Bad Data: a problem in the middle of the solution
The problems faced by lack of data are not always simply synthesized to “little data”.
Quantity is as important as the quality of information. When this does not happen, industries face something called Bad Data (a term used to define the collection of information that lacks quality aspects).
Some examples of data that can have negative effects are:
Outdated data:
Blind investment in empty samples can be quite costly, especially in the industrial environment. Data from machines that worked at their peak productivity months ago are great indicators for production expectations. But to maintain a good rhythm and have control of variability, it is necessary that the information happens with a flow and consistency.
Incomplete data:
Gaps in data generate great interference in industries, at the immediate level – in daily monitoring – and also in historical statistics. The manual collection of information leaves the margin of risk to human error, thus creating possible holes in the process, influencing the entire decision-making.
Non-Compliant Data:
Compliance is a term that can be translated as “compliance”. There are guidelines on the use of data and the idea of compliance has to do with complying with these guidelines. The extrapolation of these rules is the so-called non-compliance data. Whether it is the improper obtaining of sensitive data, or the manipulation of data – as can occur in industries that rely on manual collection of processes – this error tends to occur when internal or external rules are disrespected.
Incorrect data:
As in the previous items, errors in data can occur due to human influence (in its reading or review), either intentionally or accidentally. In any case, compromised data is a concern, as all expectations of benefits around data collection can be broken: reliability, optimization of resources and time, in addition to risk reduction, are all affected by poor data quality.
Why invest in data quality?
Investment in data is a necessity for monitoring and analyzing performance within industries. But just bringing a data collection tool will not solve its problems.
As previously presented, obtaining samples is not enough. The quality, coherence and consistency of information are as important as quantity. Otherwise, several areas may be harmed.
Investment in data quality, however, not only ensures the integrity of the machines or the process, but is also a great advantage for the industry at the management level.
Below, we list some areas where the collection and analysis of quality data can improve existing strategies:
Decision making
Well-defined and credible information increases the leadership power of managers looking to move into new territories in their strategies. Healthy data exponentially reduces risks and mistaken guesses, making the perspectives of industries much clearer.
Compliance
This term carries weight when it comes to credibility and integrity within industries. Policies and regulations – governmental or internal – are what ensure that data is secure, qualitative and useful for use. “To comply”, loosely translated as the verb “comply”, refers to these concerns about private information, which can only be successfully achieved if data quality is a priority for companies.
Productivity
There are several ways to increase productivity. But, in fact, investing in more efficient error solving is what will effectively help the productivity of the teams. A more dynamic reading, carried out on accurate and well-organized data, optimizes time and exponentially increases positive results.
Competitive Advantage
The unbiased study of the industry’s capabilities and difficulties assists the management vision and improves its performance, as well as refines long-term plans and goals. In addition, investing in an environment less troubled by errors and disorder is a great differential for contributors who deal with these problems on a daily basis. From overall process control to everyday applications, ensuring data quality is ensuring competitive advantage.
Concluding
Bad Data is a risk that industries cannot take! With its consequences ranging from machinery errors to possible government implications.
With the advancement of the industry and the increase in the digitalization of processes, ensuring that the information being collected is more than abundant and fast is also one of the aspects that will differentiate good management from others.
The investment so that problems do not occur influences much more than just the “conformity” of the industry to the market; It is an opening door to continuous and long-term improvements, for the company and everyone involved in the process.
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