Business Intelligence
and
Analytics
A Comprehensive Overview
Jack G. Zheng
Spring 2024 (V12)
http://zheng.kennesaw.edu/teaching/it7123
https://www.edocr.com/v/r4dg6mjr/
IT 7123 BI
Overview
1. What is business intelligence (BI) and analytics?
– Are they the same or different?
2. BI/Analytics process and technology
– BI/Analytics as an information and decision process
– BI/Analytics as a computing and information system
3. BI evolution and trend: traditional BI and modern
BI
4. BI/Analytics education and career
2
This lecture notes provides a high-level overview of business
intelligence and analytics. This overview is comprehensive and covers
as many aspects as possible, but it keeps them at a high level. More
details are provided in more learning modules.
Sections
3
What is BI and Analytics?
• Are they the same or different?
• How about data analytics, business analytics, data science, big
data, etc.?
4
What is Business Intelligence?
Business Intelligence is a set of methods,
processes, architectures, applications, and
technologies that gather and transform raw data
into meaningful and useful information used to
enable more effective strategic, tactical, and
operational insights and decision-making.
5
Adapted from Forrester Report
“Topic Overview: Business Intelligence”, 2008
https://www.forrester.com/report/Topic+Overview+Business+Intelligence/-/E-RES39218
More BI from Forrester
https://www.forrester.com/business-intelligence
BI is an umbrella term for a set of methods, processes, applications, and
technologies that focus on analytical data processing, which includes data
gathering/ingestion, data storage, data analysis, reporting, and other tasks.
Business
• Does “business” only mean commercial entities and activities?
• Traditionally or narrowly speaking, “business” implies companies,
corporates, and their operations and commercial activities.
• The term “business” has become more general and represents many
types of entities and activities
– It refers to more like “activity” or “issue”
• BI/Analytics can be applied in all “business”
entities (organizations, functional areas or domains)
to drive “business” performance. These entities may include:
– Companies (for profit) and financially related
• Retail, manufacture, real-estate, financial, sports, media, advertising, entertainment,
healthcare, publication, energy, etc.
– Non-profit organizations, institutions, associations, communities, etc.
– Government: citizen service, city planning, crime, immigration, etc.
–
Individuals: personal health, exercise, learning, eating, power consumption,
etc.
6
Think of “business”
more like the “business”
in “mind your own
business!”
Data
•
Data is the source of insights and decision. The analysis is the essential part to transform data to information
and knowledge. Many times, the process is complex, as have to deal with different types of data and all kinds
of data use problems.
•
Different types of data
– Numeric vs. textual
– Structured vs. unstructured
– Standard format vs. proprietary format
–
Internal vs. external data, system stored vs. file-based data
– Raw fact data vs. simulated/forecast/estimated data
– Simple fact data vs. calculated metrics data
•
Common data use challenges
– Structured, unstructured, semi-structured
•
Information and knowledge management is the management of both structured data (15% of information) and unstructured data
(85% of information), according to the Butler Group.
•
80 percent of business is conducted on unstructured information (Gartner Group).
–
Information overloading
•
too much data and information with varied formats and structure
•
difficulty of data organization for effective access and retrieval
•
difficult to find useful information (knowledge) from them
• Multiple copies of data exists sometimes with conflicts
– Big data
• Variety, Velocity, Volume, Veracity https://www.ibmbigdatahub.com/infographic/four-vs-big-data
– Data everywhere
• Data in separate systems and different sources; internal and external
• Problem of spreadmart http://en.wikipedia.org/wiki/Spreadmart
• Over 43 percent of organizations have more than six content stores. (Forrester Research).
– Difficulty of access
• We may have that data, but we cannot access it (or difficult to get it), because of technical issues or administrative issues.
–
Lack of data
•
The data is simply not available.
•
The collection of data may need additional process and is costly.
7
DIKW and Intelligence
•
The DIKW hierarchy depicts relationships between data, information, knowledge (and wisdom).
– Data: raw value elements or facts
–
Information: the result of collecting and organizing data that provides context and meaning
– Knowledge: the concept of understanding information that provides insight to information, thus useful
and actionable
– Wisdom: the understanding of interactions and an integrated view, and the understanding of implications
and indirect results beyond a target domain.
