook’s services:

• Blanket prohibition against monetizing data of children and teens under 18: Meta and all its related entities would be restricted in how they use the data they collect from children and teens. The company could only collect and use such data to provide the services or for security purposes, and would be prohibited from monetizing this data or otherwise using it for commercial gain even after those users turn 18.
• Pause on the launch of new products, services: The company would be prohibited from releasing new or modified products, services, or features without written confirmation from the assessor that its privacy program is in full compliance with the order’s requirements and presents no material gaps or weaknesses.
• Extension of compliance to merged companies: Meta would be required to ensure compliance with the FTC order for any companies it acquires or merges with, and to honor those companies’ prior privacy commitments.
• Limits on future uses of facial recognition technology: Meta would be required to disclose and obtain users’ affirmative consent for any future uses of facial recognition technology. The change would expand the limits on the use of facial recognition technology included in the 2020 order.
• Strengthening existing requirements: Some privacy program provisions in the 2020 order would be strengthened, such as those related to privacy review, third-party monitoring, data inventory and access controls, and employee training. Meta’s reporting obligations also would be expanded to include its own violations of its commitments.

Meta has 30 days to respond to the FTC’s proposals.

TPSC is a YouTube channel dedicated to cybersecurity and privacy. The channel took a brand-new laptop that had never been used and used Wireshark to monitor the computer’s traffic, starting from the moment it was booted up.

Unsurprisingly, the computer immediately connected to a number of Microsoft services, including Bing, MSN, and the Windows Update service. While it’s not surprising a Windows machine would connect to Microsoft, it is surprising that the Bing traffic was happening without the web browser ever being opened or used.

Even more surprising, Windows 11 also connected to McAfee, Steam, and Comscore’s ScorecardResearch.com, to name just a few. The last one is particularly alarming, as it is an ad-tech company. In fact, when TPSC first tried going to the website to see what ScorecardResearch.com was, the channel’s browser adblocker would not even load the page since it is a known ad and tracking domain.

To make matters worse, Microsoft connects and sends data to these servers without expressly asking the user’s permission. Instead, the company relies on a vague clause in the Microsoft License Terms to constitute permission.

Privacy; Consent to Use of Data. Your privacy is important to us. Some of the software features send or receive information when using those features. Many of these features can be switched off in the user interface, or you can choose not to use them. By accepting this agreement and using the software you agree that Microsoft may collect, use, and disclose the information as described in the Microsoft Privacy Statement (aka.ms/privacy), and as may be described in the user interface associated with the software features.

Tom’s Hardware reached out to Microsoft and was given the following statement:

“As with any modern operating system, users can expect to see data flowing to help them remain secure, up to date, and keep the system working as anticipated,” a Microsoft spokesperson said. “We are committed to transparency and regularly publish information about the data we collect to empower customers to be more informed about their privacy.”

A legitimate case can be made for Windows 11 connecting to Microsoft services, but there is absolutely no valid justification for connecting to and sending telemetry to an ad-tech company.

Interestingly, TPSC ran the same test with Windows XP and found that it only connected to Microsoft update servers, greatly undermining Microsoft’s claim that Windows 11’s connections to third parties were necessary to “remain secure, up to date, and keep the system working as anticipated.”

As we have stated at WPN many times, there is NO EXCUSE for a company that charges handsomely for a product to then turn around and try to monetize its customers’ data, let alone try to do so without express and explicit permission. And no, a couple of sentences buried in a long, legalese licensing document that few people will ever read does not count as express and explicit permission.

Microsoft should be ashamed of itself for this behavior, and one can only hope this revelation will put the companies in the crosshairs of the EU’s GDPR.

In the meantime, TPSC’s question, “Has Windows become spyware?” is one that deserves an answer.

Nate Silver gained fame for creating FiveThirtyEight, a political analysis website that attained a degree of success predicting political outcomes. The site eventually branched out into sports, using its analytics to predict winners and losers, being acquired by ESPN in 2013. The site was later transferred to ABC.

