How to mark leaves of polynomial based on its position in tree

I’ve been struggling with this for a few days, and so I thought I might ask this here. Part of the problem lies in the fact that I do not even know the mathematical solution, which runs the risk of this question falling out of the scope of this site. Nevertheless, I’ll proceed:

Consider this unexpanded polynomial of Symbols constructed out of heads Plus and Times only:

poly = ((a + b) (c + d (e + f)) + (g + h) i) j + k

What I’d like to do: Without expanding the polynomial, mark the leaves of the polynomial by integers based on its position in the tree. This labeling of leaves must have the property such that

  1. When Expanded, each factor in each term is given a unique label
  2. No more labels are used than necessary for the whole polynomial (number of distinct labels equals overall order of polynomial in all its variables).

For poly, a solution is

((a[1] + b[1]) (c[2] + d[2] (e[3] + f[3])) + (g[2] + h[2]) i[1]) j[0] + k[0]

The result is not unique, but observe that the expanded form satisfies both requirements 1 and 2.

a[1] c[2] j[0] + b[1] c[2] j[0] + a[1] d[2] e[3] j[0] + 
  b[1] d[2] e[3] j[0] + a[1] d[2] f[3] j[0] + b[1] d[2] f[3] j[0] + 
  g[2] i[1] j[0] + h[2] i[1] j[0] + k[0]

It is not necessary that terms with fewer factors use specific labels in any order: for example a labeling of the polynomial in which the 2nd last term of the expanded form is h[1] i[3] j[0] (missing label 2) or in which the last term is k[2] is acceptable.

Moreover I’d like a solution that is faster than just expanding the polynomial and labeling each term.


My original attempt was based on traversing the tree,

WH5eL How to mark leaves of polynomial based on its position in tree

and raising/lowering the value of the label based on whether it passes through Plus or Times. Unfortunately, none of my solutions based on this give the correct answer.

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A Guide to Making AI Explainable – Yes, It’s Possible!

Screen Shot 2018 06 18 at 4.33.19 PM A Guide to Making AI Explainable – Yes, It’s Possible!

The possibilities of artificial intelligence are endless. AI helps businesses create tremendous efficiencies through automation, while enhancing an organizations ability to make more effective business decisions. However, it’s no surprise that companies are beginning to be held accountable for the outcomes of their AI-based decisions. From the proliferation of fake news to most recently, the deliberate creation of the AI psychopath Norman, we’re beginning to understand and experience the potential negative outcomes of AI.

While AI, machine learning, and deep learning have been deemed to be ‘black box’ technologies, unable to provide any information or explanation of its actions, this inability to explain AI will no longer be acceptable to consumers, regulators, and other stakeholders. For example, with the General Data Protection Regulation in effect, companies will now be required to provide consumers with an explanation for AI-based decisions.

FICO has been pioneering explainable AI (xAI) for more than 25 years and is at the cutting edge of helping people really understand and open up the AI black box. As you move forward with your AI journey, we’ve curated a list of blogs that uncover the importance of and trends leading to xAI.

According to GDPR, customers need to have clear-cut reasons for how they were adversely impacted by a decision. But what happens when your model was built with AI? This blog post uncovers the requirement of making AI explainable.

AI comes with many challenges, including trying to decipher what these models have learned, and thus their decision criteria. This blog lists ways to explain AI when used in a risk or regulatory context based on FICO’s experience.

Ready to make AI explainable? This post illustrates how you can achieve better performance and explainability by combining machine learning and scorecard approaches.

In 1996 we filed a patent for Reason Reporter—indicative of how long, in fact, FICO has been working with Explainable AI. Simply enough, Reason Reporter provide reasons associated with the neural network scores Falcon produces. The not so simple part? This post demonstrates how we utilize the reason reporter algorithm during model training.

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How To Drive Blockchain Technology Value In Life Sciences

 How To Drive Blockchain Technology Value In Life Sciences

Blockchain: Everywhere you turn in life sciences digitization, this ledger system is involved. Fully 83% of executives in life sciences expect blockchain integration in healthcare within five years. Last year’s proof of concept study clearly showed strong results using blockchain in identity trust. PC Magazine mentioned the use of blockchain for legal proof of ownership of unique cannabis strains.

