News & Insights

Guest data captured at the point of booking is quietly falling apart before the stay even begins, and peak season is when it costs the most. A guest books a weekend away for the August bank holiday. Room type, dates, total cost, all confirmed on screen. Then nothing. No confirmation email lands in their inbox. Ten minutes later, they're calling the front desk to check the booking actually went through. Nobody did anything wrong here. The booking engine worked. Payment went through. What broke is quieter than that, and far more common than most hospitality businesses realise. The address you have isn't always the address you think you have A significant share of hotel bookings now arrives through an OTA (an online travel agency, like Booking.com or Expedia) rather than direct. That's not new. What's less well understood is what actually lands in the property management system when they do. Many OTAs pass through a masked or proxy email address rather than the guest's real one, generated specifically for that booking and often expiring shortly after the stay ends. It looks like a valid email address. It behaves like one, right up until the property tries to use it for anything beyond the original booking confirmation. Direct bookings aren't much safer. Industry estimates put the proportion of invalid addresses from manually entered guest data at 20 to 45%, a misspelt domain, a transposed digit, a typo made booking late at night on a small phone screen. None of that shows up as a problem until the moment it matters: a pre-arrival email, a late check-in code, a parking permit, a table reservation confirmation. Why this one email matters more than most Booking confirmations aren't treated like other guest communications, because guests don't treat them like other emails. Open rates for confirmation emails run considerably higher than standard marketing sends, and the expectation isn't "eventually"; it's immediate. A guest expects that confirmation within minutes, not by the end of the day. If it doesn't land straight away, the natural read isn't "it's still processing"; it's "something's gone wrong". A guest who doesn't receive a confirmation doesn't shrug it off. They worry, then they call, then someone on your team spends five minutes confirming something that should have taken none. Peak season is when this compounds None of this is especially visible in a quiet month. A handful of bounced confirmations, a few guests calling to check, easily absorbed. Peak season changes the maths. Higher volumes mean more bad records moving through the system at once, and less staff time available to catch each one before it becomes a guest's problem rather than a data problem. The property that could quietly absorb ten failed confirmations in March is dealing with a hundred in August, right when front desk and reservations teams are already stretched thinnest. Peak season doesn't create this problem. It just makes the one that was already there impossible to ignore. What this actually costs The cost isn't just the phone call. A guest who's already anxious about whether their booking is real arrives in a different frame of mind than one who received a warm, accurate pre-arrival email three days before. Upsell opportunities in that pre-arrival window - an early check-in, a room upgrade, a spa slot, depend on the guest actually receiving it in the first place. None of that happens if the address behind it was never valid to begin with. The fix sits earlier than most teams look The instinct when a confirmation bounce is to fix it after the fact: resend, follow up by phone, apologise. The more useful moment is earlier, verifying email and phone at the point they're captured, whether that's a direct booking form or a reconciliation step against whatever an OTA has actually handed over. In practice, that looks like validation built into the booking form itself: if a guest mistypes their email, the system flags it before they hit submit, the same way a checkout might flag an invalid card number. This is exactly what Fetchify does for hospitality businesses: verifying contact details at the source, so the confirmation, the pre-arrival email, the parking permit, all have somewhere real to land. Caught there, it never becomes a guest-facing problem at all. Getting this right at the point of entry does more than protect a confirmation email. It's a faster, less frustrating booking process for the guest, since a flagged typo takes a second to fix rather than derailing the whole form. It's cleaner data feeding into retargeting and post-stay marketing, since accurate contact details are what make audience targeting worth doing in the first place. And it's a real inbox to send a review request to once the stay is over, rather than one more email quietly bouncing into nothing. If your team is capturing guest contact details manually, or reconciling them from OTA bookings, catching the invalid ones before check-in is more straightforward to fix than it sounds. More on how Fetchify helps hospitality businesses keep guest data accurate below.

