Why a focus on quality over quantity makes your data more reliable

No matter the type of organization, data has become the lifeblood of day to day operations

The health, performance, and sustainability of any organization depends on the collection and use of data that can help highlight trends, maintain records, and drive decision making. As much as the people or the equipment, data is an indispensable asset.

Despite the importance of this valuable asset, many organizations do not think very much about the quality of the data they hold. They may collect data, and may even use those data to create reports, but even those organizations may not be aware of the increased value of their data if they had taken their time to make data quality a central focus of their work processes.

The ideal time to begin thinking about data quality would be as a brand-new organization is just beginning to operate. But, due to the natural growth of organizations, most have probably started small with very little in data holdings, but before too long many find themselves with a loose assortment of different types of data, with no overall plan of how to manage or use them. Sometimes there are different formats or software programs used to collect and store the data. Each new holding was added as needed without much thought given to how everything related to each other. In these cases, it can become cumbersome to try and retrieve information from so many sources and to try and align those different sources together. It is usually at this point when the data becomes more of a hindrance than a help that the decision is made to consider improving data quality.

In order to have a data quality focused organization, the people need to be the first consideration. There needs to be buy-in from senior management. As there is the potential for changes to be made that will require an investment of resources, the executives need to understand why they should expend the cost. Specific examples of how improved data quality can improve efficiency will go a long way towards allowing them to see the need for an overhaul. If, for example, there is a need to combine data from an invoice database that has a date in “mm/dd/yyyy” format and an ordering database that has a “dd/mm/yyyy” date format, we could explain how much time is spent by staff reformatting the date. If we tell them that in the long run, having both dates be the same format will make ordering more efficient, that will be a concrete example of how improved quality means better work.

Ask management what reports they would like to see, what questions they have that they cannot currently easily answer. Explain to them that improved data quality means improved reports. The internet is a great resource for report templates that can show what could be possible if the organization had clean, reliable data.


Full Engagement

Not just managers, but all staff need to be engaged in the concept of data quality. Everyone needs to do their part to ensure that the data is of the highest quality. Take time to speak to the staff, to find out their frustrations and issues with the data entry process. There are often times that staff will invent their own workarounds or little shortcuts to make their day to day work go more smoothly. Gather these pieces of information because those who work with the data on a day to day basis often have a better idea of what is working well and what is not. Be sure to find out both. Ask also for “wish list” items. Not all staff requests will be able to be accommodated, but there are times that a new, better process could be implemented that no one else had thought of before.

If new data collecting processes do end up being implanted, either suggested or not, the staff will be more likely to accept and follow them if they feel as though they have contributed. They will also be more accepting of change once they know why the change was made. As in the example above, if the staff inputting data into the ordering database were told to change the date format without being given a reason, they may be less inclined to do so consistently than if the staff were told that the change was to make it easier to combine information from the invoice database. They will be able to see how the change will benefit them and be more likely to remember to do so.

If there are external sources of data that your organization uses, such as those from suppliers, or from administrative data sources, do try to collaborate with those sources. These organizations may already have their own data quality framework that they use. By asking what their preferred format is, we may get ideas to use in our own data quality initiatives. Or, maybe we could ask that they send you data in a format more compatible with our data. That may not be possible in all cases, but for those where it is, it’s worth having a discussion.

Always be open to change. In a vibrant, dynamic organization, things are always changing. New technologies, new staff, and new polices will require a data quality policy that is just as flexible. Do not be afraid to re-think your processes. Sometimes the best designed process just does not work in day to day life. Do not be afraid to scrap it. Continue to seek feedback from staff, having an open-door policy when it comes to data handling and processing. As the organization changes, the data needs will change, and those on the front line will see those changes before anyone else. Take feedback on an ongoing basis, so that data quality is an everyday work priority instead of a project carried out once every couple of years when the situation becomes critical.