•
The model can be loosely related to the levels of transactional processing (OLTP) and
analytical processing (OLAP)
8
Transactional
Processing
Analytical
Processing
For more extensive reading: http://en.wikipedia.org/wiki/DIKW_Pyramid
Different opinion: https://hbr.org/2010/02/data-is-to-info-as-info-is-not
Analytical Data Processing
Transactional Processing
• Focus on data item
processing (insertion,
modification, deletion),
transmission, and even some
non-analytical query
Analytical Processing
• Focus on queries,
calculation, reporting,
analysis, and decision
support
9
For a more detailed
comparison of OLTP and
OLAP:
https://techdifferences.com/di
fference-between-oltp-and-
olap.html
• Change product price.
•
Increase customer credit limit.
•
Import data from another source
• What are the top 10 most
profitable products?
•
Is there a significant increase
of operational cost?
Note: transactional and analytical
processing above refer to general
concepts; OLTP and OLAP also
refer to a specific type of
technology and system.
Two Types of Data/Information Processing
Narrowly speaking, intelligence comes from
data (facts), based on DIKW. In this sense,
BI focuses on analytical data processing.
Insights and Decision
•
Insights and decisions are the intelligence part of BI. Intelligence also represents the techniques
and methods.
•
Insights is a bit different from decision.
–
Insights is the deep understanding and comprehension of the “business” and the data. It may not directly
lead to actions.
– Decision is more actionable.
• Decisions can be made based on
– Facts, or data
– Simulation (models)
–
Intuition, perception, sense
– Group negotiation
• Problems in decision making
– Management/operation by intuition
– A gap between data and knowledge (useful information leading to a decision).
– Lack of effective feedback and alignment systems, no improvement cycles
– Need good analytical processing and models
• Evolving analytical needs in decision support
– Real-time, most recent data
– Business user driven, agile, instant
– Exploratory and interactive
10
Extended reading – DSS
Traditionally BI has been also understood as Decision
Support System (DSS) – known as data driven DSS (data
directly contributes to decision without intensive and
advanced analytical techniques).
Read about a brief history of DSS
http://dssresources.com/history/dsshistory.html
Performance
• A common goal of BI is to drive performance.
• Performance measures or indicators (known as
KPI, key performance indicators) are defined
and tracked using BI approaches and systems.
– https://kpi.org/KPI-Basics
• Different businesses have different kinds of
“performance”
– Financial performance
– Academic performance (institutional effectiveness)
– Public service performance
– Individual work performance
– Sports performance
11
Sample BI/Analytics Applications
•
Business management
–
Strategic planning
–
Performance management
–
Process intelligence
–
Competitive intelligence
–
Project and program management
•
Retail, marketing and sales
–
Customer behavior analysis
–
Targeted marketing and sales strategies
–
Customer profiling
–
Campaign management
–
Inventory management
•
Human resource/capital
–
HR analytics
–
Talent management
•
Workplace analytics
•
Data type specific
–
Text analytics
–
Location intelligence
•
Personal
–
Personal health, exercise, learning, eating, power
consumption, etc.
•
IT management
– Web analytics
–
App analytics
–
Security management
•
Supply chain and logistics
–
Supplier and vendor management
–
Shipping and inventory control
–
Fleet analytics
•
Financial sector
–
Portfolio management, stock analysis
–
Insurance
•
Government
–
City/region planning, urban analytics, crime, demographic,
–
Traffic management, power usage
–
Citizen service, immigration
•
Education
–
Learning analytics
–
Institutional research/effectiveness
–
Academic analytics
–
Student engagement and success
•
Other industry/sector-specific
–
Power and energy management
–
Healthcare management
–
Social analytics
–
Sports and games analytics
12
BI/Analytics can be applied in many “businesses” (functional areas, activities, or
domains) to drive “business” performance.
Evolution of BI/Analytics
• The evolution of BI resides in both “business” and “intelligence”
– Expansion of entities, domains, and users that use BI
– Evolution of processes, techniques, technologies and systems
13
1980s
Executive information systems (EIS), decision support systems (DSS)
1990s
Data warehousing (DW), business intelligence (BI)
2000s
Dashboards and scorecards, performance management
2010+??
Analytics, big data, data science, augmented BI, …
“With each new iteration, capabilities increased as
enterprises grew ever-more sophisticated in their
computational and analytical needs and as
computer hardware and software matured.”
