Today’s veterinarians are relying on data more than ever before, which has fueled a growing demand for high quality data management. Without discerning which data is useful for commercial operations, practices simply cannot compete with their peers. Even so, many entrepreneurs and veterinary professionals have a limited understanding of data quality management. Fewer still, know how to implement it into their existing digital structure.
Here’s a breakdown of data quality management and how to implement it into your practice. Be sure to pay attention to the common mistakes that too many veterinarians make when analyzing their data.
Defining Data Quality
When determining the quality of your data, focus on the following dimensions:
- The quality of your data. This refers to the accuracy of the information you’re working with and whether it produces actionable insights for your practice.
- The reliability of your data, such as whether it is recent and error-free. Some data is too outdated or erroneous to provide any value to your organization.
- The wholeness and consistency of data. This means that your data is showing you a complete picture and won’t produce irregular results when you analyze it for commercial purposes.
Maintaining high data quality standards is important if you want to master business consolidation. Few practices will want to combine their data with yours unless it is insightful and manageable. Ensuring a smooth consolidation thus requires a stellar data quality management model. Veterinary practices that seek to grow must focus on collecting accurate, current, and consistent data.
Be sure to regularly purge your data archives of erroneous information that will make your data management practices look sloppy. In time, your practice will become a far more enticing partner to corporations looking to expand their footprint in your area.
Understanding Data Quality Management
Data quality management can help you accomplish several goals. It lets you correctly predict future patient behavior. It identifies costly deficiencies in the business model of your practice. It particularly helps ensure your decision-making process is well-informed. Above all else, high quality data management standards ensure your organization is an alluring partner for business consolidation.
This consolidation allows your practice to pool large sums of capital for the bulk purchasing of equipment and medicine. It will enable your practice to more easily secure financing for future expansions. It can even introduce new expertise into your ranks that allows your organization to offer patients profitable new services.
You will want to familiarize yourself with leading data quality management tools so that your organization isn’t grappling with a huge sum of data without the means to understand and make use of it. With the right management tools, your practice will be able to consolidate its data more easily and quickly with others.
Veterinary practices thinking about business consolidation should focus on these three steps:
- Finding the right tools for the job.
Reviewing a guide on the selection of the right tools for high quality data management can ensure your organization properly equips itself before grappling with data. If your business seldom uses a data management tool, consider looking at alternative options. Sticking with the wrong tool because you do not want to invest in a new one is a critical mistake.
- Recruiting the right individuals for your team.
A savvy data expert can identify, collect, and clean data for your practice like no other. Do not be afraid to invest in high-salaried workers if they deliver stellar results. These data gurus will understand the importance of data quality statistics and know how to manage the ETL (extract, transform, load) data process. Don’t fret if you lack the expertise needed to evaluate these data gurus before hiring them. A good data quality manager who knows the goals and limitations of your brand can make these decisions on your behalf.
- Devising an appropriate strategy for your company.
This requires reviewing your current data management practices to identify any deficiencies. Team leaders should focus on the dimensions of data quality to guarantee your investment into data management isn’t going to waste. Always solicit the feedback of your data quality manager when devising such a strategy.
When focusing on the dimensions of your data quality, also concern yourself with the timeliness or relevance of your data. Constantly ask whether the data you’re working with is up to date and available when your organization is facing a tough call and must make an informed decision. Finally, assess the uniqueness of your data. This ensures that you’re not falling victim to data duplication and the myriad of problems it can produce.
Data Quality Management and Business Consolidation
Your veterinary practice will only be able to be consolidated when you’ve mastered data quality management. To do so, pay attention to the key feature of data quality analysis that’s highlighted by McKinsey – designing a data governance regime that will deliver value to your practice. Deriving actionable and valuable insights from your data ensures your practice can competitively negotiate the terms of consolidating with another brand. The best data quality techniques will thus optimize your own practice while making your brand a more enticing partner in the eyes of corporators.
Ask whether your employees are saving time thanks to your data quality management model. Question whether potential clients will be attracted to your company based on its data management practices. Always consider whether your data quality manager is a vital part of your team. Finally, ask how your data quality management practices are helping you consolidate and grow within the veterinary industry. Before long, your brand will grow to new heights as you corner the market with your savvy data practices.