The Importance of Data Quality in AnalysisYou know data analysis is important; in today’s day and age, how could you not?! Everywhere you turn, you’re being asked about your own data, giving companies permission to use it, or even using it yourself to make better decisions. And while data analysis is quite useful, there’s an important factor to keep in mind: data analysis is useless without clean and high-quality data.
We know what you’re probably thinking: data is data! Any kind of data can be very useful when it comes to making decisions. Well, bad news: there are lots of things to be cautious of when using data to make decisions and when analyzing your data from the start. Data can be misleading, altered, or even completely wrong. How? Why?! We’ll cover these questions and explain the importance of quality data further in this article.
What is Data?
Understanding the importance of quality data isn’t the first nor second step to ensuring you’re running a solid data analysis. Before you begin, understanding data itself is key. Data is information that has been gathered that, on its own, doesn’t carry any specific weight or meaning. However, once data has been processed or moved, it can lead to highly valuable insights.
Data isn’t just numbers; data comes in the form of text, numbers, graphs, symbols, images, and much more. Generally speaking, data is split into three categories:
Numerical data: this kind of quantitative data includes any sort of number, such as times, weights, height, volume, and more.
Categorical data: characteristics and identifying information, such as gender, race, or marital status fall into categorical data, which is quantitative.
Ordinal data: bringing together these two types of data, ordinal data consists of both categorical and numeric data, such as a 1 to 5 review form that allows customers to also leave written comments.
We know you’ve heard people talk about data constantly; you’ve probably heard it so much that you’re actually unsure of the exact uses of data! Data is incredibly important, however, and greatly affects the following five areas:
Decision making: data-based decision making is an absolutely crucial part of business! And there’s no better way to make a decision than by using data to back up your choices. Data can help you defend your choices and guide you towards smarter decisions.
Problem solving: when something that seems random and unexplainable happens, what can you do?! Use data, of course! Data can help show you why something happened and get to the root of the issue.
Understanding information: Good decisions are data-backed, but even better ones are fully understood. Instead of just seeing the outcome, you can use data to see what occurred during each and every step of the process.
Improving processes: by seeing what’s working and areas for improvement, you can improve productivity, highlight problem areas, and help to streamline business operations.
Understanding customers: at the end of the day, you want to create a product or service that customers want. To do this, you need to understand their thinking and what makes them either buy a product or decide to choose a competitor.
Now that we’ve defined data and why it’s important, let’s dive right into the good stuff: quality data.
What is Data Quality?
All data comes from our records or client information, so it must all be accurate and helpful, right?! Unfortunately, that’s not the case. Factors like outdated client information or missed deadlines can lead to the following problems: poor customer relations, inaccurate analytics, bad decisions, wasted money, and overall lower business performance. On a high level, companies that use high quality data perform better, thanks to more accurate measures.
Data quality vs. data integrity
As someone interested in data, you’ve probably heard of data integrity, which lots use interchangeably. While similar, the two differ: data quality is just one part of data integrity, which focuses on three additional areas: data integration, location intelligence, and data enrichment.
Data integration highlights the need for all data, regardless of its origin, to be integrated properly into systems to provide for a quality analysis. When you add location intelligence to your data analysis, you’re adding even more details--details that can provide more insight. Finally, enriching your data means that you are providing even more information, such as consumer or business details, that contribute to a more complete final picture.
What makes data high quality?
There are lots of factors that make data high or low quality, but let’s dive into five:
Accuracy: is the data you’re looking at true? Is it free of bias? Inaccurate data can lead to incorrectly drawn conclusions.
Completeness: data that’s missing from within a dataset can offset the final outcome; ensuring that your dataset is complete means that all fields are filled for all products, allowing you to get an accurate picture of the company’s status.
Reliability: your data needs to be consistent throughout your entire company; clients with contradicting information can make your data analysis faulty.
Relevance: is this piece of data important to your final conclusions? Wasting your time analyzing random data points can not only use up valuable resources, but also cloud the truly important analysis.
Timeliness: ensuring that data is recent and up-to-date helps guarantee that the information is accurate, avoiding problems down the line.
Be aware of “dirty data”
So now that you know what kind of data is good, let’s explore the other side: the dirty data. This kind of data can be harmful to your overall company performance:
This shouldn’t be a surprise! Data regulations are popping up right, left, and center in practically every corner of the globe and it’s the responsibility of the company to ensure that their data storage and collection is lawful. And for companies that operate internationally, guaranteeing that the data meets every guideline, no matter their home location, can be quite the challenge.
Meeting these regulations, however, is absolutely essential. Companies that ignore these rules can be heavily fined, in addition to suffering reputational consequences.
Our tip: stay up-to-date with local regulations and ensure that you clean and check your data frequently to make sure you’re not holding onto old data.
One of the biggest challenges in analyzing data is collecting information from across multiple departments. What if the sales team has a client’s email listed one way and the finance department has it another way? Or a client shows up twice in the database, therefore providing inaccurate results? Streamlining the data analysis and management process can help you avoid inconsistency issues.
Our tip: use a data management tool that can be universally employed throughout your company, eliminating contradictory information and duplicate accounts.
Imagine you’re trying to determine the average age of your customers at a certain location, but realize you’re missing age information for more than half of your database? Or you’re trying to see if men or women are more likely to buy your product, but don’t have complete gender details for all clients? These gaps in your data may seem minor when looking at just one client, but can seriously skew your results upon analysis.
Our tip: search your database to find missing fields, pull these clients and fill these gaps, and focus on obtaining this information for all clients in the future. It seems tedious, but will benefit you in the long run.
Real Life Examples of Data Analysis Using Quality Data
Now that you have a good picture of what quality data is, how it affects quality analysis, and the dangers of dirty data, let’s dive right into some real-life examples of how quality data can have a positive impact:
Medical care: through global databases that have basic patient information, such as age, gender, symptoms, and diagnosis, but no further identifying information, doctors can use the decisions and records of past patients to compare their current patient’s status, see what’s worked in the past, and make the best possible decision for their treatment.
Ensuring that they’re working with only the cleanest data sets is absolutely essential for patient wellbeing.
Recommendations: one of the data’s best uses is to see what customers like, what they don’t like, and what they’re searching for. With this information, product teams can create in-demand products or services that are desired by the customer; they can also avoid creating products that customers won’t buy, therefore saving time and resources.
Missing information or gaps in the data can lead to faulty recommendations and clients can be more likely to use another service that aligns better with their likes and dislikes.
Insurance: through collected data, insurance companies can offer clients more accurate and better deals, in addition to using the data as a reward; drivers with a good driving record can benefit from additional perks.
Inaccurate data can cost the insurance company if they get the offer a lower premium than what the client really needs.
You get it, right? Quality data is absolutely crucial when it comes to data analytics and if your dataset is outdated, missing information, or contains duplicate data, your final analysis could be completely off. At Ironhack, we know the importance of clean and quality data--in fact, that’s why we offer our Data Analytics Bootcamp to prepare the next generation of data analysts. Interested? Here’s what you need to know.