What is PAF? The Postcode Address File (PAF®) is Royal Mail’s definitive database of every deliverable address and postcode in the UK. It covers over 32 million delivery points and is updated monthly. If your business relies on accurate address data, at checkout, in your CRM, or for deliveries, PAF is the source that keeps it current. July 2026 in numbers Royal Mail made 54,025 changes to PAF this month. That is not a small number. It represents new homes that need delivering to, businesses that have moved or closed, streets that have been renamed, and addresses that were simply wrong and have now been corrected. Every one of those changes is a record in someone’s database that may now be out of date, and a delivery, a campaign, or a customer communication that could go wrong if the data hasn’t been updated. Delivery point changes at a glance Here’s the full breakdown of what changed, amended, and was removed from PAF in July:

The Address Is Only Half the Journey. Meet nShift. Fetchify ensures the address is correct at checkout. nShift makes sure the parcel gets there. A validated address is a great start, but it's only half the journey. From there, the parcel still has to find the right carrier, the right label, and the right doorstep. That's a different problem, and it's the one nShift solves. What nShift does nShift is a delivery management platform connecting businesses to 1,000+ carriers across 190 countries through a single integration, checkout, delivery options, carrier selection, tracking, and returns, all in one place instead of a separate system per carrier. Customers using nShift see a 20% increase in conversions at checkout and up to 60% fewer "where is my order" queries. Superdry is a good example with 515 stores, 21 websites, and shipping to 100+ countries. As the business scaled internationally, nShift let them onboard new carriers fast and get full visibility across every shipment. As Gordon Knox, Superdry's Business Transformation and Logistics Director, put it, onboarding carriers quickly was essential to a growing international business; that's exactly what they got, plus the data to hold carriers accountable on cost and service. Why we're recommending them Some of nShift's own clients already use Fetchify to validate their checkout data, so we've seen firsthand what good address data unlocks downstream in nShift's platform. That's the real reason for this partnership: the two products solve adjacent halves of the same problem, and we've watched it work in practice. Fetchify validates the address in real time, catching typos, missing flat numbers, and misspelt street or town names as the customer types, without slowing the checkout down, and confirms phone and email are live at the same time. That clean, structured data flows straight into nShift, which picks the right carrier automatically and keeps the customer updated with branded tracking. No reformatting. No manual fixes. No booking failures from bad data. The result: higher conversion, fewer failed deliveries, fewer support tickets, because the two weakest links in the checkout-to-doorstep chain (bad addresses, clunky carrier logistics) are both handled properly. Who this is for Any ecommerce business shipping physical goods stands to benefit, especially if you're juggling multiple carriers, shipping cross-border, or scaling into markets where one carrier doesn't cover it. If delivery reliability has become as much of a pain point as address accuracy, this is worth a look.

Why membership organisations can't afford to confuse data failure with genuine attrition, and what to do about it. Membership organisations are meticulous about tracking renewals. Lapse rates, retention percentages, and win-back campaign performance. The numbers are watched closely because every member lost represents real, recurring revenue that is hard to replace. But there is a category of membership loss that most organisations are not measuring at all, because it does not look like a loss. The renewal notice went out. The direct debit ran. The email was sent. On paper, everything worked. The member just never received any of it, because the contact details in the CRM are no longer correct. That is not attrition. It is a data failure. And across an industry that collectively manages tens of millions of member records, the scale of that problem is significant. The context that makes this more urgent Discretionary memberships are under pressure. The cost-of-living squeeze that tightened household budgets from 2022 onwards has made memberships that feel optional the first thing to go when money is tight. Even organisations with healthy long-term growth are seeing more volatility in year-to-year renewals as a result. In that environment, the last thing any membership organisation can afford is to also lose members it could have kept. Where membership data goes wrong Membership databases face a specific version of the data decay problem. Individual consumer databases decay because people move house, change email providers, and update their details without telling organisations they have. Membership databases face all of that, and an additional layer. For organisations with corporate or trade members, a single record represents an organisation rather than a person. The contact within that organisation (the membership secretary, the finance director, the branch representative) changes. People move on, retire, change roles. When they do, the relationship between the membership organisation and its member frequently