Data mart design for managing IT department in a

Data mart design for managing IT department in a

telecommunications company

Jasna Mirković, M.Sc*, Ljiljana Kašćelan, PhD

**

* Telecom Montenegro/ IT department, Podgorica, Montenegro

** University of Montenegro/Faculty of Economics, Podgorica, Montenegro

[email protected]

Abstract – This paper deals with the data mart design in the

area of analysing problems and requests in the IT department

of a telecommunications company. Efficient resolution of

problems and execution of requests is very important to

operate smoothly and ensure that the services this company

offers to users are available on time. Main decision-making

processes have been identified during the research in the IT

department of a telecommunications company. In that respect

adequate data mart was designed. The concept of realisation

of the data mart was presented using business intelligence

tools. The implemented system was tested on data from the

case study of a company – “Telecom Montenegro” AD, where

outputs with good performance were successfully generated.

I. INTRODUCTION

Having the right information on time represents basis for making good decisions. No matter how good a manager who manages the business is, the success is not guaranteed if relying only on intuition without timely information.

In modern business conditions a manager must know what information to look for and how to get them before the competition. Therefore, business intelligence tools are means for getting the information in a timely manner. If these tools are properly and skilfully handled, they will give good results.

The subject matter of this research is data mart design in the area of managing IT department, as in [3], which means that:

It must be implemented in the manner to be included in integral data warehouse

It should correspond to specific requests of the users

It should enable development of OLAP model under the presented requests

The basis for designing data mart lies on the following assumptions:

Main decision-making processes have been identified in the area of managing IT department in telecommunications companies, i.e. data mart targets have been identified

Logical model was defined and RDBM system was selected;

Logical model was mapped and physical model was developed;

OLAP models were developed based on user’s requests;

Model was tested on data of real system “IT Help desk application” of the company “Telecom Montenegro”.

The results obtained pointed out that model corresponds to the presented requests and the time of responses in real system has parameters that correspond to standards of the business intelligence system.

II. DEFINING THE OBJECTIVES OF DATA MART

The work of the IT Department of the telecommunications company and the decisions that are passed in this area reflect on business success of the company in many ways. The main objective of IT Department is to provide the support to the operations of the company through adequate support of internal users and IT systems that enable the operations of the company. The time of responses in resolving problems and requests influences directly the revenues of the company.

For an optimum organisation of IT department and monitoring of its work, in addition to various analyses, the most important ones with regard to the IT Department management are the following:

1. Analysis of problems/requests of internal users – This analysis will enable easier and better insight in all problems/requests reported in specific time period, as well as the analysis of duration and pricing of resolving problems. In this way, information needed for more efficient and effective resolution of problems and requests have been obtained. Detecting problems that appear most frequently enables their prevention and saves time of the employees.

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2. Analysis of users – This aspect answers the questions on end users that report problems and requests: who are they, which sectors do they come from, which users repeat requests, which users make the same mistakes.

This analysis may give answers, for instance, which internal user needs additional training and in what area.

3. Analysis of executors – These analyses are particularly important for IT managers as they enable detection of most efficient executors and those that are not efficient. It also gives an insight in areas where executors need additional education. It is also possible to establish key performance indicators (KPI) through this analysis which will be set up as a target to all employees from certain group and encourage achievement of targets and higher efficiency.

Main targets of data mart are:

To provide implementation of OLAP model which will be enabled by previous analysis;

The possibility of integration in data warehouse system of entire company;

The possibility of its implementation using some of standard tools for development of data warehouse;

The possibility of integration with standard OLAP tools;

III. DATA MART DESIGN – SELECTION AND

SPECIFICATION OF BUSINESS FACTS AND

DIMENSIONS

Modelling of data through star schema i.e. using fact tables and dimension tables is selected for data mart design. Fact table is in the centre of star schema connected by surrogate keys with dimension tables. Dimension tables are denormalized with surrogate keys that enable storing of dimension history.

Fact table for data mart with targets recognised in the previous chapter is the following table:

helpdesk (HELPDESK_F) – which will record all resolved problems/requests by IT department. Attributes of this table, in addition to surrogate keys of dimension tables, are: price_of_resolving_problems and duration_of_resolving_problems.

In data mart design, the selection on the level of granulation should be taken into consideration which should be respected in all tables that make star schema.

Granules represents the main, the smallest level of data to be represented in the fact table for a given business process. Granules in the fact table should be determined at as low level as possible, which represents individual transaction. Lower level of granulation gives more robust but more flexible design of star schema, which means that it is more ready to answer to various types of queries and enable possible introduction of new dimensions.

