Retailers should fear poor-quality records.
Poor quality records produce bad data, later on. The US alone loses $3trillion every year because of this.
Retail sector loses profit as well as reputation when poor records are entered into the system. Maintaining data quality to the ultimate level is the most important part here. You should miss nothing if you want to grow your retail business.
In this piece, we are going to cover how poor data impacts the retail operation with a close inspection. Also, we are going to discuss how to get out of the issues of poor data here.
Let's start!
What is Data quality in retail?
Maintaining the accuracy, consistency, and completeness of data within the retail operations comes under the parameters of data quality in retail.
The flow of retail operations fluctuates throughout the year and depends on the market demand. So, managing the quality of data during the high-demand period is extremely important. It helps retailers have control over their inventories and influence customer satisfaction.
Retailers who have considered data quality as a distinct parameter bring positive changes in their daily retail operations.
Once retailers considered the importance of data quality and acted on it, they realized the following things;
- Omnichannel presence (retailers served uniformly in every vertical; online & offline)
- Increased interoperability (chances of miscommunication reduced)
- Welcomed faster data-driven solutions to operations
- Compliance with regularity norms and laws (GDPR, CCPA, etc.)
Interesting facts about data quality
Over time, the complexities of data quality have increased. Earlier, it used to take 4 hours or less to find and resolve data issues. But now, it takes more than 4 hours to resolve one data issue incident.
Effects of poor quality data in retail
Invalid or erroneous data can negatively impact retail operations. From IT to sales, it can ruin the entire operations of a retail company. Over time, if the issues are left unnoticed, they can negatively impact employees, too.
Imagine your sales professionals struggling to find the right prospect data. On the other hand, your delivery professional could not find the orders that were generated from sales. A complete mess. It only happens in retail when the level of bad data increases in the data operations.
Bad data = financial loss
Bad decisions are the result of bad data.
Inaccurate data always harms the decision-making process and eventually leads to costly mistakes. Many famous incidents have happened before where organizations lost millions or billions of dollars just because of poor records.
Every three months, users change their email addresses. The availability of free mail service providers is one of the reasons, whereas the security of personal information is another. Not just email addresses, people change their addresses and phone numbers every now and then.
You're a retail store. Suppose one of your old customers called you for an order, and you processed the order. Please note here that you haven't updated your customer database in the last 6 months. As a result, there is a high chance that the order reached the wrong address. Plus, you have to re-deliver the order, which will definitely decrease your profit percentage.
Whose fault is this loss?
Of course, yours. Because you did not consider updating your customer database in each quarter.
Diminish productivity
Now looking inwards, bad product data always diminishes productivity in retail businesses. Suppose some entries are made with wrong information in your product database. Once you find it, it's very much possible for your team to make corrections in that product data immediately. However, it will kill the time (which is completely unnecessary) of your team dealing with such errors. Eventually, it will lead to productivity issues in the long run.
From recording product information to order fulfillment, bad data can enter the database from anywhere. As we all know, data rots over time. Sometimes, due to the fall of the data enrichment practice of the organization, it happens. Ultimately, it ends up killing the productivity of the retail operations.
On the other hand, bad records also affect customer experience. Retail operations are mostly customer-oriented, hence a minor issue in the data can impact the entire operation negatively. Most retail businesses struggle with bad data, even if they have adopted an AI-powered system to manage their data operations.
Unsatisfied employees
Poor data always annoys employees. Most of the time, employees have to make corrections again and again. So, it produces employees' frustration.
Let's understand this with an example.
It's quite common that retail operations are completed via multiple departments. Take here the two most popular departments. One is the customer handling department, and the other is logistics. When bad data takes over the customer database, the customer handling department will suffer the most. It's quite natural that they'll get maximum complaints regarding wrong deliveries. On the other hand, the logistics department will have to return all the wrong items delivered to the actual address.
Ultimately, employee frustration will increase over time as they have to perform the same tasks again and again. Besides them, one thing that hampers the most during this course is your brand reputation. Increasing complaints from customers are a bad sign, and it can put your brand position in danger if you allow bad data to interrupt here.
Improving data quality is the solution!
Maintaining data quality intact can solve issues of bad data. Following the data quality parameters can help improve the quality of your data. You need to focus on the following aspects;
- Completeness
- Accuracy
- Uniqueness
- Validity
- Consistency
- Integrity
Once you're done with the quality, next you have to focus on data cleansing. Make sure you clean your data every quarter, and focus on data enrichment too. If this task is hectic for you, you can delegate it to any reputable data cleansing company for your help. Working with dirty data is really dangerous when you're operating in the retail sector.
