The role and responsibilities of a data quality manager are varied depending on the industry in which he or she works. However, resolving issues, maintaining company standards and being an effective communicator are just some of the responsibilities that are necessary.
Also called a quality assurance manager or a quality control manager, this person is responsible for coordinating activities to complete these standards of quality. They also advise on how these quality systems are managed, they report performance and they measure against set standards. A data quality manager often sets policy standards and may be a Salesforce Admin, Marketing Operations professional, CRM Manager, or even an outside partner.
Quality data managers also meet with other staff, employees and managers in the organization to ensure that the system for quality management is operating correctly. When it is needed, they will recommend changes and explain how these changes can be implemented into the system by providing the necessary training, best data quality tools and techniques that will allow the company to meet its company standards. Data management tools, like RingLead Data Management Solutions, can make cleaning, organizing and enriching data an automated and simple process saving professionals valuable time, money and energy.
This type of manager will also provide insight into how much work can effectively be completed within one work shift so that the highest work standards are maintained.
Data Quality Manager – Where Are They Required
Quality data managers work in a variety of sectors. There are in high demand the production and manufacturing sectors, and they are placed primarily in support functions. However, they are found in other sectors as well such as technology, finance, public service, transportation, food production, engineering and petrochemicals. More or less, any company with a database can benefit from database management.
With the popularity of CRM systems like Salesforce and Microsoft Dynamics, and Marketing Automation Systems like Marketo and Pardot, there is an even greater need for data quality managers to make sure these databases are clean, standardized and enriched, so that sales and marketing departments can see the greatest ROI on these systems. When your CRM and Marketing Automation Systems are deduped and enriched, companies see better engagement, conversion and deliverability rates, and overall higher revenue and growth.
There are certain skills that an individual possess in order to be a successful quality data manager.
The following list includes some of the most important skills a quality data manager will need in order to complete day to day tasks. They should:
- Be able to negotiate
- Be able to write reports well
- Have excellent customer service skills
- Be able to plan and lead projects to completion
- Be effective at communicating
- Be motivators
- Have analytical skills
- Be familiar with the tools and methodology needed for successful quality control
- Be able to interpret data
In information technology, although the skills and responsibilities may be similar as other sectors, there are some skills specific to IT that quality data managers should have. They inspect processes and equipment, maintain a data quality checklist, set data quality objectives and also check the efficiency and functionality of these processes. The process of testing is also determined by the quality data manager, and he or she makes modifications if needed. If you have an eye for detail and enjoy analytical thinking, you should consider a career as a quality data manager.
Think you have what it takes to be a data quality manager? Join RingLead’s Premier Partner Program today to help implement RingLead Data Management Solutions (DMS) in companies across the globe, streamlining their data management processes through scheduled cleanse tasks, and real time duplicate prevention alerts. DMS is the only cloud-based, fully scalable, automated platform that clean, protect and enrich all of the data inside your database in real time, getting rid of the mundane and tedious task of deduping and normalizing data by hand.
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