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Data Credibility: Why It Matters in Data Visualization
Data credibility – it’s an important topic. With the volumes of data that organizations manage growing every day, business leaders have a greater responsibility than ever to ensure the data they use is accurate and up-to-date.
Data visualization is a powerful tool for many business leaders. It helps them gather insights from data that would otherwise be difficult and time-consuming to analyze.
According to Bian, organizations that have the most advanced analytics capabilities are twice as likely to be in the top quartile of financial performance within their industries and twice as likely to use data very frequently when making decisions.
Data visualization is clearly a powerful tool. But where does data credibility come into the mix? In this article, we will examine what credible and reliable data means and where it comes from.
Why is data credibility important?
Data credibility refers to how complete and accurate a data set is. Reliable data is a vital component for creating data trust within an organization.
However, according to a survey from Talend, less than half of executives believe their organizations’ data reliability is “very good.” This is bad news for business leaders because they need reliable data to make informed decisions. In addition, sharing inaccurate data with employees or consumers can break trust when it is later proven false.
What are the benefits of reliable data?
Reliable data holds numerous advantages for businesses versus unreliable data or no data at all. Accurate data is an essential tool for making business decisions. Data from customers and the market will help you make strategic decisions that push your organization in the right direction and beat out your competition.
In addition, accurate data makes your employees happy. When they have accurate data, they can work more efficiently. Instead of wasting time fixing data errors, they can put their time into more important (and interesting) tasks.
Another reason accurate data is important is that it helps your organization toe the line when it comes to regulations. Across industries, there are many regulations around data and how it can be gathered, stored, and used. Reliable data gives you peace of mind, knowing you are compliant.
What ethical problems does non-credible data pose?
At the other end of the spectrum, non-credible data can have serious negative impacts on your business.
Biases can originate for a variety of reasons, such as human bias. This could simply be an error in inputting data, or it may come from the result of cherry-picking data that supports your hypotheses.
For example, your sales team may want to show that customers are happy with their service. They will then likely show you all the positive reviews they have received. But there may also be several negative reviews (that would provide useful information on how to improve your sales) that are not being shown.
In addition, businesses have to combat the issue of out-of-date data. Both online and offline data can quickly become outdated. Customer data can change quickly and easily be siloed across departments, with different teams having access to different information. This creates obvious problems in everything from product development to customer service. Data revision is crucial for a business to keep its information reliable and useful.
How to ensure data is reliable for your visualizations
Your data visualizations need to be created with accurate and reliable data if you want to benefit from them. But how do you ensure that your data is reliable?
The first step is to track the methods you use for data collection, analysis, and interpretation to check for data quality. Knowing the research process gives you and anyone else examining your data a process to follow to validate your visualization. Theoretically, they could generate similar or equal results if they followed the same method.
Confirmability is another vital area to consider with your data. You want to be able to prove that data visualizations are neutral and not subject to bias.
Beyond looking at your data source, businesses must also look at how they store it. To get the most out of your advanced analytics capability and ensure that data is kept accurate, collection and ownership must be allocated across business functions.
You need a data strategy to plan how to generate insights, prioritize opportunities, and the allocation of your analyst’s time. This will ensure that data is kept accurate and up-to-date and that data can easily be shared across the functions in your business.
Data-driven decisions, powered by Astrato
Credible data can have a significant impact on business as a whole. When collected and stored correctly, data provides critical insights that often prove invaluable when it comes to helping C-suite executives make confident and business-critical decisions.
Interested in what Astrato can offer your business? Book a demo and try Astrato for yourself today .