How to drive blockchain technology value In life sciences

Life sciences companies are just starting to invest in digital ledgers technology (DLT) like blockchain. A 2017 survey from IDC showed that a quarter of IT professionals in the industry were already using DLTs or implementing it. Another third was evaluating or planning on evaluating the technology. But why have DLT systems started to become so popular for life science?

DLTs can provide instantly verifiable accountability to the research and development process. Beyond that, it also helps record accurate shipment data. This is important in an industry where environmental control is vital to success. It’s expected that the use of DLTs will rise as investment in digitization increases. But how do we know that this process is happening in life sciences?

Digitization increases the use of Big Data, analytics, cooperative work, and transparency. DLTs such as blockchain improve these aspects without compromising data integrity or security. It’s expected that supply chain, regulatory compliance, and product safety are probably the first blockchain projects for many life sciences companies. Sterilization processes or cold storage and transport of biologics are expected to lead the way for DLT options. Providing irrefutable documentation of this information builds trust between shareholders.

Using blockchain in clinical trials

Let’s look at an example of blockchain use in clinical trials. A pipeline drug can have a number of supply chain issues. Shipping and receiving of the experimental drug and test samples need to be tracked. Proper security, global payment management, and data sharing must also be managed. Smart contracts help improve payments to suppliers and research organizations. Internet of Things sensors can be implemented with shipping processes to update blockchain records. This helps catch potential environmental variances in medications during shipping.

Tracking document exchanges, data sharing, loT genealogy, and internal manufacturing processes can be monitored through DLTs. Now imagine adding this to current track and trace regulations for medications. Electronic product code information service requirements are much easier to manage using systems like blockchain. It provides an unforgeable ledger record that minimizes reaction time to public health crises resulting from medication problems. At the same time, it protects patient privacy by providing limited access to blockchain records.

Measuring overall DLT performance

What kind of metrics can be used to measure performance changes using DLT systems? One option is to look at reported errors compared to the overall process volume. Comparing the existing numbers against a blockchain system can often help your company locate and identify specific discrepancies and issues in the system. Because DLT systems have a unique signature with each transaction, it cannot be modified. This builds trust between supply chain parties, making the reconciliation process faster, easier, and more secure.

To implement blockchains into an organization, stakeholders must first decide which information should fall in a public versus a private blockchain. Sensitive, proprietary, or private information should be kept on a private blockchain so that it is protected. At the same time, data access and sharing must be considered. Who is allowed access and who has the responsibility to read and write to the system needs to be determined prior to starting the conversion process. Before the process goes live, your audit trails will also need to be tested.

By connecting clinical trial management programs to distributed ledgers, your organization’s supply requests, test results, and sensor data can be automated. This process can help speed up and improve the efficacy of the clinical trials. The level of transparency for the data can also help with your patient recruitment. The blockchain ledgers make it easier to improve contract payments and fair compensation while limiting overpayment risk.

How do you successfully add blockchain to your operation? Make sure you benchmark your current business processes. Map that process both before and after adding a DLT system to your operation. Take care in management of ownership and access to both internal and outside information. Ensure that patient and critical data is protected securely. Above all, before starting, figure out whether you have the expertise and resources to set up the system in the first place.

The advantages of using blockchain in life science ensure that this part of digitization is here to stay. DLT systems will help speed up and improve the quality of clinical trials. Companies that take advantage of these benefits will reap great rewards.

Learn how DLT systems can work with your business through SAP Leonardo.

Learn more about this topic in the IDC whitepaper: The Value of Blockchain Technology in the Life Sciences Industry.

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Rhinoceros Rub

 Rhinoceros Rub

Rhinoceros enjoys chest rub.

“we all like this.”
Image courtesy of https://imgur.com/gallery/IxzI3Sk.

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How to Find the Object Type Code for Any Entity

access blur book 1128240 300x225 How to Find the Object Type Code for Any Entity

Each entity in the system will have an Object type code. For simplicity of understanding, think of it as a numbering system for all the entities, regardless if they are out of the box or custom. As a CRM Expert, you might encounter a situation where you will need to identify the Object Type code for an entity.