According to a tweet by Silver, he doesn’t expect to remain at the company once his contract expires this summer.

Layoffs hit FiveThirtyEight particularly hard today, with deputy managing editor Chadwick Matlin, business ops manager Vanessa Diaz, senior audience editor Meena Ganesan, senior science reporter Maggie Koerth, senior designer Emily Scherer, and sports editor Neil Paine all tweeting news of their layoffs.

While certainly convenient, the plethora of mobile apps many people use can be a major security risk. The more apps a person uses, the more their personal data is scattered across various platforms and services, opening additional attack vectors if those services are compromised.

Google is working to limit the threat with a new data retention policy:

Google Play has launched a number of recent initiatives to help developers build consumer trust by showcasing their apps’ privacy and security practices in a way that is simple and easy to understand. Today, we’re building on this work with a new data deletion policy that aims to empower users with greater clarity and control over their in-app data.

For apps that enable app account creation, developers will soon need to provide an option to initiate account and data deletion from within the app and online. This web requirement, which you will link in your Data safety form, is especially important so that a user can request account and data deletion without having to reinstall an app.

Developers have until December 7 to begin answering questions in their app’s Data Safety section. Developers that need more time can apply for an extension, giving them until May 31, 2024 to comply.

Google’s decision is good news for users and will hopefully give them more control over their data.

Cybersecurity firm Mitiga analyzed Google Cloud’s online storage and found that the platform’s logging mechanism comes up woefully short in terms of providing useful information. This is especially concerning since these logs are used by security professionals and law enforcement to identify the scope of a potential breach.

According to Mitiga, Google’s current logging system cannot effectively differentiate between a threat actor viewing data versus exfiltrating it:

Even with the detailed logging constraint applied, Google logs events of reading Metadata of an object in a bucket the same way it logs events of downloading the exact same object. This lack of coverage means that when a threat actor downloads your data or, even worse, exfiltrates it to an external bucket, the only logs you would see will be the same as if the TA just viewed the metadata of the object.

While this issue doesn’t inherently make Google Cloud any more insecure than the next cloud provider, it does mean that customers impacted by a data breach on Google Cloud may have a much harder time taking the appropriate investigative action.

Mitiga reached out to Google Cloud and received the following response:

“The Mitiga blog highlights how Google’s Cloud Storage logging can be improved upon for forensics analysis in an exfiltration scenario with multiple organizations. We appreciate Mitiga’s feedback, and although we don’t consider it a vulnerability, have provided mitigation recommendations.”

Moreover, ELT and ETL processes are quite affordable for every interested company or institution. So, as you may have already guessed, we are going to discuss ETL data modeling practices today. We believe that this will have a positive impact on your business growth.

First of all, you need to know the differences and similarities between ETL and ELT. What’s more, it is recommended to understand the most used words on this topic, like data lakes and data warehouses. Let’s start! 

How do ELT and ETL processes work? 

The ELT (extract, load, transform) is the process in which the data is modified after having been transferred to the receiving database, without making any changes. The first stage of this operation is to extract the data. The maneuver of loading the data to the database it receives is the intermediate stage where the ELT admits that the target system executes the respective transformations.

For this reason, we recommend you apply ELT and ETL processes while working with large amounts of data. In addition, unstructured data, the source, and the target database use the same technology when the amount of converted data is massive. 

The transformations made on the data are improved by the database that receives them. Usually, it can be NoSQL databases or Hadoop clusters. As we can see, in the ETL process, the data circulates from the origin to its final destination. So, the whole responsibility for the success lies in the ETL and the chosen database technology. 

What is more, ELT and ETL processes are responsible for:

• mobilizing large amounts of data;

• integrating them;

• introducing them into a common site. 

What are the main differences between ETL and ELT?

ETL and ELT are very similar. Yet, the most obvious difference is in the order in which the ETL and ELT processes in Big data execute the various required operations. These methods are best handled in different situations.

