Businesses thrive on data provided to them through various sources. The times may have changed, and the data collection & collation may have turned digital. Still, the fundamentals of business administration remain the same – collect the data and use it to generate leads for your businesses. The quality of data is fundamental to any company’s rise or continued success, for that matter.
Today’s age has already been characterized as the age of data, with millions of data points ready to be analyzed. For this very reason, many IT service providers design custom-made software for your business to mine data flawlessly. This software can manage your data inventories, data collections, and data analysis or even help you manage your kubernetes registry for the smooth running of your business application.
Many companies have fallen prey to bad data since the age of data has dawned upon us, with tons of user data readily available. Social media and online listings have made companies greedy with the plethora of data; however, they tend to forget that bad data is equally harmful to their businesses. Just because this bad data is difficult to translate into monetary terms doesn’t mean it’s not affecting your business.
How Does Bad Data Come into Being?
When it comes to understanding bad data and the impact it can have on any organization, it’s important to understand how or why it exists. How did the data being consumed become categorized as bad data? Is there a benchmark to follow when segregating data as either good or bad?
Gaining insight into how bad data comes to be, would answer these questions:
- Human Error: Data quality is bound to drop if a mistake has been made by the person who was entering it. Even though human error is almost impossible to eliminate, the chances of bad data being collected become higher when data is entered manually into CSV or Excel files in large amounts. Another reason this happens is when data entry protocols haven’t been standardized.
- Disparate Data: Data systems in many organizations are sometimes built to be stand-alone modules, focused on collecting particularly-distinct data, while not communicating with other data collection systems. When different sources are integrated from these systems at the time of building data sets, duplicate, inaccurate, or missing data goes through unnoticed. Systems can also pick up and perceive data differently from each other; one system may allot one meaning to a piece of data, while another system recognizes it differently.
- Data Invalidity: As businesses evolve, so do their needs for consuming different kinds of data. Sometimes, it takes a while to pick up precisely on the kind of data that is needed to study.
The Harmful Effects of Bad Data
As mentioned above, bad data is a real threat to business integrity, with companies losing a lot of revenue to such data. According to a survey, companies observed losses of more than 13 million Dollars due to below-par data quality. Let’s look at how this bad data quality affects businesses:
1. Dissatisfaction of Customers
‘The customer is king’, says every business owner; however, you risk ruining your customer relations if you’re acting on bad data. The unreliable data makes your customers wary of the business as they can’t trust what’s written on your website or whether they trust your communication in general.
Often, errors and poor data lead to customer returns in large numbers, which is an embarrassment for the business. As few as 1 in 3 companies look towards evaluating the quality of their data, which often leads to repeated mistakes. Such errors lead to a bad name for the business, destroying its reputation and leading to low sales.
2. Competitors’ Advancement
Another major drawback of poor-quality data leads a business on the path behind its fiercest rivals. Your rivals are one step ahead of you all the time as you grapple with misrepresentations or incorrect data. Incorrect leads further jeopardize your marketing strategy as you cannot effectively portray to prospective customers what you’re selling.
Bad-quality data leads to ill-planned sales campaigns with disastrous results for the business. The business is unaware of the needs of its buyers and is often seen shooting it in the foot by campaigning negatively or against public sentiments. It leads to more bad news for the company, which also faces a backlash from the existing customers and results in little to no loyalty to the brand.
3. Financial Setbacks
The cost of doing business overshoots any projected budgets once you get acting on low-quality data. Communication with customers and potential clients, dissatisfied customers, and failure to implement cutting-edge software in your business adds to the overall cost of conducting business. Hence, the cost of doing business is nowhere as profitable as you would’ve hoped at the start of your venture. There’s the cost of missing out on implementing software applications that are agile which could have benefitted your company in the long run, because of poor data quality.
4. A Dent to Reputation
A reputation to maintain is a business’s primary priority while conducting its business in any sphere, be it digital or physical. More so in the case of the digital sphere, it has been seen that prospective users heavily rely on existing customers’ reviews for the same brand to buy something.
It means bad reviews or customer care results in a bad reputation for your brand, which will eventually trickle down into more bad news. You may fix a dent in reputation, but it shouldn’t be on the back of the poor quality of data as it is an avoidable error on the company’s part.
How to Improve Data Quality
Companies should avoid bad data at all costs as it leads to dissatisfied customers and diminishing returns for a company. A company should always ensure the data they’re using as input to formulate business strategies, or a sales pitch, is sound and of the highest quality. You can use your web forms to get customer credentials or third-party data vetting agencies to ensure data quality.
Custom-made data management tools for your business can make a big difference to your data quality. A little investment in checking the quality of the data you’re inputting can save the company from a lot of embarrassment and financial costs down the line.