=Paper=
{{Paper
|id=Vol-2499/paper4
|storemode=property
|title=Knowledge Sharing in BI Ecosystems: Case of E-Municipalities
|pdfUrl=https://ceur-ws.org/Vol-2499/paper4.pdf
|volume=Vol-2499
|authors=Lauma Jokste,Rūta Pirta,Kristaps Pēteris Rubulis,Edgars Savčenko,Jānis Vempers
|dblpUrl=https://dblp.org/rec/conf/ifip8-1/JokstePRSV19
}}
==Knowledge Sharing in BI Ecosystems: Case of E-Municipalities==
https://ceur-ws.org/Vol-2499/paper4.pdf
Knowledge Sharing in BI Ecosystems: Case of E-
Municipalities
Lauma Jokste1, Rūta Pirta1, Kristaps Pēteris Rubulis1, Edgars Savčenko2, Jānis Vem-
pers2
1
Riga Technical University, Institute of Information Technology
Kalku 1, Riga, LV-1658, Latvia
{lauma.jokste, ruta.pirta, kristaps-peteris.rubulis}@rtu.lv
2
Ltd. ZZ Dats
Elizabetes 41/43, Riga, LV- 1010, Latvia
{edgars.savcenko, janis.vempers}@zzdats.lv
Abstract. The paper investigates the ecosystem perspective of business intelli-
gence and data analytics with emphasis on knowledge sharing. It is argued that
knowledge sharing is an efficient way of justifying and promoting usage of busi-
ness intelligence solutions and that municipalities are particularly well-suited for
collaborating in the business intelligence ecosystem. The paper proposes a pre-
liminary conceptual model of the business intelligence ecosystem. The model
considers formalized representation of knowledge in a form of reusable patterns,
supports accumulation of feedback information about value of the patterns and
distinguishes usage of open and proprietary data items. Examples of business in-
telligence knowledge sharing are provided.
Keywords: Business intelligence, knowledge sharing, e-municipalities, patterns
metamodel
1 Introduction
BI (BI) and data analytics is being widely adopted in providing smart municipal ser-
vices [1]. Municipalities have similar objectives and functions, driven by principles of
openness and transparency and often have limited capabilities to adopt digital technol-
ogies. Therefore, a collaborative data and BI ecosystem approach [2] is particularly
appealing to them for implementation and exploitation of BI and data analytics solu-
tions.
Knowledge sharing is one of the key areas of concern in current BI research and the
ecosystem is the most widely considered architectural pattern [3]. Cross-company com-
parisons are considered one of the main advantages of knowledge sharing. One of
the main challenges of wider adoption of BI and data analytics is lack of convincing
business cases with clear justification of expected returns on investment. Sharing
knowledge about successes and failures of BI and data analytics usage is a potential
solution to this problem in the e-municipalities framework.
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
38
This research is done as a part of the industrial research projects conducted jointly
by the university and the company. The overall objective of the projects is to establish
the BI ecosystem facilitating efficient adoption of BI solutions in Latvian municipalities
with emphasis on demonstrating information value and identification for suitable BI
application cases. The research process follows the action design research (ADR) meth-
odology [4]. ADR addresses real world problems which require strong organizations
stakeholders and research community collaboration [5]. Our research is collaborative
work between company that develops software and services to municipalities and re-
searchers who help to find the most appropriate solution for proposed problem.
The objective of this paper is to propose an initial version of conceptual model for
sharing knowledge of developing and using BI solutions. The conceptual model defines
key actors involved and mechanism for knowledge sharing based on patterns. The
model emphasizes on open and proprietary data and information items. It is used to
analyze several BI application cases in municipalities and potential for knowledge shar-
ing. The contributions of the proposed research are expansion of BI solutions with com-
ponents for ecosystem-wide knowledge sharing and proposing to use patterns for defi-
nition of reusable BI components.
The rest of the paper is organized as follows. Section 2 provides background infor-
mation. An early version of the conceptual model is proposed in Section 3. Section 4
shows several examples of potential scenarios of knowledge sharing among the munic-
ipalities.
2 Background
Modern data analytical solutions are characterized by their diversity and centralized
data analysis patterns such as data warehousing are no longer sufficient. That has
caused rise of analytics ecosystems [2], where various parties collaborate and exchange
data processing and analysis services. There are various modes of collaboration de-
pending on entities shared. The highest level of collaboration is achieved if data, ana-
lytical services as well as value attained are shared among the members of the ecosys-
tem. There are various actors involved in data ecosystems [6]. Their examples are data
providers, service providers, application developers, infrastructure and tools providers
as well as data users.
