1. Business intelligence strategies for small and medium enterprises are becoming more viable due to trends like cloud computing, open source solutions, and mobile access. These solutions allow SMEs to implement BI solutions at lower costs and with greater flexibility compared to traditional enterprise implementations.
2. Key criteria for a successful BI implementation at a SME include aligning the project with business needs, starting small and growing solutions iteratively, ensuring ease of use, reducing total costs of ownership, and designing flexible systems that can adapt to changing business requirements.
3. SMEs have various BI solution options - traditional on-premise systems, open source tools, cloud/SaaS offerings, and integrating operational data analytics into business processes
data mining, data preprocessing, data cleaning, knowledge discovery, association, classification, clustering, introduction, why data mining, application
data mining, data preprocessing, data cleaning, knowledge discovery, association, classification, clustering, introduction, why data mining, application
This is a presentation in a meetup called "Business of Data Science". Data science is being leveraged extensively in the field of Banking and Financial Services and this presentation will give a brief and fundamental highlight to the evergreen field.
SITEC eCommerce Class 4
Title: Online Payment
Presenter: Chan Kok Long, Executive Director (iPay88)
Date: 11 August 2015
Venue: The Canvas @ Damansara Perdana
Implementing the Data Maturity Model (DMM)DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s Data Management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current-state Data Management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational Data Management and Data Management Maturity
Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
Discuss foundational DMM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Arne Rossmann outlines why the Business Data Lake works and which Services the Business Data Lake should provide. Organizations can use the Business Data Lake concept best when they standardize, industrialize and innovate.
Presented by Arne Rossman, Capgemini Germany, at the OOP Conference, 31 January 2017
Prepared as an interactive session focuses on participating in shifting the paradigm toward learner-centered curriculum delivery. The key factors for adult education are maturity, accumulation of experience; readiness for learning; being problem vs. subject centered; intrinsic vs. extrinsic motivation; and curriculum that is anchored as problem-centered rather than content-oriented. Where there is a challenge achieving true innovation, this paper and the planned interactive session offers a series of framework based on Porter's idea on Value Chain Analysis to identify factors which leverage activities along a "chain" of delivering an educational service to self-directed learners. Specifically, course design for online delivery is the scenario for open dialog on shifting the paradigm. This paper contains diagrams for discussion slides that introduce the topic and support open dialog among practitioners at the TCC conference 2011
Big Data Analytics and its Application in E-CommerceUyoyo Edosio
Abstract-This era unlike any, is faced with explosive
growth in the size of data generated/captured. Data
growth has undergone a renaissance, influenced
primarily by ever cheaper computing power and
the ubiquity of the internet. This has led to a
paradigm shift in the E-commerce sector; as data is
no longer seen as the byproduct of their business
activities, but as their biggest asset providing: key
insights to the needs of their customers, predicting
trends in customer’s behavior, democratizing of
advertisement to suits consumers varied taste, as
well as providing a performance metric to assess the
effectiveness in meeting customers’ needs.
This paper presents an overview of the unique
features that differentiate big data from traditional
datasets. In addition, the application of big data
analytics in the E-commerce and the various
technologies that make analytics of consumer data
possible is discussed.
Further this paper will present some case studies of
how leading Ecommerce vendors like Amazon.com,
Walmart Inc, and Adidas apply Big Data analytics in
their business strategies/activities to improve their
competitive advantage. Lastly we identify some
challenges these E-commerce vendors face while
implementing big data analytic
In today’s competitive world, every business has to fight huge competition to achieve success. So it is necessary for every business organization to collect large amount of information like employee’s data, Sales data, customer’s information, market analysis reports, etc.
MEDICI’s new ‘Indonesia FinTech Report 2021’ analyzes the country’s FinTech sector and trends in the last three years—a deep-dive by segments & subsegments, funding patterns, M&As, ecosystem partnerships, industry drivers, and perspectives drawn out of regulatory, geopolitical, economic, and market dynamics.
Master Data Management - Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) can provide significant value to the organization in creating consistent key data assets such as Customer, Product, Supplier, Patient, and the list goes on. But getting MDM “right” requires a strategic mix of Data Architecture, business process, and Data Governance. Join this webinar to learn how to find the “sweet spot” between technology, design, process, and people for your MDM initiative.
