In another study by Deloitte<\/a>, a startling 71% of participants who were shown data about them said that their data was only 0 to 50% accurate.<\/p>\n If you’re looking for ways to cleanse and enrich your customer database without having to spend days doing it manually, we can do it for you. On average, it takes 50 hours to fully cleanse, standardize and enrich a database of 1000 customers. To learn more about how we can do this for you in a fraction of the time,\u00a0contact us for a free consultation<\/a>.<\/p>\n Many companies in the study were either unaware of this problem or surprised by the bad data. Half of them were aware of it and not surprised, but hadn\u2019t done anything about it.<\/p>\n It\u2019s an expensive problem to ignore.<\/p>\n Bad data has cost companies $611 billion in the US alone<\/a>.<\/p>\n Without good data, everything suffers:<\/p>\n Bad data is an emergency. It\u2019s non-negotiable.<\/p>\n To stop flushing money down the data toilet, we\u2019ve compiled this guide on how to pick the right data quality tool.<\/p>\n In this guide, we will cover:<\/p>\n Let’s start with the recommendations, then we’ll go into how to choose them.<\/p>\n The top data quality tools in 2020 are:<\/p>\n We break them down below based on your company size.<\/p>\n Our favorite tools for the most basic needs are the following:<\/p>\n In this case, you have around 10,000-1 million contact points. The above methods would be too manually intensive and make no sense here. Your annual revenue is $2-$50 million. You’re scaling but your needs aren’t as extensive as an enterprise.<\/p>\n You need a tool that works across your CRM, email marketing provider, and other databases. Ideally you need a tool with no learning curve, no need for training, and that works in your current systems.<\/p>\n We took a close look at 13 of the <\/em>best data cleaning tools for cleaning customer data.<\/em><\/a> Check it out if you need a data check before moving onto a new CRM.<\/em><\/p>\n Large enterprises with over $1B in revenue have very complex databases and needs. You need data conversion, data mapping, data transformation, ETL, data migration, data integration, EDI, data warehousing, data extraction, data quality and data virtualization, and more.<\/p>\n In this case, we recommend the following tools:<\/p>\n According to our own data, it takes a human about 3-5 minutes<\/strong> to clean one data point.<\/p>\n That\u2019s for non-complex, complete data, with maybe just a misspelling or a couple of items in the wrong fields. If there are complex duplicates or you need more data, it takes much longer.<\/p>\n This is the basic metric to use when deciding whether you should do this in-house or not. If you have a few 100 contacts in your database, then it\u2019s just a few days of labor. In this case, it might be okay to do it manually, in-house.<\/p>\n But if that were the case, you probably wouldn\u2019t be on this page looking for a data quality tool.<\/p>\n One of our clients, Vetrotech<\/a>, had over 80k data points in its database. It would\u2019ve taken their staff 4,200 hours to clean it. That’s $75.6k and one staff member working on only this, full-time for 2 whole years.<\/strong> By the end of the 2 years, most of the data would’ve decayed already.<\/p>\n The task would\u2019ve been futile.<\/p>\n We cleaned all of this data in under two weeks.<\/strong><\/p>\n Our tool worked great for Vetrotech, but it might not be what every company needs.<\/p>\n Here\u2019s how to choose the tool that\u2019s right for you.<\/p>\n We created the ultimate email list management bundle for busy marketing leaders who want to make the most of their data<\/p>\n Taking a look at the current state of your data will help you understand what you need. Ask yourself and your team:<\/p>\n What percentage of your data is clean, accurate, and usable?<\/p>\n<\/li>\n Do people ever complain about the data? What\u2019s the most common complaint?<\/p>\n<\/li>\n What systems do you currently have to manage data quality? Are they being followed?<\/p>\n<\/li>\n Can this data be trusted?<\/p>\n<\/li>\n Does the data do what you need it to?<\/p>\n<\/li>\n What does your current data cleaning or maintenance process look like?<\/p>\n<\/li>\n Who is responsible for the data?<\/p>\n<\/li>\n<\/ul>\n Take into consideration the above minimum time frame (3-5 minutes per data point).<\/p>\n In this case, a data point means at the very least first name, last name, job title, company, phone number and email address. But it may include other things.<\/p>\n See how long it would take your staff to do this task, based on the number of data points you have.<\/p>\n If the cost of labor is greater than the cost of a tool, then the financial investment is warranted.<\/p>\n This of course depends on how much you pay for labor and how often you clean your data.<\/p>\n If you use overseas virtual assistants (VAs) and you only clean your data once a year, maybe that’s cheaper.