BI and data analytical solutions are typically developed according to commonly used
reference architectures. Dimensional storage centered architecture is used in data ware-
housing, lambda architecture is used to combine stream and batch processing for big
data and [7] combines structured and unstructured data processing and analytics. Rao
et al. [8] provide an extensive review of constituent parts of BI and data analytics solu-
tions. Data warehousing components are formally defined in the Common Warehouse
Meta model [9] though the model has not been updated recently.
In last decade implementation and usage of BI solutions in local governments and
municipalities are evolving rapidly. BI technology allows to quickly analyze data into
reliable information which can be further used for decision-making process [10] and
allows to ensure better services for citizens [11]. Municipalities’ innovation plans often
include modernizing decision-making process which proves the need for BI solutions
39
for municipalities [12]. [13] point out that there are not many existing solutions that can
be applied for municipalities, which leads to the need for customizable solutions for
local government units. [14] have in depth researched the need for BI solutions in dif-
ferent organizations. The main value acquired from BI solutions is based on data that
organization generate on daily bases which means that for municipalities BI solutions
are important regarding data about citizens, tourists, legislations, laws, territorial as-
pects and municipality itself. [15] emphasize that BI solutions for municipalities serve
as mechanism for identifying citizens’ needs so services provided by municipality can
meet the needs of citizens thus ensuring maximum benefit of such services.
The ecosystem perspective and open data collaboration in particular has been for-
malized in the data ecosystem model [16]. The ecosystem model shows its actors, rela-
tionships among the actors and resources exchanged. There is a gap between research
done on BI and data analytical solutions and data ecosystems. The former still treats
these solutions as relatively self-contained with mainly inwards flows of data and re-
sources and the latter focuses on interactions among players in the ecosystem while
neglecting data processing and analysis features.
Recently a capability-based approach to support mixed open and proprietary data
ecosystems has been proposed [17]. This approach distinguishes among raw data meas-
urements, meaningful business information and knowledge. The business information
drives digital companies, the raw data measurements provide situation specific data
sources to extract the necessary information and knowledge describes reusable data
processing solutions. All these items are either open or proprietary and reasoning about
data sharing possibilities can be performed. The capability-based approach is supported
by appropriate tools and a pattern repository is a component responsible for knowledge
management. The pattern repository [17] provides knowledge management services for
knowledge creation, discovery and usage. The main feature of this pattern repository is
that it is able to track knowledge usage in different applications and to aggregate feed-
back about usage efficiency. This feedback can be used in knowledge discovery to
guide selection of appropriate patterns.
Using patterns is a promising approach how to “improve something”, because pat-
terns by their definition state that they offer instructions how to achieve the desired
result [18]. According to [19] patterns are “a three-part rule, which expresses a relation
between a certain context, a problem, and a solution. [20] also define forces as forth
major pattern component. [18] have developed business process improvement patterns
metamodel which defines the structure of the patterns and serves as the basis for select-
ing an appropriate pattern and its proper application. In Addition to typical pattern com-
ponents, their metamodel includes building blocks, mechanisms, effects and perfor-
mance indicators.
3 General Approach
The BI ecosystem is primarily analyzed from the perspective of an IT company imple-
menting BI solutions at various organizations, e.g., municipalities. The IT company
proceeds with gradual and evolving roll-out of these solutions starting at one user site
40
and transferring the implementation knowledge to other users. In the case of munici-
palities, they have similar functions though their organization architectures, BI readi-
ness and technological landscapes are very different. Lack of understanding about value
of BI and proven returns on investment are the most significant impediments of BI
implementation. While literature often emphasizes collaboration as a progressive chain
starting with data sharing [2], knowledge sharing is the most important aspect for the
municipalities.
3.1 Conceptual Model
Interactions among parties involved and relevant concepts are defined to establish foun-
dations of knowledge sharing in the BI ecosystem. The interactions are modeled (Fig.
1) by expanding the open data ecosystem model [6]. The User needs a BI solution to
analyze its business. The solution is provided by the Consulting company, which uses
services provisioned by the Service provider and the Infrastructure and tool provider.
