13 April 2021
In the era of Big Data and Industry 4.0, everything is driven by data. Whether in finance, industry, or research, data is constantly being created, analyzed, and used. This data, therefore, plays an essential role in the proper functioning of companies and their value creation process. To ensure that this data can continue to be used over time, it is necessary and vital to have good data quality.
Data quality is an important element for any company, whatever its domain of activity since it is about maintaining the consistency of its data over time. It is about ensuring that over the years, the data will not lose information and will remain viable and useable. Everyone knows that data tends to deteriorate during its exploitation. But in concrete terms, what are the criteria that qualify data? Depending on the field and the person involved, the criteria may differ somewhat, but the broad outlines will always be the same. In its report on data quality, Forbes Insights has identified four main criteria:
The data must correctly reflect what it represents. A location must allow the equipment or building to be located. The data must also be up to date to play its role and convey the right information. Of course, the accuracy depends on the context but also on the end-use. The location of industrial equipment, for example, will need more accuracy than an entire building.
Data must be complete. Lack of information can lead to misunderstanding of circumstances and lead to serious consequences, no matter what the field. Whether in finance, maintenance or IT, incomplete data can prevent accurate identification of the situation, resulting in misdirected strategies or directives.
The format of the data depends on the source or the region of the data, so it is necessary to find a way to compare them. The date format changes depending on whether you are in the USA (Month – Day – Year) or in Europe (Day – Month – Year or Year – Month – Day). It is also important to manage potential duplicates and errors. By standardizing the formats, it is possible to obtain comparable and consistent data and to limit misinterpretations.
Data must be reliable and fit for purpose. Without credibility, the data will give useless results because they have no real basis. The data, whether from internal or external sources, must therefore be authoritative.
Having bad data can indeed have serious repercussions. It means bad or missing information. This results in distorted knowledge and therefore potential risks and problems in the company’s business or accounts. According to some studies, the impact could cost up to 25% of the turnover. More concretely, here are some of the consequences of poor data quality:
When there is no trust in the data you have, there is quickly no trust in the results that come from it either. According to KPMG’s 2016 Global CEO Outlook, 84% of CEOs are concerned about the quality of the data they rely on. A lack of confidence could cause leaders to turn to more traditional means and temper their willingness to invest in data quality improvement solutions. Instinct, experience, and trusted opinions are essential, but basing decisions on these alone puts the business at greater risk than analyzing quality data.
With better data, you get more information. If a company and its competitor are targeting the same customer and the competitor has more information, the competitor will have a more mature understanding of the need and will be at an advantage because of the data they can leverage. Data is an asset. It must be handled correctly in order to gain and maintain a competitive advantage.
Poor data quality can result in significant revenue loss, such as incorrect customer data resulting in lost business. Like an insurance company with bad information about its policyholders’ assets, this would have a strong impact on revenues and a real loss of income. Estimating the location of its insureds rather than accurately surveying them could, in the case of a flood zone, for example, create a large loss of revenue.
A company’s reputation is vital to it, so it is essential to prevent anything from damaging it. This does not always have a big impact, it is even possible that one does not realize it, but it can be very damaging. Without complete and up-to-date data, unintentional errors are possible, such as misspellings of a client’s name or sending communications to a deceased person. Mistakes with media impact are, unfortunately, also possible, such as the catastrophic launch of Apple Maps in 2012. Due to bad data, both incomplete and inaccurate, the services competing with Google Maps were unusable and caused a lot of ink to flow. By correcting this aspect, in particular, Apple Maps now offers a quality service that is completely consistent.
Sirfull is a software vendor with a strong industrial culture and French know-how, which develops solutions that enable its customers to anticipate changes in their market.
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WELDING SOFTWARE COMPARISON
Search through qualified PQR and Welder Qualifications approval ranges
Automatic Approval Range Calculation
Upload your existing PQR documents into the software
Code Checking between WPS and PQR to ensure compliance (Warning you of any non-conformance)
Automatic generation of WPS
WPS translation into a secondary language
WPS template customization by user
PQR Management and Qualified welder identification in compliance with identified WPS
Notifications for welder qualifications that are about to expire
Build in technical library of consummables that could be customized
Weld mapping on equipment plan (bubbling function)
Welding project management
Filler and materials certificates management
Full traceability of weld
Weld status management (historization)
WPS and Welder performance management (percentage of repairs based on the number of welds completed)
Multiple company management
User matrix rights
Weld sketch designer in the software
NDTs report management
Automatic initialization of NDT schedules and specific monitoring dashboards
NDT report creation
Welding data book pdf generator / customizable table of content / annexe document importation
Manufacuring end report pdf generator / customizable table of content / annexe document importation
Welding data book and Manufacuring end report translation into a secondary language
Customizable dashboard to follow up on the welding performance indicators such as quality rates, welder performance, non-compliance etc.
Weld cost calculation
ERP and/or IT systems integration interface
Connection welding source
Excel import / export
Mass data importation (available with Swift Data additionnal module)
User friendly navigation