To identify the Entity name using the Object Type Code, follow these steps:

1. Enter your CRM instance.

2. Open any entity under any model, such as Accounts, Contacts, Cases, etc.

062218 1820 HowtoFindth1 How to Find the Object Type Code for Any Entity

3. Open a New Record within the Entity

062218 1820 HowtoFindth2 How to Find the Object Type Code for Any Entity

4. Once a new record is opened, pop out the record by selecting the icon on the top right side of the page.

062218 1820 HowtoFindth3 How to Find the Object Type Code for Any Entity

5. Once enlarged, the Object Type Code is placed after the “etc.” equal sign within the URL.

062218 1820 HowtoFindth4 How to Find the Object Type Code for Any Entity

Identifying the Entity of an Object Type Code

Sometimes you may receive an error message that states the Object Type Code but not the entity name or a Custom Entity Object Type Code that is not available online.

To identify the Entity name of an Object Type Code, follow these steps:

1. Follow the same steps in “how to Identify the Object Type code of an Entity” as shown above.

2. Once the record is enlarged, you can find the Object Type Code is placed after the etc. equals sign within the URL.

062218 1820 HowtoFindth5 How to Find the Object Type Code for Any Entity

3. After the etc. equals sign, delete ONLY the number shown, replace it with desired number, and select Enter.

4. Now it will open a new record with the Entity Object Type Code you entered.

There you have it! For more Dynamics 365 tips and tricks, be sure to subscribe to our blog!

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PowerObjects- Bringing Focus to Dynamics CRM

Supreme Court Ecommerce Tax Decision Opens Pandora’s Box for Retailers

Posted by Ian McCue, Content Manager

The federal government took a major step toward regulating ecommerce on Thursday when the Supreme Court upheld a South Dakota law that allows the state to gather sales tax from online businesses with at least $ 100,000 in sales from state residents or 200 transactions in the state annually. This overturned a ruling from 1992, Quill Corp. v. North Dakota, that prevented states from collecting taxes from companies without a physical presence in their state.

The 5-4 decision is meant to level the playing field between online sellers and traditional brick-and-mortar retailers. In the coming months and years, states will change their laws on ecommerce taxes based on this decision. Ecommerce sales added up to $ 450 billion in 2017, 13 percent of all retail sales.

Forty-five states have some kind of sales tax and online sales represent billions of dollars-worth of taxes every year, per NPR. Some states already have workaround laws in place to collect sales tax online, but experts predict they will scrap those in favor of legislation modeled after South Dakota.

Additionally, certain large online retailers already collect sales tax. Amazon charges taxes on all direct sales but not those from third-party businesses who sell through its site. Wayfair, one of three companies who challenged the initial South Dakota law, collects tax on about 80 percent of orders, according to The Hill.

However, this opens Pandora’s box for many smaller ecommerce companies that sell nationally but do not impose taxes. This provided certain online retailers with a competitive advantage – not only might it attract more customers to shop on the site, but the company did not need to worry about calculating or paying taxes on out-of-state orders.

Chief Justice John Roberts was not in favor of the ruling and expressed concern that the decision will slow the growth of ecommerce.

“Ecommerce has grown into a significant and vibrant part of our national economy against the backdrop of established rules, including the physical-presence rule,” Roberts wrote in the dissenting opinion. “Any alteration to those rules with the potential to disrupt the development of such a critical segment of the economy should be undertaken by Congress.”

Even before this decision, small businesses face a complex web of tax laws.

“Texas taxes sales of plain deodorant at 6.25 percent but imposes no tax on deodorant with antiperspirant,” Roberts wrote. “Illinois categorizes Twix and Snickers bars — chocolate-and-caramel confections usually displayed side-by-side in the candy aisle — as food and candy, respectively (Twix have flour; Snickers don’t), and taxes them differently.”

The President of the National Retail Federation, Matthew Shay, called for lawmakers to draw up universal rules that can be applied to all states and will minimize complications. While this would be ideal for retailers, it may not be realistic.

“Congress must now follow the Court’s lead and pass legislation implementing uniform national rules that provide consistency and clarity for retailers across the country,” Shay told The Hill.

This ruling will make robust commerce technology and tax compliance software a necessity for even small online sellers. They need a scalable way to automate taxes that may vary based on where a customer resides and a way to repay that money to the state. Technology providers will need to move quickly as ecommerce tax laws spring up.