So, here are to your attention some of the most crucial differences between ETL and ELT:  

1. ETL is the process of extracting, transforming, and loading data. 

2. ELT is the process of extracting, loading, and transforming data. 

3. Within ETL, data is moved from a data source to an intermediate data store. 

4. ELT uses a data warehouse to perform basic transformations. There is no need for data staging. 

5. ETL can help ensure privacy and compliance by cleaning sensitive and secure data before it is loaded into the data warehouse.

6. ETL can perform complex data transformations and can be more cost-effective than ELT. 

The difference between ETL and ELT can be explained quite easily. However, realizing the whole picture and the cool benefits of ETL over ELT requires some time. Plus, a deeper understanding of how ETL works with data warehouses and how ELT works with data lakes is also necessary.

The result of using both ETL and ELT 

Organizations, companies, and institutions always need to make the most of the advantages of these two computer methodologies. They use ELT and ETL processes in Big Data, where ELT is responsible for quick introductions of unstructured data. As for ETL, it is helpful to make them more flexible and secure. 

For this reason, the vision has expanded towards ETLT, which executes the following steps that you need to know:

• Extraction. Data from the different sources are collected and transferred to the development area for preparation.

• Loading. At this stage, data is uploaded to the Data Warehouses.

• Transformation. It is the last stage, but not the least. It is where operations are executed to transform and integrate data from various sources.

This result of the operations carried out on the existing data. It allows adjusting the times and technologies used to improve the amount of work. Therefore, the first group of changes is faster and more effective in providing the data with the necessary preparation and greater security.

Can the ETLT bring any benefits to the ETL and ELT processes?

The ETLT in Big Data provides us with the advantages of both – ETL and ELT processes. In this way, it manages to lighten the introduction of data. At the same time, the ETLT provides the security and quality required in modern companies, organizations, and institutions that use such technologies.

ETLT processes are commonly used when it is necessary to filter, anonymize or mask data for regulatory reasons, before capturing it in the data warehouse.

How to choose the best solution 

As we have already mentioned, the ELT and ETL process in Big Data have become fundamental and essential for numerous organizations. The ETL can brag about many years of popularity. As well, it has a sufficiently high maturity and flexibility. This is really impressive, as it was not designed to work correctly with structured data and relational databases.

As for ELT, it was created to execute activities with NoSQL solutions. For this reason, the difficulty of the operations it performs is less. Still, the size of the data it can process is greater than those processed by ETL.

In this order of ideas in the execution of the two processes, it is possible to observe the accuracy, in terms of the type and structure of the data in the ETL processes. This hinders future transformations. At the same time, in ELT processes it is normal to make movements of unstructured and structured data with equal procedures.

In summary, the data lake is unlimited, but you should have deep knowledge and comprehensive documentation about the ETL and ELT processes. This will help you achieve the required transformations and maximum quality for the use of the stored data. 

Luckily, you can choose an option to work with professional IT services, like Visual Flow, to save your time and energy. This way, your team, and customers will get a better experience with data analytics. Moreover, obtaining a data warehouse is a reasonable investment that can raise your profits.

Twitter has engaged in a number of layoffs since Musk bought the company, as the new CEO has worked to slash costs in the interest of profitability. One of the major challenges the company has faced is the loss of advertisers over some of Musk’s more controversial decisions.

Given Musk’s need to gain and keep new advertisers, one would think that ad engineers would be the one jobs safe from layoffs. Unfortunately, according to The Information, that is not the case, with some 40 ad engineers and data scientists being laid off.

The layoffs targeted areas that Twitter’s leadership considers to be failing, such as the ads product, and unimportant, such as data science, the person said. Twitter’s ad revenue has reportedly plunged in recent months, as advertisers respond to Elon Musk’s loosening of rules around content moderation and the general chaos as Musk shakes up the company.

According to BleepingComputer, source code for Toyota’s T-Connect software was left online for roughly five years. T-Connect allows users to connect their smartphone with their cars. The feature integrates phone calls, navigation, notifications, music, and vehicles status information.