Solomon Negash (2004), Business Intelligence, CAIS (13)
https://www.researchgate.net/publication/228765967_Busin
ess_intelligence
The search for the perfect “business
insight system”, from Performance
Dashboard, by Wayne Eckerson
http://download.101com.com/pub/td
wi/files/performancedashboards.pdf
Analytics
•
Analytics has emerged as a catch-all term for a variety of different
business intelligence (BI) and application-related initiatives. …it is
applying the breadth of BI capabilities to a specific content area. In
particular, BI vendors use the “analytics” moniker to differentiate
their products from the competition. Increasingly, “analytics” is
used to describe statistical and mathematical data analysis.
–
https://www.gartner.com/en/information-
technology/glossary/analytics
•
Analytics refers to a more systematical, automated, and flexible
process of data analysis for revealing insights and decision
support in more extensive application areas (beyond
organizational contexts), e.g. sports, disease, network traffic, etc.
–
http://pestleanalysis.com/differences-between-business-analytics-
and-business-analysis/
•
Analytics initially referred to advanced statistical modeling using
tools like SAS and SPSS. … Now, analytics refers to the entire
domain of leveraging information to make smarter decisions. In
other words, reporting and analysis.
–
The Evolution of BI Semantics http://www.b-eye-
network.com/blogs/eckerson/archives/2011/02/whats_in_a_word.
php
•
Analytics is geared more toward future predictions and trends,
while BI helps people make decisions based on past data.
–
Christian Ofori-Boateng
https://www.forbes.com/sites/forbestechcouncil/2019/06/21/data-
analytics-versus-business-intelligence-and-the-race-to-replace-
decision-making-with-software/
14
The Evolution of BI Semantics
http://www.b-eye-
network.com/blogs/eckerson/archives/2011/02/whats_in_a_wor
d.php
Analytics can be
viewed as the
evolved, expanded,
or improved BI
Depending on perspectives, Analytics
• is part of BI
• includes BI
• goes beyond (the traditional) BI
• = (the new) BI
Analytics or BI?
• We tend to call analytics rather than BI in the following scenarios. But their
processes and technologies are very similar.
• Non-(traditional) business activities such as
– Learning analytics: learning progress and performance
– Talent analytics: human resources
– Web/app analytics: web traffic or app usage analysis
– Sports analytics: gaming strategies and performance
•
Involving more varied data types
– Text analytics
– Multimedia analytics
• Non-organizational contexts; mainly based on public data and for public
communication.
– Social media analytics
– Election/voting analytics
•
Individual or small group data monitoring/analysis
– Personal health analytics
– Communication analytics
15
BI/Analytics and Related Terms
•
Data analytics = analytics
•
Business analytics
–
Analytics used mainly for business (company) contexts.
–
Business analytics (BA) is the practice of iterative, methodical exploration of an organization’s data with emphasis on statistical analysis and data
mining. Common analysis techniques include regression, forecasting, correlation, factor analysis, and others.
–
https://www.tableau.com/learn/articles/business-intelligence/bi-business-analytics
•
Data science
–
An interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms
–
Focus on advanced analytics and presentation models and methods
–
Using autonomous or semi-autonomous techniques and tools, typically beyond traditional BI to discover deeper insights, make predictions, or generate
recommendation.
–
A good data scientist = data hacker + programmer+ analyst+ coach+ storyteller+ artist (http://analyticsindiamag.com/data-science-the-most-desirable-
job-in-the-21st-century/)
–
“In some ways, data science is an evolution of BI.” https://www.linkedin.com/pulse/data-science-business-intelligence-whats-difference-david-rostcheck/
•
Knowledge management
–
Broadly speaking, intelligence, or knowledge, also comes from human experience and tacit knowledge, in various format like story, experience,
practices, image, video, etc.
–
In this sense, BI is also related to knowledge management (either BI under KM or vice versa)
http://capstone.geoffreyanderson.net/export/19/trunk/proposal/research/Knowledge_management.pdf
•
All these new terms try to differentiate them from the (traditional) BI. However, if one considers BI is a dynamic and evolving field, then all these
new terms can be viewed as extensions/expansions of BI; they all still fall under the umbrella of the general BI.
