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Editor's Note: Today's blog comes from Katie Cruze at who gives us the top 5 reasons why data quality is important.

Data, for most companies, is often collected for record-keeping purposes. Data is collected when an inspection is completed, an employee’s performance is reviewed, and maintenance is recorded, or even when a safety meeting is conducted. The record is then usually kept for future reference in order to achieve a greater objective, such as making better business decisions. Another reason data is collected is to make the decisions that will positively impact the success of a company, improve its practices and increase revenue. For many companies, managing quality data can seem like an overwhelming task. However, having accurate and business-ready data is an absolutely integral component to ensure that companies do not experience the negative impacts that can accompany “bad” or “dirty” data.

The 5 Key Reasons Why Data Quality Is So Important

There are five components that will ensure data quality; completeness, consistency, accuracy, validity, and timeliness. When each of these components is properly executed, it will result in high-quality data. It is also imperative that everyone who uses the data collected has a general understanding of what the data represents. The extent of a data initiative is not limited to the data produced by the company’s own research, it must include data obtained from external sources as well. High-quality data will ensure more efficiency in driving a company’s success because of the dependence on fact-based decisions, instead of habitual or human intuition.

  1. Completeness: Ensuring there are no gaps in the data from what was supposed to be collected and what was actually collected.

Solution: This can be resolved by halting submission if the data is not complete. With Paper and Pencil Interviewing (PAPI), this can be exceptionally difficult as this method is prone to human error. On the other hand, the Computer Assisted Personal Interviewing (CAPI/electronic) method uses smartphones and tablets that allow the same data collection but the data is recorded on a device instead of paper. By using the mandatory fields function, data completeness is easily achievable. The respondent will not be able to complete and submit the data without the mandatory fields being filled. This will also ensure less time being wasted fixing mistakes resulting from incomplete data.

  1. Consistency: The types of data must align with the expected versions of the data being collected.

Solution: This can be ensured by using the drop-down menus in a data collection application, which will result in data that is consistently collected in the expected format. Instead of free-form writing, there are predetermined numbers of options from which to choose from. There will be consistency across the board and allow for complete search results.

  1. Accuracy: Data collected is correct, relevant, and accurately represents what it should.

Solution: Accuracy is more challenging to remedy than data completeness and consistency. Accurate data is often the result of trained and competent employees. However, there is still room for human error. In order to reduce the likelihood of inaccuracies, it is vital to implement extra measures like adding picture capture, GPS location, and timestamps to recorded events.

  1. Validity: Validity is derived from the process instead of the final result.

Solution: When there is a need to fix invalid data, more often than not, there is an issue with the process rather than the results. This makes it a little trickier to resolve.

Paper-based methods are more difficult to fix when it comes to issues of invalid data because changing forms can be expensive, wasteful, and the more widespread the company is, the harder it is to change. With the electronic (CAPI) option, it will immediately take seconds to implement the change company-wide as all the data is collected on an electronic device. There are no old surveys to throw out or extra printing costs. And certainly, no one left using an old version of the survey.

  1. Timeliness: The data should be received at the expected time in order for the information to be utilized efficiently.

Solution: REAL-TIME DATA. Anything slower becomes an inadequate source of information. With real-time data and analytics, companies are better equipped to make more effective and informed decisions. There is a pressing need to eliminate the lag time between when a survey is completed in the field and when it is received.

Electronic methods allow field employees to collect the same data they would on paper but it would be safely recorded on a smartphone or tablet upon completion and then instantly submitted to the database. Another way to achieve timeliness is to employ Dattel Asia, ASEAN’s leading face-to-face data collection service provider that utilizes tablets, digital tools, and artificial machine learning systems to collect the true voice of the respondents across a vast urban and rural gamut. To date, Dattel has more than 310,000 unique and verified respondents, over 250 full-time Field Data Associates as well as over 40 quality assurance officers across 3 countries. Their platform consists of cutting edge in-house proprietary tools that ensure data is collected with 100% transparency. Their process comprises in-house developed artificial intelligence and machine learning systems that ensure quality checks and cleaning on all the data collected. Dattel Asia promises 100% transparency, and all clients get access to real-time data collection systems where you can monitor projects as their Field Data Associates collect data. Real-time data collection and analysis has never been easier.

Below are statistics that highlight a clear correlation on how vital and essential quality for data is to a company’s operations, sales, productivity, and revenue.

According to research:

  • 33% percent of Fortune 100 companies will experience an information crisis by 2017 because of their inability to effectively value, govern and trust their enterprise information.
  • 77% of companies believe their bottom line is affected by inaccurate and incomplete data. It is also believed that is 12% of revenue is wasted because of poor quality of data. This is a shocking statistic. Nevertheless, companies that did put a focus on high-quality data saw a revenue increase of 15% to 20%.
  • Companies have witnessed a staggering 40% of their initiatives fail to achieve targeted benefits due to poor data quality. This is a significant effect on operational efficiency.
  • When a data quality initiative is implemented it can lead to 20%-40% increase in sales for a business