Top Data Mining Techniques and Their ApplicationsPromptCloud
In this presentation we have covered why data mining is important and various techniques used for data mining. Apart from that, examples of applications have been given for each technique. This presentation also explains how an enterprise can source web data via crawling services to bolster data mining models.
Business intelligence for SMEs with Data AnalyticsTzar Umang
Know the importance of Data on your business, how it can shape up your Business Operations and day to day decision making from a narrative representation of your Data.
Webinaarin nauhoitus osoitteessa http://youtu.be/7vmMNMbPIMo
Vilkkaan verkkokauppoihin saumattomasti liittyvä, pilvestä toimitettava Nosto automatisoi tuotesuosituksien esittämisen yksilöllisesti jokaiselle asiakkaalle.
Noston teho perustuu siihen, että jokaiselle asiakkaalle esitetään kohdennetusti juuri häntä kiinnostavia ajankohtaisia tuotteita, mikä parantaa verkkokaupan kaupallista tehoa kuten konversiota ja keskiostosta. Noston avulla verkkokauppa voi näin ollen myös tuottaa asiakkailleen henkilökohtaisen, aikaisempaa miellyttävämmän ostokokemuksen sekä tavoittaa uudella vaivattomalla tavalla uskolliset asiakkaat.
Webinaarissa tutustuttiin tarkemmin miten ja miksi Nostoa kannattaa hyödyntää esittelemällä "best practicet" sekä näiden käyttökohteet.
Myös seuraavaa käytiin läpi:
-Nosto-tilin ja palvelun avaus Vilkkaan hallintaliittymän kautta
-Noston perusominaisuuksien lisäys, hallinta ja sijoittelu uudelleen
Vilkkaan hallinnassa
- Uusien suosituselementtien sijoittelu
- Noston hallintaliittymän käytön perusteet
- Mistä eri moduleista Noston suosituselementit koostuvat
This is a presentation in a meetup called "Business of Data Science". Data science is being leveraged extensively in the field of Banking and Financial Services and this presentation will give a brief and fundamental highlight to the evergreen field.
SITEC eCommerce Class 4
Title: Online Payment
Presenter: Chan Kok Long, Executive Director (iPay88)
Date: 11 August 2015
Venue: The Canvas @ Damansara Perdana
Implementing the Data Maturity Model (DMM)DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s Data Management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current-state Data Management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational Data Management and Data Management Maturity
Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
Discuss foundational DMM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Arne Rossmann outlines why the Business Data Lake works and which Services the Business Data Lake should provide. Organizations can use the Business Data Lake concept best when they standardize, industrialize and innovate.
Presented by Arne Rossman, Capgemini Germany, at the OOP Conference, 31 January 2017
Prepared as an interactive session focuses on participating in shifting the paradigm toward learner-centered curriculum delivery. The key factors for adult education are maturity, accumulation of experience; readiness for learning; being problem vs. subject centered; intrinsic vs. extrinsic motivation; and curriculum that is anchored as problem-centered rather than content-oriented. Where there is a challenge achieving true innovation, this paper and the planned interactive session offers a series of framework based on Porter's idea on Value Chain Analysis to identify factors which leverage activities along a "chain" of delivering an educational service to self-directed learners. Specifically, course design for online delivery is the scenario for open dialog on shifting the paradigm. This paper contains diagrams for discussion slides that introduce the topic and support open dialog among practitioners at the TCC conference 2011
Big Data Analytics and its Application in E-CommerceUyoyo Edosio
Abstract-This era unlike any, is faced with explosive
growth in the size of data generated/captured. Data
growth has undergone a renaissance, influenced
primarily by ever cheaper computing power and
the ubiquity of the internet. This has led to a
paradigm shift in the E-commerce sector; as data is
no longer seen as the byproduct of their business
activities, but as their biggest asset providing: key
insights to the needs of their customers, predicting
trends in customer’s behavior, democratizing of
advertisement to suits consumers varied taste, as
well as providing a performance metric to assess the
effectiveness in meeting customers’ needs.
This paper presents an overview of the unique
features that differentiate big data from traditional
datasets. In addition, the application of big data
analytics in the E-commerce and the various
technologies that make analytics of consumer data
possible is discussed.
Further this paper will present some case studies of
how leading Ecommerce vendors like Amazon.com,
Walmart Inc, and Adidas apply Big Data analytics in
their business strategies/activities to improve their
competitive advantage. Lastly we identify some
challenges these E-commerce vendors face while
implementing big data analytic
In today’s competitive world, every business has to fight huge competition to achieve success. So it is necessary for every business organization to collect large amount of information like employee’s data, Sales data, customer’s information, market analysis reports, etc.
