A data warehouse is a big storage of data from a wide range of resources which is crucial in guiding decisions. It was formerly used for the population data organization and the resource analysis by the government. In my view it is also a good system when used in the business sector. Data warehousing is the activity accumulates structured data from many sources so that it can be compared and well analyzed to enhance business planning and intelligence. The users of this tool has a better executive insight into corporate performance. This enhances its functionality in the business situations. It is also evident the sources the sources used by managers nowadays help them in getting information necessary for making decisions (Reddy, Srinivasu, Rao & Rikkula, 2010). Sometimes the tools are not efficient and manager’s end up getting unclassified and incomplete information and this makes it hard for them to compute valid decisions. It was formerly used as a reference to negotiate contradicting records and accounts in businesses but in my view it can be used to accumulate information that can be of use even in the managerial field.
I preferred to use this tool due to its effectiveness in operation. Availability of information coming from many sources make it have a sufficient bank of data required for detailed business analysis. It also improves the quality of data, and this produces consistent impressions and conclusions. (Inmon, Strauss, & Neushloss, 2010). I prefer the data warehouse to decision support because the latter only provide relevant information if the user was involved in the initiation, development, and evaluation. It is also user driven. It cannot analyze on its own. The other knowledge management tools are way too expensive to install and maintain for example content management systems.
Maintenance of data history
The data warehouse keeps information copies of all the transactions carried out in the organization at a particular time. That makes future retrieval of the data possible in the same state as it was stored.
The data is restructured in a way that it becomes relevant in the business
The data warehouse restructures the data uploaded to that system to a form that even a stranger in the organization can work with them (Bouman& Van Dongen, 2009). An example is the business accounts which are computed in a double entry system where the accountants can use it to calculate the books of final reports.
It brings together related data coming from various sources for more straightforward analysis. It is now more accessible for the management of the business to conduct the functional study without having to waste time categorizing the data into the various types.
Contrary to other knowledge management tools the data warehouse has consistency in the way the data flows to the business (Hazen, Boone, Ezell & Jones-Farmer, 2014). Practical techniques are applied to make sure that at no time there lacks the relevant data required in making tactical decisions that affect day-to-day operations in the organization
It arranges and also disambiguates repetitive data. This helps to improve the quality of the records kept in the business for example inventory valuation. It will also assist in avoiding the errors caused by the accidental repetition of data.
An example is the customer relationship and services. Time to time data is stored in the system, and once a complaint is raised, the organization can use the kept transactions information to solve those conflicts amicably. The operational staff also benefit from the data warehouse when doing a self-performance assessment.
Scholars have discovered that many organizations lack a system which is capable of storing data in an organized manner. In the business activities which is my area of interest, the past data is very crucial in the present (Miller & Han, 2009). An example is a manufacturing firm needs to have all the inputs bought, used and the remaining so that they can be able to determine their financial position for that fiscal year.
Some of the purchase records may be cumbersome to keep in the large and busy organizations, but the data warehouse knowledge management tool will help them much in different areas. The transactions will be classified along with their dates and commodities purchased so that even in the future it will be possible to maintain the stock records in the business.
Nowadays many business firms are expanding regarding size and complexity. The new challenge posed by this advancement is that the data received takes many forms and maintaining, organizing them is difficult and expensive. The data warehouse will provide the opportunity to the organization to store and manage all data immaterial of its nature for possible future use.
In every day in the business operations take place in the premises. They are mostly recorded in the inventories and books of accounts. When the data warehouse is introduced the copies of the transactions information is put into the staging area. In the staging area, the data from all activities taking place in the business are kept in the same format. For example, if the money coming from the international transactions is from different countries, then they should be converted into one standard unit (Chen, Chiang & Storey, 2012). An example is exchanging currency into the US Dollar.
