How To Put Data Quality Monitoring At The Forefront Of A New Business

The founding of a business can leave you with a lot of problems to address. However, it also represents a great opportunity to put best practices in place early. Especially if your business depends on data, the right quality monitoring processes can make a major difference. You can take these 6 steps to put data quality monitoring at the forefront of your new operation.

Develop a Data-Centric Culture

A business can't leave all the work to its data quality monitoring software, even if it has the best package available. Your company needs to have a data-centric culture where everyone understands the importance of quality. Team members, C-suite occupants, and partners all can benefit from knowing the company's commitment to data monitoring. You can outline standards and practices, include them in manuals and guidelines, and use them in training sessions.

Build With an Eye Toward Data Quality Monitoring

With a barebones business, you have a chance to make every choice with an eye toward data quality. You can select hardware and software systems based on specific criteria. Network appliances, databases, adapters, and code can all integrate as a stack that maximizes the quality of your data.

Outline Your Process

Your operation is going to ingest, store, process, and analyze data. Outline what the process will be from the moment intake begins. Starting with your sources, as data monitoring techniques can ensure the integrity of the information. You can integrate data monitoring software with definitions to minimize the likelihood of problems during intake. Also, you can iterate this approach across storage systems, analytics engines, and even reporting tools.

Take Proactive Steps

As you outline the process, you may notice there's a glaring weakness in the system. Fortunately, your business is young so you can solve this problem early. Be proactive. Identify the issues and potential data quality monitoring solutions. Implement them before bad practices have a chance to permeate the larger operation.

Test and Refine

Data monitoring is an endless process. Testing matters, and you'll make refinements based on what you learn from the tests. Run your systems hard. Hit them with garbage data and see how they perform under stress. Break things so you can fix them quickly.

Be Patient and Persistent

You won't get everything right on the first try. Be patient so you can build the definitions files for your data monitoring software. If the system encounters issues, be persistent about fixing them. Put the talk about a culture of data quality monitoring into practice every day.