Simple Tips to know about SAAS-CLOUD

The Data Privacy Statement is a crucial document that sets out how businesses use the private data of EU citizens.


The European Parliament and the European Council have developed the General Data Protection Regulation (GDPR), a legislation aimed at protecting and securing data rights for the citizens of the European Union (EU). The GDPR applies to companies that carry out business transactions with European Union citizens. The companies’ mobile phones, their desktop applications, and websites are among the prime objects that are governed by this regulation.

The GDPR was completely reenacted, replacing the earlier law on data protection, the Data Protection Directive, on May 25, 2018. The new data protection law, the GDPR, is the extant law on this subject, replacing the Directive that was in force for about two decades.

Computer Network

It is mandatory for companies that collect or process data of EU citizens, to inform them how these personal data is collected, used, shared, secured and processed. This is the soul of the new regulation. Any company that deals with information pertaining to what is described as personal data by the EU has to show compliance with this regulation. The cost of non-compliance is exorbitant: it can attract fines of up to €20 million, or up to one-twenty fifths of the company’s total annual revenues, depending on which of the two is higher.

It is to help companies understand the core aspects of data privacy relating to 21 CFR Part 11 and SaaS-Cloud, that Compliance4All, a leading provider of professional training for all the areas of regulatory compliance, is organizing a webinar. The aim of this 90-minute learning session, which will be organized on April 11, is to help participants understand how to write a Data Privacy Statement for compliance with the GDPR regulation. The Data Privacy Statement is a crucial document that sets out how businesses use the private data of EU citizens.

To gain knowledge of how to craft this extremely vital document, the appropriateness or lack of which can be the difference between compliance and penalties, please log on to to register.

The expert at this webinar is David Nettleton, an FDA Compliance Specialist for 21 CFR Part 11, HIPAA, and Computer System Validation. David will describe just what companies need to do to be compliant with Part 11 and the European equivalent Annex 11 for local, SaaS/Cloud hosted applications. He will show the proper ways of writing a Data Privacy Statement that meets the compliance requirements set out by the GDPR.

He will explain this through an explanation of all the four primary compliance areas that this law applies to:

  • SOPs
  • Software features
  • Infrastructure qualification, and
  • Validation

The aim of this learning is to show to participants the right manner of using electronic records and signatures. Doing it in the right manner goes a long way in helping to increase productivity and in ensuring compliance.

These are the core objectives that the learning from this webinar will impart:

  • Which data and systems are subject to Part 11 and Annex 11
  • How to write a Data Privacy Statement
  • What the regulations mean, not just what they say
  • Avoid 483 and Warning Letters
  • Requirements for local, SaaS, and cloud hosting
  • Understand the current industry standard software features for security, data transfer, audit trails, and electronic signatures
  • How to use electronic signatures, ensure data integrity, and protect intellectual property
  • SOPs required for the IT infrastructure
  • Product features to look for when purchasing COTS software
  • Reduce validation resources by using easy to understand fill-in-the-blank validation documents.

This webinar on simple tips to know about SaaS-Cloud: Data Integrity Compliance with 21 CFR Part 11, SaaS-Cloud, and EU GDPR is suited for GMP, GCP, GLP, Regulatory Professionals, QA/QC, IT, Auditors, Managers and Directors, Software Vendors and Hosting Providers.


About the speaker: David Nettleton specializes in performing gap analysis, remediation plans, SOP development, vendor audits, training, and project management. He has completed more than 185 mission critical software validation projects.

His latest book, “Risk Based Software Validation – Ten easy Steps”, relates to the development, purchase, installation, operation and maintenance of computerized systems used in regulated applications.

3-Hour Boot Camp for the Detection of Microbial Pathogens in Foods and Feeds

The session then describes how to confirm that results obtained by commercially-available kit, are comparable to or exceed those obtained using the reference method.

All methodologies described in this presentation are also used by FDA labs. FDA applies them to education, inspections, data collections, standard setting, investigation of outbreaks and enforcement actions.

This presentation uses the latest FDA thinking and guidance documents to assist you in re-establishing those requirements that need to be fulfilled in the evaluation for microbial methods used in your testing laboratories. It also re-establishes performance evaluation (verification & validation) criteria, necessary for the use of commercially-available diagnostic test kits and platforms.

