Today, it is important to assure that the information provided by companies and organizations are of the highest possible quality because it can literally make or break a company. In business, information is power and in order to have the power to influence decisions, the data should be of the best quality. When data is not of the highest quality, businesses often suffer from low productivity, ineffective marketing efforts, and even lose customers.
Data quality tools, as the name suggests, are tools used to manage, collect, analyze, and act upon data that are qualitative in nature. The data quality tool used by a particular company will depend on the type of business that it has. If the business is more specifically oriented, then the tool should also focus on that particular topic. Some examples of data quality tools are:
Today, as our society continues to become more digitally active, the importance of data quality and the protection of it has become increasingly important. There have been many debates whether or not outsourcing data quality responsibilities is a good idea. The answer, of course, lies in the company’s ability to remain competitive and in the long run. Today, businesses must protect their valuable intellectual property to prevent competitors from stealing ideas and data and to prevent themselves from being taken advantage of.
Qualities Of Good Data
It is quite simple to make the point that one of the main Qualities Of Good Data Entry is that it has to be qualitative. After all, if data is used for any reason at all, that reason has to be qualitative. There is little point in using quantitative data, when you are making an argument about something that is qualitative. You simply cannot have quantitative data without being able to relate it to the subject matter with which it was generated.
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The whole purpose behind business data quality is to try and correct any aspect of the current data extraction techniques which are causing problems in accuracy. Unfortunately there are many areas in the business world where this is very much the case. As data loss is a major business problem these days, it has become all too common for companies to be neglecting aspects of their data quality simply because they do not see the need to do so. Here are some examples.
* Businesses often make the mistake of prioritizing the analysis part of data management too much. Instead of concentrating on what should really matter most to the end result, they spend too much time worrying about the details of each analysis. This is a problem, and a consequence of the fact that decisions are made by people who do not have a background in statistics. This is why you should never ever approach your analysis choices as purely a matter of prioritization.
* Poor data quality can often lead to poor decisions based upon lack of consistency. This is perhaps the most common example of poor data quality. The only thing worse than taking an action based on “gut feeling” is doing so based upon “gut feelings”. All human beings have a tendency to act on “agenda” that is not completely obvious. For example, you might decide that you want to open a new cashier training unit in your store because it seems like a good idea.
However, you quickly realize that it would be impossible to gather the quality data that you need in order to make this decision, because you are relying on your gut feelings to guide you. You simply cannot take any risks because you know that opening a cashier training room will mean losing revenue over the long-run. However, having no solid data collection process in place makes it very easy to make decisions based on “gut feeling”, and therefore, create bad quality data collections.
* Another huge problem that arises from the above example is the possibility of “data silo syndrome”. As previously mentioned, human beings are prone to acting on “gut feelings” that are not entirely clear to them. However, having no means of obtaining solid, objective information that can be objectively assessed and used to make decisions based upon consistent criteria makes human beings subject to “data silo syndrome”. In simple terms, having irrelevant or bad data collection procedures can actually make it very difficult for organizations to make good business decisions based on objective criteria.
* Having poor decision-making processes doesn’t help your business; in fact, it could cost it sales and reputation. The bottom line is that if your company does not maintain consistent standards of data quality, it will suffer the consequences. On one hand, your customers will view your product or service as unreliable, and on the other hand, your competitors will see your company as having poor data quality which allows them to make competitive decisions against you.
When building your internal policy addressing data quality, it is important that you take a “quality first” approach. First, ensure that you have a consistently and reliably collected set of data sources. Then, once you have these sources, try to improve the internal policy that governs the organization in such a way that your data quality meets the exact requirements that you want. Finally, constantly monitor the condition of your data sources and adapt your internal policy accordingly.
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