Businesses Discover the Shocking Cost of Bad Data
Poor data is an overlooked crisis affecting many businesses, so steps to improve data quality must be taken.
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Big data has become incredibly important for many companies all over the world. Unfortunately, the growing emphasis on big data has led to some poor decision-making. Many entities are prioritizing data scalability at the expense of data quality. As a result, bad data is costing them a lot of problems.
In the USA alone, bad data - any poorly structured or managed data - costs the country over $3 trillion every year. Whether it’s created from data engineers accidentally adding an extra zero, a discrepancy in how things are formatted, or even problems with the data system itself, a lot can go wrong with data.
Bad data is far from just a pain for data scientists; it actively bleeds into all areas of business, radically impacting how everyone in the workplace goes about their daily lives. This article will explore the impacts of bad data, pointing out how it occurs and exactly how it's affecting your business.
Let’s get right into it.
What Forms Does Bad Data Come In?
While people debate about what bad data is and where it comes from, often categorizing the errors themselves with specifics, it’s much easier to think of bad data from one of three sources. Each of these sources makes up a bulk of the possible errors that lead to bad data and points to the reasons that they have occurred.
These three sources are:
- Policies - Data isn’t only about internal metrics. If your business has to deal with external data, especially on an international scale, then discrepancies in policy can lead to significant errors within your data. For example, the global address system is significantly different from country to country. That’s not to mention something like the USA being practically the only country in the world that still uses the ineffective imperial system. Therefore, discrepancies in policy can seriously impact data harvesting, and without the processes in place to fix the errors, they ruin datasets entirely.
- Processes - When it comes to exchanging data within an industry or across two or more companies, the difference in internal processes can lead to a significantly different style of collecting and processing data. For example, if a business structures its cloud data warehouse differently from the other they are farming data from, they could have issues when merging and formatting the data, leading to extensive errors.ut certainly not least, people are one of the leading causes of bad data in organizations. If people don’t know how to operate data in regard to policies and processes, then errors will begin to snowball. That’s not to mention manual input errors. No matter how amazing your data scientist is, mistakes can happen, decimal points can shift one to the right, and your data can become skewed.
Whether it’s policies, people, or processes that lead to your business receiving and using bad data, the impacts will be largely the same. Considering that the erroneous data costs around $9.7 million per year per business, it’s important to understand exactly what this data could be doing to your organization.
How Does Bad Data Impact Your Business?
Bad data comes in many forms, but the impacts are almost always headed in the same direction - reducing the effectiveness of your business strategies and hindering growth. Whether it be your marketing department or your product development team, poor data can completely derail your team projects, leading you down dead ends and wasting time.
There are four central ways that bad data impacts your business negatively. These are:
- Slows down decision-making
- Leads to higher costs
- Disrupts day-to-day processes
- Reduces customer engagement
Let’s break these down further.
Slows Down Decision-Making
Data is a vital part of decision making, with well-structured data allowing departments to make certain decisions. For example, after running some A/B testing, a company could clearly see which of their marketing approaches garners more results, helping them to focus on campaigns that will serve their brand to a greater extent.
Yet, when this data isn’t available, or worse, completely wrong, companies could severely waste time during the decision-making process. When things just don’t seem to add up, and your data is leading you to dead ends, it becomes difficult to streamline the decision-making process.
Equally, if your business has bad data to the extent that the wrong eclipses the right, then it could even lead to your company making the wrong decision. Not only does this increase your costs, but it leads to you missing out on opportunities for expansion or refinement of your product.
Leads to Higher Costs
Part of running an effective business is to analyze your data and create a summary of how your costs could be reduced through optimization. This particularly rings true for the supply chain, which is a centralized area where companies can often find places to save money. Whether it be improving their product monitoring software or decreasing the amount they spend on logistics, data provides them a base to work from.
Yet, bad data completely destroys this insightful process, throwing up a range of statistics and ratios that may have absolutely nothing to do with your company. Considering how easy it can be to accidentally create a false data point, this happens a lot more often than you may initially think.
If your business wants to save money while boosting potential output, you should always be consulting data that is produced internally, as well as how your internal metrics stack up when compared to industry averages. If either your internal data or your processing of external data is wrong, then you could lead your business down the wrong path, miss signals that point to a potential cost reduction, and slow down the progress of expanding the company.
Disrupts Day-to-Day Processes
Even if employees don’t actively consider data analysis part of their jobs, you’d be hard-pressed to find a job that doesn’t need basic analytical skills. From constructing graphs to understanding trends, data creeps into almost every job role within the world of work. When that data is incorrect, it leads to a significant decrease in the potential productivity of your employees.
The employees that notice something odd about the data that they’re working with will spend time figuring out what exactly has gone wrong, eating up valuable time and resources. Even those employees that don’t notice the presence of bad data will spend time pursuing ideas based on data that was ultimately incorrect.
Either way, bad data is a lose-lose for your company, pushing employees in the wrong direction and wasting time in a range of departments.
Reduces Customer Engagement
One of the most effective ways of boosting your customer engagement when it comes to marketing or your product presentation is by using A/B testing on all of the campaigns you run. From small changes like the font your product uses to larger shifts of your landing page, testing allows you to find out exactly what resonates with your customers.
With these data-driven adaptations, you’re able to effectively craft your brand around user feedback, moving toward creating a product that your audience loves. However, if your data is off, your whole marketing strategy and brand presentation can be thrown. In fact, marketing departments that use bad data in their campaigns will lose around 550 hours due to its presence.
Every single year, the least effective marketing campaigns can be traced back to bad data, with over 30% of bad campaigns benign due to wrong impressions about what customers wanted to see. With lower engagement rates, falling interest in your brand, and a failure to connect to your audience, bad data can be a brand-killer.
In our modern age, with trillions of GBs of data produced every day, it’s no wonder that bad data has become such a massive problem. What starts as one small formatting error can spiral into a huge problem, costing a business millions and wasting hundreds of hours of employee time.
The first step towards recovering from bad data is to assess where it came from, putting in place checks to ensure that neither people, policy, or processes are getting in your way. Although it may take a few hours to get started, the hours and money you’ll save your business down the line by investing in smart data infrastructure will astound you.
As we move further into a digital age of business, data - clear, correct, and clean data - is vital for ongoing progress.
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