breaks down not because the member chose to leave, but because communications are still going to someone who is no longer there to receive them. The result plays out across three specific failure points: EMAIL The most common and least visible failure. A contact leaves, their email address is deactivated, and every communication sent to that address (renewal notices, event invitations, membership benefits updates) vanishes. Hard bounces accumulate quietly. The member organisation receives nothing and assumes the membership is simply not being renewed. The membership body assumes disengagement. Neither has the full picture. BANK AND DIRECT DEBIT DETAILS For memberships renewed by direct debit, banking changes are a silent killer. A company changes its banking provider. A new finance director updates account details. The existing direct debit mandate becomes invalid, payments fail, and depending on how the failure is handled, the membership lapses without the member organisation ever intending to cancel. Card payments carry a similar risk. An expired card on file can produce the same quiet failure, particularly for individual members renewing on their own card. ADDRESS AND CONTACT DETAILS Physical correspondence, including renewal packs, membership cards, and formal notices, still matters for many membership organisations. When a member company moves, changes its registered address, or restructures its office function, paper communications go astray. The record in the CRM shows an address that was correct at the point of joining. Three years later, it reflects a reality that no longer exists. The numbers behind the problem The UK's largest membership bodies collectively manage memberships in the millions. MemberWise's Influence 100 list puts total membership across the top 100 UK bodies at over 40 million. Apply the standard data decay rate of 30% per year to a sector managing membership records in the millions, and the scale of the problem becomes clear. For an organisation with 100,000 members that has not run a data cleanse in the past twelve months, somewhere in the region of 30,000 of those records may now contain at least one material inaccuracy. Why it's harder to spot in membership organisations In eCommerce, data quality problems show up quickly. A failed delivery generates a return. A hard bounce triggers an alert. The feedback loop is short enough that the problem surfaces before it compounds too far. In membership organisations, the feedback loop is annual. Renewals happen once a year. A contact detail that goes stale in February may not cause a visible problem until the following January, when the renewal communication fails to land. By then, twelve months of communications have been going to the wrong place, the member has had no contact from the organisation, and the lapse looks, from the outside, like a deliberate decision. What good data management actually covers Many membership organisations now offer self-service portals where members can update their own contact and payment details directly, and that is genuinely useful. When members engage with it, the CRM stays current without any manual intervention. The practical limitation is engagement. Members update their details when something prompts them to: a failed payment, a bounced communication, or a prompt at renewal. Between those moments, contact details drift. Validation and data cleansing work alongside a portal rather than instead of it. Validation at the point of update, whether a member is joining, renewing, or updating their details, catches errors as they enter the system. Address, email, and bank account validation each do a specific job: • Address validation confirms correspondence will reach the right location, checked against the current Royal Mail PAF data. • Email validation identifies inactive addresses before renewal notices go out. • Bank account validation confirms direct debit mandates are still valid before payment runs are processed. Data cleansing handles the records that validation at capture cannot reach: the existing database. A cleanse run against current address and contact databases identifies records that have drifted since joining, flags emails with persistent bounce history, and surfaces direct debit details that are no longer valid. Done ahead of a renewal cycle, it means communications go out to an accurate list rather than one that reflects the membership as it existed twelve or eighteen months ago. The organisations that manage this well are not necessarily the ones with the lowest lapse rates. But they are the ones that know, with confidence, which part of their lapse rate is real attrition and which part is recoverable, because their data tells the difference. Starting the conversation For most membership organisations, data quality sits in the gap between the membership team and the IT or CRM function. It is everybody's problem and nobody's priority, until a renewal cycle underperforms and the question of why becomes harder to answer. The most effective way to move the conversation forward is to quantify it: how much of your lapse rate is genuine attrition, and how much is invisible data failure that a bounced email, a failed direct debit, or an unverified record has been quietly hiding. Find out where your membership data stands Fetchify's validation tools cover address, email, and bank account data, helping membership organisations keep records current at the point of capture and across existing databases. Speak to the team or explore the tools below.

