Why data quality matters to any industry
Data is the most critical resources available to today’s marketers. By interpreting data correctly, one can seamlessly explain the customer experience, engagement opportunities, closed deal rates, campaign performance, and more.
The challenge is making those interpretations meaningful, and this can happen only with high-quality data. Quality data enables management teams to tell an insightful story, and confidently describe the influence on revenue and brand loyalty. Without that confidence in the accuracy of data, marketers will be unable to embrace the trends or new opportunities in the business world.
Data quality is the first thing you need while building any business. If the data is not accurate, it is difficult for the staff to connect the dots, and eventually, the business may collapse. A study by DiscoverOrg revealed that sales and marketing departments lose around 550 hours and $32,000 per sales rep because of bad data. Since bad data can adversely affect the business, it is crucial to learn proactive measures to combat and treat it at its source.
Here are the characteristics of good data:
Accurate and up-to-date: It refers to how well the current data describes the real-world conditions. Good data includes all the latest details from the last engagement date with no errors. If the data is incorrect or old, you may end up targeting the wrong group of audience.
Unique and relevant: By having relevant records that are free from redundancies, you can save your time as well as money. To establish good quality database, firms must source the data from authentic sources and combine them to create a single record for each entity.
Comprehensive: The data is of high-quality if it contains only complete records. That is, a customer name should have an email address, mailing address, fax number, and phone numbers attached to it. Similarly, company names need to have a job title, revenue, location, and other necessary details.
Consistent: Steady pattern is essential to keep the database manageable. Businesses may rely on multiple systems to collate as well as store the data; hence you may find phone numbers, addresses, birthdates, and more in a various format leading to duplicate data sets or incorrect information.
Why is data quality important for your business?
Data continues to become increasingly important for any industry. Data can help with practically everything for businesses. It can help identify which audience to target, vendor to choose, faculty to recruit, or decide between multiple strategies. A database can do this only if the data in it is clean and fresh.
Company growth requires a robust approach, building on strengths to improve the trade as a whole. Weak quality data makes it harder to achieve, meaning you waste your valuable resources in unfruitful ways. Hence, the better is your data quality, the more you can get out of it.
New technologies such as marketing automation and AI are also influenced by data quality. These tools have enormous potential in today’s business world, but success heavily depends on the value of data. The more useful data you provide, the algorithms can produce faster and better results.
Similarly, data quality is critical because of specific compliance-related issues. The data protection regulations continue to evolve, forcing the companies to manage their data properly. This is especially significant for user’s personal information or sensitive financial data but can apply to other types of data as well.
What can you Do to ensure high quality data?
Collecting good quality data can be challenging at times. Problems may occur due to causes such asthe need to integrate various data systems across different departments or applications, implemention of new software, manual entering of data, use of inappropriate tools to process the records, and more. There are certain things firms can do to improve data quality in these circumstances.
Taking the following steps can help you ensure the collection and also maintenance of quality data:
- Build a reliable integration system:It is evident that most organizations rely on numerous sources to gather the data. This leads in the creation of different databases with slightly altered sets of the same information resulting in erroneous and duplicate records. One cannot resolve such issues manually if it is a large data set fed by multiple systems. Instead, usage of specific tools will help in identifying the data and organizing it consistently. These tools build a single master database providing a composite view of all the records. You can then create a data subset from this comprehensive list based on your data-driven marketing needs.
- Set a clear and organized data system: Your data will never remain stagnant. You may gain new customers, new prospects, and use new tools to manage the data. As a result, data grows continuously along with your customer base. Hence it is essential to ensure that your systems are clear, organized, and ready to scale in conjunction with upcoming changes. By staying on top with a well-set plan, you can maintain the quality of your master database throughout your business journey. Also, documenting data quality issues can help with this ongoing effort by ensuring that same mistakes are not repeated. The focus on continuous improvement of your data collection plan helps in producing exceptional campaign results, conversions, revenue, and stronger customer service.
- Implement data collection and correction plans: An appropriate data collection plan is necessary to ensure that the data you are collecting is of high-quality. Identify the type of data your business needs to meet the goals and learn different methods to collect and manage it. You must also define the roles of all data specialists involved in the process of data collection and communication between departments. Since you may have to deal with various departments, be specific about your plan to avoid confusion. Besides, you will also need to create rules for correcting data. These rules must define the person responsible for correcting data and the methods they use to fix it. This is highly essential for ensuring consistency in your database.
- Develop benchmarks to data quality: Create quality standards to determine which data to keep, which to delete and which to correct. Research and develop parameters for each data collection methodology to identify the typical pattern of errors or redundancies that may occur because of data source. Also, it’s essential to interpret the data for each pipeline stage so you can diagnose the problems in sales as well as marketing alignment. For instance, if your audience are unhappy with the content you mail them, you may have to reexamine your lead database. It may contain the records of the audience who are not interested in your product or services. Everyone involved in managing your data must agree on and understand these benchmarks to confirm consistency across your organization.
- Understand your marketing ecosystem: You can determine the possible channel conflicts and also the sources of erroneous records by clearly learning the data flow across your marketing automation, CRM systems, compliance process, as well as reporting tools. For instance, your vertical classifications may not align rightly across networks, or the data is scattered everywhere instead of directing it to the right storage. By knowing the data and systems infrastructure, businesses can tighten up the database and the reports for better insights.
- Acquire from third-party sources: Sometimes it’s difficult for the organization to build a comprehensive data set of every single prospect. Executing the campaigns using such partial database can be as useless as having no data at all. In such cases, consider tapping into the third-party sources. These aggregators collect large amount of data from various sources into one data set. One of the main advantages of third-party sources is its scale. It has the potential to expand your audience count quickly. Also, you can uncover valuable trends and insights because of its immensely larger volume.
Success with new technologies depends on data quality
Organizations are already looking at various ways to adopt the new wave of software and technologies related to CRM, automation, IoT, AI, and more. Success in embracing these new approaches depends on the ability to get data management right. This becomes extremely important as connected devices and sensors multiply in business, causing exponential growth in data. The industries that train their teams to manage data correctly and recognize its inherent value will have the advantage over others. That is why, if you are a slow adopter or a new entrant, it is essential to develop a strategy for collection, as well as maintenance, of high-quality data as early as possible.