The presentation further describes evaluation criteria for methods to detect, identify and quantify all microbial analytes that may now be, or have the potential to be associated with foods and feeds, i.e. any microbiological organism of interest (target organism) or the genetic material i.e. DNA, RNA, toxins, antigens or any other product of these organisms.

Session #: 1
Duration: 1 hour
Learning Objectives: This section sets the context for the overall presentation and then provides validation criteria and guidance for all FVM-developed or any existing method(s) that has been significantly modified.

  • Purpose & Scope
  • Administrative Authority & Responsibilities
  • General Responsibilities of the Originating Laboratory
  • Method Validation Definition
  • Applicability
  • Requirements

Criteria and Guidance for the Validation of FDA-Related Methods

  • Validation Definitions
    • The Reference Method
    • The Alternate Method
    • The Originating Laboratory
    • The Collaborating Laboratory
  • The Method Validation Process
    • Emergency Use
    • Non-Emergency Use
  • Validation Criteria
    • Validation Criteria for Qualitative Methods to Detect Conventional Microbial Food-borne Pathogens
    • Validation Criteria for Identification Methods
    • Validation Criteria for Quantifiable Methods to Detect
    • Conventional Microbial Food-borne Pathogens
  • Method Validation Operational Aspects
    • General Considerations
    • Assessment of Validation Results

Session #: 2
Duration: 1 hour
Learning Objectives: This session describes guidelines intended to support method validation efforts for developers of molecular-based assays e.g. PCR, to be used to confirm the identity of exclusion of isolated colonies. Methodologies from this session can be used for either conventional or real time PCR assays.

The session then describes how to confirm that results obtained by commercially-available kit, are comparable to or exceed those obtained using the reference method.

Criteria and Guidance for the Validation of FDA-related molecular Based Assays

  • Inclusivity & Exclusivity
  • Target Genes & Controls
  • Comparison to the Reference Method

Criteria and Guidance for the Validation and Verification of Commercially Availbale Microbiological Diagnostic Kits and Platforms

  • Definitions
    • Validation of an Alternative Method
    • Verification
  • Criteria
    • Commercially-available Microbiological Diagnostic Kits Whose Performance Parameters Have been Fully Validated in a Multi- Laboratory Collaborative Study Monitored and Evaluated by an Independent Accrediting Body e.g. AOAC-OMA, AFNOR, etc.
    • Commercially-available Microbiological Diagnostic Kits Whose Performance Parameters are Supported by Data Obtained Through an Independent Laboratory Validation Protocol and Evaluated by an Independent Accrediting Body e.g. AOAC-RI

The next big disrupter, conversational Artificial Intelligence {Voice search}

The digital assistants then engage directly with their corresponding search engine to tap into the knowledge graph as well as their specific knowledge repository to provide a response and an answer.

Within the search marketing space, there has been a lot of talk about voice search. Many are projecting voice search as the next big thing — in fact, as the next marketplace disruptor.

But the truth is, voice search probably isn’t going to be the next big thing. Yes, voice search is disrupting text-based searches, and this is causing a few raised eyebrows. However, voice is only a small part of the disruption that’s happening today.

I agree with the dissenting points of view that voice search isn’t the next big disrupter; because I believe that conversational AI is.

Conversational AI is what’s really disrupting and shifting the consumer behavior, and voice search is just a component of that bigger picture.

There, I said it.

Now, let’s talk about it. It’s hard to distinguish between voice search and voice-assisted engagements through digital assistance (aka conversational AI). So, my intention here is to outline the differences between these two entities and explain what you, as marketers, need to do to take advantage of both.

Voice search vs. conversational AI

When you think about voice search, it’s actually not that revolutionary. The AI-based technology of natural language processing that enables voice search is pretty awesome and amazing; however, voice search is just a mode in which people are engaging with search engines.

There are three ways that people can engage with the search engines. They can engage through typing or text, their voice or conversation, and through images. Voice search essentially involves doing a query using voice instead of text. That’s the only difference.

