In the business world, everything revolves around data. Every decision and action in a company, no matter how big or small, is based on information collected from various sources via a multitude of channels. Everyone uses data to gain insights into different aspects and factors relevant to their organization so they can build and choose effective strategies and solutions that will help them reach their objectives. With the right data on their hands, businesses can improve operations and performance, unlock new opportunities, maintain their competitiveness, and ultimately boost their bottom line.
The good news is that thanks to tech advances and the large number of innovative instruments available these days, companies benefit from more data than ever before. The bad news is a lot of the data that flows into businesses is inaccurate. It stands to reason that when companies rely on flawed data, they make flawed decisions that have far-reaching consequences. It’s estimated that poor data causes US businesses to lose a total of $3.1 trillion annually. That’s a lot of money going down the drain as a result of the risks, downtime, malfunctions, missed opportunities, errors, and sometimes irreparable damages stemming from data inaccuracies.
Therefore, companies don’t need more data or any kid of data to thrive; they need good quality data that is accurate and can be trusted. But as we’re going to discuss further, this doesn’t come without challenges.
What makes data accuracy so difficult to achieve
According to relevant literature, data accuracy is one of the ten dimensions of data quality, with the others being accessibility or coverage, completeness, consistency, currentness, relevancy, reliability, timelessness, validity, and uniqueness. For data to be considered accurate, it has to truthfully reflect the objective reality, so there’s no denying that this characteristic carries a lot of weight in businesses’ decision-making process.
One might think that all it takes for businesses to ensure data accuracy is to check every piece of information they gather, but unfortunately, it’s never as simple as that. There are various factors that make data accuracy a rather difficult task to achieve.
For starters, companies are flooded with enormous amounts of data generated by the introduction of advanced technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT), alongside the use of conventional methods and tools like surveys, interviews, experiments, spectrometers and so on. This places a heavy burden on many professionals’ shoulders as they have to sift through more information to separate good data from bad data.
The fact that usually the data doesn’t come from the same source or in the same format adds to the complexity of the process. This lack of uniformity makes it nearly impossible to merge all the different types of data together into one place for proper analysis, leaving room for inaccuracies to seep in.
There’s also the risk of human error that needs to be considered and addressed. Although most companies employ automated data collection systems to streamline tasks, human intervention is still necessary in certain situations, and people are prone to making mistakes.
Lastly, it has to be mentioned that data is constantly evolving as new trends emerge and consumer needs and requirements shift. This means that a lot of companies still use outdated and inefficient data collection methods and strategies that can affect the accuracy of the information they amass.
How to mitigate data accuracy challenges
Ensuring high data accuracy might not be a walk in the park, but there are ways to address these obstacles and obtain better outcomes.
One of the most efficient solutions in this respect is data profiling. This refers to a set of analytical processes that look at different data features in order to identify and correct anomalies in their structures. Data profiling allows you to spot errors early on and eliminate risks effectively.
Another way to avoid data inaccuracies is to update tools and processes. If you’ve been using the same instruments or working with the same providers for a long time, chances are you might need to make some changes. For example, should your company require spectrometers for the evaluation of product components and characteristics, it’s best to resort to industry leaders like https://www.avantes.com/ to procure the necessary equipment.
Employing data cleaning tools is also something you might want to take into consideration. Even if your company already uses advanced tools and equipment that facilitates the collection of accurate data, errors might still find a way to creep in. In such cases, innovative software solutions for data cleaning can step in to detect and then fix or remove faults and abnormalities, be it duplicates, outdated information, formatting issues, and so on.
In a company, data accuracy is not the responsibility of just one person. It implies a team effort, so it’s equally important to ensure all employees involved in data collection and management possess adequate levels of data literacy and are able to employ best practices in this respect. When team members understand the necessity of data accuracy and know how they can contribute to it, it’s much easier to keep common issues at bay.
To track the issues at the source and ensure the health of your data and the systems that facilitate their flow, you need to focus on data observability. This implies constantly monitoring all your data pipelines and networks and checking for errors so you can eliminate them promptly.
Bottom line
In a day and age where companies sit on massive piles of data that drive their every decision, data accuracy stands as a critical component of business success. Handling data is a complicated endeavor and the high cost of inaccuracies underscores the need for effective solutions and methods that are able to address obstacles and help companies make more informed choices. Keep in mind that increasing data accuracy is a never-ending quest, so be ready to make constant changes and keep up with shifting trends.
