In today’s business environment, data has become the lifeblood of any organization, and the quality of data can significantly impact business operations and decision-making. Therefore, it’s crucial for companies to make data quality a business priority.
As the CIO at a midmarket manufacturing company, I understand the challenges that organizations face with data quality. Given that context, I’ll cover why data quality is essential and how it can help organizations achieve their goals. As part of this analysis, I will also provide tips on how to improve data quality and how to create a culture of data quality within your organization.
Data Fuels the Business
At Acceleration Economy, we’ve been referring to data as “the new oil”. This metaphor has been around for some time, but it seems as though recently it has become particularly appropriate. The analogy suggests that, like oil, data has become a valuable commodity that can be extracted, refined, and used to fuel business operations and create new products and services. Just as oil was the key resource that powered the industrial revolution, data is now the fuel for the information age. In the same way, you couldn’t just take oil from the ground and fill up your car with it, you also can’t get much out of raw data until it has been scrubbed and prepared for analysis.
My fellow practitioner-analyst Wayne Sadin says that “decision makers don’t need data, they need information.” Data doesn’t become information until it has context and we have ensured that the data will be used reliably and consistently. We refer to this aspect of data as “data quality” and it is more important to business than ever before as cloud-based systems have made it easy to pull large amounts of data from so many sources, including Internet of Things (IoT) connected devices, customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, automated bots, and a virtually endless supply of cloud data services.
Which companies are the most important vendors in data? Check out the Acceleration Economy Data Modernization Top 10 Shortlist.
Why Data Quality Is a Top Business Priority
Recently, when my company went through the process of migrating from an on-premises ERP to a cloud-based system, we encountered significant challenges in preparing master data to move from one system to another without losing any important details. Then, after deploying the new system, we had to find a way to combine data from the old system with equivalent data in the new system in a way that allowed business users to analyze the data as if it were all contained in one continuous system.
Even though the basic information was the same, differences in the way information was stored and represented in the database made it difficult to organize and make it available in a consistent and reliable way. You will find out very quickly when production numbers, shipment quantities, and sales figures do not match from one system to another or differ in some way from what is expected. Sometimes those differences come down to a tiny detail in the attributes of one record in the database.
In today’s data-driven business environment, the importance of maintaining high-quality data cannot be overstated. Poor data quality can have far-reaching consequences, from incorrect decision-making to lost productivity and revenue. Here are just a few reasons why data quality should be a top priority for any business:
- Impact on decision-making: In my company, for example, it is important to have accurate sales data to know which products need to be manufactured and when they need to be ready. Any errors in this calculation could result in filling up valuable warehouse space with unsold product.
- Improved efficiency and productivity: We rely on accurate production and quality control data to set our target machine run rates and reliable production schedules.
- Better customer experience: Our customer service representatives take calls from customers looking for information about their order status. It’s critical that those employees have easy and quick access to reliable data to get the customers the information they need.
How to Improve Data Quality
Improving data quality is a continuous process that requires ongoing effort and investment. Just as with any initiative, you have to start somewhere, and you can make incremental progress toward an end goal. I am starting to put together a plan for my company to achieve a high level of data quality. Here are some of the areas, in no particular order, on which I am focusing:
Define data quality standards: This includes defining data accuracy, completeness, consistency, and timeliness. By establishing clear standards, businesses can ensure that everyone is working toward the same goals.
Conduct regular data audits: Establish a regular schedule for data audits and assign a team to perform them. To identify any issues that need to be addressed, audits should include a review of data for accuracy, completeness, consistency, and timeliness.
Implement data validation rules: This can include rules that check for data accuracy, formatting, and completeness. For example, a rule might require that all email addresses entered into a database have the correct format.
Invest in data management tools: These kinds of tools help businesses automate data quality processes and reduce the risk of errors. This can include tools for data cleaning, data integration, and data governance.
Creating a Culture of Data Quality
Improving data quality is impossible without the help of technology tools and smart practices. However, in my experience, and this is where leadership comes in, it’s essential that the people you work with are invested in, motivated towards, and knowledgeable about the process and goals of data quality, too. Here are three ways to set the stage for a data quality culture at your organization:
Educate employees on the importance of data quality: This can include training sessions, workshops, and regular communications about data quality issues. Our recent ERP migration has been a good opportunity to educate users in different departments on data quality topics.
Encourage collaboration between departments: This can include cross-functional teams that work together to identify and correct data quality issues. This has been critical for my company — IT collaborates with production, planning, sales, and executive management to ensure data quality.
Reward data quality achievements: This can include incentives for teams that achieve data quality goals or recognition for individual employees who demonstrate a commitment to data quality.
Conclusion
If data is “the new oil,” then companies that can refine that product — turning data into usable information — will be better positioned to succeed in the information age. Data quality is a critical business priority that can impact decision-making, efficiency, productivity, and customer experience. Improving data quality is a continuous process that requires ongoing effort and investment, but a crucial first step towards improving data quality is creating a culture that prioritizes it, involving employees from different departments to work towards this common goal. Over time, you will improve the accuracy, completeness, consistency, and timeliness of your data, leading to better business outcomes and increased competitiveness in the marketplace.
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