Introduction
The name business intelligence (BI) is a name that was formed in the mid 1950s to describe the act of changing un-grouped data from a company’s or an organization’s contrasting functional data into a common data warehouse that could help the organization to come up with or in reporting and presenting information.
The person uses this collected data network through an easy-to-use interface which functions to display the outcome of the extraction, transformation and loading procedure. This is commonly known as the ETL procedure and it is used to expand the data warehouse.
This particular network also acts as an ordered reporting environment that distributes the completed and available functional reports and business decisions all over the various departments of the organizations. It is noted that in the recent times, service oriented architecture (SOA) has decided to start supplanting and augmenting data warehousing and business intelligence implementations (Biere, 2003).
This action of merging data warehousing and business intelligence has a business advantage in that the reporting and making of decisions are carried out or are dependent on a common functional view or better still on one model of the truth. Access to business intelligence information has become timely and it is also known that graphic dashboards have been developed with the main aim of keeping tracks of major business operations.
As a result, this has made business intelligence more graphic intensive. Practically, charts and business graphics now form a common part of reports. The graphic dashboards were named so because of their resemblance to the usual automobile or car dashboards. They work by giving operational information at a glance (Moss & Atre, 2003).
Business intelligence is made up of various techniques of analyzing data and conveying the information that the would-be customers or users will need. The categories include: Geographic Information Systems (GIS) used in collecting spatial data; Standard statistical method used for collecting quantitative data for the purpose of forecasting, predictive models, and decision trees; and Semantic analysis method which is usually used for textual data (Biere, 2003).
The Geographic Information System and Business Intelligence
Geographic information system is an old but well developed and very informed technology that sprung up in the university’s computer science development in the later years of the 1960s. The main reason for coming up with geographic information system was to relate the available collected data with geographic referenced map graphics and pictures so as to help people understand the impact and effects of geography on factors such as behaviour and outcomes of results.
Geographic information system and business intelligence have been known throughout history to follow different growth and implementation ways. However, these two technologies have been merged due to the overwhelming request by customers to use a more complete technology and also for practical reasons. Within different organizations it is known that the visibility of these two technologies has improved to meet regulatory needs.
To meet the needs and requirements of business intelligence and geographic information system beneficiaries, leading business intelligence providers have continually used the two technologies and have provided innovative answers to their rapidly increasing number of end users (Weber, Grothe & Schaffer, 1999).
The outcome is that the new users have answered to this with new applications that influence the synergy of the merged technologies. Geographic information system can be merged with other data technique analysis and business intelligence.
There are various factors that have made their integrations very easy as will be discussed in the paper. The merger of geographic information systems with other data technique analysis provides numerous benefits to the whole organization without interfering with or interrupting the already available information technology data.
In today’s world, geographic information system recognizes the location component of data and relates the data to geographic feature found in a GIS. These features are geographic representations of real features like roads, river, forests, lakes oceans, mountains, buildings, game parks and game reserves, deserts and many more. They also include conceptual features like political boundaries and service areas.
When one relates information to these features, she or he enables users to arrange data depending on the geographic area of each record in the data. For example, they get to know data on mountains, water sources, can easily locate roads, homes and schools while conducting a survey. This type of geographic data operations is shown as a map and clearly shows spatial relationship and influence that cannot be pointed out in traditional tabular views of data (Hall & Jordan, 2010).
A geographically sorted data gives a chance for the usage of new data that might not have anything in common with the already existing data apart from location, for example, schools and other governmental organizations that engage in data collection such as insurance companies.
GIS analysts can map the addresses they will need or those of insured facilities and overlay flood plain boundaries so as to identify all the facilities within the flood prone areas. This information enables them to compute the logistics on reserves from potential catastrophic floods and can help schools to come up with a budget. Similarly, private and public organizations can also carry out the same investigation to find out the potential impact on facilities, supply chains and employees (Hall & Jordan, 2010).
Geographic information system and business intelligence came into use or were implemented at a time when the general information technology was developing to embrace common ways of compiling, distributing, storing and using data. Most business environments, both public and private organizations, had an increasing demand to operate effectively so as to be able to meet the requirements of their increasing number of clients.
