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<title>Data Quality</title>
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<remark role='italicblue'>Definitions and Mechanisms</remark>
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<title>Chapter Objectives</title>
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<para>After reading this chapter you will be able to:</para>
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<itemizedlist mark="circle">
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<listitem>Explain what data quality is.</listitem>
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<listitem>Describe the mechanism for improving data quality</listitem>
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<title>What is data quality?</title>
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<para>Good data quality should start with reliable and appropriate collection tools and methods. If the data collection process is made easy, the chances of getting good quality data are increased. Building quality into the process of data collection right from the beginning will assist data quality control.</para>
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<para>The essential components of good quality data are as follows:</para>
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<listitem>Current and On time and at all levels. Old data is of historical value only. Decisions must be made based on current, updated information.</listitem>
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<listitem>Available at all levels</listitem>
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<listitem>Comprehensive - collected from all possible data sources.</listitem>
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<listitem>Reliable and accurate enough to support decisions. If data is not accurate, then wrong impressions and information are being conveyed to the user. Recording data is subject to human error and steps must be taken to ensure that errors do not occur or, if they do occur, are picked up and immediately rectified.</listitem>
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<listitem>Usable, if not discard the data.</listitem>
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<listitem>Comparable i.e. using the same definitions of data items. If we don’t measure by using the same tool we can’t compare each other’s results. Comparability can be ensured by using the same numerators and denominators in formulas.</listitem>
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<para>The three ‘C’s’ of good quality data are that it must be:<sbr/>
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<listitem>Correct</listitem>
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<listitem>Complete</listitem>
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<listitem>Consistent, keep up to date with population, definition changes etc.</listitem>
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<title>What mechanisms are used to improve data quality? </title>
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<para>The most effective way of ensuring good data quality is to check the data yourself by visually scanning for the common sources of errors like gaps, spelling errors or duplicates.</para>
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<para>Another way to ensure good quality data is to set up systems to make sure that the data we collect is of good quality right from the start. The following systems and procedures can be applied:
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<listitem>Training staff in data collection, data quality checks and the use of information for action</listitem>
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<listitem>Ensuring that standardized data element and indicator definitions are made available and understood at data collection points . Keep a list posted in your facility</listitem>
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<listitem>Looking for possible weaknesses in the system, resulting in double counting or missing of entries</listitem>
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<listitem>Making data collection as easy as possible: user friendly tools, limited data set, limited number of forms and register, limited duplication of entries</listitem>
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<listitem>Pre-testing any new data collection tools before introducing them</listitem>
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<listitem>Having clearly defined responsibilities at every step in the information cycle</listitem>
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<listitem>Having procedures in place to formally check data quality</listitem>
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<listitem>Providing feedback to staff on the quality of the data they submit</listitem>
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<listitem>Helping staff to understand why they collect data. Provide feedback on how the data is used by managers and how they can use data themselves either to take local decisions or to lobby for specific management decisions or actions</listitem>
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<listitem>If errors are identified, look for the source of the error and correct where possible</listitem>
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<listitem>Identify gaps in staffing and motivate strongly for vacant posts to be filled</listitem>
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<listitem>Timeliness - Have a calendar for when data is due at each level in the data management process</listitem>
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<para>Chapters 3 and 4 describe the data quality improvement measures that have been implemented in DHIS2 w.r.t. the capturing of aggregated and patient data.</para>
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