Ever felt the curiosity to know what is data integrity and what are its dimensions? Well, well, before being aware of, how to achieve certainty regarding quality in data collection, a person has to have a clear idea about data integrity and its dimensions. Data integrity precisely is the assurance and consistency of data accuracy, completion, reliability, and uniformity over its life cycle. It is also an important prospect to the design, implementation, and usage of any system that stores, processes, and recovers data. There is a tendency to use the term data quality as a proxy of data integrity, but two of these hold different meanings and definitions. Data integrity refers to the features that determine the reliability of the information in terms of physical and logical validity while data quality refers to the aspects that determine the authenticity of information including planning, decision making, operations, etc.
Data quality consists of six dimensions-accuracy, completeness, consistency, usefulness, uniqueness, and validity.
Accuracy - It is one of the major components of data quality. Accurate data collection helps in drive sales, reduces financial investments on ineffective strategies, and enhances the data quality. Authentic or accurate data assists to land a progressive decision followed by actions of an enterprise.
Completeness - In a data quality framework, completion of data is very much necessary. Completion of data refers to the full availability of data in the data set. The measurement of complete data is done by finding out the missing record entries.
Consistency - Consistency in data quality is the reflection of the same information and synchronization among each other in the entire business. In more firm words, data values taken from separate data sets must not clash with each other.
Usefulness - Proper and accurate data increases the confidence level while taking any decision and decreases the risk of heading towards the wrong way. It provides information to the user that is required to do a task efficiently.
Uniqueness - This assures the restriction of duplicate data entries. Each and every data entered should be unique, otherwise the risk factor increases of accessing wrong information. So TrackBee has introduced a unique system to restrict duplicate data entries. For an instance, in the organization’s database, there is twice an entry of one entity, and the latter one is duplicate, the system will automatically recognize the duplicate one and will terminate it in the background.
Validity - This aspect in the data quality dimension, indicates information that doesn’t obey or follow specific business rules and formats, for example, date of birth. In some systems, if a person doesn’t enter his/her birthday in the format as instructed, it remains invalid. But popular survey engines have structured the format in such a way that while entering the date of birth, the date picker will automatically pop up, resulting in zero chance of invalidity.
Poor data quality can seriously turn out dangerous while taking decisions resulting in massive damage to the reputation of a business. There can be various reasons for poor data collection. Some of them are an excessive amount of data collected, error in communication and data coding, unclear and wrong interpretations, inefficient collection process, incompetent field staff, and poor collection tool, etc.
Excessive data collection - An excessive amount of data collection increases the possibility of unnecessary information, complexity in coming down to a decision, too much time consumption, getting out of focus from essential data leading to analysis paralysis.
Error in communication and data coding - One of the major causes of poor data quality is a gap in communication or weak communication. For example, if field enumerators send or upload collected data without checking it and later find out that the records contained an error, then that team of enumerators will end up looking careless and unprofessional. Preparation is the key to handle difficult conversations. The art of giving clear and actionable feedback should be mastered. Increasment in attention, location, and situation helps to avoid poor data collection. Similarly, error in data coding is another reason of poor data quality. Like human languages, machines to have languages with their own kind of syntax rules to follow. Humans still can manage up with less perfect grammar but survey programming strictly deals with and focuses on the topic and subject matter, to get a clear and on-point outcome along with the outlier’s queries and opinion for addressing the areas of progress.
Unclear and wrong interpretations - Unclear and wrong interpretations lead to huge confusion ultimately ending up with incorrect data entry and improper decisions that lose clients and hamper the reputation of the organization. 0ne should use clear, lucid, and uncomplicated language for the respondent’s understanding and avoid leading questions like how much good was the movie? This already is giving a biased perception about the movie, that it falls under the category of good. Instead, “how was the movie” would have sounded neutral. The same goes with double-barreled questions where a respondent has to answer two questions at a time in an interrogative sentence. It leaves the respondent into confusion letting either of the two answers like which one is your most liked and less liked Bengali and English novel?
Instead, if we had set the question, which is your most liked and less liked novel in Bengali? Which is your less liked and more liked English novel? It would be much easier to get an on-point answer.
Inefficient collection process - Inappropriate or inefficient data collecting methods can call for huge penalties. It exceeds the budget, consumes a lot of time to complete the task which could have been done long before if conducted on the right track. Using wrong applications to carry out a data processing task is of equal importance.
Incompetent field staff and poor collection tools - An unskillful enumerator can mess and ruin the process of collecting authentic data. Lack of personal and technical expertise, inadequate attention to the details, poor sense of timing can change a lot of proper and on-point information, again resulting in defamation of the enterprise. A proper data collection process helps to gather data from a targeted group of people to assess pre-defined parameters. Poor data collection tools like inappropriate and unrequired information, wrong methodology, unprofessional and untrained field team harm the goodwill and prominence of the company.
The perfect address of data quality can only be achieved if accuracy, relevancy, timeliness, and completeness are on point.
Digital data collection, data validation, uses of advanced features like respondent location, photographs, signature and background recording, data monitoring, data cleaning, and staff training magnify and boost the data quality.
Digital data collection - To address the data quality, this process helps an organization to gather a lot of quantitative data in the form of multiple-choice, dates, images, and numbers. The tools used by the enumerator to collect digital data are mobile phones and tablets instead of paper.
Data validation - Validating data is a very important part of the data quality address. Data validation deals with checking the accuracy, relevance, and quality before using and importing it. It minimizes the chances of error. For an instance, TrackBee always validates the data like phone number, email id, date, etc. while filling the survey and uploading it to the database. As a result, chances of mistake are nil (depends on survey programming). Benefits of advanced feature like respondent location, photographs, signature, and background audio recordings are, the exact location of the field enumerators, as well as the respondent, can be tracked through GPS by using portable tools like mobile phones and tablets, clicked photographs, taken down signatures and recorded the background audio, which is not feasible to do in pen and paper. For an instance, a field team has been instructed to carry out a survey and collect data about a poultry farming project at Haryana. Now, if the team chose the manual pen-paper survey then there will be a lot of room for mistakes and hazards. Background audio recording, location tracking, photographs, and digital signature have to be opted out as there is no provision to do all these. But if the field team uses, advanced features like capturing signatures and photos, visualizing data, downloading responses, customizing dashboard, identifying survey duration, using templates for preparing survey in less time (all these are available in TrackBee survey engine) integrated into the aforesaid tools (mobile phones, tabs), then location tracking, clicking photographs, signature, and background recording can all be done.
Data monitoring - it is another major part of addressing quality data. Observation and attentiveness while data arrival and during entry are very much necessary, to avoid improper data upload. For example, graph-based dashboard and analytical representation, tabular format, data output in Excel, CSV, and SPSS format can be used to restrict inappropriate data entry.
Data cleaning - Data cleaning is the process of identifying incomplete and irrelevant data and then cleaning inappropriate and corrupted records from a database, to keep the relevant and authentic data intact. Example- output through Excel, CSV, and SPSS format, either by manually downloading SPSS and Excel or using the system and getting the data in table format; letting corrupted data flagged and rejected online.
Staff training - Field enumerators training brings countless profits like getting the data exactly the way the organization wanted. Reduces the risks of fault, helps to cater relevant and accurate data, consumes less time, rapid growth in productivity, etc. thus quality data can be collected more easily resulting inexperienced data analysts preparing reliable reports those are supposed to be more than just a collection of information and hence will provide the most comprehensive insights, that can direct future actions.
So to conclude with, for ensuring quality in data collection, we have to keep the above-mentioned factors in mind while collecting data then building up a proper dataset for analysis will easily be accomplished.
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