As a result, they found a propriety system a major hindrance to their daily business operations. To solve this problem, various IT providers and different organizations proposed that they should try to adapt a method that had concepts of using standard and common ways of integrating data. From the time these standards were adopted by the providers of IT, it became very easy for different applications to interact as they shared the same foundations of technology.
It is also noted that internet technology also improved tremendously and became a major communication protocol that enabled the exchange of information between the various functional departments of an organization. As the standards were being put to use, business intelligence and GIS concentrated more on dealing with and giving information they considered very important to their major users (Prabhu, 2004).
The BI providers made connectors for most common file formats used by businesses while on the other hand the GIS experts worked hard to create connections of geographic features formats that were in worldwide use then. The increased use of internet and the adoption of standards as a data and information medium partly contributed to mission of enterprise implementations of applications.
BI application operators had technology grounds and applications that could help in meeting the requirements of enterprise implementations. However as discussed earlier, both BI and GIS operations work differently. It was therefore thought that merging these two applications would give opportunities for proliferations of the technologies and the benefits of these two technologies would be realized through using operational units that are recently not using similar technologies.
Consequently, this would lead to the expansion of integrated applications in the entire enterprise. These same sentiments were shared by innovators in governmental organizations that needed to obtain actionable information from the existing data (Prabhu, 2004).
BI experts also found out that the already adopted standard technology architectures would make the merging of GIS and BI very easy. If anything, every technology gave solutions and answers to issues that were seen as major obstacles to enterprise implementations of respective applications.
GIS experts saw that using tabulated data obtained from numerous databases and file systems would have been hard and expensive, while on the other hand the BI experts solved this problem by using the ETL method or using connectors that enabled BI applications to use native language.
However BI providers found it most difficult to work with geographic data format, CAD data and imagery. To add to this problem, they also had difficulties with the projections used in GIS maps. But then again the GIS providers solved this issue by using standards of the interoperability of their data (Prabhu, 2004).
Data Warehousing and Business Intelligence
Data warehousing is an important aspect to talk about when discussing business intelligence. Data warehousing has different definitions a few of which are listed below:
Data warehouse is a recent development in database management in which copies of all the databases in an organization or a company are kept or arranged in one single location and can easily be accessed by employees from any locality. It is also the method of designing, building and maintaining a data warehouse system (SCN Education, 2001). Data warehouse can also be defined as a collection of an organization’s data that has been compiled and kept electronically.
They are made in such a way that they make easy the reporting and analysis of information (Thierauf, 2001). Data warehousing is related to business intelligence (BI). It tells us that the major thing that the project personnel should do is to collect information from final consumers because they are usually not well conversant with the data warehouse matters and as a result they need the guidance of a business sponsor.
It is very important to do a proper choice of data warehouse personnel and tools and also business intelligence software. The personnel can either be external consultants who are normally more experienced and have elaborate knowledge in the field, or they can be permanent employees. Permanent employees are found to be much more economical as opposed to consultants who are very expensive (Hall & Jordan, 2010).
The next step involves executing data warehouse design which involves several steps such as quality assurance, incremental enhancement, the physical environmental set up and many more.
The design of the data warehousing must be done taking into consideration various issues such as what is supposed to be done to successfully finish the phase, the approximate amount of time that is needed to finish it, and the documents that will be needed at the end of the task to show every single step taken during the data warehouse task. The documents help consultants to relay their outcomes to clients.
Models of Data Warehousing
The models of data warehousing are divided into dimensional model, conceptual model and logical model. Dimensional model is normally used in data warehousing systems that are used for translational type systems. It deals with aspects such as category of information for instance time and attributes. It also contains a fact table which is a table that gives information on the measure of interest such as amount of sales in a business.
On the other hand a conceptual data model shows the level of relationship between various areas. A logical data model gives a much detailed description of data without taking into account how they will be implemented in the database. Business Intelligence is made up of various techniques for analyzing data and conveying information to the would-be decision makers.
Standard Statistical Methods and Business Intelligence
This is a method that uses statistical surveys to compile quantitative information involving attributes of a population. This analysis may centralise on opinions or factual information based on its use and it mostly involves asking questions directly to individuals. “These questions can be given by a researcher whereby this type of examination is called a structured interview or researcher administered survey,” (Thierauf, 2001, p. 67).