Companies using a cloud system for financials and ecommerce are well-positioned for this change because as the vendor modifies the platform to address these challenges, updates are automatically pushed out to all customers.

A business platform that unifies commerce and financials is especially advantageous because it can calculate, capture and report on taxes across channels. The system automates tax-related tasks on all sales, whether an in-store purchase or online orders from customers spread across states, so staff doesn’t waste time dealing with such headaches.

Time will tell how this Supreme Court ruling impacts online retailers and how quickly, but change is brewing. What is clear is businesses with a unified platform that can painlessly manage sales tax will be better prepared for the change and continue to power the remarkable rise of ecommerce.

To learn more about the latest trends and news in ecommerce, check out this recap from the 2018 Internet Retail Conference and Exhibition.

Posted on Fri, June 22, 2018
by NetSuite filed under

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Health care bots are only as good as the data and doctors they learn from

 Health care bots are only as good as the data and doctors they learn from

The number of tech companies pursuing health care seems to have reached an all-time high: Google, Amazon, Apple, and IBM’s Watson all want to change health care using artificial intelligence. IBM has even rebranded its health offering as “Watson Health — Cognitive Healthcare Solutions.” Although technologies from these giants show great promise, the question of whether effective health care AI already exists or whether it is still a dream remains.

As a physician, I believe that in order to understand what is artificially intelligent in health care, you have to first define what it means to be intelligent in health care. Consider the Turing test, a point when a machine becomes indistinguishable from a human.

Joshua Batson, a writer for Wired magazine, has mused whether there is an alternative measurement to the Turing test, one where the machine doesn’t just seem like a person, but an intelligent person. Think of it this way: If you were to ask a random person about symptoms you experience, they’d likely reply “I have no idea. You should ask your doctor.” A bot supplying that response would certainly be indistinguishable from a human — but we expect a little more than that.

The challenge of health care AI

Health is hard, and that makes AI in health care especially hard. Interpretation, empathy, and knowledge all have unique challenges in health care AI.

To date, interpretation is where much of the technology investment has gone. Whether for touchscreen or voice recognition, natural language processing (NLP) has seen enormous investment including Amazon’s Comprehend, IBM’s Natural Language Understanding, and Google Cloud Natural Language. But even though there are plenty of health-specific interpretation challenges, interpretation challenges are really no greater in this particular sector than in other domains.

Similarly, while empathy needs to be particularly appropriate for the emotionally charged field of health care, bots are equally challenged trying to strike just the right tone for retail customer service, legal services, or childcare advice.

That leaves knowledge. The knowledge needed to be a successful conversational bot is where health care diverges greatly from other fields. We can divide that knowledge into two major categories: What do you know about the individual? And what do you know about medicine in general that will be most useful their individual case?

If a person is a diabetic and has high cholesterol, for example, then we know from existing data that the risks of having a heart attack are higher for that person and that aggressive blood sugar and diet control are effective in significantly lowering that risk. That combines with a general knowledge of medicine which says that multiple randomized controlled trials have found diabetics with uncontrolled blood sugars and high cholesterol to be twice as likely as others to have a cardiac event.

What is good enough?

There are two approaches to creating an algorithm that delivers a customized message. Humans can create it based on their domain knowledge, or computers can derive the algorithm based on patterns observed in data — i.e., machine learning. With a perfect profile and perfect domain knowledge, humans or machines could create the perfect algorithm. Combined with good interpretation and empathy you would have the ideal, artificially intelligent conversation. In other words, you’d have created the perfect doctor.

The problem comes when the profile or domain knowledge is less than perfect (which it always is), and then trying to determine when it is “good enough.”

The answer to “When is that knowledge good enough?” really comes down to the strength of your profile knowledge and the strength of your domain knowledge. While you can make up a shortfall in one with the other, inevitably, you’re left with something very human: a judgment call on when the profile and domain knowledge is sufficient.

Lucky for us, rich and structured health data is more prevalent than ever before, but making that data actionable takes a lot of informatics and computationally intensive processes that few companies are prepared for. As a result, many companies have turned to deriving that information through pattern analysis or machine learning. And where you have key gaps in your knowledge — like environmental data — you can simply ask the patient.