MEDICI’s new ‘Indonesia FinTech Report 2021’ analyzes the country’s FinTech sector and trends in the last three years—a deep-dive by segments & subsegments, funding patterns, M&As, ecosystem partnerships, industry drivers, and perspectives drawn out of regulatory, geopolitical, economic, and market dynamics.
Master Data Management - Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) can provide significant value to the organization in creating consistent key data assets such as Customer, Product, Supplier, Patient, and the list goes on. But getting MDM “right” requires a strategic mix of Data Architecture, business process, and Data Governance. Join this webinar to learn how to find the “sweet spot” between technology, design, process, and people for your MDM initiative.
Top Data Mining Techniques and Their ApplicationsPromptCloud
In this presentation we have covered why data mining is important and various techniques used for data mining. Apart from that, examples of applications have been given for each technique. This presentation also explains how an enterprise can source web data via crawling services to bolster data mining models.
Business intelligence for SMEs with Data AnalyticsTzar Umang
Know the importance of Data on your business, how it can shape up your Business Operations and day to day decision making from a narrative representation of your Data.
Webinaarin nauhoitus osoitteessa http://youtu.be/7vmMNMbPIMo
Vilkkaan verkkokauppoihin saumattomasti liittyvä, pilvestä toimitettava Nosto automatisoi tuotesuosituksien esittämisen yksilöllisesti jokaiselle asiakkaalle.
Noston teho perustuu siihen, että jokaiselle asiakkaalle esitetään kohdennetusti juuri häntä kiinnostavia ajankohtaisia tuotteita, mikä parantaa verkkokaupan kaupallista tehoa kuten konversiota ja keskiostosta. Noston avulla verkkokauppa voi näin ollen myös tuottaa asiakkailleen henkilökohtaisen, aikaisempaa miellyttävämmän ostokokemuksen sekä tavoittaa uudella vaivattomalla tavalla uskolliset asiakkaat.
Webinaarissa tutustuttiin tarkemmin miten ja miksi Nostoa kannattaa hyödyntää esittelemällä "best practicet" sekä näiden käyttökohteet.
Myös seuraavaa käytiin läpi:
-Nosto-tilin ja palvelun avaus Vilkkaan hallintaliittymän kautta
-Noston perusominaisuuksien lisäys, hallinta ja sijoittelu uudelleen
Vilkkaan hallinnassa
- Uusien suosituselementtien sijoittelu
- Noston hallintaliittymän käytön perusteet
- Mistä eri moduleista Noston suosituselementit koostuvat
A fresh look at clearly defining your marketing strategy by defining the 3 Ps of Price, Product & Promotion.
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By using a facilitator for your next strategic planning session you'll get better inputs, better information and a better use of time and money towards creating an implementable strategy. Call SME Strategy to facilitate your next strategic planning session, or strategy event. http://smestrategy.net/services/meeting-facilitation/
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Wanbury Deploys 1KEY BI as an efficient reporting tool so that it could have a clear view of the performance of its departments.
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2. Table of content:
1. General Overview : why mid-market companies are adopting BI
Traditional view of BI
Implications for SME’s
What makes BI available for SMEs?
2. Criteria for the successful implementation of BI
3. Solutions available in a market
Traditional BI / In-house
Open Source
On Demand/In the cloud
Operational Data Analysis
4. Conclusion
3. Traditionally Business Intelligence was overviewed as a process, tools and technologies required to
turn the data into information and information into knowledge and knowledge into plans that drive
the actions that improve business performance1.
The structure of Business Intelligence was different from one company to another, in this
iness
report I will present a basic structure of Business intelligence as it used to be in the past. It
consisted of 4 layers: on the bottom we have the Data Infrastructures-this is where the
this w
information is stored-information and data from operational systems is collected and
information
stored in this layer. Most of the BI software products and vendors will subscribe to the data
warehouse approach as a part of their data environment – in fact they will insist that it’s
mandatory.
The next layer is Data integration It is the layer where data is turned in to information by
integration.
extraction and integration processes, unfortunately in some cases information is generated
through different types of processes The data can be collected directly from
processes. ta
operational/transactional data sources or from Data warehouses. This will create errors in
.
data sources.
