Data Value in Decison Process: Survey on Decision Support System in Small and Medium Enterprises

Transcription

1 miprobis - Business Intelligence Systems Opatjia, Slovenia Data Value in Decison Process: Survey on Decision Support System in Small and Medium Enterprises Maurizio Pighin(*) and Anna Marzona (**) (*) Department of Mathematics and Computer Science University of Udine (Italy) (**) LiberaMente srl Udine (Italy) 1

2 Agenda The economical context on analysis Survey targets and methodology Survey Results Conclusions 2

3 Economical context The province of Udine with its 4,905 sq km is about 62% of the territory of Friuli Venezia Giulia It is the largest province in the region also for: Concentration of population, with 529,000 inhabitants, representing 44% of regional total Number of employees, with 228,000 employees, representing 44% of regional total Number of businesses, with 49,500 businesses, 48% of the regional total. High rate of entrepreneurship: one production company every 9.4 inhabitants. The companies are mainly small ones (such as considering up to 49 people). 3

4 Economical context Production specialization, metal-mechanical production with 1,600 units (26% of total manufacturing) woodworking and furniture production with 2,020 units (33% of total manufacturing). Strong propensity to export. European Union with 60% of export value America (especially Northern) (11% of exports) Asia (8% of exports). 4

5 Agenda The economical context on analysis Survey targets and methodology Survey Results Conclusions 5

6 Survey targets Survey on mechanical companies represent the trends of the entire territory heterogeneous in size, incoming, type of products and processes We inquiry how many companies use data warehouse systems what is their profile what are the goals and methods of use 6

7 Survey targets In general we expect that greater use of data warehousing systems on mediumlarge size companies small businesses are less interested in these systems not so important amount of data less computerized processes low proneness to investment low attention to new technologies and innovative practices companies with data warehouses are those with higher technology with the percentage of exports and foreign relations 7

8 Agenda Introduction The economical context on analysis Survey targets and methodology Survey Results Conclusions 8

9 Profiling companies - Dimension Group A: more than 100 employees Group B: employees Group C: employees Companies In sample 38% of companies income is between 5 and 15 million euros Group A 16 Group B 14 Group C 15 Total 45 9

10 Profiling companies - Age Company Average start year Average years of activity St. Dev. Group A Group B Group C Total The average age of companies is about 36 years of activity companies of group B and C, significantly more recent (29 and 31years) companies of group A on the market for about 45 years. 10

11 Profiling compaines Export-Quality High level of export and foreign relations High percentage (75.5%) with quality certification 94% of group A 53% of group B 75,5% of group C Company % avg. Export Group A 65% Group B 41% Group C 25% Total 43% 11

12 Profiling IS Specific function group A and B have a function dedicated to the Information System Group C in 74%: Information System is kept by executives or top management Company % specific function for I.S. Avg. number of I.S. staff Group A 100% 2,4 Group B 93% 1,4 Group C 26% 2 Total 73% 2 12

13 Profiling IS Computerized areas The areas mainly computerized are Administration, Sales, Purchase, Logistics and Production The percentage drops down in the areas of Quality and Control, while still not widely used are the CRM subsystems. Area Group A Group B Group C Average Accounting 100% 100% 100% 100% Logistics 94% 93% 67% 84% Sales 94% 100% 93% 96% Purchase 100% 100% 87% 96% Production 100% 86% 67% 84% Quality assur. 87% 57% 60% 69% CRM 44% 21% 13% 27% Control 87% 64% 53% 69% 13

14 Profiling DW Data analysis areas The areas most involved in the data analysis are Administration, Sales and Purchase Logistics, despite having a high percentage of computerization, is less often the subject of data analysis. Area Group A Group B Group C Media Accounting 100% 100% 87% 95% Logistics 69% 43% 33% 49% Sales 88% 93% 100% 93% Purchase 88% 86% 80% 84% Production 88% 71% 73% 78% Quality assur. 75% 36% 47% 53% CRM 31% 21% 6% 20% Control 88% 36% 60% 62% 14

15 Profiling DW - Knowledge group A: 94% know the existence of DW the percentage drops to 50% of companies of group B and 47% of group C Company % Knowle dge Group A 94% Group B 50% Group C 47% Total 64% 15

16 Profiling DW - Usage 24% use DW systems for data analysis among the companies that still do not have this tool, 26% will adopt one in the future, and 11% in the short term. 20% in group C orientation of small organizations into decision support systems. introduction of DW was fairly new except some rare cases, DW systems were introduced in the last 3-5 years. Company % Usage % Future usage % Fu tu re usage in short term Group A 50% 31% 6% Group B 21% 14% 7% Group C 0% 33% 20% Total 24% 26% 11% 16

17 Profiling DW Correlation with export The companies that use DW systems have the high percentage of export need to keep under control the remote activities The initial assumption is reflected by the survey Company % Export Using DW 66% Not using DW 36% 17