Fetchify has added Canada Post's address data to its datasets, bringing the same quality of address coverage to Canada that our customers already rely on for UK addresses. We talk to our customers a lot. And over time, a consistent theme emerged: businesses operating across multiple markets needed the same standard of address data in Canada that they relied on from Fetchify everywhere else. So, we did something about it. Fetchify has added Canada Post's address data to its datasets, giving our customers access to the most authoritative address coverage available in Canada. What the data covers This data is Canada Post's licensed address directory, covering over 14 million physical locations across Canada. Every address carries a unique, permanent code that maps to a specific physical location, making it the definitive reference point for Canadian address validation. Canadian addresses also follow a different structure to the UK, with alphanumeric postcodes rather than numeric, which is exactly the kind of variation that trips up validation built around a single country's format. Coming directly from Canada Post, which means it is maintained, authoritative, and consistent in a way that approximated or third-party alternatives simply are not. It is the definitive source, and that is what makes it worth using. Who does this matter for Canadian address quality is most critical for businesses that operate across multiple markets and need consistent data standards everywhere they trade. A global brand selling online in the UK, Europe, and North America cannot afford to have its Canadian address validation performing at a different standard to everywhere else; the delivery failures, the checkout friction, and the customer experience problems show up just the same. For businesses with significant Canadian order volumes, the difference between good and poor address data is measurable in: Checkout completion rates, where validation that fails to recognise a valid Canadian address creates friction or abandonment First-time delivery success, where address inconsistencies mean parcels miss their destination and generate redelivery costs Customer data quality, where addresses captured incorrectly at checkout accumulate in the CRM and compound over time These are the same problems that poor address data causes in any market. Canada simply had fewer options for solving them reliably. Accessing the gold standard for Canadian address data If Canada is part of your footprint, the case is a simple one. Royal Mail's PAF is the reason UK address validation works as well as it does; it's the definitive source, and nothing else really competes with it on that ground. Canada Post's data plays the same role for Canadian addresses. If you want that level of confidence on the Canada side of your business, too, this is how you get it, through the same integration your team already uses. Need access to this dataset today, or want more details? Reach out to your account manager or contact us at support@fetchify.com . 

How data decay is quietly removing your best customers before they ever decide to leave. Somewhere in your CRM right now, there is a customer you think you lost. They stopped buying about eighteen months ago. They went into a lapsed segment, got a couple of reactivation emails, did not respond, and were eventually written off. The assumption was that they moved on. What actually happened, in a surprising number of cases, is much simpler. They moved house. The reactivation emails went to an inbox they no longer check. The direct mail went to a flat that has a different tenant. The customer was not gone. They were just unreachable. And because the database had no way of flagging the difference, they were counted as churn. This is how data decay works. Not in dramatic failures, but in a steady accumulation of records that have quietly stopped being accurate. Around 30% of customer data goes stale every year, not because anything went wrong, but because people move, change jobs, switch email addresses, or get married. Left unaddressed, that figure compounds. A database that has not been properly maintained for three years may have a third of its records either partially or wholly unreachable. The problem is that it is almost invisible until it is already significant. A handful of bounced emails does not raise an alarm. Neither does a slightly elevated returns rate. The metrics look broadly normal because the volume of bad data is not yet high enough to distort them. By the time it is, the damage is done. The churn you cannot account for Most businesses have a reasonable handle on the customers they actively lose. Cancellations are tracked. Lapsed accounts are flagged. Retention programmes exist precisely to address the customers who stop buying. What those programmes cannot reach is the customer who never formally left. They sit in the CRM as a lapsed record. They count toward the database size. They get included in reactivation segments. They cannot receive the communication because the address on their record is no longer valid. The downstream effect is real. A repeat customer whose address changed after a house move never receives the offer that would have brought them back. A lapsed member does not see the renewal reminder and lets the subscription quietly expire. In both cases, the organisation records an attrition event. In neither case did the customer actually decide to leave. A customer who moved house is not the same as a customer who left. That distinction tends to matter quite a lot when you are trying to work out where your retention budget should go. Why reactivation campaigns underperform When a win-back campaign comes back with poor results, the instinct is to interrogate the campaign. The subject line gets tested. The offer gets more aggressive. The timing gets adjusted. All of that is reasonable. None of it helps if a meaningful share of the list cannot receive the email in the first place. A lapsed customer segment typically contains three types of contact: people who genuinely disengaged and are unlikely to respond, regardless, people who might respond to the right message, and people who would respond, but the email never arrives because the address has changed. The frustrating thing is that you cannot easily tell these groups apart from the outside. Low open rates and low click-through rates look the same whether the cause is disengagement or data decay. Email is only part of it. Physical address decay affects direct mail and delivery. Phone number decay affects SMS and outbound calling. Each channel erodes at its own rate, and most organisations are not tracking the accuracy of their data across all of them. 30% of customer database records become inaccurate within 12 months, without any action by the customer. What changes when the data is clean A data cleanse does not just improve deliverability, though it does that. It changes what the numbers actually mean. When ghost records are removed from a lapsed segment, the remaining file is smaller but more meaningful. Reactivation revenue from that cleaned list is real revenue, not a percentage improvement calculated against contacts who were never going to respond. The churn figure, once recalculated without the unreachable records, is often more positive than expected. Some of what looked like permanent attrition turns out to be recoverable. There is a GDPR dimension too. Article 5(1)(d) requires that personal data be kept accurate and, where necessary, up to date. The ICO can issue fines of up to £17.5 million for data accuracy failures. Most organisations are not at serious risk of enforcement, but most organisations also have not checked how their database holds up against a standard they are legally required to meet. The more common consequence is commercial rather than regulatory. Marketing budgets applied to an inaccurate list simply do less than they should. The same spend, against a validated file, produces measurably better results. Not because the campaigns improved, but because the contacts can actually receive them. The practical starting point Addressing data decay does not require a significant IT project. For most organisations, the starting point is a cleanse of the existing CRM: matching records against current address databases, identifying email addresses with persistent bounce history, removing duplicates, and flagging phone numbers that are no longer in service. Done once, it resets the foundation. Done regularly, and combined with validation at the point of data capture, it prevents the drift from accumulating again. The customers in those unreachable records did not all decide to leave. Some of them are still out there, still buying in your category. They just moved. Improve your data health and protect your business today. Reach out to our team below for a free data health check.