The solution operates with data provided by the Data provider. However, users often
do not have knowledge about BI utilization opportunities and consulting company has
possibilities to reduce implementation effort by reuse. Therefore, an actor referred as
to the Sage is introduced and it is responsible for knowledge management. The Sage
maintains BI implementation and usage knowledge. Users like municipalities inquire
the knowledge base about BI solutions used by similar users and get inspirations and
suggestions. The Consulting company uses the knowledge base to retrieve technical
information (i.e., design patterns) about the implementation of relevant features. The
users provide feedback on usage success of the implemented features. This information
could be shared with other members of the ecosystem, notably, data and service pro-
viders to improve data quality and services.
Data Consulting
Data Solutions Users
provider company
Usage
Feed-
Data Data Resource know-
Patterns back
services ledge
Infra &
Service tools Sage
provider provider
Data and service value
Fig. 1: Actors of the knowledge sharing in the BI ecosystem.
The main concepts of the knowledge sharing in the BI ecosystem are defined in Fig-
ure 2. It is assumed that users (e.g., municipalities) have some data processing and
analysis needs expressed as goals. A BI solution is to be provided or updated to support
fulfilment of these goals. It consists of various components as prescribed by various
41
reference architectures. Examples of the components are data storage, reports, dash-
boards and ETL activities. These components operate with data items. The differentia-
tion between data and information is made. The former refers to raw data supplied by
data providers and the latter represents information created within the BI components
by means of processing. Information is directly applicable in business processes and
decision-making. It is expected that information needs are relatively similar within the
ecosystem while data sources and raw data could be quite variable.
Knowledge of using BI and data analytics is represented as patterns. The patterns
are rated according to feedback obtained during usage of the BI solution. The feedback
might be qualitative such as users’ ratings or quantitative such as process efficiency
measurements. The quantitative evaluation is supported by key performance indicators
(KPI) defined according to the goals. Members of the ecosystem use the aggregated
feedback to identify appropriate BI application cases. The patterns are assumed as open
within the ecosystem while data and information can be either open or proprietary. That
influences the ability to reuse patterns and a degree of configuration required.
class ConceptsView
Describes
Rating Feedback Pattern
value
0..* 0..*
0..* 0..*
Describes usage Design
1 according to
KPI BI solution BI components 0..* Item Open Item
1..*
Support
Measures
Goal Information Processing Processed Data
Needed Obtained by by
1..*
0..* 1..* 1..* 1..* 1..* 1..*
Fig. 2: Key concepts of the BI ecosystem with knowledge sharing.
The knowledge of using BI and data analytics is represented in a form of patterns
metamodel (Fig. 3). Metamodeling has been recognized as suitable way of describing
patterns completely and consistently [18]. Metamodel was developed based on existing
identification of pattern components [17],[18] by adding additional components spe-
cific for knowledge sharing patterns such as Guidelines for pattern application and us-
age and Feedback for performance indicators retrieval. The problem and context are
defined in a formalized manner as goal models and context models, respectively [18].
That allows users to match their business requirements and context to knowledge stored
in the pattern repository.
The solution can be represented in various formats depending on the type of prob-
lem. It can be a data structure for data storage purposes, a query for data retrieval pur-
poses, data mining algorithm for data analysis purposes, dashboard design for data
42
presentation purposes or analytical model (e.g., in XML format) for analytical pur-
poses.
Fig. 3: Knowledge sharing patterns metamodel
3.2 Technical Architecture
The BI and data analytics solution consists of typical components characteristic to this
type of solutions [6]. It is expanded by adding components responsible for requirements
and knowledge management (Figure 4).
The data capture module is responsible for extraction of raw data from their sources.
The data forwarding component is responsible for channeling data for further pro-
cessing and performing data transformations. Classical ETL processing can be used as
well as data streaming for real-time processing. There are various ways for storing data
including dimensional data storage, data cubes and data lakes. These types are com-
bined according to the business needs. The computational module is responsible for
computationally intensive data processing and calculations. The data analytics module
concerns interactive presentation and analysis of data. The data analysis can be per-
formed using data coming directly from the forwarding module or from the storage.
The additional components to support knowledge sharing are the requirements and
knowledge management modules. The requirements management module is used to
specify customer’s goals and context. That is used to find appropriate BI usage
knowledge maintained by the knowledge management module. Patterns provided by
the knowledge management module are used to setup up the BI solution. The patterns
used are registered in the knowledge management module to enable accumulation of
customers’ feedback. Semantic consistency of the goal and context definitions as well
as pattern definitions should be ensured.
