Have you heard the expression "garbage in, garbage out"? It's often used in programming, but it's relevant to pretty much anything related to data - if you have bad data, anything you do with it, any decision you base on it, and any way you use it, your results will not be good.
But can bad data actually harm your business?
Bad data is data that is inaccurate, misleading, low quality, or downright damaging. Every business gets some bad data at some point, but it's vital that you recognise and resolve the issue.
Usually, bad (or dirty) data comes under one of three main umbrellas:
All of these make it difficult, if not impossible to interrogate, use, or rely on the information you store. Let's look at the implications of different types of bad data, and how it can affect your business...
No matter what types of customers you have, bad data will have a negative impact for your business.
Having good customer data allows you not only to provide a great service, but enables you to go above and beyond what people expect, to truly delight your customers.
On the other hand, if you use the wrong name, refer to the wrong service, forget something you've agreed to, or miss important dates, your customers are going to start looking for pastures new.
Customer data isn't just about name, service/product, job title, company details, and so on. It also includes things like emails, phone call records, and meeting outcomes. If you're not storing and tracking all of that information, it's easy to miss something. How often do client or customer expectations deviate from your expectations? If you have a record of everything, you can easily go back to the source and make sure everyone is on the same page.
Bad sales data can lead to your sales team spending time on deals that will never close, or focusing too much on one big deal and not bringing in enough quantity. Or it can lead to you losing deals because you don't have the right information about your prospects.
And it can lead to poor financial decisions, expectations for revenue that never materialise, causing cashflow crises and overspending.
How much business do you think you'll close in the next 4 weeks? What's that worth to your business? What happens if all of it closes? What about if none of it closes? Do you have the resource to deliver, or do you need to start recruiting?
Are the deals in your pipeline real deals, or are they hanging on for dear life even though they're never going to come through? Are the people attached to those deals the decision makers, or are you dealing with lower-level staff? Do you understand the needs of your prospects?
How long, on average, does it take to close a deal from start to finish? What's your average sale value? How many sales do you need to make to hit your targets? What are your conversion rates during the sales process?
Without having all of this information, how can you know how your sales are performing? You might know that you had 3, or 27, or 200 new sales this month, but is that enough?
If you don't know how many sales you need and what your conversion rates are, you won't know how many leads you need to reach your goals, which means you won't know there's a problem until you're missing your revenue targets. If you don't know your average time to close, you might be expecting more than your pipeline can realistically deliver in that period.
Do you know what money you've got coming in and going out over the next week or month?
If your financial data is bad, it can tank your company faster than anything else on this list. Even a great company with lots of clients can run into cashflow issues, which can quickly escalate if you're not on top of things.
And if you want to access some credit to get you over a hump, well, you'll need good financial records in order to be able to do that.
Do you know when your next tax bill is due, and how much you're going to have to pay? If you're thinking, "well, my accountant handles that," take a step back and think again. If anything goes wrong, it's not your accountant's business on the line. Know your numbers.
Do you know your run rate (how long your business can keep going without any more cash coming in)?
Do you know who is behind on paying their invoices? Or are you providing services without being paid for them? Do you have policies to handle what happens with non-payment, and if so, are you following them? If you don't have good date about your financial position, it's impossible to follow even the best processes, leading to operating at a loss.
When you employ people, you have plenty of data about them - from personnel information to performance and duties.
If you don't have a process for keeping your payroll up to date, for example, you could end up paying out twice for the same person.
If you don't keep your performance data up to date and visible to the right people, poor performance can slip through the cracks - not only does that harm your productivity, it can also have a severe impact on morale. If team members see a colleague getting away with performance issues, their motivation will plummet.
A poor employee can also cause damage to your business, depending on their role. Imagine a customer has a bad experience with a member of your team. Apologies are made but nothing really changes. The same issue crops up again, but there's a new manager in the role who didn't have data about the previous incident. As far as the customer is concerned, you simply don't care about them.
This isn't about the quality of your data as such, but it's a hugely important piece of the data puzzle. If you have bad data security, you open your business up to significant risk of breaches and fines, bad press, and reputational damage.
We've got a free 5-min security quiz to help you find (and fix) those holes that might be lurking, but whatever you do, make sure you're on top of security.
Breaches are when even good data can turn very nasty on you.
We've covered a lot of different types of bad data in this post, and there are different ways to handle each of them, but there are some core areas you should look at:
Process is the best way to keep your data good. Have a process for gathering the data (to make sure you get everything you need), a process for storing the data, a process for adding to the data, and a process for updating/amending the data.
You should also have a process for getting rid of unwanted, unusable, or irrelevant data, to keep the integrity of what you have.
This is such a key element of process that it deserves its own section. If no one is specifically accountable for something, it won't get done properly, consistently, or at all.
If you have a role that manages data, they might be the person who is accountable for following your process, updating and cleansing data, and coordinating with others.
If you have a team, you'll want to assign accountability to different people. In a sales team, all reps might be accountable for any contacts that they own, for example. Or the team leader is accountable for getting the team to update the database each week.
Having one central location for your data - in this case customer, sales, and prospect data - means that everything you know is accessible, available, and up to date. Don't keep this kind of information in spreadsheets, that's the ideal breeding ground for bad data.
A good CRM will allow you to store plenty of information about your customers and prospects, as well as deals and your sales pipeline, and give you reporting capabilities to interrogate that data. Combined with your processes and accountability, this should help keep your data cleaner.
Making sure all teams use the same system helps to eliminate the issues of bad data caused by silos. If the marketing team are sending emails, the product team have upset the customer, and the account management team haven't been in touch at all, the sales team should not be wading in for a service review or pricing increase. With everything in one place, at least they'll know that.
Finance systems and employee/HR/payroll systems are equally important, helping mitigate all the issues discussed earlier in this post. Look for something that will do everything you need it to, without too much additional complexity.
If you find one that only does some of what you need, and you have to bolt multiple systems together to get the right result, you introduce more room for errors, either human or technological, that can get in the way of cleaning up your data.