How to Set Your AI Project Up for Success
Picking the right AI project for your company often comes down to having the right ingredients and knowing how to combine them. That, at least, is how Salesforce’s Marco Casalaina tends to think about it. The veteran artificial intelligence and data scientist expert oversees Einstein, Salesforce’s AI technology, and has made a career out of making emerging technologies more intuitive and accessible for all. With Einstein, he’s working to help Salesforce customers — from small businesses to nonprofits to Fortune 50 companies — realize the full benefits of AI. HBR spoke with Casalaina about what goes into a successful AI project, how to communicate as a data scientist, and the one question you really need to ask before launching an AI pilot.
You’ve been working in AI for a long time now. You worked for Salesforce years ago, then at other companies, and now you’ve come back to lead. How would you describe what it is you do in this work?
I bring machine learning into the things that people use every day — and I do it in a way that aligns with their intuition. The problem with machine learning and AI — which are two sides of the same coin — is that most people don’t know what either really mean. They often have an outsized idea of what AI can do, for example. And of course, AI is always changing, and it is a powerful thing, but its powers are limited. It’s not omniscient.
The point you’re making about how imagination can take hold explains a lot of the issues businesses run into with AI. So, when you’re thinking about the kinds of problems that AI is good at solving, what do you consider?
When I talk to customers, I like to break it down into ingredients. If you think about a fast food taco, there are six main ingredients: meat, cheese, tomatoes, beans, lettuce and tortillas. AI isn’t that different: there’s a menu of certain things that it can do. When you have an idea of what they are, it gives you an idea of what its powers are.
I’m intrigued! So, what are AI’s ingredients?
The first ingredient is “yes” and “no” questions. If I send you an email, are you going to open it or not? These give you a probability of whether something is going to happen. We get a lot of mileage out of “yes” or “no” questions. They’re like the cheese for us — we kind of put that in everything.
The second ingredient is numeric prediction. How many days is going to take you to pay your bill? How long is it going to take me to fix this person’s refrigerator?
Then, third, we have classifications. I can take a picture of this meeting that we’re in right now and ask, “are there people in this picture?” “How many people are in this picture?” There are text classifications, too, which you see if you ever interact with a chatbot.
The fourth ingredient is conversions. That could be voice transcription, it could be translation. But basically, you’re just taking information and translating it from one format to another.
The tortilla, if we’re sticking to our analogy, is the rules. Almost every functional AI system that exists in the world today works through some manner of rules that are encoded in the system. The rules — like the tortilla — hold everything together.
So how do you, personally, apply this in your work at Salesforce? Because I think people often struggle with figuring out where to start with an AI project.
The questions I ask are, “What data do we have?” And, “What concrete problems can I solve with it?”
In this job at Salesforce, I started with something every salesperson tracks as a natural part of their job: categorizing a lead by giving it a score of how likely it is to close.
Data sets like these are a key source of truth from which to develop an AI-based project. People want to do all kinds of things with AI capabilities, but if you don’t have the data, then you have a problem.
Getting into the next phase of this, let’s talk about the lifecycle of finding a project and deploying it. What are the questions you find yourself asking when thinking about how to get from pilot to rollout?
What problem you’re trying to solve — that’s the first question you need to answer. Am I trying to prioritize people’s time? Am I trying to automate something new? Then, you confirm that you have the data for this project, or that you can get it.
The next question you need to ask is: Is this a reasonable goal? If you’re saying, I want to automate 100% of my customer service queries, it’s not going to happen. You’re setting yourself up for failure. Now, if 25% of your customer service queries are requests to reset a password, and you want to automate that and take it off your agents’ plates, that is a reasonable goal.
Another question is: Can a human do it? Most of the time AI can’t do anything that humans can’t do.
Let’s say you’re an insurance company and you want to use a picture of a dented car to find out how much it’s going to cost to fix it. If you might reasonably expect that Joe down at the body shop can look at the picture and say, this is going to cost $1,500, then you could probably train AI to do it too. If they can’t, well, then an AI probably can’t either.
How long do you want to spend in a pilot phase? Because a lot of what you’re doing, other people are trying to do, too.
AI projects tend to have uncomfortably long pilot periods — and they should. There’s two reasons for this.