43
Business intelligence and data analytics solution
Data
Data capture Data storage
forwarding
Administration
and monioring
Data Data
analytics compute
Configure
Requests
Requirements Provides
Knowledge
management Registers
management
Feedback
Fig. 4: Components of BI solution with knowledge sharing support.
4 Sample Application Cases
Several BI application cases are identified and tentatively formulated as patterns. These
cases arise in municipalities pioneering BI application is these areas and are assumed
as candidates for reuse in other municipalities. Cases were identified together with IT
company which is developing software for municipalities and is aware of municipali-
ties’ needs for analytical solutions. Reusable BI components are identified for each case
as building blocks which can help for other municipalities to build similar BI solutions.
Patterns are described in tabular form and follow the structure of Knowledge sharing
patterns metamodel given in figure 3. Identification of BI application cases also serves
as the preliminary validation of knowledge sharing patterns metamodel. Patterns struc-
ture was initially validated by software development and business analysis experts from
IT company by evaluating patterns structure from both – software development and
implementation and business analysis adequacy perspectives.
4.1 Road Maintenance Case
Municipalities are responsible for maintenance of local roads including winter mainte-
nance. Tracking of maintenance activities is a complex task and touches multiple con-
cerns such as cost efficiency, environmental impact and road safety. Reporting solution
has been developed for one of the municipalities and pattern has been defined specify-
ing this solution (Table 1).
Table 1: Pattern defining BI component for providing winter road maintenance overview.
Item Description
Goal To know status of winter road maintenance work performed
44
To calculate cost of winter road maintenance work performed
To calculate environmental impact of winter road maintenance
work performed
To summarize customer feedback
Context Road network; Climate conditions
Solution Information: km traveled, km plowed, km de-ice, liters of de-icing
liquid consumed, kg of salt consumed, customer feedback
Data: GPS data, de-icing liquid tank sensors, customer feedback
BI component: Winter road maintenance overview dashboard
(could be provided in a machine readable format).
Feedback KPI: km plowed; KPI: liters of de-icing liquid consumed; KPI:
number of customer complaints
Guidelines 1. GPS data should be mapped to GIS data
2. De-icing liquid tank sensors are not always available and
could be substituted by number of refills.
Reusable BI 1. Calculation algorithms
building 2. GPS to GIS data mapping
blocks 3. Sensor data gathering and processing
4. Winter road maintenance overview dashboard components
5. Changes in Municipalities Business data model (new attrib-
utes etc.)
6. Changes on BI Dimensions model (new facts in facts tables,
new relationships etc.)
The goal and context can be represented using models as described in [21]. The so-
lution can be specified using data processing and analytics standards or widely used
formats, for instance, the dashboard can be represented using Grafana JSON model1.
Other municipalities search the pattern repository and might find this pattern useful. If
that is the case, usage guidelines are followed to configure the BI solution for the new
application case.
4.2 Pubic Services Applications Delivery Analysis Case
Municipalities are responsible about public services delivery to their citizens. Different
channels are established for citizens applications handling, services delivery and con-
sultations. Traditional channels can include face-to-face contact, telephone or postal
mail. Digital channels encompass websites, mobile-based services and public access
points such as kiosks. Each channel effectiveness measurements are needed to meet
legalization requirements and resources planning (citizens service centers locations,
working hours etc.). Data can be used also for cities scoring2. Reporting solution has
been developed for one of the municipalities and pattern has been defined specifying
this solution (Table 2).
1
https://grafana.com/docs/reference/dashboard/
2
https://www.boston.gov/cityscore
45
Table 2: Pattern defining BI component for providing public services delivery overview.