Specifically, for the selected fact, helpdesk granules could be: problem/request which internal user reports and the employee in IT department resolves. This enables the possibility of data manipulation at atomic level – each single problem/request of any type regardless of period when it is reported. In this way, it is possible to come to the following information:

when certain problems/requests have been reported and resolved.

Who solved them, i.e. who reported certain problem/request.

What is the average number of resolving problems/requests in specific categories, and the like.

It should be noted that the selection of granulation level of dimension tables depends on the level of granulation of fact tables. The level of granulation for all tables must be the same.

Dimensions for fact helpdesk are the following:

Time dimension (Datum) – which should reflect hierarchy of time: year → quarter → month → day

Executor dimension (IZVRSILAC_DIM) – Each problem or request is resolved by employee in IT department.

User dimension (KORISNIK_DIM) – each problem or request is reported by an internal user. This dimension corresponds the hierarchy of type: department → user

Problem dimension (PROBLEM_DIM) – Insight in problems and requests that are reported by end users to the employees in IT department. This dimension corresponds the hierarchy of type: type_of_application → list_of_application → list_of_problems

IV. DATA MART REALISATION

Tools to be used for realisation of data mart are Oracle Warehouse Builder (OWB) and Oracle Discoverer.

OWB is a tool for designing, implementation, development and managing data warehouse system, as well as the system of business intelligence, in general. This tool combines key components of ETL tools and tools for

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designing, thus enabling integration of ad-hoc inquiries as well as OLAP solutions. It enables realisation of Data mart through three stages, the defining stage, generating stage and data loading stage, as in [1], [5].

The defining stage includes:

Defining of targeted warehouse schema – defining of multidimensional models and aggregations;

Defining of sources of data

Defining of mapping operations i.e. transformation and data loading – ETL procedure.

In the generating stage, scripts for creating warehouse schema and ETL procedures are generated and executed. The initial data loading is performed through the execution of ETL procedures.

Targeted warehouse schema for data mart designed in the previous chapter and defined by OWB, is shown in Picture 1. The Picture 2 shows mapping of facts helpdesk.

Oracle Discoverer was used for creating OLAP models. Administrative component of this tool, as in [7], enables designing and presenting the groups of data from corresponding business areas, which user accesses through user component Discoverer Plus. End User Layer (EUL) represents a component that hides database complexity and its change from end user and which is located between database dictionary and Discoverer. From operational standpoint, EUL generates SQL statements at client’s level and communicates with database. When a user selects an object within a business area, EUL generates a SQL statement that defines the selection over table and view. When a user makes a query, EUL executes this SQL statement, sends it further to the database which returns results to the user’s component. In this way, end user does not need to be familiar with SQL to access and search for data. This inter level protects database integrity since nothing that user does on Discoverer reflects on database but it reflects on metadata within EUL.

Picture 1. Star schema

Picture 2. Mapping of facts helpdesk

All OLAP models were realised for all three analysis that were defined as the targets of this data mart, as in [2], [6]. One of OLAP models defined using this tool is shown on Picture 3.

V. CASE STUDY

For the analysis within case study, a data of telecommunications company “Telecom Montenegro” AD were taken from helpDesk information system of IT department, as in [3]. This company deals with provision of telecommunications services, and IT department is responsible for entire information system of the company without which the company could not perform its business.

Information system helpDesk is organised in a way that internal users report requests through simple interface and also the problems occurred during their work. Furthermore, employees in IT take over and resolve these problems and requests, and the information on who took over the problem and when the problem was resolved was sent to internal user. The reporting of problems and requests using phone or email to system manager of the division was valid until 1 January 2006, which required that the manager would spend significant portion of time as a mediator between end users and employees of IT and it was difficult to follow up the quantity of these requests. Automation of information system of IT department was done at end-2005 and it was put in operations on 1 January 2006. Total 5975 of requests were evident in 2006, total of 12192 requests were evident in 2007, and in 2008 there were 14461 requests.

Source data used for data mart design in this research represent business data of IT department of “Telecom Montenegro” on problems and requests of internal users during 2006, 2007 and 2008. Source data were placed in SQL server relational database where they were taken in the form of Excel tables.


Picture 3. Workbook of number of reported problems with average solution

Initial data loading (about 40 000 records) into designed data mart was less than two minutes. Almost in all cases, initial importing of data into workbooks is 1 - 3 minutes, if data for all years are loaded. After that, all operations in slicing last less than one second, as well as operations drill-up/drill-down if all hierarchies are already present in the workbook. If some of hierarchy is not present in workbook, the operations drill-up/drill-down require data uploading, which has the same level as the initial uploading of workbook. This leads to the conclusion that it is better to implement workbook with all anticipated hierarchies if the time of responses matters.

Several interesting analysis, as in [3], realised through OLAP models over case study data from the aspect of IT managers follows.