First, to determine whether it actually works the way it should. Do people trust it? Is it explaining itself sufficiently for the weight of the problem? At one extreme there’s things like an AI-driven medical diagnosis, which can have a huge impact on someone’s life. You better tell me exactly why you think I have cancer, right? But if an AI recommends a movie I don’t like, I don’t really care why it’s telling me that. A lot of business problems kind of fall somewhere in between. You need to share just enough explanation so your users will trust it. And you need this pilot period to verify that your users understand it.
Second, you need to measure the value of the AI solution versus baseline — human interaction. Think about automating customer service queries. For customers using the chatbot, how many of those are actually answering the right questions? If I use the DMV’s chatbot and say, “I lost my license” and it says, “Fill out this form and you’ll get a replacement,” well, that’s what I was asking for. But if your chatbot can’t answer your customers’ questions, you end up with frustrated customers who hate your chatbot and end up talking to a human anyway.
Pivoting for a second here, you’ve been in this job for a few years at this point. What are some of the big things you’ve learned over that time?
We’ve learned how to find and use data sets to solve problems. Now, we help people understand how the data that they’re putting into their business systems — just by virtue of doing their jobs — can be used to develop machine learning that helps them solve problems more efficiently. But we’ve also learned how important a role intuition plays in that process.
So, we released a product called Einstein prediction builder about two years ago. A lot of customers are using it now, but it didn’t have the same rapid adoption curve as some of the more self-explanatory services like lead scoring.
Einstein prediction builder allows you to build a custom prediction for questions like, “Will my customer pay their bill late or not?” We realized that to get to that prediction, people have to make a bit of a mental leap: I would like to know the answer to this question, so I want to make a prediction about that.
That was tough for a lot of customers. Now, we have a new product, a recommendation builder. It’s a little bit more self-explanatory, because we’re also introducing a template system. For example, it will recommend what parts to put on the truck when a field representative is sent out to fix a refrigerator. We’ll lead the horse to water, right, from the Salesforce perspective, by having the automated step there, and work with customers to understand what parts they might need for the scenarios they might face.
As data scientists in the AI field, we have a tendency to think about algorithms, or maybe slightly higher level abstractions. I’ve learned we really need to get into our customers’ heads and express the solution to the problem in terms that they will relate to. So, I’m not just making a recommendation, I am specifically recommending the part that goes into a project; I’m not just making a prediction, I am specifically answering the question, are you going to pay your bill or not?
And then you have to decide, if I make that prediction, I give you a probability of the guy paying late, what are we going to do about it?
If you’re speaking to leaders who are thinking about this, it sounds like part of what you’re what you’re talking about is the need to stay grounded when considering what problems you should try to solve with AI and what you have on hand that can help you do it.
Right, it’s going back to the question of: Can a human do it? If they can, okay, maybe AI is a great way to take that task off a human’s plate to free them up for other magical things.
When to Give Employees Access to Data and Analytics
As business leaders strive to get the most out of their analytics investments, democratized data science often appears to offer the perfect solution. Using analytics software with no-code and low-code tools can put data science techniques into virtually anyone’s hands. In the best scenarios, this leads to better decision making and greater self-reliance and self-service in data analysis — particularly as demand for data scientists far outstrips their supply. Add to that reduced talent costs (with fewer high-cost data scientists) and more scalable customization to tailor analysis to a particular business need and context.
However, amid all the discussion around whether and how to democratize data science and analytics, a crucial point has been overlooked. The conversation needs to define when to democratize data and analytics, even to the point of redefining what democratization should mean.
Fully democratized data science and analytics presents many risks. As Reid Blackman and Tamara Sipes wrote in a recent article, data science is difficult and an untrained “expert” cannot necessarily solve hard problems, even with good software. The ease of clicking a button that produces results provides no assurance that the answer is good — in fact, it could be very flawed and only a trained data scientist would know.
It’s Only a Matter of Time
Even with these reservations, however, democratization of data science is here to stay, as evidenced by the proliferation of software and analytics tools. Thomas Redman and Thomas Davenport are among those who advocate for the development of “citizen data scientists,” even screening for basic data science skills and aptitudes in every position hired.
Democratization of data science, however, should not be taken to the extreme. Analytics need not be at everyone’s fingertips for an organization to flourish. How many outrageously talented people wouldn’t be hired simply because they lack “basic data science skills?” It’s unrealistic and overly limiting.
