Data quality management is a crucial aspect of any business’s growth and operational strategies. But achieving high data quality requires that specific roles, responsibilities and tools be in place.
SEE: Job description: Chief data officer (TechRepublic Premium)
Data management takes a team with both technical and business expertise. They must work together to understand what data is needed by different stakeholders, how it should be collected or generated, how administrators should store it, and whether they should update it periodically. In addition, companies need front-end (collection) and back-end (analysis) teams who analyze incoming data streams to identify problems before propagating across your enterprise.
Putting together a team with these skillsets and responsibilities can be challenging, especially for companies that are just getting started with a data-driven mentality. To achieve higher levels of data quality through effective data quality management, consider investing in professionals who can handle these data roles and responsibilities.
- What is data quality management?
- Data quality management roles and responsibilities
- Data quality tools
What is data quality management?
Data quality management is the process of ensuring that data meets a set of predetermined requirements. It is a data strategy that looks at the entire data lifecycle, from collecting and compiling raw data to analyzing and presenting it in meaningful ways. Data professionals play a key role in every step, overseeing how information is collected, processed, stored and distributed.
Data quality management roles and responsibilities
The data quality management process is a multifaceted one that involves various professionals with varying responsibilities. These are some of the most important roles to include on a data quality management task force:
Data quality managers
Data quality managers are responsible for overseeing projects related to data quality as well as assessing what needs to be improved. The responsibilities of data quality managers include the following:
- Working with clients to identify and define requirements for data quality management projects.
- Analyzing data that needs to be managed to determine how well it aligns with these requirements.
- Creating metrics to measure progress toward specific project goals.
- Implementing new policies or processes that will result in improved data quality.
- Monitoring progress against metrics over time.
Chief data officers (CDOs)
A chief data officer, or CDO, is a C-level executive who is accountable for an organization’s data assets. As their core responsibility, CDOs ensure that their company’s data assets meet strategic goals. The role of the CDO has been evolving in recent years from strategic data management to business process management as more organizations become reliant on data-driven decision-making.
The responsibilities of a CDO vary by organization but generally include the following:
- Establishing organizational goals related to data quality management.
- Developing policies for the use and control of organizational data assets.
- Overseeing the implementation of those policies and establishing mechanisms for measuring compliance.
- Prioritizing data quality projects.
- Integrating data quality across organizational departments.
- Training staff on best practices.
- Advocating internally and externally for improvements in organizational data practices.
- Overseeing the data quality management process to make sure data that is collected and used by the company meet business requirements.
- Developing strategies for how to use data to achieve business goals.
SEE: Strategies for adopting data stewardship without a CDO (TechRepublic)
A data steward is a professional responsible for enforcing policies around data usage and security as set by the organization’s data governance strategy. In addition, data stewards may be responsible for allocating resources to maintain and update databases, ensuring that policies are being followed, and monitoring and reporting on data quality.
The responsibilities of a data steward can change from project to project depending on the scope of their role and their role in the organization.
As the data gatekeeper, the data steward takes an active leadership role in planning projects, reviewing reports, participating in development sessions, designing new processes, and advocating for changes when necessary.
Data Stewards work with teams across different functional groups to establish common ground on how best to use and manage data-related information throughout the enterprise; this effort often requires negotiating cross-functional differences among stakeholders with different needs or priorities.
SEE: How do I become a data steward? (TechRepublic)
A data analyst is a data professional who collects, analyzes and interprets raw data to uncover patterns. Data analysts can be found in many industries, including retail, finance, government and healthcare.
Their responsibilities vary by industry but generally include:
- Collecting data from various sources.
- Analyzing collected data.
- Designing and maintaining data systems and databases.
- Making predictions based on their findings.
- Communicating clearly with colleagues across departments.
- Working with programmers, engineers and organizational executives to enhance processes, modify systems and build data governance policies.
Data analysts must have excellent organizational skills to keep track of large amounts of information. They must also communicate effectively with people across departments, such as IT staff or business development professionals who are not involved directly with the analytical process.
Data analysts work closely with the data scientists who are responsible for creating predictive models based on historical trends and predicting what will happen in the future. These two positions require similar skill sets, though one may specialize more in statistical analysis while another specializes more in predictive modeling.
A data custodian is a data professional who is responsible for the storage and security infrastructure of all or part of the enterprise. Data custodians oversee the storage, aggregation and use of datasets. In addition to storing, managing and securing data on behalf of other users or departments within an organization, data custodians are often responsible for ensuring that organizational requirements for privacy regulations are met in accordance with the organization’s data governance strategy.
Their responsibilities may include managing risks associated with information access, modification and deletion, and determining how admins should retain long data. The individual may also need to perform tasks related to systems user management, configuration management, systems development lifecycle management, capacity planning, disaster recovery planning, backup procedures and media management.
Data modelers are systems analysts who work with data architects and database administrators to create a data model that defines the different aspects of the data architecture. They build an organizational structure for a company’s data by deciding what data will be stored in databases and how to structure it.
SEE: Job description: Big data modeler (TechRepublic Premium)
Data modelers’ responsibilities include:
- Creating models that define different aspects of the data architecture.
- Building an organizational structure for company data by deciding what data will be stored in databases and how to structure them.
- Deciding how data is moved between systems so that it can be analyzed or accessed by people or programs.
- Ensuring an appropriate level of data quality across all applications and systems.
- Developing guidelines for managing change and modeling new requirements.
Big data engineers
A big data engineer is an IT professional who uses big data technologies to analyze large datasets. Big data engineers design, build, analyze, test, maintain, monitor and manage complex company data infrastructure systems.
SEE: Hiring kit: Data engineer (TechRepublic Premium)
A big data engineer’s job includes managing relational databases, columnar databases, distributed file systems, caching algorithms, information retrieval methods and other related techniques.
Data architects and designers
A data architect or data designer is responsible for designing a company’s data architecture. This includes gathering requirements from business stakeholders, analyzing the current data structure to determine what needs to be done and building an architecture for the future.
SEE: Hiring kit: Data architect (TechRepublic Premium)
Data architects are strategic thinkers who understand how any changes in the technology landscape will impact a company’s data environment. They take care of all the technical aspects of developing data architectures and ensure they align with other organizational initiatives. They also manage relationships with IT partners and vendors and must have excellent communication skills.
Data quality tools
Having the right people in place is an important first step toward higher data quality, but it’s equally important to give them the tools and resources they need for success.
There are a variety of tools available for this purpose that range from simple to complex. Choosing a data quality management tool will depend on the size and scope of your company as well as how much staff time and resource you can devote to this task.
Some of the top data quality tools to consider include Cloudingo, Data Ladder, IBM InfoSphere QualityStage, Informatica Master Data Management, OpenRefine, SAS Data Management, Precisely Trillium and Talend Data Quality. These tools differ in price, complexity and feature sets, so make sure to consider all of these factors when selecting which tool best suits your company’s specific needs.
Getting started with the right data quality management team and responsibilities is a huge first step that many companies never take. It involves planning, comprehensive knowledge of your business’s data strategy and a commitment to hiring and training the right people for the job. To achieve the highest levels of success right out of the gate, hire a data leader like a chief data officer who can shape and reshape your data quality management hiring plans as your company’s goals evolve.
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