Once the information in a data warehouse th
ormation then information can be analyzed and
interpreted, usually it happens by creating algor
algorithms.
4. If the company wants to do more than just simple reporting, then they will need to build
Online Analytical Processing cubes: a multi dimensional view of information that allows
users to drill the data. The problem with such an approach is that we need to know the
parameters of the cube before building it. If the parameters are not identified early, new
features will need to be added in order to build the OLAP process again2.
The next level of the BI framework diagram above refers to the analytical environment,
which is a domain of business users who use the analytical tools to query, report, analyze,
mine, visualize and most importantly act on the data. Analytical tools allow business users
to either view a report as a static document or navigate the report (search, drill down…) to
change the view of the level of detail they see.
These types of processes were limiting Business Intelligence to the types of organization
which could an implement this kind of infrastructure.
Implications for the SMEs
Why business Intelligence was not available for SMEs3?
• High Pries
Prices were too high; many companies could
not even afford to enter the BI scene.
• Low flexibility
Once the BI is implemented, it was almost
impossible to change the criteria’s or the
structure of BI within a reasonable time;
however the business needs are changes very
frequently.
• Large Infrastructural Requirements
Requirements of infrastructure were
dependant on an organization’s ability to
invest in its IT infrastructure.
• Time implementation consideration
According to Gantry group, 83% of BI
implementations take 6 months or more.
• Limited number of solutions available
All available solutions were coming from big corporations
5. What makes BI available for SMEs?
Trends affecting Business Intelligence availability for SMEs4
• Collaboration
Increasing ability of use of
technology within the
organization
• Competition
Due to the high barriers of
entering into the BI
market, new players
targeting the SMEs
segment entered the
market
• Integrated Applications
The integration of analytics
in operational solutions
• Power to the People
Increased availability of BI
tools for non-technical people
• The need for Speed
Relatively new technology in In-memory analytics enabled SMEs to run what-if
scenarios, while putting large data sets into the hands of users in a manageable and
easily navigable manner
• Cloud BI
The ability to implement BI without heavy IT architecture
• Open sources mainstream
Developing “developing tools” that were user-friendly. The reduced the need for
having developers in-house
• Fast food BI
On-demand offerings becoming more attractive as low cost solutions
• Visualize it
The visualization capabilities made BI more attractive and increased the motivation
to expand the use of BI towards graphic means of understanding data
• Social Media5.
Companies started looking into BI from not only internal sources but externally as
well. Social networks are a good example, as contemporary social networking affects
businesses greatly. Companies are therefore moving toward social activities and
bringing the concepts of social networks in-house
6. 2. Criteria for the successful implementation of BI for SME’s
The choices available to companies looking to implement BI, reporting and analysis to their
operations are vast. There are virtually hundreds of products offering BI solutions online
and most of them are technically similar.
The recent innovations in technology made these solutions available to SMEs. However,
before starting to invest in BI it is important to understand where to start and what to look
for in order to make sure that the companies make the best investment decisions.
There are six critical factors to consider when delivering successful Business Intelligence
solutions in the SME market.
1. Aligning business and IT
This is one of the most important points which are usually overlooked. Top executive
business sponsors have to be assigned for project implementations. As the sources online
provides6: The well-designed BI solutions provide little business value if no one use them.
The role of the Business sponsor is to act as an agent between users and IT to promote
Information Culture to get users to switch from old to new ways of making decisions and
analyzing information.
2. Start Small, think Large
It is wise to take a deliberate approach to implementing BI. Identify biggest issues in an
organization and start the initiative from there, or organizations can identify the area
which benefit from a first – time BI deployment. For example, according the research made
by Aberdeen group Customer Services, Finance and Sales departments are the most
demanding BI solutions departments in organizations.
Research consistently shows7
that the new BI projects that
identify their values in the early
stage of projects are more likely
to succeed than those given a big
budget to start. That is why it is
suggested to start small and
grow from there.
Although BI should be viewed as
an ongoing program rather than
a one-off project, the deployment
of individual solutions should be
done iteratively.
Moreover, this approach will
make it easier to demonstrate
the value and financial return of
BI and securing funds for the
similar projects in the future.
7. 3. Time concern for the whole solution.
Companies should look at the time required to implement a complete working solution: the
one which make it possible for business users to answer business questions. Usually there
are 2 parameters to measure 1: time when the first application deployed to an end user. 2:
time for the total deployment of the entire project.