As business leaders look to democratize data and analysis within their organizations, the real question they should be asking is “when” it makes the most sense. This starts by acknowledging that not every “citizen” in an organization is comparably skilled to be a citizen data scientist. As Nick Elprin, CEO and co-founder of Domino Data Labs, which provides data science and machine learning tools to organizations, told me in a recent conversation, “As soon as you get into modeling, more complicated statistical issues are often lurking under the surface.”
The Challenge of Data Democratization
Consider a grocery chain that recently used advanced predictive methods to right-size its demand planning, in an attempt to avoid having too much inventory (resulting in spoilage) or too little (resulting in lost sales). The losses due to spoilage and stockouts were not enormous, but the problem of curtailing them was very hard to solve — given all the variables of demand, seasonality, and consumer behaviors. The complexity of the problem meant that the grocery chain could not leave it to citizen data scientists to figure it out, but rather leverage a team of bona fide, well-trained, data scientists.
Data citizenry requires a “representative democracy,” as Elprin and I discussed. Just as U.S. citizens elect politicians to represent them in Congress (presumably to act in their best interests in legislative matters), so too organizations need the right representation by data scientists and analysts to weigh in on issues that others simply don’t have the expertise to address.
In short, it’s knowing when and to what degree to democratize data. I suggest the following five criteria:
Think about the “citizen’s” skill level: The citizen data scientist, in some shape and form, is here to stay. As stated earlier, there simply aren’t enough data scientists to go around, and using this scarce talent to address every data issue isn’t sustainable. More to the point, democratization of data is key to inculcating analytical thinking across the organization. A well-recognized example is Coca-Cola, which has rolled out a digital academy to train managers and team leaders, producing graduates of the program who are credited with about 20 digital, automation, and analytics initiatives at several sites in the company’s manufacturing operations.
However, when it comes to engaging in predictive modeling and advanced data analysis that could fundamentally change a company’s operations, it’s crucial to consider the skill level of the “citizen.” A sophisticated tool in the hands of a data scientist is additive and valuable; the same tool in the hands of someone who is merely “playing around in data” can lead to errors, incorrect assumptions, questionable results, and misinterpretation of outcomes and conclusions.
Measure the importance of the problem: The more important a problem is to the company, the more imperative it is to have an expert handling the data analysis. For example, generating a simple graphic of historical purchasing trends can probably be accomplished by someone with a dashboard that displays data in a visually appealing form. But a strategic decision that has meaningful impact on a company’s operations requires expertise and reliable accuracy. For example, how much an insurance company should charge for a policy is so deeply foundational to the business model itself that it would be unwise to relegate this task to a non-expert.
Determine the problem’s complexity: Solving complex problems is beyond the capacity of the typical citizen data scientist. Consider the difference between comparing customer satisfaction scores across customer segments (simple, well-defined metrics and lower-risk) versus using deep learning to detect cancer in a patient (complex and high-risk). Such complexity cannot be left to a non-expert making cavalier decisions — and potentially the wrong decisions. When complexity and stakes are low, democratizing data makes sense.
An example is a Fortune 500 company I work with, which runs on data throughout its operations. A few years ago, I ran a training program in which more than 4,500 managers were divided into small teams, each of which was asked to articulate an important business problem that could be solved with analytics. Teams were empowered to solve simple problems with available software tools, but most problems surfaced precisely because they were difficult to solve. Importantly, these managers were not charged with actually solving those difficult problems, but rather collaborating with the data science team. Notably, these 1,000 teams identified no less than 1,000 business opportunities and 1,000 ways that analytics could help the organization.
Empower those with domain expertise: If a company is seeking some “directional” insights — customer X is more likely to buy a product than customer Y — then democratization of data and some lower-level citizen data science will probably suffice. In fact, tackling these types of lower-level analyses can be a great way to empower those with domain expertise (i.e., being closest to the customers) with some simplified data tools. Greater precision (such as with high-stakes and complex issues) requires expertise.
The most compelling case for precision is when there are high-stakes decisions to be made based on some threshold. If an aggressive cancer treatment plan with significant side effects were to be undertaken at, for instance, greater than 30% likelihood of cancer, it would be important to differentiate between 29.9% and 30.1%. Precision matters — especially in medicine, clinical operations, technical operations, and for financial institutions that navigate markets and risk, often to capture very small margins at scale.