When we think about the difference between voice search and conversational AI (the voice assistance component) what’s important to recognize is that searches are continuously happening. It’s just how people are conducting the search that’s shifting and disrupting the marketplace.

Voice assistance is using your voice to engage with some sort of intelligent technology — like a digital assistant, a chatbot, or potentially even a voice skill — to ask a question and find an answer or to control other technology and the IoT.

Here’s the big differentiator: Instead of using Google, Bing, Yahoo, etc. directly, we are now asking questions of, and talking with, third parties like Alexa, Cortana, Google Assistant, Siri, and the like.

Those third parties are typically digital assistants that engage with our voice. So, nowadays, I say, “Hey, Siri”,“Hey, Cortana”, “Okay, Google” or “Hey, Alexa,” whenever I have a question or something I want information about.

The digital assistants then engage directly with their corresponding search engine to tap into the knowledge graph as well as their specific knowledge repository to provide a response and an answer. Search is the intelligence platform powering intelligent agents.

That’s conversational AI, and it’s changing the way people engage with search.

Say goodbye to the age of touch as the primary interface

What we are seeing with this, in terms of voice assistance, is a shift in how people engage, where the search results are coming from, and how that response is derived. As marketers, we are used to developing programs and marketing plans in an era where touch and screens are the primary user interfaces between consumers and devices.

“The age of touch as the primary user interface between consumers and devices is being disrupted. We’re entering the age of conversational interfaces that are powered by our voice and gestures.” — Me.

We’re entering the age of conversational interfaces powered by our voice, sometimes even our gestures if there’s an AR/VR technology component in place, and it doesn’t even have to involve a screen. Increasingly, these devices do have screens, but their job mostly involves listening and delivering a spoken response.

And as marketers, we have a real opportunity on the horizon.

Voice search — It’s all about position zero and owning your graph

When you type a query into a search engine, hundreds of options pop up. It’s different with voice. When people engage in a voice search using a digital assistant, roughly 40 percent of the spoken responses today (and some say as many as 80%) are derived from “featured snippet” within the search results.

In search speak, that’s position zero. When you are that featured snippet in an organic search, that’s what the assistant is going to default to as the spoken response. Siri, Google, Cortana and Alexa don’t respond with the other ten things that are a possibility on that search page. Just the one.

When you consider this, it’s clear why position zero is becoming really important, because, while you might be number two in the text-based searches, you’re getting little to no traffic if people are engaging with intelligent agents and listening to the spoken response.

The opportunity here is to become that position zero, so you can win the search and win the traffic. But how? It goes back to the best practices of organic search, basic SEO, and having a solid strategy.

It’s embracing schema markup and structured data within your website, so you are providing search engines with signals and insights to be included in the knowledge graph. It’s claiming your business listings so that the data is up-to-date and correct. It’s understanding the questions people are asking and incorporating that question and conversational tone into your content.

Simply put: It’s understanding the language your customers are using so that you can provide value and answers in their own words and phrases. So, let’s conclude with that.

Say goodbye to the age of touch

Why You Must Keep Carrying Out Risk Management Throughout Your Business [Lifetime]

This places risk management right up there at the very top as among the most crucial elements of a business.

What makes risk management critical? Well, business that comes with no risk is no business at all. It is like the side effect of a medicine. It is impossible to think of the medication without the side effects. Risk being inhered into a business and intricately woven into it; it is necessary to understand why you must keep carrying out risk management throughout your business’ lifetime. This places risk management right up there at the very top as among the most crucial elements of a business.

Management and business experts have propounded the theory that risk can only be managed or contained, and never eliminated. This is the extent to which risk is bound to business. The enormity of risk management can be gauged from the fact that we only hear a term such as risk management, and seldom risk elimination. This indicates the inescapability of risk from a business.


Carrying out risk management throughout the business lifetime is the key

Among the most important aspects to keep in mind about risk management is that it is not something an organization does at some point of time and forgets. It is something that should be carried out every now and then at every point of the business lifetime. Another core point related to this fact is that risks are never single or static. You could face several risks, connected to each other or not, at one point of time.

Further, risks keep changing from time to time. As one risk gets managed, another could spring up. This explains just why as an organization, you must keep carrying out risk management throughout your business’ lifetime. There are very many core reasons for carrying out risk management during the lifetime of the business whenever the occasion demands it.