Picture 4 shows distribution of solving users’ requests within the group of employees dealing with removal of problems on work stations in 2008.

The analysis shows unequal distribution of resolving problems. To wit, 88% of requests were resolved by three employees out of total of nine employees. Time spent on resolving problems also differs.

Picture 5 shows drill made based on the description of problems. This report shows that certain executors were “faster” than others within the same category. Overall analysis points that time of employees dealing with resolving these problems has not been used in the best manner; therefore, the appropriate actions should be taken, for instance, rearrangement of employees or reduction of their number in IT department, further training and follow up of the progress of employees being ‘poor’ executors.

The following analysis (Picture 6) shows distribution, duration and average of resolving problems and requests by types of applications, observed only in 2008 aimed at detecting the area of the highest engagement of human resources. The analysis shows that majority of IT resources were engaged on type of application “Billing”.

Picture 4. Distribution of resolving user’s requests within group of employees dealing with removal of problems concerning work stations

Picture 5. Distribution of user’s requests within the group of employees dealing with removing problems concerning work stations – drills based

on the description of problem

Further analysis (Picture 7) shows that there are several billing systems within type of application “Billing”, and majority of reported problems and requests are within the category “Tytan-Billing” (Billing of landlines), and then “Billing-mobile phones”, while time spent on resolving these problems/requests is almost equal. This point out that Tytan billing system has several problems in its work, but those problems require shorter time for their resolution.

Further analysis of problems/requests of Tytan billing system drills to the description of the problem (Picture 8) and points out that the majority of requests comes from the category “TK Centre: Error in process”.

The category “Other” remains neglected as these are problems that cannot be grouped in any of the categories. If number of errors in processes is reduced, it would save up to 21% of time of the team that deals with resolving these requests and problems.


Picture 6. Distribution, duration and average of resolving problems and requests by types of application

Picture 7. Distribution, duration and average of resolving problems and requests for the type of application ‘Billing’

This team consists of six employees, which means that the team could be reduced to 5 persons or the efficiency of the team should be improved – both activities directly influence the profit of the company.

The following analysis (Picture 9) represents monitoring of key performance indicators, number of resolved problems/requests within 3 days, and comparative analysis for 2006, 2007 and 2008.

It can be noted that KPI trend improves in the most important part for the support of internal users “Billing”. However, the trend within domain “IT: Work Stations” is rather poor. It means that actions should be taken in this area to improve the efficiency considering the analysis from the previous chapter.

VI. CONCLUSION

This paper defined designing of data mart for the support to the decision-making process of IT department in a telecommunications company. The process of data mart design included the following:

Picture 8. Distribution, duration, and average of resolving problems and requests for type of application ‘Tytan’ billing system, drill to the

description of the problem

Picture 9. Number of resolved problems/requests within 3 days, by years

Identification of main decision-making processes in IT department of telecommunications company;

Definition of sources of data for identified processes;

Projection of data mart design for the support to identified decision-making processes;

Definition of concepts and methodologies for the realisation of this data mart using “business intelligence” tool;

Realisation of defined targets of data mart, i.e. OLAP model;

Data mart design based on identified decision-making models gave expected results. It means that the proposed model gave efficient and qualitative outputs for all identified decision-making processes. This paper also gave the concept of realisation of data mart. Realised system was tested on data from the case study of the company –


“Telecom Montenegro” AD, where successfully generated outputs with good performances showed accuracy of the proposed design. Further research could be focused on the possibilities of integration of data mart defined in this manner into uniform data warehouse and expansion of possibilities, i.e. higher number of analysis.

LITERATURE

[1] Kašćelan, Lj. : “Oracle Warehouse Builder and stages in development of data warehouse” , SYMOPIS 2004 – Conference Proceedings, 2004

[2] Kašćelan, Lj., “Creation of OLAP model in Oracle Discoverer ”, INFO-M, No. 13/2005, p. 18-22, 2005

[3] Mirković J.,: “Model of business intelligence system for managing IT department of a telecommunications company”, Master’s Thesis, Faculty of Economics, Podgorica, 2009

[4] Panian Ž, Klepac G: ”Business intelligence”, MASMEDIA, Zagreb, 2003

[5] Oracle Warehouse Builder user's guide 10g Release 1 (10.1),

PDF version, http://download.oracle.com/docs/pdf/B13916_04.pdf , Oct 2007

[6] Oracle Business Intelligence Discoverer Plus user's guide, Release 10.1.2, PDFversion,http://download.oracle.com/docs/pdf/B13915_02.pdf , Dec 2007

[7] Oracle Business Intelligence Discoverer Administration guide, Release 10g, PDFversion,http://download.oracle.com/docs/pdf/B13916_02.pdf , Dec 2007


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Data mart design for managing IT department in a