Challenge experts to scout for bias: Advanced analytics and AI can easily lead to decisions that are considered “biased.” This is challenging in part because the point of analytics is to discriminate — that is, to base choices and decisions on certain variables. (Send this offer to this older male, but not to this younger female because we think they will exhibit different purchasing behaviors in response.) The big question, therefore, is when such discrimination is actually acceptable and even good — and when it is inherently problematic, unfair, and dangerous to a company’s reputation.
Consider the example of Goldman Sachs, which was accused of discriminating by offering less credit on an Apple credit card to women than to men. In response, Goldman Sachs said it did not use gender in its model, only factors such as credit history and income. However, one could argue that credit history and income are correlated to gender and using those variables punishes women who tend to make less money on average and historically have had less opportunity to build credit. When using output that discriminates, decision-makers and data professionals alike need to understand how the data were generated and the interconnectedness of the data, as well as how to measure such things as differential treatment and much more. A company should never put its reputation on the line by having a citizen data scientist alone determine whether a model is biased.
Democratizing data has its merits, but it comes with challenges. Giving the keys to everyone doesn’t make them an expert, and gathering the wrong insights can be catastrophic. New software tools can allow everyone to use data, but don’t mistake that widespread access for genuine expertise.
Accion Business Loans: 2022 Review
The bottom line: Accion loans are a good option for borrowers who’ve been in business for three months or more and have been turned down by other lenders.
Pros and Cons
A broad range of loan amounts from $5,000 to $100,000.
Loans are available to businesses in operation for as little as three months.
Expanded credit guidelines for borrowers.
Customized loan terms.
No prepayment penalty.
It can’t be used to get a business off the ground.
Shorter loan repayment periods of one to five years.
Slow processing speed compared to online lenders.
Not available in all U.S. states.
Accion Opportunity Fund is a nonprofit community lender offering customized loans to small business owners throughout most of the U.S.
Over 80% of Accion clients identify as women, people of color or immigrants. In addition to small business loans, educational resources and coaching support in English and Spanish are also provided.
Accion is best for borrowers who:
Prefer customized options. Loan terms are structured based on your business needs.
Don’t have perfect credit. Factors other than your credit score can be used to determine qualification.
Have new businesses and can’t get funding elsewhere. Businesses only need to be in operation for three months to apply.
Accion loan features
From $5,000 to $100,000.
5.99% to 14.99% for Small Business Progress loans.
4% subsidized rate for Southern Opportunity And Resilience, or SOAR, loans for businesses located in certain southern states.
3.99% to 6.99%.
12, 24, 36 or 60 months.
5-7 days for loan application to be processed.
Where Accion stands out
Expanded credit guidelines for borrowers
Accion says that most of its borrowers have not been able to get loans with traditional lenders because they have poor credit, no credit history or require a small loan amount. Accion can use more than a borrower’s credit score to determine qualification for a business loan.
Customized loan terms
Accion can structure a loan to meet your specific business needs. After submitting an application, you may be able to choose from several loan options with different term lengths, interest rates and payment amounts. In addition, if Accion can’t provide a loan, it will refer you to one of its partners or provide other financing options for you to explore.
Additional services offered
Accion does more to help small businesses than just offering loans. Business coaching and mentoring are also available. You can set up an appointment for one-on-one assistance provided by a business expert. Your coach can also help you enroll in training programs to enhance your leadership skills. In addition, its resource center offers videos, articles, and interactive learning materials.
Where Accion falls short
Funds can’t be used to start a business
Accion loans are designed to support existing small business owners. But, again, your business must be in operation for a minimum of three months to qualify for an Accion loan. That means you won’t be able to use loan funds to start a business.
Loan programs aren’t available in all U.S. states
Accion loans are available in most U.S. states, but you won’t be eligible if your business is located in Montana, North Dakota, South Dakota, Tennessee or Vermont. Also, Southern Opportunity and Resilience (SOAR) funding is limited to businesses located in Alabama, Arkansas, Delaware, Florida, Georgia, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Texas, Virginia, or Washington, D.C.
Accion loan requirements
Credit score: No minimum required.
Time in business: Minimum of 3 months in business.
Annual revenue: Varies depending on the loan program.