Unfortunately, the source code also contained an access key to the server storing customer data, including both email addresses and management numbers. Fortunately, Toyota says customer names, phone numbers, and credit card information were not stored in the same database and remain secure.

The company also claims there is no evidence anyone accessed the data that was stored in the compromised server, but cannot be sure.

“As a result of an investigation by security experts, although we cannot confirm access by a third party based on the access history of the data server where the customer’s email address and customer management number are stored, at the same time, we cannot completely deny it,” explains the company, machine translated by BleepingComputer.

SAS Viya is a powerful analytics platform but was previously not available as a cloud-based, pay-as-you-go service. The announcement from SAS is a significant departure for the company, which has a long history that is firmly entrenched in the enterprise as it expands its cloud-based offerings.

“Our commitment to cloud and AI innovation is critical to our customers’ success – whether they are building simple reports or developing advanced AI solutions to answer critical business questions,” said Bryan Harris, Executive Vice President and Chief Technology Officer at SAS. “Making SAS Viya available to entire analytics organizations is our goal, regardless of skill level, team or preferred programming language. We want Viya to be the foundation of our customers’ innovation stories.”

SAS customers will be able to leverage the power of the company’s analytics innovations with SAS Viya on Microsoft Azure, including SAS Visual Analytics, SAS Visual Statistics, SAS Visual Data Mining and Machine Learning, and SAS Model Manager.

“We’re excited to offer customers a new way to gain access to SAS Viya while continuing to provide all the powerful capabilities our users already know and love,” SAS CEO Jim Goodnight said. “We’ve spent decades building the most comprehensive suite of analytics on the market, with next-generation AI that addresses the complete analytics life cycle. We design and test our software to be the fastest and most productive. And now we’re making it all available with a few clicks in the Microsoft Azure Marketplace.”

Analysts believe the move is a major win for SAS and its customers.

“In this strategic move, SAS makes access to its analytics easier,” said Dan Vesset, Group Vice President, Analytics and Information Management at IDC. “SAS Viya is one of the most comprehensive analytics platforms on the market today. Presenting users with the complete analytics life cycle – from data to a deployed and managed model – available for immediate use and deployment via the Microsoft Azure Marketplace, is key. And offering it with built-in support and training is expected to bring significant productivity benefits to analytics, IT and business groups around the world.”

Customers can try SAS Viya on Microsoft Azure here.

Uber acknowledged Thursday it was investigating reports of a data breach after a hacker posted a message on the company’s Slack channel saying they had hacked the company. Screenshots of the breach were shared on Twitter:

The company now says no “sensitive user data” was accessed and that its systems are coming back online:

While our investigation and response efforts are ongoing, here is a further update on yesterday’s incident:

– We have no evidence that the incident involved access to sensitive user data (like trip history).

– All of our services including Uber, Uber Eats, Uber Freight, and the Uber Driver app are operational.

– As we shared yesterday, we have notified law enforcement.

– Internal software tools that we took down as a precaution yesterday are coming back online this morning.

Medical data analysis — what is it?

Information about each patient and the population of the entire country helps not only to extend the life of a person and improve its quality, but also to improve the results of treatment through improved procedures, reducing the volume of medical waste.

Medical analytics has the capacity for reducing the cost of treatment, predicting outbreaks of epidemics, early screening of certain diseases, improving the quality of life in general, and introducing modern methods of treatment into practice. Medical staff are collecting huge amounts of data today, and they need the tools to use these numbers.

How important is the analysis of medical data?

Methods and application of machine learning make it possible to analyze huge amounts of information about the immune status of a particular person.

Using the data you can:

• plan medical care for people and predict the course of diseases;
• identify and implement the most effective measures decreasing the number of hospital readmissions;
• reduce the risk of blood poisoning and kidney failure, intervene at an early stage avoiding negative consequences;
• optimize outcome management and costs of medicines;
• develop tools to improve the quality of patient care.

Personalized medicine is focused on treatment decisions based on all information about the patient. To do this, more and more data will need to be processed in the future. For example, each person’s “genetic blueprint”, DNA, will need to be checked for genetic changes.

