Strategic Information Technology lesson 4

Lecture Notes

Welcome to Lesson 4! In this lesson, you will describe the business intelligence (BI) concept and outline how databases aid in business intelligence. Also, you will explain the major components of business intelligence. BI can expand exponentially with the use of decision-making tools such as databases.

This lesson describes several examples of business intelligence and why organizations need it. A discussion is also made of the importance of databases in providing the inputs needed for these systems especially because the key to effective management is high quality and timely information to support decision making. Further, Lesson 4 describes business intelligence, followed by a description of databases and data warehouses as components into gaining business intelligence. A discussion of primary IS components utilized by organizations for gaining business intelligence is then made. The lesson concludes with an introduction of different technologies utilized at various decision-making levels of modern organizations to gain business intelligence.

Business intelligence, in theory, has many applications. Forbes differentiates the term by talking about the rhetoric and intelligence used to react to and summarize events that have already occurred versus the utilization of techniques of advanced BI that include “predictive analytics and alerts”(Forbes, 2014). Earlier in these lessons, we mentioned the usefulness of learning where a company is heading and not just where it has been or is now and the advantage this gives us over our competitors. Often an added burden is reconciliation of which data set is being used by in-house analysts and the difficulty in arriving at a consensus of its contents and the path forward.

Database management systems (DBMS) are tools to help compile, store, and retrieve data and turn it into useful information to make present and future business decisions based on accurate data. Through these systems, queries can be used that ask the database a question based on our parameters, and results will be printed or listed for us to view. The integrity of the database depends on the integrity in which the data has been entered into it. It also depends on the initial design and framework put into place prior to any data entering. Databases require scrupulous attention to detail to make sure that the initial fields will provide users with the correct information. When fields have been added after the initial design, they will have to be backfilled with prior entry information, which often leads to missing information and inaccurate reporting. Become familiar with the fields, records, tables, attributes, queries, etc. so that you can understand the basic machinations of the database. Focus on knowing what the database can do for you as a manager and how it can assist you in making sound business decisions. Concentrating too much on the details will obscure your vision of its effectiveness.

Another paradigm you will learn about in this lesson is data warehouses. They are exactly what you think, warehouses of data. These data warehouses “integrate multiple large databases and other information sources into a single repository” (Valacich & Schneider, 2014). By stockpiling the data into one single source, analyses can be conducted from its contents. Some of you may be familiar with the term “selling data.” We are very protective of our data and want to make sure it is not sold to telemarketers, online marketers, etc. We try to limit the deluge of unsolicited information and sales pitches we receive in our everyday life. Examples of companies that use data warehouses include Walmart, UPS, and Alaska Airlines. Other companies employ the use of data marts, with each containing a “single aspect of a company’s business, such as finance, inventory, or personnel” (Valacich & Schneider, 2014).

Let’s pick up on our earlier discussion of BI, as the lesson first introduces the concept, shifts to databases, and then shifts back to BI components and management. One of the many pieces of BI includes information and knowledge discovery tools used to find data within already existing data. That is, to extract the pieces of data that will be used in making decisions. If, for example, you are trying to print or preview a target customer list with specific criteria to concentrate sales efforts, you can query your database for specific information. These are management support systems upon which decisions can be based with a certainty level. Reports come in all shapes and sizes and require you to know first what the output of such reports will logically be so that you can build your database and enter your data within it in such a way that the results you seek will be provided. Again, we are working backwards, as we mentioned in an earlier lesson with a look at our vision first, then searching for long-term plans, and finally short-term goals. What is it we want to know? What do we plan to do with this information? Collecting information for the sake of collecting it is not goal-oriented and will not provide you with what you ultimately want to know.

We will look at ad hoc queries and reports, exception reports, and other analytical reports for the purposes of helping us answer a question that can place our company in a solid strategic position. We also can look at data mining, which will provide us with predictive information and relationships about our data that sets us apart from our competitors. We will then theorize, based on solid information, about what we anticipate that our customers will want and need and then provide them with this before our competitors do: we tell them what they will need/want and then we draw them in providing them with rationales about the wisdom of purchasing our product or service.

Organizations can no longer simply focus on being efficient. They can no longer stay competitive by squeezing more efficiency from operational applications such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), or eCommerce. Advanced business analytics tools and technologies are needed for business processes and business operations to become more effective. For example, eCommerce technologies can be used to efficiently process a customer purchase, but data warehousing and mining can effectively analyze customer buying habits and target offers to specific customer segments for a better response and improved cross-sell and up-sell ratios.

Business analytics process is automated so analysts can focus on action and not have to search through multiple mail systems, call center logs, and enterprise applications. Combining data with process awareness:  Organizations with ambitious strategic goals need to be more than efficient - they need to be effective. On the operational level, a relational database might make processing a customer's orders less expensive, but analytics such as data mining can use advanced customer segmentation to increase cross-sell ratios in real-time during a customer interaction – when it counts. On the executive level, expert systems can bring decades' worth of knowledge to a problem, helping managers with situation assessment and long-range planning.

References

Groenfeldt T. (2014). Forbes. Business Intelligence (BI) Isn’t. Very Intelligent. Yet.

Valacich, J., & Schneider, C. (2014). Information Systems Today: Managing in the Digital World (6th ed.). Upper Saddle River, NJ: Pearson Education, Inc.