How to apply for a loan from Accion
After completing an application online, you’ll receive a quote. Accion says that the quote won’t affect your credit score. You will need to provide some basic information about your business, including revenue and expenses. Accion will then review your loan options with you, including interest rates, repayment amounts and the repayment period. If there are no options that work for you, Accion can refer you to other resources.
If you decide to move forward with the loan offer, you’ll be asked to provide documents that Accion can use to verify the information you provided on your application. After that, your loan will be finalized; you’ll sign loan documents and then receive funds.
Alternatives to Accion
An SBA loan is another option to consider. These loans are offered through banks but partially guaranteed by the Small Business Administration. This can make it easier to qualify because the lender takes on less risk. In addition, funds from an SBA loan can be used to start a business. This differs from an Accion loan, which requires your business to operate for a minimum of three months to qualify. SBA loans also offer flexibility when a borrower has less-than-perfect credit.
Kiva is another nonprofit that is an option to ponder. You can get up to $15,000 at 0% interest if you qualify. Kiva loans don’t require a minimum credit score or collateral. Still, there are other eligibility requirements, such as the business must be based in the U.S. and you can’t currently be in foreclosure, bankruptcy or under any liens. One unique Kiva provision is that borrowers are asked to demonstrate their strength of character by having friends and family make loans to them.
Compare business loans
If you’d like to compare loan options, NerdWallet has a list of best small-business loans. All of our recommendations are based on the lender’s market scope and track record, the needs of business owners, rates, and other factors so that you can make the right financing decision.
Best Merchant Services of 2022
Merchant services allow a business to accept credit and debit card transactions by transmitting the customer information to the card network and issuing bank and giving businesses access to the payments received.
The companies that offer merchant services vary in the related products and services they offer, and in their pricing models (flat rate, interchange-plus, membership). Here’s our list of the best merchant services and what sets them apart.
Helcim: Best overall option
Payment processing: In addition to the interchange rate — which is set by the card issuer and generally ranges from 1% to 3% — you also pay a processing fee (hence the term “interchange plus”). For a monthly card processing volume of up to $25,000, the markup is 0.3% plus 8 cents per in-person transaction and 0.5% plus 25 cents per keyed and online transactions. Lower rates are available for higher volume levels.
Hardware: Card reader is $109. Stands, printers and other equipment available through Helcim Shop.
Why we like it: Helcim’s transparent pricing, lack of monthly fees and volume discounts are what pushes it to the top of the list. It’s easy to sign up for an account online by providing some basic information. And without a contract or cancellation fees, there’s no penalty to close your account. Funds from your transactions are deposited within one to two business days. Customer support is available weekdays from 7 a.m. to 7 p.m. Eastern time and on the weekends from 9 a.m. to 5 p.m. Eastern time. You can sync data with both QuickBooks Desktop and Online. Other integrations include WooCommerce, Magento and Zone 4.
Square: Best flat-rate option
Payment processing: Flat-rate pricing model that charges 2.6% plus 10 cents per in-person transaction, 3.5% plus 15 cents per keyed transaction and 2.9% plus 30 cents per online transaction.
Software: Free option.
Hardware: Free card reader. A register costs $799 or $39 a month for 24 months.
Why we like it: Square is our top pick for flat-rate pricing with no monthly fees, low transaction rates and free virtual terminal. It can accommodate all types of credit card transactions. You receive your funds as fast as the next business day for free, or you can pay a fee to receive funds instantly. Square offers free dispute management for chargebacks and doesn’t charge processing fees for customer refunds. Free phone support is available during the week from 6 a.m. to 6 p.m. Pacific time. Square integrates with QuickBooks, Xero, Stitch Labs and other popular apps.
Accept payments without worry
See our payment provider recommendations that fit your business.
Dharma: Best for e-commerce
Payment processing: In addition to the interchange rate, a processing fee is charged. For Visa, Mastercard and Discover, that’s 0.15% plus 8 cents per in-person transaction. Rates for American Express in-person transactions are 0.30% plus 11 cents. And you’ll pay 0.2% plus 11 cents for keyed and online transactions.
Software: $25 monthly fee.
Hardware: Terminals start at $229 and a Clover Mini standalone device can be purchased for $749.