–
“In its more comprehensive usage, BI is all of the systems, platforms, software, technology, and techniques that are essentia l for the collection, storage,
retrieval, and analysis of data assets within a given organization.” – Dataversity 2015 Report on BI vs Data Science
16
More perspectives from the industry
• http://www.dataversity.net/distinguishing-analytics-business-intelligence-data-science/ and
https://www.slideshare.net/Dataversity/analytics-business-intelligence-and-data-science-whats-the-progression
• https://www.betterbuys.com/bi/business-intelligence-vs-business-analytics/
• https://solutionsreview.com/business-intelligence/data-science-vs-data-analytics-whats-the-difference/
• https://www.sisense.com/blog/whats-the-difference-between-business-intelligence-and-business-analytics/
https://www.slideshare.net/Da
taversity/analytics-business-
intelligence-and-data-science-
whats-the-progression
BI/Analytics Process and Technology
• BI/Analytics as an information and decision process
• BI/Analytics as a computing and information technology
17
Process and Technology
• BI/Analytics can be viewed both from the perspective of
process and technology
•
Information and decision process
– BI and analytics share similar process to transform data to
insights
– A process consists of multiple steps (or activities,
corresponding to capabilities), arrange in varied order
– Each process may be different depending on a number of
factors, including data sources, quality, analytical needs, etc.
• Computing and information technology
– The technology directly implements and supports BI
capabilities and activities.
– Technology can be in the form of applications, systems,
architectures, platforms, tools, products, technique, algorithm,
hardware, software, etc.
18
Analytics: A General Process
19
See details of the process in
one of the core readings at:
https://www.g2.com/articles/
data-analysis-process
Extended reading: a different perspective is presented in this video when talking about
data lake (we will cover data lake in a later module)
https://www.youtube.com/watch?v=zlBZrG8dDMM
A Decision Process
Another view from the corporate decision perspective
http://www.slideshare.net/junesungpark/business-process-based-analytics
20
Analytics/BI: A General Process in more Technical Terms
21
Results are presented and
delivered in different human
comprehendible formats
(such as tables and charts),
to support decisions. These
results are delivered through
multiple media and tools.
Cleaning and
transforming data into
clean and common
models and formats.
The collection or the
ingestion of raw data
from different sources
by different means, and
in different formats.
Data
Gathering
Data
Cleanse
Data
Analysis
Data
Presentation
Data
Storage
The refined data will be modeled
(if needed) and stored in a
particular place (e.g., a file or a
data management system) and
ready for analysis.
This step involves analytical
components, such as descriptive
analysis, statistical analysis,
data mining, and other
advanced analytics to extract
information and knowledge.
Queries/reports can also
directly present results to
users without intensive
analysis. This is usually
used for data exploration
and descriptive reports.
Data Preparation
Data can be
analyzed
immediately in
many agile
analytical cases,
without a formal
managed storage.
BI/Analytics: Systems and Platforms
• A BI system is a computer information system that implements (part or
whole) and streamlines BI capabilities and processes.
• BI or analytics can be done with multiple independent tools and
technologies, but a complete system can greatly facilitate the process.
• The values of a BI/analytics system
– Provides an integrated data (analytical) processing platform
– Enables easy and fast access of data and information at all levels (raw
data, analysis results, metrics, etc.)
– Streamlines a controlled and managed process of data driven decision
making
• Enterprise level vs. personal level system
– An enterprise level BI system emphasizes more on control and
performance.
– While a more user-oriented analytics platform enables nontechnical users to
autonomously execute full-spectrum analytic workflows from data access
and preparation to interactive analysis and the collaborative sharing of
insights.
22
General BI Capabilities Conception
23
Figure from: Business Intelligence, Rajiv Sabherwal, Irma Becerra-Fernandez, John Wiley & Sons, 2011
http://books.google.com/books?id=T-JvPdEcm0oC – narrated slides https://slideplayer.com/slide/5861482/
This is consistent with the
general BI or analytics
process but more from an
information behavior angle.
Critical Capabilities of a BI and Analytics Platform
•
Infrastructure
–
Manageability: Capabilities that track usage of the ABI platform and manage how information is shared (and by whom).
–
Security: Capabilities that enable platform security, administering of users, auditing of platform access and authentication.
–
Cloud analytics/BI: The ability to support building, deployment and management of analytics in the cloud, based on data stored both in the cloud and on-
premises (platform-as-a-service and analytic-application-as-a-service).
•
Data Management
–
Data source connectivity: Capabilities that enable users to connect to, query and ingest data, while optimizing for performance.