<\/p>\n If you are more cautious with your data, pay higher wages, and clean it continuously, once a week, month, or quarter, then a tool is significantly cheaper.<\/p>\n Related Post: <\/strong>10 Things You Need to Know About Email Hygiene For Large Lists<\/strong><\/a><\/p>\n It\u2019s not only about the cost of labor, but also the time taken away from your employees. Manual data cleaning has the unfortunate nature of being both vital to your business, yet incredibly mundane and tedious.<\/p>\n It\u2019s also low-value work that can be automated. So using your human resources to do this task doesn\u2019t make sense.<\/p>\n The first questions will be ROI-related:<\/p>\n Is a tool cheaper than my workers? And do I want results faster than my workers?<\/p>\n The next consideration is strategic.<\/p>\n Do you want to digitize your business and have automated processes?<\/p>\n Or would you rather stay as a traditional company with manual processes?<\/p>\n When your overall strategy relies heavily on automation tools, data cleaning needs to be automated, too.<\/p>\n Does it make sense to automate your marketing and sales process and then get a human to manually clean data between steps?<\/p>\n We don\u2019t think so.<\/p>\n A fully personalized and automated customer journey only works with automated data cleaning.<\/p>\n Before choosing a tool, you should consider which stakeholders use your company’s data. If you don\u2019t get buy-in from them, you may later find that it\u2019s not compatible with their technology stack or processes.<\/p>\n First question first: who\u2019s in charge of customer data?<\/p>\n Who runs the CRM? Even if it\u2019s you, you should still involve the other stakeholders. If it\u2019s not you, this is the first person to speak to about it.<\/p>\n Get everyone together for a meeting. Find out what their expectations and needs for clean data are.<\/p>\n Chief Marketing Officer<\/strong><\/p>\n Your CMO, VP of Marketing, Marketing Manager, whoever heads the marketing department. Most modern CMOs are data-driven and need data for:<\/p>\n You\u2019ll need their input to see what tech stack they\u2019re using and what kind of integrations they need. Many CMOs don\u2019t believe they are responsible for digital transformation. However, they have a clear idea of the impact they want to make and what they need to do so.<\/p>\n Sales department head<\/strong><\/p>\n Most sales departments today have a wide tech stack and an automated process. They\u2019ll take data from a variety of sources: contact and pipeline data from the CRM and transactional data from the ERP system<\/p>\n A sales department\u2019s figures are only as good as their data. They need to be able to trust it.<\/p>\n Otherwise the valuable time of sales reps is spent updating data instead of doing revenue generating activities. You could say that every sales rep is like a great cook. They do magnificent work but don’t like to clean the kitchen afterwards.<\/p>\n Therefore the CRM looks messy most of the time despite having procedures in place preventing the entry of faulty data. This may work if sales territories don\u2019t get reshuffled or new reps don\u2019t have to take on the sales leads and customers of their peers.<\/p>\n Additionally, sales automation tools don\u2019t work when the underlying data is incorrect or simply not there.<\/p>\n A head of sales also needs internal data. They track which reps perform best, what leads have the shortest sales cycles, and how many cold calls they need to make before closing a customer.<\/p>\n In the best case, their data needs are aligned with marketing, but usually they differ.<\/p>\n Some of them, despite being data-conscious, might not be very data-savvy. So a complicated new data quality tool might be too difficult to implement.<\/p>\n Related Post: <\/strong>Guide For the Modern Marketer: How to Manage Sales Leads in 2020<\/strong><\/a><\/p>\n Head of IT<\/strong><\/p>\n Sometimes this department is in charge of the CRM.<\/p>\n But not always.<\/p>\n The IT department implements and facilitates the flow of information. But it doesn’t necessarily define the process for making sure all that information is correct.<\/p>\n Find out what\u2019s going on in that department and what they need from a data quality tool. How can the tool work with what the IT department already does?<\/p>\n There might be a tool in place that nobody uses right now. How can you get everyone\u2019s buy-in? The Head of IT\u2019s greatest concern is keeping the tech stack manageable. Whatever tool you pick should be easy to integrate or, best case scenario, shouldn\u2019t change the tech stack at all.<\/p>\n Other stakeholders<\/strong><\/p>\n There are other people in your company who may be affected by bad data.<\/p>\n While they won\u2019t play a central role in choosing your new tool, it\u2019s good to be aware of their situation.<\/p>\n For instance, sales reps who can\u2019t reach people in their CRMs because the phone numbers are incorrect.