Why we like it: Dharma specializes in helping e-commerce businesses and has one of the lowest rates for card-not-present transactions. Businesses with monthly card sales over $100,000 or more than 5,000 transactions may qualify for volume discounts, as well as restaurants with average ticket amounts of less than $25. Funding is guaranteed in two business days. Customer support to process your card transactions is available 24 hours a day. You can export data into an Excel file to import into QuickBooks.
Stripe: Best flat rate for online sales
Payment processing: Flat-rate pricing model that charges 2.7% plus 5 cents per in-person transaction and 2.9% plus 30 cents per online transaction.
Software: Free option.
Hardware: Card readers cost $59 and up. A POS register is $249.
Why we like it: Stripe is best for online sales because it supports processing payments in multiple currencies, allowing customers to charge in their native currency and businesses to receive funds in theirs. Payments are typically processed in two business days. Stripe integrates with a large number of apps and automatically syncs with QuickBooks and NetSuite. You can use the developer tools in Stripe Terminal and pre-certified card readers to build your own in-person checkout system.
Payment Depot: Best for large transaction amounts
Payment processing: In addition to the interchange rate, 15 cents per transaction is charged. This could be less depending on the plan selected.
Software: Plans starting at $79 per month.
Hardware: Free and up. Terminals and POS systems from Clover, Ingenico and other brands available for purchase.
Why we like it: Payment Depot offers membership plans that give businesses access to wholesale interchange rates at a set fee per transaction. It’s an independent sales organization that handles merchant accounts for Wells Fargo Bank. You can get access to next-day deposits based on the membership plan you select. Support is available 24/7 through the bank. Payment Depot integrates with Shopify, OpenCart, QuickBooks, PrestaShop, Shift4Shop, BigCommerce, WooCommerce, Magento, Zen Cart, Revel, NCR and Authorize.net.
PaymentCloud: Best for high-risk businesses
Payment processing: Rates determined on a case-by-case basis.
Software: $10 and up monthly.
Hardware: A card reader and terminal included with the account. Mobile POS systems, terminals, POS registers, kitchen printers, kiosks and other devices can be purchased.
Why we like it: PaymentCloud specializes in services for high risk industries, although they also offer services to low and medium risk businesses. Payment processing is available for in-person, online, mobile, keyed and cryptocurrency transactions. It has over 10 banking relationships that can be used to secure a merchant account for your business. Next-day payment processing is offered as part of retail POS services. The platform integrates with QuickBooks and most shopping carts including BigCommerce, WooCommerce, Shopify and Magento.
National Processing: Best for customized rates
Payment processing: In addition to the interchange rate, fees based on business type are charged. For example, 0.14% plus 7 cents per transaction for restaurants, 0.18% plus 10 cents per transaction for retail businesses and 0.29% plus 15 cents per transaction for e-commerce business are applied.
Software: $9.95 per month or more based on industry.
Hardware: A mobile reader is included with most plans at no additional cost. Based on the plan you select, a terminal and PIN pad may also be included. A large number of POS devices are available including Clover hardware.
Why we like it: National Processing customizes its fees based on industry and risk. For example, the rate a restaurant pays is less than that of a retail organization. Also, processing services are offered for some high-risk businesses. You can expect to receive your funds in two days with an opportunity for next-day deposits. Phone support is available 24/7. Integrations are offered for popular business apps including QuickBooks, WooCommerce, Ecwid, Zendesk, BigCommerce, OpenCart and Shopify.
QuickBooks Payments: Best for QuickBooks loyalists
Payment processing: Pricing varies. QuickBooks Online users pay 2.4% plus 25 cents per in-person transactions; 3.4% plus 25 cents per keyed transactions; and 2.9% plus 25 cents for invoiced transactions.
Software: Free and up.
Hardware: A PIN pad costs $389 and a hardware bundle that includes a cash drawer, receipt printer, wired barcode and PIN pad is $900. Additional devices available.
Why we like it: For loyal QuickBooks users, QuickBooks Payments can process online, in-person and invoiced transactions. Payment for the next business day is typically available when the cutoff time of 3 p.m. Pacific time is met. Phone support is available Monday through Friday from 9 a.m. to 8 p.m. Eastern time. QuickBooks Payments integrates with Shopify, Amazon, eBay, WooCommerce, Magento, BigCommerce, Walmart and Etsy shopping carts.
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