Item Description
Goal To know amount of delivered public services (per each service and
channel)
To know amount of received services applications and consulta-
tions on each channel
To measure public services that are delivered on time
To calculate cost of one service application handling and consul-
tation on each channel
To plan citizens service centers locations and working hours
To summarize customer feedback
Context Citizens service centers locations; Citizens movement
Solution Information: number of delivered services on each channel, public
services delivery time (defined and actual), public services deliv-
ery costs, citizens flow, customer feedback
Data: E-services platforms data, call centers data, queue machines
data, accounting data, customer feedback, cameras
BI component: Public services delivery dashboard, API for data
sending to government centralized Public services platform and
other sources (as municipality website)
Feedback KPI: delivered public services (per service, per channel); KPI:
public service delivery costs (per service, per channel); KPI: num-
ber of public services delivered on time; KPI: number of customer
complaints
Guidelines 1. Camera data should be mapped to approx. numbers of citizens
in different locations on different times
2. Public services delivery data from different channels and data
sources must be aggregated in unified format
3. Statistics data must be sent to Public services platform once
per year
Reusable BI 1. API for data sending to Public services platform
building 2. Changes in Municipalities Business data model (new attrib-
blocks utes etc.)
3. Changes on BI Dimensions model (new facts in facts tables,
new relationships etc.)
4. Data aggregation algorithms for camera data
5. New dashboards
4.3 Spatial Analysis of Municipal Investments Case
Each Municipality of Latvia vary in area and population which leads to different pop-
ulation density both between municipalities and within different territorial units of one
municipality. Municipalities have to invest financial resources to evolve road infra-
structure, recreation infrastructure and to create territorial improvements. In the same
time municipalities gain territorial income from real estate taxes, personal income
46
taxes, rent for land and buildings owned by municipality and company taxes. Munici-
palities want to determine investments efficiency, by territorially comparing invest-
ments made against the income received. This analysis can further help to plan territo-
rial investments. Reporting solution has been developed for one of the municipalities
and pattern has been defined specifying this solution (Table 3).
Table 3: Pattern defining BI component for spatial analysis of municipal investment.
Item Description
Goals Geospatially display data of financial resources investments made
by the municipality
Geospatially display data of income from taxes and rent
In Geospatial Information System (GIS) split municipality terri-
tory in smaller analyzable units (parish authorities, villages, pop-
ulated areas etc.)
Calculate Return on Investment (ROI) index for each analyzable
unit
Context Citizens population in municipalities territorial unit
Solution Information: citizens population in each territorial unit, financial
resources investments, income from taxes and rent
Data: Data from municipalities financial systems (rent and real es-
tate tax incomes, planned and made investments in territorial
units), anonymized data on the amount of Personal Income Tax
residing in the planning territorial units from State Revenue Ser-
vice data warehouse, data from municipality population account-
ing system
BI Component: Investment, income and calculated ROI data dis-
played in GIS
Feedback KPI: Financial resources investments per territorial unit; KPI: In-
come (taxes, rent) per territorial unit; KPI: Citizens population
density per territorial unit; KPI: ROI index per territorial unit
Guidelines 1. Municipality’s territory in GIS should be splatted into smaller
analyzable units
2. ROI should be calculated in annual terms, because rent and
taxes incomes can vary depending on season
Reusable BI 1. ROI calculation algorithm
building 2. Displaying investment, income and ROI data in GIS
blocks 3. Data extraction from municipality systems
4. Changes in Municipalities Business data model (new attrib-
utes etc.)
5. Changes on BI Dimensions model (new facts in facts tables,
new relationships etc.)
47
5 Future Work and Conclusion
The preliminary version of the knowledge sharing BI and data analytics ecosystem has
been proposed in the paper. Several application scenarios for municipalities are identi-
fied jointly with a BI consulting company. The scenarios show that knowledge sharing
is important for the municipalities and patterns need to be configured to fit particular
data and enterprise architecture of individual municipalities.
The conceptual model will be further elaborated and the technical solution will be
developed in the framework of the research and innovation project. Additional BI ap-
plication scenarios will be identified jointly with the IT company stakeholders and mu-
nicipalities, BI application patterns will be defined and potential for reuse will be iden-
tified. The analytical cycle will be proceeded by the build and evaluation cycles, where
the main emphasis will be devoted to feasibility of the pattern evaluation feedback loop.
The knowledge sharing mechanisms will be piloted with participating municipalities
and field observation will made of functioning of the ecosystem. More thorough vali-
dation of knowledge sharing patterns metamodel and BI ecosystem for municipalities
will be carried out during the practical development, implementation and evaluation of
this project.
Acknowledgment
The research leading to these results has received funding from the project "Compe-
tence Centre of Information and Communication Technologies" of EU Structural funds,
contract No. 1.2.1.1/18/A/003 signed between IT Competence Centre and Central Fi-
nance and Contracting Agency, Research No. 1.1 "Analytical Data Warehouse Design
Framework for E-government".
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