Benefits of data analytics in medicine

Technological development makes it possible to process small and large amounts of data, to study rare diseases. This is the exclusivity and originality of data analysis.

The development of the necessary technologies helps to implement the results of analyses in the work of a particular doctor and patient. The doctor receives a computer program where the data of his patients are collected. He can see on the monitor the values ​​of medical indicators of patients from his past practice, which are closest to indicators of his new patient being studied at the moment. It allows identifying similar cases and optimizing the treatment regimen.

Analyzing information about how a patient adheres to the doctor’s instructions after discharge from the hospital will help the medical institution to predict the hospital readmission within several months and take appropriate measures.

The study of the patient’s condition data can improve his treatment

Data science plays a key role in monitoring patient health and informing physicians of options to prevent potential problems. Specialists use powerful predictive tools for early detection of chronic and systemic diseases.

Data processing algorithms also help to model exactly how medicines will act on the human body. It allows companies to reduce laboratory experiments, costs, and develop innovative medicines for the treatment of serious diseases.

It is important to take into account the specific challenges in the healthcare system as it involves the collection and analysis of sensitive patient data. It is also very important to understand that the value of digital infrastructures is in the intelligent, controlled use of data for the benefit of the individual and society in general.

In California, for instance, when entrepreneurs submit documents for filing with the California Secretary of State, they are automatically creating a public record. Accordingly, all of the documentation and information that is provided for filing is therefore made available to the general public for copying and viewing. Internet search engines and third-party websites will also be able to access and use the information as they wish. Details such as the name of the person filing as well as any mailing and street addresses, phone numbers, or email addresses that are provided in public filings, will be made available.

The California Constitution, as well as the California Public Records Act mandate that the Secretary of State provides public access to its records for public viewing and copying. The Golden State has advised that “Corporations and limited liability companies can update address information on the Secretary of State’s records by filing a Statement of Information. For other types of filings, updated address information for filings with the Secretary of State can be submitted through a subsequent amendment or amended filing.”

How to Protect Personal Information When Starting a Business

A California registered agent, also referred to as an agent for service of process, is a federal law requirement for every formal business entity in the state of California. This individual or business entity will receive all official paperwork from the California Secretary of State. They will also receive any service of process that a business may be served with— in the instance of a legal dispute.

Many business owners, especially single-member LLCs, tend to choose to act as their own registered agents. While this is legally allowed, entrepreneurs put themselves at risk of having all of their private information– home address, personal contact details– made public on the California Business Entities Search. A way around this is by hiring a registered agent who will accept legal correspondence on behalf of the company, hence protecting the location of the owner’s home or private business. 

Benefits of a Registered Agents Service

Apart from just superior privacy, a California registered agent service also provides business owners with flexibility since registered agents are required to be available every working day of the year, at all normal business hours. Outsourcing the service allows entrepreneurs to be more flexible in the hours and locations that they work. 

They also allow for discretion in sensitive matters since receiving service of process for a lawsuit at the actual business premises should not happen in front of employees and clients. Registered agents help to avoid this issue since they will receive any service of process and other documents at their personal office; they can then discreetly have them delivered via mail or online.

Registered agents are required to be a resident or have permission to operate in a state, which can be an issue when a company looks to expand its operations to other states. Since most registered agent services have permission to operate in all 50 states across the United States, entrepreneurs will not have to go through the hassle of the additional paperwork and procedures when expanding.

It is vital that businesses remember to file their required paperwork timeously to avoid fines and keep the company in good standing with the state– which is important in maintaining the limited liability protection of the business. A registered agent service will help the business with the filing of important paperwork and will maintain a compliance calendar and remind the business when annual reports or any other filings are due.

Final Thoughts

Privacy is an important aspect to keep in mind when starting up a new business, especially for entrepreneurs that run their business from a private residence or office space. While most states make the businesses filing information public, owners are able to protect their personal information by using a registered agent service that will handle all of the correspondence on behalf of the business.