The table below illustrates how organizations manage large quantities of data sources and
the effect they have on the time of completion of BI project versus the experiences of mid-
sized and smaller companies.
4. Ease of use
As I mentioned earlier, companies can design and build the most sophisticated BI solutions.
However, if the system is difficult to use then the investments on this project will bring no
value. Due to the lack of measurement standards to identify the level of complexity of
systems, easy-of-use criteria are often overlooked during the selection of technology and BI
systems6.
Companies needs to consider what it will take for their employees to use the systems they
deploy. One of the ways to measure the complexity of a system might be the time it takes a
business manager to learn to use the product. Alternatively, the recommendations from
other customers of the same system measured by the time it took to train the customers to
use the system. Finally, I believe it is important to measure the time of learning and
developing new application with this tool by power-user or IT and Business analyst.
5. Reduce the total cost of ownership
The total cost of ownership includes many costs of implementing the solution, in addition
to the cost of the software and a license. Companies need to consider Cost of Data
integration as the cost of linking the data with BI might be costly depending on how easy it
is to get an access to the data over the time period.
Secondly, the cost of implementation might depend on the processes behind the technology
and how the technology interpreted the data. If the solution is used OLAP cubes, the total
cost of ownership might be higher as the future changes in reporting and analytical
parameters will bring similar costs in developing new OLAP cubes.
8. Finally, time consumption is one of the main factors that affect the total cost of ownership.
Companies have to recognize that the time to achieve the first complete workable
application.
6. Flexibility and Adaptability
Business practices have proven that questions asked by business people today will be
different than in the future, as the business environment is dynamic and always evolving.
Therefore, mid-sized companies face big challenges in designing and managing BI systems
that constantly change to adapt to the business needs of their immediate environment6.
According the Gantry Group survey 75% 6of all analytical applications needs to be modified
within 6 months of their initial deployment. The ability to change and modify BI systems as
quickly as business needs require will determine the success of the project.
3. Solutions available in a market for SME’s- what are your Options?
By considering all of the above-mentioned factors, organizations can choose one of the
currently available solutions in the market
The first solution is the Traditional or In-house solution.
These refer to general BI solutions that target SMEs and they include:
• Web-based options that can be downloaded from internet
• Role-based offerings that follow the same principles as traditional BI
implementations. However, these solutions will target a specific type of end user,
thus enabling SMEs to implement solutions that target particular areas of their
organization; like sales/finance department etc.
• Data warehouse appliances with reporting/dashboarding solutions.
The benefit of using this type of solution is that the trial versions and free offerings may
exist online. For example open – source solutions are increasing relevant to BI and the
vendors across the market compete by creating free versions of software to gain market
share. The other benefit of traditional BI is to work towards an organization-wide approach
to BI which means the organizations can build and develop solutions required by the
business needs of the organization without any limitation by the third parties.
Furthermore, there is general flexibility for the organizations to build BI to suit
organization specifications.
However, companies has to consider that the time taken to implement some solutions are
quicker than others, cost of internal sources required, the company might consider
employing more resources, not only to build the solution but to maintain it as well.
Second solution is Open Source solution9
Open source BI offers free codes to developers to implement their own solutions onsite for
free. These solutions are:
• Community based
• Low to no cost (includes developer cost only)
9. • Enterprise solutions available for traditional implementation but with OS pricing
models. Currently large vendors provide open source solutions where organizations
can adapt the solution to the company’s needs by changing open source code and
this does not require a developer’s expertise in-house. Therefore the company’s
external IT department can modify and adapt the open source solution.
The benefit of open source solutions are that user friendly products are developed to stay
competitive in a market. As software and solutions are developed by a community, there is
a feedback and testing available during the implementations from the multiple developers
working on the same project within other organizations.
Third solution is On Demand & In The Cloud10
These solutions are offered as a service vendor hosts. The vendors keep customer data at
their data center or in the cloud. These include:
• Dashboards
• Embedded/Operational analytics
• Data
• Report
Benefits are a quick to implement, because in many cases, as data has been transferred, the
solutions are turned on. There is no in house management required and generally very low
cost is incurred during the delivering process as the solutions and the costs are
subscription-based.
