Sample Business Paper on The business environment

Sample Business Paper on The business environment

            The business environment is always volatile. The biggest change is that seen since the turn of the century, whereby such issues as cross-culture management, HR outsourcing as well as internet marketing, sales or administration have become critical in doing business. Consequently, there is a constant need for organizations to make critical decisions to maintain or develop new competitive advantages over their rivals (Greitemeyer 78). According to Brijs, an organization with supreme business intelligence about the environment it works in is bound to be more successful than their rivals (32). The process of business intelligence includes the collection, analysis, management as well as storage of relevant data. Business intelligence is essential when it comes to edging out the competition in business. This article is set to highlight the principle concepts of business intelligence that include data warehouse, data lake, and data marts in relation to raw data collection, analysis, managements, and storage.

Data Warehouse

All well-placed decisions begin with the collection of relevant data that is market-related. A data warehouse is defined as a central depository unit that is used to categorize data, in a particular file and format manner that is friendly to an organization in making its decisions (Humphries, Hawkins, and Dy 21). The data brought into a data warehouse is non-volatile and in its most basic form (Sabherwal and Becerra-Fernandez 78). Later, the data is categorized, reported, managed, and analyzed to offer reliable as well as secure information that best suits the organization’s decision making needs.

Data Lake

According to Inmon, a data lake is defined as storage source that hosts a large volume of data in its raw form until it is needed (67). Dissimilar to a data warehouse, a data lake uses a flat architecture format that is not file or folder scheme (Gorelik 34). In other words, a unique tag called a ‘bagtag’ identifies data in a data lake, and the tag is activated only when a quarry related to the information needs to be answered (Rosenzweig 78). Finally, the data in a data lake is structured in a manner that deals with small set questions that ultimately would lead to an organization making the best possible decision.

Data Mart

In the instance that a data warehouse is a centralized depository, a data mart is a subset that is used to introduce relevant data from varied sources to the organization (Thierauf 55). A data mart, unlike a data lake or data warehouse, only holds specific subject data in summary form (Inmon 11). For an organization to make educated decisions, there is a significant need to have amalgamated various data marts.


Figure 1: Data Mart Drawing



Use of the Data Warehouse, Data Lake and Data Mart in an Organization

The twenty-first century has seen a variety of new trends that have guaranteed a high competitive advantage such as internet marketing, HR outsourcing as well as cross-cultural management (Schmarzo 116). All the above processes are a derivative of market movements. The first process through any business intelligence process is collecting relevant information from a variety of sources. The data warehousing process is categorized into three classifications of collection, analysis as well as storage also known as ‘schema on write’. Data warehouses are significantly used in the technology industry, which is highly competitive and similarly volatile. Clients in the technology industry are always attracted towards a variety of factors making the collection as well as the use of relevant data, which a fitting tool in attaining a high competitive advantage.

  Lenovo technologies are manufactures of a number of electronics including smartphones, electronic tablets and computers (laptops). Currently, Lenovo Laptops are the most sought after products in a highly competitive computer industry. Companies such as Apple Inc., Dell, and HP have always dominated the computer industry. However, over the last decade, Lenovo has emerged as the primary player. The reason for this is predicated on a highly efficient data processing that has seen the organizations develop some of the most successfully marketed and sold products in the market such as the ‘Yoga series’ of laptops. The Current market favors the portability of electronic tablets from the sale of the iPad and the Galaxy Tablet. On the other hand, the processing power of laptops is still preferred. Form such information developed the Yoga laptop series. From the example above, information from various data marts, in particular sales and marketing data marts have provided insight into the organization’s line of business that instigates success. It is from information from these data marts that are collected and stored in the Company’s Data Warehouse as well as Data Lakes.  The nature of data marts may seem to restrict access departmental wise; however, this is not the case. The manufacturing set up is not restricted to the shop floor but is influenced by the sales and marketing department. Additionally, an organization’s sales department collects information from both their rivals as well as their previous product performance. The information from a variety of sources (data marts) is fed into the organization’s data warehouse. The data introduced is then analyzed, and from that point, new products are proposed.  Finally, information that is used is later stored in a data lake and may be used for company requirements. Lenovo, the ‘think pad’ series of laptops, was developed from IBM archived information.

In summary, the current market is significantly volatile. Therefore, there is a consistent need for organizations to come up with new strategies to maintain a competitive advantage. Corporate organizations ought to come up with intelligent strategies that are most suited to their environment. The principle concepts of business intelligence that include data warehouse, data lake, and data marts are core aspects that every business should keep in mind. These strategies are a derivative of an organization’s business intelligence processes.


Works Cited

Brijs, Bert. Business Analysis for Business Intelligence. CRC Press, 2012.

Gorelik, Alex. Enterprise Big Data Lake: Delivering on the Promise of Hadoop and Data Science in the… Enterprise. O’Reilly Media, 2016.

Greitemeyer, Jörg. Business Intelligence: An Analysis Based on the Resource-Based View.             diplom. de, 2002.

Humphries, Mark, Michael W. Hawkins, and Michelle C. Dy. Data Warehousing: Architecture     and Implementation. Prentice Hall Professional, 1999.

Inmon, Bill. Data Lake Architecture: Designing the Data Lake and Avoiding the Garbage Dump.             Technics Publications, 2016.

Inmon, William H. Building the Data Warehouse. John Wiley & Sons, 2005.

Rosenzweig, Phil. “Misunderstanding the Nature of Company Performance: The Halo Effect and             Other Business Delusions.” California Management Review 49.4 (2007): 6-20.

Sabherwal, Rajiv, and Irma Becerra-Fernandez. Business Intelligence: Practices, Technologies,     and Management. John Wiley & Sons, 2011.

Schmarzo, Bill. Big Data MBA: Driving Business Strategies with Data Science. John Wiley &       Sons, 2015.

Thierauf, Robert J. Effective Business Intelligence Systems. Greenwood Publishing Group, 2001.