18 Profiling DW Correlation with market High-tech companies tend to adopt innovative tools The initial assumption is reflected by the survey % DW Product market usage Electronic and automation 66% Tool s 66% Components and subsupply 25% Mechanic workshop 20% Machinary production 20% Carpentry and assembly 16% Installations - Third party work - Metal furniture - 18

19 Profiling DW Architecture - source The data that flow into the data warehouse comes from ERP sources (in 100% of cases) other external sources (73%) other internal sources (63%) DW as instrument of data reconciliation Architecture % Company 1 level 80% 2 levels 10% 3 levels 10% 19

20 Profiling DW - Supplier 80% - DW built by the supplier of the ERP system 20% - DW designed by other suppliers or consultants A single known partner who already knows the company s information system (better comprehension of its dynamics and needs) 88% - one-level architecture in DW built by ERP supplier 50% - two-levels architecture in DW built by specific consultants ERP vendors offer solutions for Business Intelligence, but usually of a lower profile compared to solutions proposed by specialized consultants. 20

21 Profiling DW Kind of tools OLAP tools drill-down or roll-up features Data Mining simple data analysis package, like classification and prediction or association analysis. Tool Group A Group B Group C Average Reporting 100% 100% - 100% OLAP 75% 67% - 73% Data Mining 13% 33% - 18% 21

22 Profiling DW Internal use and investment The general trend global monthly analysis investigate some small data on a daily basis In 90% of cases data is updated daily and automatically Budget spent by companies to acquire data warehousing systems is on average between 10,000 and 20,000 Annual budget for planned maintenance or for any developments of the system is less than 10,000 Role of users Group A Group B Group C Average Area managers 88% 67% - 82% Staff 63% 67% - 64% CEO 38% 67% - 45% 22

23 Profiling DW Simplicity and Usefulness The simplicity of the analysis tools used, in a scale of 0 to 10, has an average answer value of about 6.5 with a variance quite low (1.25). This shows a certain uniformity of opinion, considering fairly simple the analysis tools available. The usefulness of these tools found positive answer with an average value of about 8 on a scale of 0 to 10, and variance

24 Profiling DW - Activation The activation process of a data warehousing system The process is not very simple: the mean value is 5 on a scale of 0 to 10 Exploring the reasons for this difficulty through the use of open questions, we found Determining what information to require The lack of internal knowledge the design is almost exclusively dependent on external consultants or on the same suppliers of ERP 24

25 Profiling DW - Motivation Almost 60% of companies say they have been pushed to invest in this direction to be more competitive on the market the need to have a single tool to conduct analysis and obtaining clear and usable information. Barriers to investment lack of knowledge cost often considered too high 25

26 Survey results The paper presents more tables and details 26

27 Agenda The economical context on analysis Survey targets and methodology Survey Results Conclusions 27

28 Conclusions Desire to use methods and tools of business intelligence: amount of data that modern transaction systems generate more competitive on the market, taking quick and appropriate strategic decisions based on fast and complete information synthetic indicators that allow to monitor corporate performance and to have crossed and parametric analysis on raw data provided by operational systems 28

29 Conclusions Knowledge the theoretical foundations that underlie the formation of these indicators are fairly consolidated, much less are foundational aspects and engineering skills with which to build business intelligence systems the instruments used are not always appropriate to the target 29

30 Conclusions In most cases data warehousing systems are made by the ERP vendors, relationship of trust Software companies often push to solve the informational question through their ERP develop reporting or interactive investigations as customized ERP functions use of simple OLAP navigation instruments that read directly the operational database (one-levelarchitecture) poor knowledge of tools and methodologies of business intelligence attention to operational core business, the ERP system jealousy of their customers 30

31 Conclusions Producers of business intelligence tools are very oriented to architectural and technological aspects, much less to application and organization the solutions they propose oversimplify the collection, cleaning and physical organization of data. Poor ETL instruments One-level-architecture vertical decay of performance, complexity of user views. 31

32 Conclusions Unrealistic vision of the procedures necessary for effective DW construction this kind of solutions relative new Innovative methodologies requires years of gestation proposed in formal terms perceived by the market as a whole tuned successfully transposed to the end user (especially the SMEs) 32

33 Conclusions We can state a profile of the companies that makes use of data warehousing systems: mostly medium to large companies in the market since long time correlation between the use of the DW and the percentage of export the need for control over foreign operations and the usefulness of a centralized data warehouse is high; nature of the products may be related to the use of DW high-tech companies are more likely (from the cultural point of view) to adopt innovative tools than other. 33

34 Conclusions The usefulness of data warehousing tools is still not fully understood in companies difficulties to quantify (not only in terms of money) the ROI lack of a specialized figure within the company The adoption of these tools is going to increase This evolution must go hand in hand with the transformation of corporate culture that must be open to innovation 34