The data is then stored in the warehouse in that aggregate form. The data can also be categorized into data marts. A data mart is a collection of data well analyzed and converted to fit the needs of a particular user. In the business context the transactions data can be kept in marts that can be used by each of the following; finance manager, operations manager, human resource manager, and the finance auditor. In this manner, the data kept analyzed into different marts that can serve each of the above. The benefit of this is one user’s access to the data does not affect the use of the other client (Thuraisingham, 2014). The information is in the mart is also well modified to meet the needs of the various users for example in the finance manager’s data mart answers the needs like knowing the change in the value of money over time.
The process of creating the database in the data warehouse follows the ETL format. This means the first step is data extraction (Han, Pei & Kamber, 2011). This involves obtaining data from the source to the stage base. In the stage base, the data is transformed to give meaning to the data. The final step is loading the data to the warehouse where it is stored in the form of a data mart.
This warehouse tool is the best-fit knowledge management tool that fits the needs of the business. As seen in another process, the data stored in the data warehouse is in its purest form, and this makes concluding leisurely. Take for example the human resource performance index data. Once the data is well organized in the data warehouse (Aji et al.,2013) even in the big organizations, then it is easy to monitor performance trends by the manager. The manager can know the impact of some variables on the performance of the employees.
In the implementation of this system, I would opt to use the parallel execution. Here the new system works alongside the old one for some time but after some time the new one is implemented fully. The data warehousing system is a bit complex and involving the direct implementation method will inconvenience the workers in the organization.
In that process, we will create a conceptual data model. Here we will determine the subjects relating to the data which will be expressed in the form of fact tables (Waller & Fawcett, 2013). The performance indicators for example production and profit values determine the format in which the events will be stored. The dimensions chosen are then linked to the performance indicators where the entities are entered in the rows of the tables. It is worth noting that the data should be synchronized before it is put to this process. Careful planning will enable the experts to construct a system that will have data organized most accurately.
The next step is carrying out the sustainability plans of the data warehousing structure. To this effect, time-to-time evaluation tests are conducted to check on any dysfunctions in the system (Zhou et al., 2010). This move is essential some of the data kept in this system are very delicate, and any misinterpretation will negatively affect the business operation.
The maintenance approaches are also crucial in making of any knowledge management tool. It will help in ensuring that repairing breakdowns become easy in case they occur.
Conclusion
The knowledge tool of the data warehousing has been widely used by the governments to keep the data of the citizens. I saw it as a good idea to introduce the tool in the business area to ease in the storage, organization, loading, and acquisition of business information for use by the involved parties. This will enable the organizations to keep vast amounts of data mostly involving stock (Koh & Tan, 2011) and transactions for use in the future.
References
Aji, A., Wang, F., Vo, H., Lee, R., Liu, Q., Zhang, X., & Saltz, J. (2013). Hadoop gis: a high performance spatial data warehousing system over mapreduce. Proceedings of the VLDB Endowment, 6(11), 1009-1020.
Bouman, R., & Van Dongen, J. (2009). Pentaho solutions: business intelligence and data warehousing with Pentaho and MySQL. Wiley Publishing.
Inmon, W. H., Strauss, D., & Neushloss, G. (2010 Han, J., Pei, J., & Kamber, M. (2011)). DW 2.0: The architecture for the next generation of data warehousing. Elsevier.
Miller, H. J., & Han, J. (Eds.). (2009). Geographic data mining and knowledge discovery. CRC Press.
Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
Koh, H. C., & Tan, G. (2011). Data mining applications in healthcare. Journal of healthcare information management, 19(2), 65.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS quarterly, 1165-1188.
Zhou, X., Chen, S., Liu, B., Zhang, R., Wang, Y., Li, P., … & Yan, X. (2010). Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support. Artificial Intelligence in medicine, 48(2-3), 139-152.
Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84.
Thuraisingham, B. (2014). Data mining: technologies, techniques, tools, and trends. CRC press.
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80.
Reddy, G. S., Srinivasu, R., Rao, M. P., & Rikkula, S. R. (2010). Data Warehousing, Data Mining, OLAP and OLTP Technologies are essential elements to support decision-making process in industries. International Journal on Computer Science and Engineering, 2(9), 2865-2873.
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