Why data collection is important

In addition to identifying problems and inefficiencies, data also gives you the power to see your strengths and to implement the same methods across your organization. Having a smart data collection system in place will save you valuable time down the road.

So many resources are wasted going back again and again to retrieve the same information. A smart system will gather and display data in a way that is easy to access and navigate, meaning everyone who is part of your organization will save time.

More and more studies are proving the crucial role that data plays in pushing ROA forward and making the most of what your organization already has at its disposal. There is plenty of evidence proving that collecting data such as maintenance schedule and monthly inspections will help you improve asset usability over time.

Access to good data helps improve the quality of life both for those working in your organization and for the people you support. You can take appropriate action with fewer frustrations and complications at all levels of the process. Simply put, data improves overall quality.

Here is a breakdown to explain the importance of data collection: 1. Data empowers you to make informed decisions Never lose sight of the fact that data equals knowledge. Data helps you identify problems The fact of the matter is that every organization has problems and inefficiencies.

Data will help you explain both good and bad decisions to your stakeholders. Whether or not your strategies and decisions have the outcome you anticipated, you can be confident that you developed your approach based not upon guesses, but good solid data.

Data increases efficiency. Effective data collection and analysis will allow you to direct scarce resources where they are most needed. If an increase in significant incidents is noted in a particular service area, this data can be dissected further to determine whether the increase is widespread or isolated to a particular site.

If the issue is isolated, training, staffing, or other resources can be deployed precisely where they are needed, as opposed to system-wide. Data will also support organizations to determine which areas should take priority over others.

Data allows you to replicate areas of strength across your organization. Data analysis will support you to identify high-performing programs, service areas, and people. Once you identify your high-performers, you can study them in order to develop strategies to assist programs, service areas and people that are low-performing. Good data allows organizations to establish baselines, benchmarks, and goals to keep moving forward. Because data allows you to measure, you will be able to establish baselines, find benchmarks and set performance goals.

A baseline is what a certain area looks like before a particular solution is implemented. Collecting data will allow your organization to set goals for performance and celebrate your successes when they are achieved. Funding is increasingly outcome and data-driven. With the shift from funding that is based on services provided to funding that is based on outcomes achieved, it is increasingly important for organizations to implement evidence-based practice and develop systems to collect and analyze data.

Prevention is the most cost-effective activity to ensure the integrity of data collection. This proactive measure is best demonstrated by the standardization of protocol developed in a comprehensive and detailed procedures manual for data collection.

Poorly written manuals increase the risk of failing to identify problems and errors early in the research endeavor. These failures may be demonstrated in a number of ways:. An important component of quality assurance is developing a rigorous and detailed recruitment and training plan. Implicit in training is the need to effectively communicate the value of accurate data collection to trainees Knatterud, Rockhold, George, Barton, Davis, Fairweather, Honohan, Mowery, O'Neill, The training aspect is particularly important to address the potential problem of staff who may unintentionally deviate from the original protocol.

Since the researcher is the main measurement device in a study, many times there are little or no other data collecting instruments.

Indeed, instruments may need to be developed on the spot to accommodate unanticipated findings. A clearly defined communication structure is a necessary pre-condition for establishing monitoring systems. There should not be any uncertainty about the flow of information between principal investigators and staff members following the detection of errors in data collection. A poorly developed communication structure encourages lax monitoring and limits opportunities for detecting errors.

Detection or monitoring can take the form of direct staff observation during site visits, conference calls, or regular and frequent reviews of data reports to identify inconsistencies, extreme values or invalid codes. While site visits may not be appropriate for all disciplines, failure to regularly audit records, whether quantitative or quantitative, will make it difficult for investigators to verify that data collection is proceeding according to procedures established in the manual.

In addition, if the structure of communication is not clearly delineated in the procedures manual, transmission of any change in procedures to staff members can be compromised.