However companies have to consider some disadvantages as well. One of them is long term
cost. On the one hand, companies do not face huge costs during the deployment of the
solution; on the other hand, the long term cost might be larger to compared with other
types of solutions. The other disadvantage is limits in customization and expansion from
the vendor side. Will the vendor have the ability and sources to allocate the future demands
and specifications? In addition, the most concerned point is the security. Many
organizations still prefer to protect their internal information which might not be
confidential when exposed to the fragilities of third party clouds.
Forth solution is Operational Data Analysis11
Organizations have started looking for ways they analyze the data differently and the ways
they access BI differently. Companies no longer want to look at the historical approach,
they want to be able to analyze information immediately in order to identify what is
happening, to be able to predict what will happen and act on this information. This
approach requires new ways of delivering BI solutions
• SME’s may choose to use dashboards and analytics to access operational data to
perform “real-time” analysis.
• This provides a different and newer ways of looking at BI usage while moving away
from a historical view of the reporting and analysis.
The benefit of this type of solutions is the ability to monitor business activities in a timely
fashion. To be able to react real-time and to be able to collaborate what is going on. This
approach will work in integrating of BI with business processes.
10. There are some considerations to consider; for example, Integration requirements with
various operational systems and expansions towards organization-wide BI.
In Conclusion, after the looking through the factors to consider and solutions available on
the market, the benefit and disadvantages, it can be seen that the strategy of successful
implementation of BI solutions really depends on organizations objectives, needs and
available sources. But there is no doubt that for the SME’s competing in an increasingly
data-driven market, the ability to access and process information with speed and accuracy
is critical. Whether in recessionary times or not, it is increasingly important for the SME’s
to efficiently aggregate information from a wide variety of sources.
Ultimately, making effective use of BI will be the hallmark of the well-managed SMEs,
demonstrating that it is an enterprise that can always answer crucial business questions,
ensuring timely, accurate, information-based business decisions12.
11. References:
1. Data or Process Centric Approach to Decision Making:
bif.seenews.com/_c/Files/BifArchive/Files/DimitarDimitrov_en.pp [Accessed on 31.03.11]
2. Advantages and Disadvantages using multidimensional cubes OLAP:
http://businessintelligence.ittoolbox.com/groups/vendor-selection/bi-select/advantages-
and-disadvantages-of-multidimensional-cubes-4222[Accessed on 31.03.11]
3. DM Review Midmarket Business Intelligence Survey Results: http://www.information-
management.com/news/10001933-1.html[Accessed on 31.03.11]
4. The Top Ten Trends in Business Intelligence:
www.ecrmguide.com/insights/article.php/11225_3907576_2[Accessed on 31.03.11]
5. When Business Intelligence and Social Networking Unite:
http://www.philsimonsystems.com/blog/technology/business-intelligence/bi-social-
networking/[Accessed on 31.03.11]
6. Criteria For Selecting a Business Intelligence, Reporting and Analysis Solution:
http://www.visualintelligence.co.nz/Content.php?article_uid=34d8da82-ac08-102c-b170-
0022191d1e2d[Accessed on 31.03.11]
7. Crawl-walk –run:
http://www.bigtrucktv.com/item.aspx?fo_liberto_crawl_walk_run&q=001[Accessed on
31.03.11]
8 . Aberdeen Group: Business Intelligence for SME’s:
http://www.slideshare.net/findwhitepapers/business-intelligence-for-the-small-to-
medium-sized-business[Accessed on 31.03.11]
9. Open source Business Intelligence: what are you
options?:http://www.ecemguide.com/insight/article.php/11225_3914651_1[Accessed on
31.03.11]
10. Advantages of Cloud BI: http://www.blackbox-bi.com/support/blogs/-
/blogs/advantages-of-cloud-bi?_33_redirect=http%3A%2F%2Fwww.blackbox-
bi.com%2Fsupport%2Fblogs%3Fp_p_id%3D33%26p_p_lifecycle%3D0%26p_p_state%3Dn
ormal%26p_p_mode%3Dview%26p_p_col_id%3Dcolumn-
1%26p_p_col_count%3D1[Accessed on 31.03.11]
11. Transforming operational data into business intelligence: http://www.misys.com/cds-
portlets/digitalAssets/7/5950_Misys_Business_Intelligence_Brochure.pdf[Accessed on
31.03.11]
12.VRSrivatsan:
http://www.bluelinkerp.com/newsroom/whitepapers/BusinessIntelligenceforSME_white
paper.asp[Accessed on 31.03.11]