Understanding the risk is the first step

Risk comes in various forms, shapes and sizes. Anyone who understands a business should start by understanding the risks in it first. Risk could be either relative to the business or the industry or it could be specific to the organization.

Any business is prone to what may be called general risks. Like mentioned, it is a risk that comes with an activity involving a certain kind of business. Irrespective of the nature, size and reach of the business or its market, there are risks such as market, changing consumer tastes, shifting market size and trends, geographical location and so on.

And then, the specific ones

In addition to the general risks that come with any business, a business has to also take into consider the risks that are specific to it, regardless of which industry it is in. Some of the specific risks that come to mind are:

  • Do our employees carry the right skillsets for this industry and this business?
  • Do we have the right resources for growing?
  • How many of our people could be off work on any given day across the business and what are our backup plans?
  • What are our plans for raising funding through the investors and what if they fail to get convinced?
  • What risk will befall our business if the top management quits?
  • What is our disaster recovery plan?
  • Do we have the resilience to handle major disasters and there any that our business is prone to, being located where it is?

Management experts pin down these risk management principles into these main elements:

  • Understanding or establishing the risk
  • Identifying them
  • Analyzing them
  • Evaluating how to manage them.

Do it before it is late

The key to risk management is to not only understand that you must keep carrying out risk management throughout your business’ lifetime, but that you must act before the risk happens. This is where the business’ ken to handling risk management lies. The organization that does this is one that has understood its business best and is ahead of the rest. What happens when risk management is applied after the risk has occurred is that the severity is hardly reduced, while risk management done before the onset of a risk ensures that the risk is contained, and its effects mitigated, which risk management truly is essentially all about.

CAPP Presents Roadmap for Telehealth Adoption to Improve [Patient Care]

How can telehealth realize its potential to improve patient care? The Council of Accountable Physician Practices (CAPP), a coalition of visionary multi-specialty medical groups and health systems, answers this question in its new primer.

“Telehealth tools have the potential to transform healthcare delivery by improving access, quality, and efficiency,” noted CAPP Chairman Stephen Parodi, M.D., associate executive director, The Permanente Medical Group. “However, there are significant barriers in realizing these benefits due to current cultural, regulatory, and payment practices. At this pivotal time, we must Telehealth into daily clinical practice services are deployed in appropriate ways that are embraced by physicians and patients alike.”

To help guide that transition, CAPP physician leaders identified six critical principles for stakeholders to consider.

1. Telehealth must integrate, not fragment, care.

Telehealth tools are most effective when they are used in the context of an already-established relationship between a patient and an accountable healthcare delivery system. In that setting, telehealth encounters with a patient’s own provider or system are simply another means of delivering integrated, comprehensive care, and supporting the capability to provide value-based care.

2. Telehealth improves quality, access, and convenience; cost-savings are not the primary benefit.

Short-term savings for payers should not be most important feature of telehealth. In the experience of most CAPP groups, telehealth visits via phone or video did not replace in-person care, but rather augmented it. Potential cost savings will be realized in the long term as telehealth tools expand access to preventive care and disease management, eliminating the need for more costly interventions in the future.

Telehealth into daily clinical practice

Tight (security) comes with an annoying compromise

Better security is great, but unfortunately, the T2 coprocessor isn’t without problem.

The launch of the 2018 MacBook Pro has been rife with controversy, with issues ranging from the performance to the keyboard. While we’re at it, let’s throw one more log on the fire, shall we?

The new MacBook Pros come with what Apple calls the T2 coprocessor — a chip first featured in the iMac Pro. Although its main reason for inclusion is Siri voice activation, it also has important implications on security and storage. Better security is great, but unfortunately, the T2 coprocessor isn’t without problem.

The return of the T2

The T2 coprocessor brings all sorts of security features to the MacBook Pros. In its press release, Apple says it has “support for secure boot” and “on-the-fly encrypted storage,” two features that first came when the T2 showed up in last year’s iMac Pro. These security features might not sound like a big deal, but they’ll have a much larger effect on users than activating Siri with your voice.