Companies looking for new “conversational AI” are filling these gaps in health care, beyond Alexa and Siri. Conversational AI can take our health care experience from a traditional, episodic one to a more insightful, collaborative, and continuous one. For example, conversational AI can build out consumer profiles from native clinical and consumer data to answer difficult questions very quickly, like “Is this person on heart medication?” or “Does this person have any medications that could complicate their condition?”

Not until recently has the technology been able to touch this in-depth and profile on-the-fly. It’s become that perfect doctor, knowing not only everything about your health history, but knowing how all of that connects to combinations of characteristics. Now, organizations are beginning to use that profile knowledge to derive engagement points to better characterize some of the “softer” attributes of an individual, like self-esteem, literacy, or other factors that will dictate their level of engagement.

Think about all of the knowledge that medical professionals have derived from centuries of research. In 2016 alone, Research America estimated, the U.S. spent $ 171.8 billion on medical research. But how do we capture all of that knowledge, and how could we use it in conversational systems? This lack of standardization is why we’ve developed so many rules-based or expert systems over the years.

It’s also why there’s a lot of new investment in deriving domain knowledge from large data sets. Google’s DeepMind partnership with the U.K.’s National Health Service is a great example. By combining their rich data on diagnoses, outcomes, medications, test results, and other information, Google’s DeepMind can use AI to derive patterns that will help it predict an individual’s outcome. But do we have to wait upon large, prospective data analyses to derive medical knowledge, or can we start with what we know today?

Putting data points to work

Expert-defined vs. machine-defined knowledge will have to be balanced in the near term. We must start with the structured data that is available, then ask what we don’t know so that we can derive additional knowledge from observed patterns. Domain knowledge should start with expert consensus in order to derive additional knowledge from observed patterns.

Knowing one particular data point about an individual can make the biggest difference in being able to read their situation. That’s when you’ll start getting questions that may make no sense whatsoever, but will make all the sense in the world to the machine. Imagine a conversation like this:

BOT: I noticed you were in Charlotte last week. By any chance, did you happen to eat at Larry’s Restaurant on 5th Street?

USER: Uh, yes, I did actually.

BOT: Well, that could explain your stomach problems. There has been a Salmonella outbreak reported from that location. I’ve ordered Amoxicillin and it should be to you shortly. Make sure to take it for the full 10 days. The drug Cipro is normally the first line therapy, but it would potentially interact badly with your Glyburide. I’ll check back in daily to see how you’re doing.

But while we wait for the detection of patterns by machines, the knowledge that is already out there should not be overlooked, even if it takes a lot of informatics and computations. I’d like to think the perfect AI doctor is just around the corner. But my guess is that those who take a “good enough” approach today will be the ones who get there first. After all, for so many people who don’t have access to adequate care today, and for all that we’re spending on health care, we don’t yet have a health care system that is “good enough.”

Dr. Phil Marshall is the cofounder and chief product officer at Conversa Health, a conversation platform for the health care sector.

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Online Learning Platforms Lead Blockchain Integration In Higher Education

 Online Learning Platforms Lead Blockchain Integration In Higher Education

The average student loan debt for 2017 graduates was $ 39,400—a 6% increase from 2016, and we are likely to see that amount grow. Students are paying increasingly high tuition for degrees that may or may not give them a positive return.

That said, it’s no secret that higher education is one of the largest industries poised for disruption. Students are discovering the availability of trustworthy and affordable online learning platforms, and blockchain technology has arrived to add a stamp of accreditation previously unavailable in the digital education world.

The proliferation of affordable online learning

No online learning platform better represents the future of learning than Khan Academy, which has grown steadily year-over-year and recently launched a free LSAT prep that is available to all prospective law students.

“As a teacher, I can’t encourage online learning enough,” said Erika Gingery, a faculty member at Settlement Music School specializing in piano lessons in Philadelphia, PA. “I assign students supplemental material through various online music learning platforms and virtual keyboards, and they actually like it. The rest of their life is on a screen, so I like to meet them where they already live.”

Driven by language learning, coding, and take-home school materials, free online learning has spiked dramatically. But so far, none of these platforms have used blockchain technology to add a layer of authority or accreditation.