Jay’s career has never followed a straight line. Electronics engineering. Automotive systems. A social app for hostels that was about to launch just as COVID closed every hostel in the world. A pivot into web development. And eventually, Fetchify - where he now leads the team building the technology that keeps millions of data lookups running accurately every day. Looking back, the route makes perfect sense. Jay has always been drawn to what’s next. To faster feedback. To building things that work and seeing them work quickly. Software gave him all of that in a way that automotive engineering, for all its complexity, eventually stopped doing. The long way round Jay studied electronics engineering and came out of university specialising in embedded systems. By 2015, he was working on automated parking systems - the kind built on sensors and split-second decisions - and for a while, he found it genuinely interesting. But something was missing. “I wanted to see results faster,” he says. “With embedded systems and automotive work, the feedback loops are long. I wanted to build something and see it working.” So, he pivoted. He taught himself mobile development and from there, a startup building a social app for hostels and hotels - a platform that matched guests by shared interests, so someone travelling alone could find other guests up for the same activities. It was a genuinely good idea, with a handful of places trialling the beta version. Then 2020 arrived, the hospitality industry stopped overnight, and the timing simply couldn’t have been worse. Most people would have counted it as a setback. Jay counts it as part of the story. Finding something that fits He joined ClearCourse, initially working on the membership CRM side of the business. When a role came up at Fetchify, he knew it was the one. Tech Lead. A team to run. Real scope to build, improve and innovate - and enough space to do it properly. “What I love most about my job is the chance to be innovative and improve the quality of the software - and the opportunity to keep learning. There’s always something new.” His approach to leading the team reflects the same values. He talks about trust a lot - giving people the space to do things the way they think makes sense, rather than prescribing the path. The team checks in daily, whether that’s to swap ideas, talk through a problem, or join a scrum call. It’s not just his immediate team either: the wider Fetchify team, and within the ClearCourse group, there’s a culture of helping out. Of people being willing to lend a hand when it’s needed. “Software development can feel like a solo job, but actually the team here is solid, and we enjoy working together.” The thing he's most excited about Ask Jay what he’s most passionate about right now, and the answer is immediate: AI. Not in an abstract, trend-chasing way - but with a specific and considered view of what it actually means for software developers and the organisations they build for. “AI is raising the bar for what developers can produce. But I see it as a two-way collaboration - a helping hand to do the grunt work, while the ideas, the creativity, the innovation still come from people. It should help people achieve more in less time. Not replace the thinking.” His long-term goal is to help other ClearCourse businesses integrate AI into their products - starting, naturally, with Fetchify. For a company built on data accuracy, the intersection of clean data and AI capability is not an abstract future conversation. It’s already the direction of travel. Beyond the screen Jay grew up in Egypt, and travel is still one of the things he values most. He heads home to family a couple of times a year, and fits in city breaks wherever he can - somewhere new, with good food and different people and things to explore. His ideal off-duty scenario involves a beach, good conversation, and absolutely no particular agenda. The gym, friends and music round it off - time away from the screen that, for someone whose working life involves building technology that processes millions of data points a day, seems like a fairly sensible skill. When he imagines the distant future - the looking-back version - he pictures a career of creation, innovation and the willingness to embrace whatever comes next. That, and a beach somewhere warm. We’re very glad the winding road brought him to Fetchify.