<\/p>\n Customer service reps who have to take complaints about misspelled names.<\/p>\n Marketer\u2019s can\u2019t segment customers & personalize communication because you\u2019re missing important personal information.<\/p>\n These complaints should paint a picture of where you need to fix your data and how quickly.<\/p>\n The main criteria you should look for is one that serves the needs you identified above. Anything more and it may be too expensive or too complicated to implement.<\/p>\n If you\u2019ll only use half the features, opt for a simpler tool. If the tool doesn\u2019t have all the features you need, you\u2019ll have to combine it with another one.<\/p>\n If you believe you’ll need more complicated features down the line, pick a tool that can scale with you.<\/p>\n Pricing is an important feature, but you can figure out what price works best for you by calculating:<\/p>\n Features for a very small company:<\/strong><\/p>\n For a very small company, you just need basic features. At this point, maybe your database is nothing more than an Excel sheet or Mailchimp audience..<\/p>\n In this case, a tool that can fix or flag incorrect information and validate emails is fine.<\/p>\n SMBs:<\/strong><\/p>\n As a company grows towards midsize, the risks of duplicated and messy data gets exponentially worse.<\/p>\n At this size, it\u2019s good to have a data quality tool that can clean data across your company’s different tools. Ideally, you\u2019ll want it to match, dedupe, clean and merge data.<\/p>\n For example, you may have a separate email marketing provider and CRM. The tool needs to be able to merge between these two.<\/p>\n Enterprise-level:<\/strong><\/p>\n This is where things get complex.<\/p>\n There are tools for enterprises that can do anything. These are the most flexible tools, but also the most complicated and expensive.<\/p>\n But there\u2019s a steep learning curve. They often require migration. Some are so complex you need a consultant to come in and educate your staff on how to use the tool.<\/p>\n Check out our comparison post on<\/em>\u00a0Cloudingo vs. RingLead vs. tye, to help you choose the perfect data cleansing software to invest in<\/a>.<\/em><\/p>\n Solopreneur<\/p>\n<\/td>\n SMB<\/p>\n<\/td>\n Small business<\/p>\n<\/td>\n Enterprise<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n If you\u2019ve taken all the above steps before choosing a tool, the choice will be clear. Each tool has different features and works for different kinds of companies.<\/p>\n![\"71%](\"https:\/\/www.tye.io\/wp-content\/uploads\/data-quality-tools.png\" "\\"data-quality-tools\\"")
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Top data quality tools<\/h2>\n
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Best data quality tools for solopreneurs and very small companies<\/h2>\n
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Best data cleaning tools for SMBs: the ultimate data quality automation tools<\/h2>\n
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Best data quality tools for big data and large enterprises<\/h2>\n
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Cleaning your data in-house<\/h2>\n
\u00a0Questions to ask to assess your data quality<\/h2>\n
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When should a company consider investing in a data quality management tool?<\/h2>\n
Cost<\/h3>\n
Speed & efficiency<\/h3>\n
When your business strategy relies on automation<\/h3>\n
What stakeholders should be involved in selecting your data quality tool?<\/h2>\n
![\"data](\"https:\/\/www.tye.io\/wp-content\/uploads\/People-at-a-company-who-need-clean-data-1.png\" "\\"People")
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What features to look for in a data quality tool<\/h2>\n
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Data Quality Tools Comparison Chart<\/h2>\n
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\n \n<\/th>\n Excel<\/th>\n tye<\/th>\n Neverbounce<\/th>\n Dataladder<\/th>\n<\/tr>\n<\/thead>\n \n Easy-to-use<\/td>\n <\/td>\n <\/td>\n <\/td>\n Cell<\/td>\n<\/tr>\n \n Scalable<\/td>\n Cell<\/td>\n <\/td>\n Cell<\/td>\n <\/td>\n<\/tr>\n \n List merging<\/td>\n Cell<\/td>\n <\/td>\n <\/td>\n <\/td>\n<\/tr>\n \n Verify emails<\/td>\n <\/td>\n <\/td>\n <\/td>\n <\/td>\n<\/tr>\n \n Identify invalid emails<\/td>\n Cell<\/td>\n <\/td>\n <\/td>\n <\/td>\n<\/tr>\n \n Remove invalid emails<\/td>\n Cell<\/td>\n <\/td>\n Cell<\/td>\n <\/td>\n<\/tr>\n \n Data scrubbing<\/td>\n Cell<\/td>\n <\/td>\n Cell<\/td>\n <\/td>\n<\/tr>\n \n Big data matching<\/td>\n Cell<\/td>\n Cell<\/td>\n Cell<\/td>\n <\/td>\n<\/tr>\n \n Requires training and migration<\/td>\n Cell<\/td>\n Cell<\/td>\n Cell<\/td>\n <\/td>\n<\/tr>\n \n Company size<\/strong><\/td>\n \n \n \n \n Pick your data quality tool carefully<\/h2>\n
![\"\"](\"https:\/\/www.tye.io\/wp-content\/uploads\/Copy-of-Untitled-4.png\" "\\"Copy")
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