–
Data preparation: Support for drag-and-drop, user-driven combination of data from different sources, and the creation of analytic models (such as user-
defined measures, sets, groups and hierarchies).
–
Dropped or combined compared to previous reports: data storage, data model
•
Analysis and Content Creation
–
Reporting: The ability to create and distribute (or “burst”) pixel-perfect, grid-layout, multipage reports to users on a scheduled basis.
–
Data visualization: Support for highly interactive dashboards and exploration of data through manipulation of visual properties and visual forms.
–
Data storytelling: The ability to combine interactive data visualization with narrative techniques in order to package and deliver analytic content in a
compelling, easily understood form for presentation to decision makers.
–
Automated insights: A core attribute of augmented analytics, this is the application of ML techniques to automatically generate findings for end users (for
example, by identifying the most important attributes in a dataset).
–
Natural language query (NLQ) or augmented analytics: This enables users to ask questions and query data and analytic content using terms that are
either typed into a search box or spoken. Automatically finds, visualizes and narrates important findings without requiring users to build models or write
–
Notable missing compared to previous reports: Advanced Analytics.
•
Delivering and sharing of content
–
Catalog: The ability to automatically generate and curate a searchable catalog of analytic content, thus making it easier for analytic consumers to know
what content is available.
–
Publish and collaborate Analytic Content. Capabilities that allow users to publish, deploy and operationalize analytic content through various output
types and distribution methods, with support for content search, storytelling, scheduling and alerts.
–
Notable missing compared to previous reports: Mobile Exploration; Embedding Analytic Content (APIs and support for open standards for creating and
modifying analytic content, visualizations and applications, embedding them into a business process, and/or an application or portal.)
24
Gartner Magic Quadrant Report 2021
Additional resources:
https://www.predictiveanalyticstoday.com/key-capabilities-of-business-intelligence-software/
https://www.selecthub.com/business-intelligence/list-bi-capabilities/
• Query
• OLAP
• Business analytics
• Statistics
• Data mining
• Advanced analytics
• Machine learning
BI System Components at a Glance
25
• Reports
• Tables
• Data visualization
• Dashboard
• Scorecards
• Strategy map
• Visual analytics
• Free-form results
• Relational database
• Data warehouse
• Data lake
• Data modeling
• Data governance
• Data quality
• Metadata
• Master Data
• Data virtualization
• Data integration
• ETL
Data
Management:
Gathering and
Storage
Analytical
Processing
Presentation
• Local files
• Website, web portal
• Reporting server
• Application server
• BI server
• Client apps apps
(browser, desktop
app, mobile app,
email, etc.)
• Devices (computer,
tablet, phone, print-
outs, etc.)
Delivery and
Sharing
Users of various types
and levels:
• Power users
• Casual users
• Analysts,
developers, public
* Data management usually
includes a data sourcing
and gathering component.
This component may be
integrated with or
independent from a data
storage system.
Applications:
• Performance
management
• Benchmarking
• Market research
• CRM
• Strategic
management
• Web page visits
A Practical Example: MSBI System Architecture
26
Image from
https://bipointblog.wordpress.com/2014/05/28/implementat
ion-of-a-bi-system-using-microsoft-bi-stack-introduction/
Note: this is only one example
of a typical and traditional BI
system architecture. There are
some more recent self-service
focused architectures.
Data Management/Storage
•
Traditional (operational) relational databases facilitate data management and transaction
processing. They have two limitations for data analysis and decision support
– Performance
• They are transaction oriented (data insert, update, move, etc.)
• Not optimized for complex data analysis
• Usually do not hold historical data
– Heterogeneity
•
Individual databases usually manage data in very different ways, even in the same organization (not to mention
external data sources which may be dramatically different).
• A special analytical data store is needed for business intelligence and analytics.
•
In traditional BI, a special database system called data warehouse or data mart is often used to
store enterprise data
– The purpose of a data warehouse is to organize lots of stable data for ease of analysis and retrieval.
– Many data warehouses are build using the relational database systems.
– The data warehouse approach is a centralized and structured approach for analytical data management.
For more recent personal BI/analytics, data is also kept locally for easy access and manipulation, without
much technical support.
• More recent developments utilized more forms of database, including
– NoSQL databases for semi-structured data
– Data lake approach which accommodates multiple models and structures of data
– Lakehouse that combines features of data lake and data warehouse
– Cloud-based systems that hide the underlying structure complexity
27
Data storage for analytics will be covered in IT 3703 module 4 and 7123 module 4.
