Give your ops team new tools that turn them into front line warriors for the customer
Get ahead of data issues with flexible and targeted quality checking and follow up
Build your ops team skills and empower their careers
We’ve all (occasionally!) experienced that feeling of leaving a call with a company service representative, where it seemed like the person on the other end of the line really got it! They knew who you were, you didn’t have to answer the same question multiple times, they surprised you by being able to refer to past interactions with the company.
That experience didn’t happen by accident and it’s a strong signal that the company in question places a high value on the data they hold and are actively managing that data to appropriate levels of quality.
Transforming data operations
Operational data management in an organization may be functionally organized under another group (typically IT) or be established as a standalone function. In either case as business becomes more connected, integrated and machine-driven, the gaps in data and substandard data quality become increasingly transparent to customers.
The often-quiet warriors of the operations team are a key part of your companys’ customer experience and resulting satisfaction, but investment in the function is often overlooked in favor of new revenue initiatives. The good news is that the tools driving the data-driven economy can be applied equally effectively to transform your operational processes. Here are three areas to consider:
Data quality processes
Many data quality processes today are run using SQL stored procedures or Excel macros. These workhorses of data management have limitations however in functionality and control. Migrating quality checks into a data science environment such as Python or R removes these limitations as well as providing a step change in speed and performance. These languages also come with rich function libraries that allow new checks to be written quickly and easily.
Are John Adams and John A. Adams the same person? Accurate entity matching is a key driver of data quality, and many data operations functions spend significant amounts of time managing this process. Applying some basic natural language and entity extraction techniques to the problem can bring significant gains in productivity and support a probability-based approach to exception management.
Part of effective data governance is putting the workflows in place to support the correct outcomes and ensure the right steps are taken by the appropriate people, in the correct sequence. Leveraging the extensive base of open source and commercial software code allows low cost development and implementation of custom workflow tools that support the key governance responsibilities across the data operations team.
Dmi works with data operations teams to build tools that transform data quality, and transition the knowledge into the team to ensure those changes are sustained through time. If you’re interested in taking your operations to the next level contact us to find out more.