‫-: Hey, welcome back.
‫In this lesson we are gonna be talking about
‫the data quality analyst role.
‫The data quality analyst role
‫is probably the most important role
‫when it comes to data quality management.
‫These are actually the analysts
‫that are gonna monitor the quality of the data.
‫They examine complex data to optimize the efficiency
‫and quality of data being collected.
‫They actually resolve data quality problems.
‫They collaborate with database developers
‫to improve the systems.
‫So they're really the main driving force
‫in terms of making sure
‫that the quality of your data
‫is to the required standard.
‫Here's a sample from a job on what you can expect
‫from a data quality analyst responsibilities.
‫So you see that
‫the data quality analyst will perform statistical tests
‫on large data sets
‫to determine data quality and integrity.
‫They evaluate system performance and design
‫and its effect on data quality.
‫Just keep in mind that for this point
‫you need really people with experience.
‫You don't want your business users to deal with
‫database developers.
‫So the data quality analysts will also communicate
‫to the database developers to improve the data collection
‫and storage procedures and processes.
‫And they'll also do a random ad hoc querying of data
‫in order to find any data issues.
‫With the guidance of the data quality manager
‫they'll also document processes and procedures.
‫So in order for your data quality analyst team
‫to be effective
‫you need to make sure that
‫they're always up to date with the latest
‫and greatest in terms of data quality framework,
‫methodologies and best practices.
‫And if you have a data quality manager
‫the data quality manager will need to make sure
‫that they put the right focus for the data quality analysts.
‫Because in large organizations
‫there's so much to do when it comes to data quality.
‫But some of the tasks
‫that usually data quality analysts perform,
‫will really not evolve any big benefits
‫for the whole organization.
‫I see this problem in a lot of companies where
‫they're focusing on the small stuff
‫because they're easy to resolve and easy to identify.
‫For example, a single data error in a database
‫takes a few days out of your team's time
‫when it's gonna be much better for this time to be invested
‫into the wider issue.
‫And once you resolve the wider problem
‫that's at the enterprise level, for example,
‫the lack of processes and procedures
‫in order to maintain data quality training for users
‫changing the actual data infrastructure
‫to be easier to maintain data quality.
‫These are the big rocks that will make sure
‫that the data quality within the enterprise organization
‫actually improves for the long term.
‫Otherwise, you have your data quality team
‫just focusing on small issues,
‫which makes them a factory of fixing minor data issues.
‫And you should not utilize your data quality team
‫just for this.
‫I believe this is what really makes or breaks
‫any data quality management program within an organization.
‫It is the utilization of the data quality team
‫whether you make them focus
‫on the right bigger fundamental issues
‫which will result in long-term benefits for the organization
‫or whether you focus them on
‫some of the minor tactical data issues
‫that pop up every single day.
‫Obviously in order to run the business
‫you need to address the tactical issues as well.
‫But in most cases this can be addressed
‫by the domain data owners.
‫So for example, if we have a lot of issues
‫popping up in the sales department,
‫usually the sales department also have their own data owner
‫and data analysts
‫and they're in a much better position of fixing the data
‫because they understand it at the local level.
‫And then you make sure
‫that your data quality analyst team focuses
‫on the more fundamental bigger data quality issues
‫within the company,
‫which in the long term will also resolve the data issues
‫within the sales department.
‫So with this we've also covered the data quality analysts
‫and what they should do within your organization.
‫So as you can see,
‫the data quality manager and the data quality analysts
‫are really the backbone of the data quality,
‫within your organization
‫and I definitely think you should have such team
‫and if you have such team
‫this team should be allocated to work on the right things.
‫Let's now continue to the other roles within data quality
‫that are not specifically hard to work
‫within the data quality management framework
‫but are crucial for the success of the data quality
‫within your organization.