Data warehouse/mart will be covered in IT 4713 module 4.
Data Ingestion, Integration and Preparation
• Data may come from multiple different sources of different formats, but need to be combined
and associated
– Operational databases
– Spreadsheets
– Text, CSV
– PDF, Paper
•
The need to bring together different data/information
– Autonomous (may not have the control and management of data)
– Distributed (from different systems and places)
– Different (in data model, format, or platform)
• General processing activities - ETL
– Extraction: accessing and extracting the data from the source systems, including database, flat files,
spreadsheets, etc.
– Transformation: data cleanse, change the extracted data to a format and structure that conform to the
destination data.
– Loading: load the data to the destination database, and check for data integrity
•
Traditional BI focuses on upfront separate ETL processes that load the data in a centralized
storage. In modern BI and analytics, data cleanse and transformation may happen just-in-time
with analysis.
– Similar or related terms: data integration, acquisition, ingestion, wrangling, blending
28
Data is never clean!
You will spend most of your time
cleaning and preparing data!
ETL will be covered in IT4713 milestone 2 (module 5 and 6).
Self-service data preparation will be covered in IT 7123 module 6 and 7.
Four Levels of Analysis
• Descriptive Analytics: Describing or
summarising the existing data using
existing business intelligence tools to
better understand what is going on or
what has happened.
• Diagnostic Analytics: Focus on past
performance to determine what
happened and why. The result of the
analysis is often an analytic
dashboard.
• Predictive Analytics: Emphasizes on
predicting the possible outcome
using statistical models and machine
learning techniques.
• Prescriptive Analytics: It is a type of
predictive analytics that is used to
recommend one or more course of
action on analyzing the data.
29
Further reading:
https://www.analyticsinsight.net/four-types-of-business-analytics-to-know/
Advanced analytics
A
Screenshot from
https://www.youtube.com/watch?v=oNNk9-tmsZY
Examples of Analysis
• Non-analytical query (search results based on certain conditions)
– Get a list of students enrolled in in the IT 6713 class.
• Descriptive analysis (summarizing)
– How many students are enrolled in online IT graduate courses for the past year?
• What if analysis
–
If inventory levels are reduced by 10%, what is the new cost of inventory storage?
• Reasoning (why) and correlation
– What is the reason for a decrease of total sales this year?
– How do advertising activities affect sales of different products bought by different type of
customers, in different regions? (synthesizing)
• Forecast and prediction
– How many students are likely to change degree next year?
• Fuzzy decision
– What new advertising strategies need to be undertaken to reach our customers who can
afford an expensive product?
– Should we invest more on our e-business?
30
Descriptive Analytics
• Descriptive reporting has been the most common in all kinds of analysis
– Structured and fixed format reports
– Based on simple and direct queries
– Usually involves simple descriptive analysis and transformation of data, such as calculating, sorting,
filtering, grouping, and formatting
– Aggregating results from multiple rows of data on multiple dimensions
– Ad hoc query and reporting
• Multi-dimensional queries
– A dimension is a particular way (or an attribute) of describing and categorizing data
– Such queries are usually arithmetic aggregation operations (sum, average, etc.) on records grouped by
multiple dimensions (attributes) at different aggregation levels.
– A pivot table or crosstab is usually used for OLAP result view (aggregated data)
• Example analysis
–
"What is the total sales amount grouped by product line (dimension 1), location (dimension 2), time
(dimension 3) and … (other dimensions)?"
–
"Which segment of business provides the most revenue growth?"
• OLAP (Online Analytical Processing)
– OLAP is a technology and system that is optimized to answer queries that are multi-dimensional
– OLAP solutions traditionally heavily rely on backend processing and dedicated IT personnel
31
Descriptive and
operational report
More open and
exploratory analysis
OLAP will be covered in IT 4713
milestone 3 (module 7 and 8).
Dimensional queries and analysis will be covered in IT
4713 milestone 4 and in IT 7123 module 8.
Advanced Analytics
• Advanced Analytics is the autonomous or semi-autonomous
examination of data or content using sophisticated techniques
and tools, typically beyond those of traditional business
intelligence (BI), to discover deeper insights, make predictions,
or generate recommendations.