The progression of online learning will lead to blockchain integration

According to the Times Higher Academics, a group of Oxford professors recently launched a new platform that’s been called “Uber for students” and “Airbnb for academics.”

This platform enables students to take classes “a la carte,” from a professor of their choice. It differs from online colleges by literally following the Airbnb model; if a professor wants to make some additional income, he or she can list their class online. A student can communicate with that professor to schedule online classes, and that study would transfer into credits at an established university.

The move allows teachers to earn the same or more pay than they would at a traditional institution, and it allows students to take a full year’s worth of courses for less than $ 20,000—significantly less than the cost of attending school full time.

So how will blockchain be involved?

  1. Proof of interaction will live on the propriety blockchain, so students will be unable to falsify studies with professors. This is especially important considering the open and independent nature of these courses, which don’t have the authority of a university’s registrar office to back them up.
  1. Payments and contracts will be automatically charged and regulated via blockchain. No payments will be made through PayPal or similar payment services.

Retrieval of academic records via blockchain

While online learning platforms will gain the most from blockchain technology in the near future, academia, in general, will benefit further down the road.

In the current academic landscape, universities must communicate amongst themselves to verify a student’s degree. A DMA candidate at Harvard, for instance, will have to ask the registrar office of his master’s degree program to send an official transcript to Harvard. This process is expensive and inefficient, and it can take weeks.

In contrast, if academic credentials lived on a blockchain ledger, students would be able to supply a key wherever they need to. In addition, credentials via blockchain would be far more trustworthy than a diploma hanging on the wall.

Blockchains will revolutionize how research is shared

Besides helping students avoid Chapter 13 bankruptcy and making the retrieval of credentials easier, blockchain technology could be a boon to open education models.

Professors could have research immediately notarized via blockchain, skip the publication process in a formal journal, and track their material’s use around the world. This process would take one day rather than months, and it would be essentially free.

In short, the only institutions that would not benefit from blockchain integration would be universities themselves—the same universities that spend millions on frivolous programs and send thousands of students into crippling debt.

For more on technology in the education industry, see 5 Digital Transformation Trends In The Education Industry.

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Cumulative Update #8 for SQL Server 2017 RTM

The 8th cumulative update release for SQL Server 2017 RTM is now available for download at the Microsoft Downloads site. Please note that registration is no longer required to download Cumulative updates.
To learn more about the release or servicing model, please visit:

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What the GDPR Means for Small US Etailers

Large corporations are not the only businesses governed by the
European General Data Protection Regulation, or GDPR, which became effective last month.

Small and mid-sized businesses also are subject to its provisions.

The regulation applies to the processing of personal data of individuals in the EU by an individual, a company or an organization engaged in professional or commercial activities.

“The common misconception is that if you don’t have an office in the EU, then the GDPR doesn’t apply to you,” said Cindy Zhou, principal analyst at Constellation Research.

However, shipping products to the European Economic Area (EEA) or sourcing them from the region are activities governed by the GDPR, she told the E-Commerce Times.

“The online marketplace has no borders,” noted Wesley Young, VP for public affairs at the Local Search Association.

That may be changing, however.

“We have seen many small businesses … exclude EU subjects from their clientele to avoid exposure to GDPR risks,” observed Andrew Frank, distinguished analyst at Gartner.

“This could impact assumptions about the frictionless global nature of e-business,” he told the E-Commerce Times.

GDPR Pitfalls for Unwary SMBs

The GDPR’s definition of personal data is “very broad,” LSA’s Young told the E-Commerce Times. “That would include IP addresses, location information, demographic information, and other general data used for targeting ads.”

The term “process” also is broadly defined, “and includes collecting and storing data, even if it isn’t further used,” he observed.

“The breadth of the GDPR’s application lends itself to be easily but unintentionally violated,” Young noted. For example, not following through on policy changes — failing to abide by new privacy policies, or not training staff to adhere to them — might be a violation.

Using data beyond the reason for which it was collected might be a violation, suggested Young, as consent has to be given for specific purposes.

The Ins and Outs of Consent

The GDPR “allows six different legal bases for collecting or processing personal data, of which consent is but one,” said Robert Cattanach, partner at
Dorsey & Whitney.

