Sales and marketing departments lose approximately 550 hours (about 3 and a half weeks) and as much as $32,000 per sales rep to bad data every year. Why? Because the data being collected is not relevant to the business or becomes distorted and corrupted throughout the data lifecycle. Instead of being able to make critical business decisions based on data-driven insights, a company may suddenly make a wrong turn costing high overheads or potentially losing a sizable portion of their clientele.
Without data quality tools to ensure that you interpret and receive reports on accurate information, you cannot achieve efficient operations and higher potential sales. This puts your organization at a competitive loss in the marketplace and could result in significant challenges growing exponentially until the data is appropriately changed, cleaned, or adapted.
What is Data Quality?
Data needs to be reliable and serve a particular purpose.We live in an incredibly fast-paced marketplace that relies on data analytics to predict respond, and adjust when demands or other fluctuations hit. Without the proper data to inform our decisions, we are left in the dark trying to navigate without road signs.
You need data quality to make operational decisions, achieve future tasks, and plan for changes in the market, internal needs, or customer demands. If you continue to ship products to a client to an incorrectly spelled address in your data, you are suffering losses that are entirely unnecessary and can be remediated quickly with appropriate data quality measures in place.
Ways to Measure Data Quality
We should always measure data quality against key factors to ensure you are getting precisely what you need to make changes. This helps avoid operational systems issues and faulty results in analytical reports. You want your data sets to be easily verifiable to a set of metrics determined by your organization. If you are new to the field, hiring a professional team that can walk you through how to measure your data quality framework can make the difference between success and failure.
Common attributes of data quality measurement include:
- Completeness - Has all the information about a set of data been properly collected, or are there gaps that will lead to questions instead of answers? A good example is customer data. If you need the geographic location of your customers but only receive the country designation and not the state, city, or county, then you do not have a precise picture?
- Validity – Have you connected different datasets to ensure every entry is valuable and not replicated, duplicated, or redundant?
- Accurate – Is the data you collected authentic and accurate to the source? If you measure the amount of an item being sold, does the data reflect the correct spelling and information of the customer making the purchase?
- Consistent – Is the information across multiple datasets factual in each instance?
- Availability – Can you access all the raw data from all the sources you are using to collect information?
- Timely – Is the data current with your needs or based on historical information that is no longer relevant?
Importance of Data Quality
Without the incorporation of data quality, you risk damaging business outcomes. The goal is to have the accurate information needed for all your decision-making. With proper data quality, you can lower operating expenses across multiple departments. One wrong decision at the top of the organizational chart can wreak havoc on downstream users who may have never even viewed the report the data is based on.
The productivity of your organization relies on high-quality data points. This is a challenge given the amount of data being collected on a minute-by-minute basis. Even if your company is a small eCommerce platform, you are most likely tracking customer interaction with your website as well as critical information like demographics, shipping, payment methods, and more.
All this information must be collected, stored, cleaned, and then presented to you and your partners through a data quality framework that ensures the information is complete and up to date. Otherwise, you could make a meaningful change to your typical business operations only to see no financial or systematic benefit later down the road.
In general, data quality should help with:
- Decision Making - The more high-quality data you have, the greater your confidence when you are facing a decision. This is the easiest way to mitigate potential risks to your business.
- Refined Targeting – Data quality plays a critical role in improving focus on your marketing, advertising, and outreach programs because it informs your team where there are opportunities and when to leverage demographic information on leads.
- Effective Content – The term “give them what they want” is true in business. Data quality will inform you what content your customers respond to the most and help refine what you produce.
- Improve Customer Relationships – Feedback is critical to today’s consumer-driven marketplace. They want to be able to tell you how their experience is going and where they feel you can make concessions. Data quality is crucial in delivering that feedback to the relevant leadership.
- Competitive Advantage - With enhanced data quality tools, you can make better dictions and discover unseen insights that you may have missed in the past. This gives you potential advantages over the competition in your niche market.
- Higher Profits - With better decision making due to quality data, you lower operational costs and increase potential sales leading to more substantial revenue generation.
Challenges of Data Quality
The collection, retention, and interpretation of data is one of the fastest-growing technology fields. The biggest challenge facing data quality tools is the vast quantity of collected data. There are so much information trading hands at rapid speeds that it are near impossible to keep up with. So, while companies invent new ways of implementing automated data quality frameworks, critical decisions must be addressed considering:
- Privacy Laws - is the data being treated with appropriate legal consideration for client privacy? This is especially critical for fields related to extremely sensitive data like healthcare and medicine.
- Data Governance – is there a set of standards and policies for the collection, storage, and sharing of information? Without that in place, you risk data being sent to the incorrect departments or leadership teams.
How Data Quality Benefits the Organization
Accurate and easy to access data quality allows for more robust decision-making and better business outcomes. It can save your company time and money by increasing revenue potential while lowering operational costs. It improves your customer relationships because you can build trust. When clients see that you are managing their information safely and securely while delivering on quality service promises, they are more willing to remain loyal customers.
Data Quality improves the overall security of a company. It allows you to uncover any mistakes being made and possible internal fraud or external threats by discovering insights that would never have been noticed by a human eye.
It comes down to being able to focus on your organizational goals instead of spending time trying to fix errors from poor data quality. You want to efficiently use your human assets as best as possible, and that productivity relies heavily on data quality in today’s marketplace.
Conclusion
Data presents a wealth of valuable and actionable information for your company to make decisions. However, by itself, data can also create problems. Ensuring you have the accurate and beneficial data quality framework to garner the most insights is the best way to take advantage of the vast amounts of information collected by your company, customers, and market niche.
To get started with a professional team that understands what data governance and tools need to be in place for your specific business, reach out to the experts at NextPhase. Our data quality control automation enabled services utilize modern techniques to ensure the information you are receiving is highly relevant, accurate, and will help you make critical decisions about where to go next.