– https://www.gartner.com/it-glossary/advanced-analytics/
– Advanced analytics are usually computing intensive
• Advanced analytic techniques include:
– Complex statistical methods
– Machine learning
– Data/text mining: using sophisticated statistical and mathematical
techniques to find patterns and relationships among data. Data
mining techniques are a blend of statistics and mathematics, and
artificial intelligence and machine-learning.
– Pattern matching, forecasting, visualization, semantic analysis,
sentiment analysis, network and cluster analysis, multivariate
statistics, graph analysis, simulation, complex event processing,
genetic algorithm, neural networks, etc.
32
Data Presentation
Data presentation is the method to summarize, organize, and communicate data
(raw or analysis results) using a variety of tools. Data can be presented in one of
the three forms: text, tables, and/or graphs. The selection of the method of
presentation depends on the type of data, method of analysis, and type of
information sought from the data.
33
Key reading:
• https://www.toppr.com/guides/economics/presentation-of-data/textual-and-tabular-presentation-of-data/
• https://www.toppr.com/guides/business-economics-cs/descriptive-statistics/diagrammatic-presentation-of-data/
Textual
Narratives and articles, with
lengthier discussions.
Popular in news and story
modes.
This is generally
more aligned with
data visualization.
Structured
layout:
table, grid,
flow list,
cards
Hybrid or
Nested
charts
maps
diagrams
Textual and Structure Layout Presentation
• Textual presentation of data
means presenting data in the
form of words, sentences and
paragraphs.
• The textual presentation of data
is used when the data is not
large and can be easily
comprehended by the reader
just when he reads the
paragraph.
• This data presentation is useful
when some qualitative
statement is to be
supplemented with key data
that is directly supporting the
statement.
• Structure Layout examples
• Flow list
– A single column/row of items
• Grid
– Exact grid with rows and columns
(each cell is one data item)
– Tiles (cells) with different sizes
– Horizontal or vertical flow of items
• Table
– Strict rows and columns, with
headers
– Each row is typical for one data
item (record)
• Card
– Grouping a various piece of data
and information in a card-like
style
34
https://finance.yahoo.com/news/exxon-mobil-rides-again-tech-205233533.html
Data Visualization
• Visualizing is basically a human physiological and psychological capability, and
plays an important role in human information behavior and decision making
– Recall or memorize data more effectively
– Enable fast perception based on instinct (see the figure on the right)
– Helps data comprehension and enhance problem solving capabilities (cognition)
– Extract/provoke additional (implicit) perspectives and meanings
– Ease the cognitive load of information processing and exploration
– Help to shape the attention and focus
– Effective communication (story telling)
• Data visualization in BI/Analytics
– Data visualization is an important part of data exploration and decision making. Given the
power of visualization, it is only natural to apply the rich communication techniques in the
field of BI and analytics.
– As organizations seek to empower non‐technical users to make data‐driven decisions, they
must consider the powers of data visualization in delivering digestible insights.
– Visualization tools have become increasingly important to business intelligence, in which
people need technology support to make sense of and analyze complex data sets and all
types of information.
– Visualization can also be part of the analysis process (visual analytics)
35
Data visualization will be touched briefly in this course. For more coverage, refer to IT 7113 Data visualization
http://zheng.kennesaw.edu/teaching/it7113 and the overview at https://www.edocr.com/v/yqwmqeba/jgzheng/Business-Data-Visualization
Data visualization is the visual representation and presentation of data
for the purpose of perception and cognition.
Presentation
• Multiple ways to present results
– Regular/periodical static reports
– Interactive reports
– Live and real time dashboard
– Free form ad hoc analysis
– Edited PowerPoint
36
“Presentation is key – be
a master of PowerPoint.”
Reports
•
Reports
–
A report is the presentation of detailed data arranged in defined layouts and formats
–
Based on simple and direct queries: usually involves simple analysis and transformation of data (sorting, calculating,
filtering, filtering, grouping, formatting, etc.)
•
Traditional reports contain detailed data in a tabular format and typically display numbers and text only.
–
It is geared towards people who need data rather than a direct understanding or interpretation of data.
–
Its purpose is mainly for printing (with styling) or exporting (raw data).
• Modern reports can be interactive and visual, but the focus is still on detailed data. Sometimes the distinction
is a bit blurred with dashboards in some practical cases.
