Abstract
This report discusses the strategies that can be employed by companies in improving data quality methods. It is important to work on data quality management because businesses cannot thrive without correct data. Today, it is data related to all businesses that stores all the information which is essential for the business. Without proper and correct data, it is probable that the business might suffer from quality issues and the customers and clients would have a hard time working with the business. It can build a bad reputation because incorrect data will lead to mistakes in the system which can be damaging to a business’s reputation. In this way, there are several strategies which businesses can work with, that can tackle the outer and inner structure of data management and provide good quality data. These methods include cleansing data, normalizing it, preventing future defects and the profiling of data. The report shall discuss the pros and cons of these strategies.
Introduction
The purpose of this report is to study and analyze the various data quality improvement strategies that can be employed by businesses and discuss the potential benefits of these strategies as well. According to the PWC report, data is the most crucial element of business, and it happens to be the backbone of acquiring efficient management. Companies in the present day mean to acquire a business culture that makes large-scale use of data. The management of the business needs to have reliable information present at their disposal in time, and it should be accurate and precise as well. The reason for this need stands the ability to make good, informed decisions as far as carrying out influential plans is concerned. To centralize the company’s focus on data can be greatly helpful in transforming the business and boosting its effectiveness. Data can lead the way to study information, and this information will provide the insight that can help to take proper and desired action, which can decrease the associated risks and costs of business, meanwhile bringing in profits and flourishing the business.
The basic most concept which needs to be understood by data analysts is to understand the source of data and what quality it possesses. The data that has to be used by the business must be substantial, of good quality and must be trustworthy, most of all. If these basic measures are not taken to understand the data, it is a wasted effort. A common error made by many businesses is their will to seek the accuracy and efficiency of data rather than spending time on an analysis of the data and checking the actions, rather than improving its quality. Hence, it is important that companies focus on the various strategies that can help improve data quality which will pay off in the long run. These include various methods such as data profiling, fixing duplicate records, preventing data defects and some others, which are aimed at improving data quality.
Strategies to improve data quality
There are several organizations which believe that adhering to the short-term solutions
for improving data quality instead of looking at the long term solutions. This is, in fact, a disadvantage because short-term measures can end in greater disturbances and challenges at the end. However today, more businesses are looking at the consequences of not taking effective measures against poor data quality. This is because of the fact that poor data quality is associated with negative consequences for the business such as lessened customer attraction, failure in the market’s shares and losing profits. If these data quality measures are not taken care of, businesses are more likely to falter and fail (Turner, 2013). Hence several strategies can come in handy to help improve data quality.
Data Profiling
Data profiling is a technique which is used to expose any kind of defects in data. This process
is also known as data archeology. It is used to analyze the data for checking the accuracy and rightness of the content, the unity of the data and its distinctiveness as well as the use of logic and reasoning in the data provided. There are many data profiling tools which can help to perform these tasks which actually sift through all the material and check records and databases meanwhile detecting and checking any abnormalities, mistakes and also profiles it accordingly (OBOlinx, 2013). There are also several mining tools that can be used for this purpose that evaluate data quality. Warehouse Miner is one such mining tool that has two functions; one which carries out a value analysis of the data and secondly, it has an overlap analysis function. The use of various methods like using histograms and even scatter plots can help discover other variables which might be affecting the quality of data.
Data Cleansing
Impure data is also a phenomenon in many information systems. Once the data is used up in
the databases, its operational system begins, and it starts picking up impurities. Hence in such a situation, data quality is compromised however if the business tackles data cleansing from operational data, then it can be easier to improve data quality. It is a laborious process, and it involves time and is definitely costly as well. It is also impossible to have all the data cleansed at once since it is an impracticable decision. However, it is also unacceptable to have all the data cleaned at all (Moss et al., 2005). Businesses need to qualify the data as the most important, urgent, critical and unimportant and have it cleansed according to the need of the hour. It is wiser to move in order of most important to least important such as dealing with the data which is critical for the business and then moving to the practical data which can be used by the business or the otherwise important data. In the end, it is acceptable to have the insignificant data unaltered because it will save time as well as capital. Hence, not all the data needs to be cleaned, and it does not have to be cleaned all at one time either. Another important factor is to know whether the data under consideration needs to be corrected or its correct form is still available. In some cases, it is useful to recreate that same data in its correct and accurate form by using minimal of time and resources. In cases where the data is too incorrect, convoluted and indecipherable, it is useless to correct the data because once the method of correction is put to use, it might end up creating more complications and problems that might not have existed before. Therefore this kind of highly incorrect data can be left by itself (Moss et al., 2005).
