Are you looking to modernize your business? Perhaps you’re investing in new technology that can establish trust and professionalism such as a fixed VoIP? Whilst there is no doubt that these qualities are important, there is one factor that should come before all else: data quality.
In today’s technological world, data is the key to success. With good data, we can learn more about our audiences, improve our analysis, and further our research. This means we can produce better and more personalized marketing materials, provide better customer service, and much more.
To maintain a successful business, data is essential. But for data to be useful, it must be high quality. For many businesses data is mismanaged and provides little value. Studies have found that 80-90% of all data is unstructured. A business must be equipped with the right tools to analyze and utilize its data fully.
Here are six simple steps that will set you well on your way to good-quality data.
1. Know the Characteristics
To properly stand a chance of having useful data, you first need to know the seven characteristics of data quality. If you and your team get to know these characteristics, you can build them into your approach when managing data. They look like this:
- Accuracy: for your data to be accurate it must contain no errors. Check and double-check before uploading any data.
- Availability: for data to be effective, it should be available to the people in your organization that need it.
- Completeness: how complete is your data? Obviously, if there is any information missing it could be damaging.
- Granularity: how in-depth is your data? Sometimes data needs to be segmented into much smaller elements.
- Relevance: is your data related to your work? Does it help you to understand your audience? If not, it is simply taking up space on your database that could be used for other purposes.
- Reliability: is your data telling you everything that you need to know? If there are sections that contradict other areas, it is unreliable.
- Timelessness: data is only useful if it is up to date. Old data can be inaccurate and provide misleading insights.
2. Improve Data Collection
The process of collecting data is often the point where quality issues start to arise. During collection, numerous challenges present themselves. The biggest is also the most obvious – you’ll be dealing with large amounts of data. Some of this data will likely be multi-layered and highly detailed.
Think about the different sets of data that you’re collecting. Data can be gathered from many sources. You could be measuring the response to a marketing campaign, using data to improve future advertising. You could be carrying out functional testing and gathering test data. But what is functional testing? Well, it helps to ensure that new software is working the way it was intended. The data produced from this kind of testing is invaluable.
If you’re not careful, you might find yourself gathering contradictory or inaccurate data. This can have unhelpful effects on your operation. If your data is to be useful, you must be vigilant when collecting it.
Some Steps to Ensuring Good Data Collection
- Make sure that you understand all your data at its most basic level. Always double-check information to ensure it’s correct.
- Set up a clear process for collecting and organizing data. This should be part of an ongoing process that will inform your different departments.
- Decide which data you need. Not all of it will be useful.
- Use a data management system. These help you to organize and store your data more effectively and allows for easier analysis.
3. Improve Data Profiling
Profiling your data is just as important as collecting it. Profiling is the process of labeling your data into subsets. Profiling helps, not only by making sure that your data is well organized but that you properly understand it.
To do this effectively you need to review all the data that you’ve collected. You may have already done this in the collection stage – but do it again. The probability of human error is high, and there’s a good chance that you may have missed a mistake.
Once you have reviewed your data, you can begin grouping it. If you profile effectively, your data groups should represent the goals of your business. For example, if you’re an omnichannel business, you’ll want to use data to create enjoyable customer experiences. If you’re looking for an omnichannel definition, omnichannel allows the consumer to interact with a business in the easiest, most convenient ways possible
Once profiled, your data subsets can be implemented into your everyday reporting, helping to improve the overall quality of your data.
4. Avoid Data Duplication
Data duplication can cause all sorts of problems for an organization. It could mean embarrassing errors, such as sending the email to a recipient multiple times. Or, that you’ve been using duplicated records that have since become outdated. Your messages might not even have reached a customer due to the use of old contact details.
As you can probably tell, to run an effective business, data duplication is a problem that needs solving.
But what precisely is it?
There are lots of forms of data duplicate data and each poses its own problem. The most common form of duplication is creating complete carbon copies of records. Carbon copies are usually easier to identify and cause the least number of problems (although this doesn’t make them harmless).
By far, the most troubling form of duplicate data is partial duplicates. A partial duplicate occurs when a certain section of one record matches another. This could lead to either you or a member of your staff inadvertently uploading both records containing different data.
Some Problems Caused by Duplication:
- Wasted budget: if you’re using the wrong data, your materials might not be reaching customers.
- Time Consuming: as well as wasted money, duplicate data means wasted time.
- Unhappy Customers: a study by Epsilon, shows that 80% of customers are more likely to make a purchase when provided with a personalized experience. If your email marketing is sending out inaccurate data to customers, you’re failing to offer a properly personalized experience.
How Do I Avoid Duplication?
Duplicate data usually results from different teams logging the same data (usually for different purposes). For a solution for this issue, start by looking at your data pipeline. A data pipeline is a series of steps for processing your information. With a more clearly defined pipeline, you’ll be able to better track the data traveling through it. This allows you to spot errors when they occur.
You should also examine the effectiveness of communication within your organization. The idea of data sharing across different departments should be common practice. If different teams share data, there’s less likelihood of duplicates being created.
5. Use Proper Analysis
Having data is extremely important, but alone this is not enough. You need to understand your data if it is to prove useful. The data that you collect is only of use if you’ve carried out an effective and thorough analysis. Here are some important steps to ensuring that you carry out the right analysis of your data:
- List your goals: before you begin collecting your data, be sure to make a list of all your goals. What do you want to gain from your data? Perhaps you want to use it to improve the running speed of your website or quality of your customer service?
- Know how to measure your goals: so, you have a clear list of objectives. Now, it’s important that you know how to measure them. For example, if you are measuring the effectiveness of marketing materials, you might focus on conversion rates.
- Choose the right data: there are two types of data. These are quantitative data and qualitative data. Quantitative data is structured and can be measured. Qualitative data is unstructured, pertaining to text, images, and video. Before you begin collecting data, ask yourself: which type is the most useful for meeting your goals?
By carrying out the right analysis, you can find different uses for datasets. For example, you may find that one dataset could be used for continuous integration. But what is continuous integration? It refers to a set of coding practices that enable software development teams to make minor adjustments to code. Data can help identify the different areas of a software to tweak.
6. Remember the Viewer
When organizing your data, ask yourself a simple question: who will be looking at it? What sort of information do they need? Remember, your data needs to be presented in a way that its intended viewer will understand. It also needs to provide them with quick access to the data that they need.
Obviously, the intended recipient should be in mind from the beginning of the data collection stage. Once data is collected, think about using visualizations as a way of making information more accessible. At the end of the day, if you don’t think about the reader, your data will provide little value.
Good Quality Data Is Essential
Without good quality data, your business is – without wanting to sound too dramatic – doomed for failure. But even though it is essential, collecting and managing data is often challenging. If your organization is to thrive, it must list maintaining good quality data as a priority.
By putting the right time and effort into data quality, you can learn more about your audience. With better audience knowledge, you can create more effective marketing materials. Good data also opens the door to providing better customer experiences.
Kate Priestman is the Head of Marketing at Global App Testing, a trusted and leading end-to-end functional testing solution for QA challenges and an industry leader revolutionizing answers to questions such as “what is QA?” and “what is test automation?”. Kate has over 8 years of experience in the field of marketing, helping brands achieve exceptional growth. She has extensive knowledge on brand development, lead and demand generation, and marketing strategy — driving business impact at its best. You can connect with her on LinkedIn.