Data, Big Data, is big news. The information and insights gleaned are invaluable – provided they are relevant, accurate, and up to date. The trouble is that the distinction between the two isn’t always immediately obvious. However, it’s critical to know the difference because mistakes can have a devastating effect on your business.
We’re going to look at what good data comprises and what exactly is meant by data cleaning. With those two elements fixed in your mind, we’re going to look at some of the myriad benefits a thorough cleaning provides B2G buyers and suppliers.
What’s Good Data?
In essence, good data is information that adds value to your business. But it’s actually much more than that. Good data answers specific questions about your database of contacts, for example, recency of purchase, source (social media, email), and product/service.
Importantly, good data is also accurate, complete, consistent, timely, and relevant (unique and valid).
Good data doesn’t just happen. It requires ongoing monitoring, management, and cleaning. The more regularly you check your data, the fewer discrepancies will crop up. Automation is one way to ensure that data processing is valid, standardised (consistent), and free of human error and bias.
How to maintain good data
Several components help to maintain good data.
1) Authority: There should be one person clearly in charge of good data, including assigning specific people or teams to keep an eye on data as it comes in and as it’s used. They should create policies for storing and handling data and play a key role in determining pre-set criteria or filters for data categorisation.
2) Management: There should be clearly defined rules about who can access data and for which purpose. Managed access prevents misuse of data and unapproved edits or changes. Management also includes checking and assessing data to minimise the chances of good data turning bad.
3) Standardisation: Standardised data capturing and categorisation simplify data storage and minimise the risk of errors and incorrect filing. Standardised or consistent data formats are easier to share because they don’t need to be to a different file type. Data is less likely to be lost or misinterpreted by automated filing systems.
4) Assessment: Assessment is essential to good data. It verifies its reliability and accuracy, without which data is meaningless. Elements to be assessed include the origin of the data, the method of data capture, and the date of first contact.
5) History: How old is the data? As a rule, the older the data the less value it has. Good data is current and up-to-date. This is particularly important when it comes to things like whether the contact is still in the listed position or has been promoted or even shifted to another division. It could be that the organisation has changed its name after a merger or doesn’t even exist anymore. Outdated and inaccurate data will invalidate all targeting efforts.
What Is Data Cleaning?
Data cleaning is like cleaning out your cupboard; some items are still serviceable, and some are not. Some still fit, while others are too big or too small. Some are out of fashion and some are timeless. Some you don’t want and some you don’t want to let go. Eventually, you end up with a neater cupboard that reflects who you are.
When you clean data you get rid of corrupted data, data that is inaccurate, no longer fit for purpose, incomplete, duplicated, and out of date. Eventually, you end up with a dataset that is relevant, valid, has value, is correctly formatted and filed, and is easy to access and interpret.
Tips to clean data
Here are three primary data-cleaning tips (and four little bonus tips).
You explore data to determine the current state of affairs. You’ll need a spreadsheet to see everything in one place. You don’t have to use special data-cleaning tools, an Excel spreadsheet will do. The Sort & Filter feature is perfect for cleaning data in Excel.
Excel actually has several features that facilitate the data-cleaning process, including conditional formatting, data validation, and various charts and graphs, not to mention getting external data from other sources and removing duplicates.
Intelligent filtering will give you the data you need. It helps to have some professional advice so that you know how to leverage all filtering features to completely optimise the cleaning process.
There are several data-cleaning steps, some of which are included below.
Duplication often occurs when you pull and pool data from multiple sources.
Remove irrelevant data
Irrelevant data is anything that falls outside of certain parameters. For instance, you’re looking for contacts who’ve made purchases within the last 18 months, but the dataset includes purchases that go back as far as five years.
Outliers are data that vary from the set parameters. They are so different that they can skew data significantly. However, some outliers are necessary to get a full picture of data analysis. Useful outliers could introduce unexpected, but pertinent information. Those are the ones you want to keep.
Only remove outliers that have no value for the dataset and could actually devalue the available data, for example, data entry errors (typos) and sampling errors.
Remove personally identifiable (PII) data
Confidentiality is non-negotiable in the public sector. This makes removing personal information one of the most important steps in data cleaning. Personal data includes home rather than business addresses, and private email addresses not related to business operations.
Manage missing data
Data can be missing for a number of reasons. It could be accidentally omitted. It could be corrupted. It could be in a format your automation system doesn’t recognise. Whatever the reason, you need to address the problem.
You have a few options.
Drop all missing values. This doesn’t necessarily solve the problem because, sometimes, like outliers, missing data provides important information about a dataset. Carefully consider the consequences of dropping all missing values before you throw them all away.
Make educated “assumptions” based on observation of other data and data patterns. The risk, obviously, is that you get it wrong and your results are invalid.
Benefits Of Data Cleaning
One of the most important reasons to clean data is to provide insightful and in-depth information that facilitates intelligent decision-making.
Another important reason is that error-free or nearly error-free data provides employees with the certainty they need to increase productivity and improve customer relations. Productivity also increases because there is more time to focus on core activities.
You can also pinpoint the most common areas where errors occur. Armed with this information, you can take steps to stem the tide and prevent future errors.
Additional data cleaning benefits include:
- Clear marketing data and analysis, so you know you’re targeting the right audience with the right message.
- Good data and analysis can streamline production lines and supply chains. Problems are identified early and damage is limited.
- Clean data and its accompanying value can increase ROI on B2G marketing campaigns. There is an increase in target audience responsiveness, which helps businesses to reach their marketing goals.
- Refined customer data lists for greater (personalised) targeting.
- A clean reputation results from clear decision-making, good customer satisfaction, transparency in marketing, and enhanced productivity. Your business gets a reputation for reliability, integrity, relevance, and discretion.
- Ensuring data is clean enables you to use data better and enhance current and future data capturing, storage, analysis, and interpretation.
- The smarter you use data and the more you optimise your data acquisition and cleaning methods, the more money you’ll save in the short- and long-term.
Data Cleaning Services
You needn’t manage all the data cleaning tasks yourself to enjoy all the benefits. You can always outsource the job to data scientists or cleaning specialists. If, for whatever reason, you would rather manage the bulk of the process yourself, but need a push in the right direction, you can engage companies like Cadence Marketing, which provides cost-effective data health assessments to determine how clean your data is and where you might need to pay closer attention.
Some companies charge a fee for these data health checks, but Cadence provides this data cleaning service in the UK for free.
What’s more, you can book a free consultation to go over your exact needs in the B2G marketing arena. Contact us now to find out more about how our services benefit you and your data analysis processes.