Preventing future defects
Companies also need to figure out how to prevent future impurities from entering the data
systems. This is only possible if the root cause of the complications and defects is detected and there can be several causes associated with this. It can include corrupted program logistics, insufficient program edits, misguided interpretation of data elements, not having familiar metadata, lack of a data verification method, unqualified data entry training, lack of a reconciliation process and not providing an incentive for producing good quality data entries (Moss et al., 2005).
It is up to the bosses of these operational programs and systems that they should
implement program checks and edits at regular intervals. The only exception for non-implementation is a good effort. If there is a program that has some quantity of bad data in the system but that data is not affecting the operation of the program, then working on that data program is unjustified and probably useless as well. Data improvement techniques can also be studied and understood by negotiating and sitting with the makers of the data rather than relying on the IT staff alone. These makers can provide good tips and suggestions for the improvement of data quality in the correct manner. Furthermore, businesses can put up a group or department that is specifically working for the data governance, and it should have posts such as data quality stewards, metadata administrators, and data administration (Moss et al., 2005).
Training for data entry must be given to those who do not have proper training regarding data entry and are prone to making mistakes. All databases cannot be checked by edit checks and employing the use of look-up tables, strong ability to type quality data and using the given procedures. Data violations and mistakes are possible at the cost of human error and even intentional mistakes caused by misunderstanding.
3.0. Prevent Duplicate Records
Duplicate records in the data can be found out by the use of email addresses. The problem with duplicate records is that it is a drain of money on the part of the business; it will also interrupt sales and disrupt the marketing process. The wiser thing to do is to catch any such duplicates at their earliest and to delete them is also preferable. By letting such duplicates live in the system will mean more systematic issues and greater need to fix them. If through running any reports and finding any major duplicates in the system, there can be a simple took employed that will solve the issue (RingLead, 2015).
4.0. Data Normalization
Data normalization is the process by which the data can be unified and brought under the same system, which will prevent misunderstandings in the system in the future. For example, different spellings might result in complications for the system in judging the data. Spellings like those for the abbreviation of the United States may be used within some systems as U.S and in others as U.S.A. This has a definite impact on scoring, smart listing and how the system marks the data within it. Data points are marked differently for the system as the spellings tend to differ from one another. Standardizing and normalizing the data will be a very thorough method of data quality management and will improve the efficiency and quality of many businesses in return (RingLead, 2015).
5.0. Conclusion
In conclusion, it can be said that data quality is a very crucial element for businesses and it is needed to make the system work efficiently without glitches and by avoiding any kind of monetary loss. Several techniques can be employed by businesses such as data profiling and cleansing, looking out for future defects, data normalization, and prevention from making duplicates. If companies stick to these strategies, they can help build stronger networks, more satisfied customers and develop more successful businesses.
References
Moss, L. T., Abai, M., & Adelman, S. (2005, July 22). Informit. Retrieved January 20, 2017,
OBOlinx. (2013, August 7). Five strategies to improve the quality of data – business operations
performance management. Retrieved January 20, 2017, from Business, http://obolinx.com/resources/2013/08/five-strategies-to-improve-the-quality-of-data-business-operations-performance-management/
RingLead. (2015, April 23). 5 hands-on strategies to improve data quality. Retrieved January 20,
2017, from Data Quality, https://www.ringlead.com/5-hands-on-strategies-improve-data- quality/#.WIIJrdJ97Dd
Turner, N. (2013, July 25). Five steps to ensure data quality success. Retrieved January 20,
2017, from Analytics Technology, http://data-informed.com/five-steps-to-ensure-data-quality-success/