Apple’s never been all that forthcoming about the exact processes these chips control, but there are a few things we know the T2 does handle. That includes Boot-up, storage, and the Touch Bar/Touch ID. Not only are these processes the Intel CPU and third-party controllers no longer must handle, it keeps them protected in Apple’s closed system of stopgaps.

A great example is the boot-up process, which is now partially handled by the T2. As detailed in initial reports about the coprocessor in the iMac Pro, the T2 verifies everything about the system before it’s allowed to move forward. As soon as the Apple logo appears, the T2 is in control, and acts as Apple’s “root of trust” to ensure that everything checks out.

Encrypted storage is equally important. Because the functions of the conventional disk controller have been replaced by the T2, the coprocessor now has direct control over the storage in your MacBook Pro.

That kind of access allows Apple to ensure every piece of data in the SSD is automatically protected and encrypted. That lets Apple to do things like secure your biometric data outside of the SSD. Right now, that’s just the TouchID sensor, but in the future that could include something like FaceID.

However, some reports were made to bring these new security features to the MacBook Pro.

More at

Improving Customer Experience with AI

Human beings don’t categorize content in the same way – and discrepancies and misunderstandings in categorization can make customer feedback useless.

A myriad of customer service channels exist today, such as social media, email, chat services, call centers, and voice mail. There are so many ways that a customer can interact with a business and it is important to take them all into account.

Customers or prospects who interact via chat may represent just one segment of the audience, while the people that engage via the call center represent another segment of the audience. The same might be said of social media channels like Twitter and Facebook.

Each channel may offer a unique perspective from customers – and may provide unique value for business leaders eager to improve their customer experience. Understanding and addressing all channels of unstructured text feedback is a major focus for natural language processing applications in business – and it’s a major focus for Luminoso.

Luminoso founder Catherine Havasi received her Master’s degree in natural language processing from MIT in 2004, and went on to graduate with a PhD in computer science from Brandeis before returning to MIT as a Research Scientist and Research Affiliate. She founded Luminoso in 2011.

In this article, we ask Catherine about the use cases of NLP for understanding customer voice – and the circumstances where this technology can be most valuable for companies.

Why Customer Voice Needs Artificial Intelligence

Making sense of the meaning in customer or user feedback (through phone calls, chat, email, social media, etc) is valuable for nearly any business. The challenge lies in finding this meaning at scale, and across so many different data formats.

Catherine tells us that, historically, businesses manage these different customer interactions by putting them into appropriate “buckets” or categories. For example, if there are 70,000 customer support email messages received in a particular month, the company might have a manual process of flagging each message as “refund request,” “billing inquiry,” “purchase request,” etc.

However, manual categorization becomes nearly impossibly challenging at scale, for a number of reasons:

  • While all customer service emails and call center calls might be labelled manually by the customer support rep who handles then, other kinds of data (tweets, chat messages, comments on online forums) may never receive the same kind of labelling.
  • A company with pre-determined “buckets” (categories) for customer service inquiries is unlikely to be able to pick up on new, emerging trends in the particular words, issues, or phrases used in customer requests. This inability to adapt and find new patterns could limit the company from seeing new opportunities for improvement, or new emerging issues for important customer segments.
  • Human beings don’t categorize content in the same way – and discrepancies and misunderstandings in categorization can make customer feedback useless.

Many companies look to technology that can detect common patterns for these messages, create categories for each found pattern, and flag them appropriately for the attention of the business owners (which includes finding new patterns). This is a job for machine learning.

“Sentiment analysis” – the process of computationally identifying and categorizing opinions expressed in a piece of text – has become a somewhat familiar term. Catherine tells us that truly understanding customer voice involves much more than simply detecting emotions within text, and includes:

  • Finding new “entities” (products, brands, people) which are gaining or losing frequency in customer feedback
  • Determining customer sentiment – not just overall – but in relation to specific entities or types of customer issues
  • Showing changes and trends in customer feedback over time
  • Understanding the different patterns of feedback across unique channels (call center, chat messages, social media, etc)

The problem is many text analytics techniques in the past require a significant amount of data and effort in building rules and anthologies in the beginning, and still be unable to provide a true picture of what is actually being said by the customers.

Here you go for full post