There is a lot of enthusiasm right now about what AI can do for ecommerce and CRM teams. Personalisation at scale. Predictive analytics. Automated outreach that learns and adapts. The pitch is compelling, and much of it is real. But there is a foundational question that almost nobody is asking loudly enough: what happens when you run AI on bad data? The answer is not that the AI fails gracefully. The answer is that it fails at scale, confidently, and in ways that are harder to trace than a simple spreadsheet error. This is not a theoretical risk. It is already happening inside the organisations that have moved fastest to adopt AI-driven tools without first addressing the quality of the data those tools run on The assumption nobody questions Most organisations treat AI as a layer that sits on top of their existing data. Feed in the CRM, connect the customer database, and point the model at the transaction history. The assumption is that AI is smart enough to work around imperfections. It is not. AI systems are pattern recognition engines. They find what is consistent in the data and treat it as a signal. If your data consistently contains errors - outdated addresses, duplicate records, lapsed contacts still marked as active - the AI learns those patterns as the truth. It bases its predictions, segments, and recommendations on a foundation that does not reflect reality. B2B contact data decays at 30% per year. For a database of 100,000 records, that means 30,000 entries become inaccurate every 12 months. When an AI personalisation engine is drawing on that data to decide who to target, when to contact them, and what to offer, it is working with a picture of your customer base that is one-third wrong AI doesn't fix bad data. It amplifies it. What this looks like in practice The problems that emerge are not dramatic. They are quiet and cumulative, which makes them harder to catch. Automated email sequences reach the wrong people or the wrong addresses, generating hard bounces that damage your sender reputation and, in serious cases, trigger blocks from email service providers. Personalisation that references a customer's last purchase or location draws on a record that has not been updated in two years. Predictive models identify high-value customers to target for retention campaigns - but a portion of those customers moved, changed roles, or lapsed long ago. Each of these is a cost. Collectively, they represent a significant drag on the performance of tools that were supposed to be driving efficiency. The irony is that AI makes these problems less visible, not more. A human reviewing a list might notice that an address looks wrong. An AI processes it at speed and acts on it. A case study: what happens when AI meets dirty data A professional services firm recently experienced this directly, who work with our sister company FLG for lead management. The team began bulk emailing an existing database through their email marketing system - a reasonable use of automation for a business trying to re-engage contacts at scale. The data, however, was old. Hard bounces accumulated quickly, and their account was flagged and blocked from sending. Fetchify cleansed the data. Contact information was standardised, and inactive or undeliverable entries were identified and removed. When they resumed outreach, the results were immediate - higher engagement, no delivery issues, and the kind of performance the automation was always supposed to deliver. The AI-driven outreach did not fail because of the tools. It failed because the data had not been maintained. Once the data was clean, everything else worked as intended. The AI readiness question organisations should be asking As AI becomes a standard component of ecommerce and CRM operations, the conversation around data quality needs to change. It is no longer just a compliance issue or an operational nicety. It is a prerequisite for AI to function as intended. Before deploying any AI-driven personalisation, automated outreach, or predictive analytics tool, the right question is not 'which AI platform should we use?' It is 'is our data clean enough for AI to learn from?' For most organisations, the honest answer is no - not without first running a data cleanse. The good news is that this is not a complex or expensive process. It is a one-time exercise that resets the foundation, followed by ongoing validation to prevent decay from accumulating again. What clean data actually enables Organisations that address data quality before deploying AI achieve fundamentally different outcomes. Personalisation engines draw on accurate records and produce recommendations that reflect the real customer base. Automated outreach reaches real inboxes and generates real responses. Predictive models identify genuine opportunities rather than ghost records. The regulatory dimension is worth noting, too. The ICO can issue fines of up to £17.5 million or four per cent of global annual turnover under UK GDPR for data governance failures. AI that acts on inaccurate or out-of-date data does not protect organisations from that exposure - it amplifies it, at speed and scale. Clean data is not an enhancer of an AI strategy. It is the essential prerequisite that makes an AI strategy viable. The organisations seeing the best results from AI aren't necessarily the ones with the best tools. They're the ones with the cleanest data. Start with a free data health check and find out where you stand.