For most e-commerce situations, the transaction arguably constitutes a contract, and “additional consent may not be required” to collect personal data necessary to conclude the transaction, he told the E-Commerce Times. However, the question of consent will arise when a merchant engages third-party vendors to track or monitor customer behavior on its website.

Monitoring or aggregating customer behavior on a merchant’s website to learn when a customer decides to place an order or abandon the search by using cookies is one option, Cattanach noted.

“The UK’s Information Commissioner’s Office has opined that implied consent may be sufficient for such site tracking,” he pointed out. Therefore, a pop-up banner stating continued use of the site means consent to the use of cookies might suffice — although some of the German data protection authorities might not agree.

For the collection of personal data, a pop-up requiring the customer to independently agree to it would be necessary.

Two major issues remain unresolved, according to Cattanach:

  • What constitutes informed consent is still “a matter of ongoing dispute”; and
  • Responses to data subject access requests — such as the right to discover what data has been collected, correct errors, and request to be forgotten — “are legally less problematic on their face but, as a practical matter, may be more difficult to execute.”

Requests to be forgotten require merchants to establish process flows for the intake of such requests; set policies for when such requests will be granted or denied; and implement pocedures for responding within 30 days.

That is “no small undertaking,” Cattanach remarked, “which is why many SMBs have just decided to avoid triggering GDPR by expunging all existing data of EU residents and blocking EU IP addresses from accessing their websites going forward.”

Records of processing were expected to be the most challenging of the data subject rights requirements by 48.5 percent of more than 1,300 U.S. business users and consumers who participated in an online survey
CompliancePoint conducted this spring.

Only 29 percent of respondents to the CompliancePoint survey were fully aware of the GDPR; 44 percent were somewhat aware and 26 percent were unaware.

Other data subject rights problems they anticipated:

  • Accountability – 41 percent;
  • Consent and data portability – 39.7 percent each; and
  • Right to be forgotten – 35.3 percent.

GDPR Readiness

Twenty-four percent of business respondents to the CompliancePoint survey said their organizations were fully prepared for the GDPR, while 31 percent said they were somewhat prepared and 36 percent said their organizations were not prepared.

Following are some of the factors that kept the organizations of CompliancePoint respondents from being GDPR compliant:

  • Waiting to see what enforcement would be applied – 45.6 percent
  • Lack of understanding of the regulations – 39.7 percent;
  • No budget for compliance – 36.8 percent;
  • Low brand visibility – 33.8 percent; and
  • Unconcerned – 27.9 percent.

“SMBs are not immune to the risk of GDPR,” said Greg Sparrow, general manager at CompliancePoint.

“The risk of fines and regulatory action are the same for businesses large and small,” he told the E-Commerce Times.

The financial penalties — 4 percent of annual revenue or 20 million euros — are large, noted Constellation’s Zhou.

However “the indirect costs in terms of impact on customer trust and brand reputation may be even greater,” said Gartner’s Frank.

CRM Software to the Rescue

CRM systems that make it relatively easy to execute functions like erasure and consent modification “can help considerably,” Frank suggested.

“SugarCRM recently released a data privacy module that automates much of the processes for managing the required data governance,” remarked Rebecca Wettemann, VP of research at Nucleus Research.

Zoho, Hubspot, Salesforce and other CRM vendors “are touting GDPR compliance,” Zhou noted.

“SMBs running cloud CRM applications will likely find the easiest path to compliance, because data privacy capabilities have been or are being built into these applications,” Wettemann told the E-Commerce Times.

That said, CRM companies are data processors by definition, Zhou pointed out, and under the guidance of the company that collected the customer data.

“Privacy policies, cookie notices and age consent forms all need to be managed by the SMBs themselves,” she said, “and are often placed on a website or on the e-commerce site which isn’t related to the CRM solution.”
end enn What the GDPR Means for Small US Etailers


Richard%20Adhikari What the GDPR Means for Small US Etailers
Richard Adhikari has been an ECT News Network reporter since 2008. His areas of focus include cybersecurity, mobile technologies, CRM, databases, software development, mainframe and mid-range computing, and application development. He has written and edited for numerous publications, including Information Week and Computerworld. He is the author of two books on client/server technology.
Email Richard.

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