It can also be in the form questionnaires. In this technique, the questions are usually ordered and standardized in such a way that the question does not have any effect on the answers to the questions. This is done to ensure validity, reliability and generalizability of the research.
All the other people taking the survey test should be given the same questions in the same order as it was done to the others. This ensures accuracy and reliability of the collected data. In organization development (OD), keenly structured examination tools are usually used on the basis of data collection, organizational diagnosis and other expected outcomes of the business (Weber et al., 1999).
This technique can be used to study concepts such as curriculum development. It becomes more useful when the researcher wants to have a one-on-one encounter with the respondents. Here the survey questions are given in the form of questionnaires. The people involved go from door to door or home to home asking questions and collecting their data at the same time.
The data collected is then quantified for later use. For example, if a given country needs to change its curriculum, first it will have to come up with the design of how it wants the new syllabus to look like, the subjects that are supposed to be taught and those that should be left out, the stages at which the subjects are supposed to be taught, the resources they would expect the teachers to use and the methods of teaching, as well as the form of behaviour reinforcement to be used to mention but a few.
The response to these questions during the piloting stage will give the developers the green light to go ahead and implement the new structured curriculum or they will have to go back to the drawing board. For instance, if majority of the people find the newly proposed curriculum to meet the country’s educational and workforce needs then it can be implemented.
On the other hand, if majority of respondents are not satisfied with the proposed curriculum then the developers will use their responses and opinions to come up with a syllabus that is suitable to their citizens and one that meets their country’s needs. Usually the outcome of this technique is very reliable and effective and can also be merged with geographic information system to help the people involved to get to various regions of the country (Thierauf, 2001).
The standard statistical methods can also be used by a country to find out how its citizens have advanced technologically. For example surveyors can set out to find out the number of people using electronics such as computers, televisions, microwaves, electric cookers, refrigerators among other electronics at home.
In such a case, the surveyors move from home to home both in the rural and urban areas collecting data about each of the equipment named above. Businesses can also make use of this technique to find out about their customers’ feelings towards their products and services. The results from the survey can help the organization to make decisions that would increase their customer base by developing products and services that meet their customers’ needs.
The advantage of this method is that it does not require users to be highly educated so as to be able to interpret the data. On the negative side, it is time consuming since it is done manually and not electronically. This technique becomes much more effective when merged with GIS because whereas the technique itself does not give maps and pictorials, it can use maps and pictorials from the GIS (Prabhu, 2004).
Semantic text mining and BI too go hand in hand as this helps in retrieving data from large amounts of data that is mainly collected in text form. Text mining helps BI experts by equipping them with skills on how to get textual data organised and ordered so that it can be easy to use. Most companies such as call centres, emails and many more collect their data in text form. This information is noted to be increasing on a daily basis and at a high rate.
By using text mining BI experts apply the knowledge of text mining examining numbered and short hand data information in the business organization. They then organize this information into manageable form that can be used by others. They do this without omitting the most important part of information that would have been otherwise lost if text mining was not used (Gao, Chang & Han, 2005).
Reference List
Biere, M. (2003). Business intelligence for the enterprise. Upper Saddle River: Pearson Education.
Gao, L., Chang, E., & Han, S. (2005). Powerful tool to expand business intelligence: text mining. World Academy of Science, Engineering and Technology, 8, 110-115.
Hall, D., & Jordan, J. (2010). Human-centred information fusion. Chicago: Artech House.
Moss, L., & Atre, S. (2003). Business intelligence roadmap: the complete project lifecycle for decision-support applications. New York: Addison-Wesley.
Prabhu, C. (2004). Data warehousing: concepts, techniques, products and applications. New Delhi: Prentice-Hall of India.
SCN Education. (2001). Data warehousing: the ultimate guide to building corporate business intelligence. Berlin: Wiley-VCH.
Thierauf, R. (2001). Effective business intelligence systems. London: Greenwood Publishing Group.
Weber, J., Grothe, M., & Schaffer, U. (1999). Business intelligence. Berlin: Wiley-VCH.