–
A report style “dashboard” (or more like a visual intensive interactive report):
https://www.cityhealthdashboard.com/ga/atlanta/city-overview
– Magic Quadrant report vs. https://www.g2.com/categories/data-visualization?segment=all
– Dashboard or report? http://www.crazybikes.com/mrc/CRAZYBIKES.R00090s
37
Reports and dashboards will be covered in IT 7123
module 10, and IT 7113 Data Visualization.
Dashboard
• Elements of a dashboard
– Data/information: the most important element
– Visual: data visuals (charts, etc.) provide a high level at-a-glance view
– User interface
•
a clean UI that unifies all elements to work together as a whole
•
supporting interactions as needed
•
The Values of Dashboard
– Provides a one-place presentation of critical information, so users can quickly understand data and
respond quickly at one place.
• Saves time over running multiple reports.
– Allows decision makers to see a variety of data that affects their divisions or departments
• This allows decision makers to focus only on the items over which they have control
• The dashboard is generally customized for each user
– Allows all users to understand the analytics. For non-technical users, dashboards allow them to
participate and understand the analytics process by compiling data and visualizing trends and
occurrences.
– More http://www.bidashboard.org/benefits.html
38
For more details, visit IT 7113 module on dashboard:
https://www.edocr.com/v/oekl31vr/jgzheng/Dashboard
Dashboard = data/information + visual + UI
A dashboard is a visual-oriented display of the most
important data and information needed to achieve defined
goals and objectives; consolidated and arranged on a
single screen so the information can be viewed at a glance.
Adapted from: Dashboard Confusion, Stephen Few,
http://www.perceptualedge.com/articles/ie/dashboard_confusion.pdf
BI Delivery/Reporting
• BI delivery, or BI reporting, is the process of providing or
delivering needed data and analysis results to users or
applications.
– BI reporting primarily enables in receiving output or results from a BI
software/ solution.
– Users consume the results for decision making. The results/ content
of BI reporting are generally in the form of actionable results that
help the organization / individual in short term, long term tactical
and/or strategic decision making.
– It is also integrated with other application that takes the results/ data
to perform any further operation /process.
– Extended reference:
https://www.techopedia.com/definition/30217/business-intelligence-
reporting-bi-reporting
• The process involves three aspects
– Management control: managed or self-service
– Forms of delivery packaging: file or app
– Deployment/distribution channels/media: server or shared space
BI Stakeholders
Producers
vs.
Consumers
(at different levels)
40
Figures originally from
http://www.bileader.com/
Dashboards.html
Technical vs. Business users
People, or users, are important
part of a larger information
system.
Users Have Different Needs
41
Figure from http://eckerson.com/articles/part-iv-seven-keys-to-a-united-bi-environment
Power users
may also be
technology savvy
and capable of
programming.
Casual users
may not be as
technical as
power users.
The Fit between Tools and Users
42
Gartner Report,
Select the Right Business Intelligence and Analytics Tool for the Right User
Published: 23 May 2016 Analyst(s): Cindi Howson
Another view put
into layers
Modern BI Trends
• Modern vs. traditional BI
43
History and Trends
44
http://www.b-eye-
network.com/blogs/eckerson/archives/2011/03/bi_market_evolu.php
From Wayne Eckerson talk
https://vimeo.com/68143902
Traditional enterprise
BI based on data
warehouse and OLAP
The Modern/New BI
• A modern BI platform supports IT-enabled analytic content development. It is defined by a self-
contained architecture that enables nontechnical users to autonomously execute full-spectrum
analytic workflows from data access, ingestion and preparation to interactive analysis and the
collaborative sharing of insights. It moves from passive collection and use of data (reporting
driven) to proactive generation of data (business development driven).
• By contrast, traditional BI platforms are designed to support modular development of IT-
produced analytic content, and specialized tools and skills and significant upfront data
modeling, coupled with a predefined metadata layer, are required to access their analytic
capabilities.
•
https://www.slideshare.net/Dataversity/analytics-business-intelligence-and-data-science-whats-
the-progression
45
Technology Insight for Modern Business Intelligence and Analytics Platforms
Gartner Report, October 2015
Analytic Workflow Component Traditional BI Platform
Modern BI Platform
Data source
Upfront dimensional modeling required (IT-built
star schemas)
Upfront modeling not required (flat
files/flat tables)
Data ingestion and preparation
IT-produced
IT-enabled (business-led)
Content authoring
Primarily IT staff, but also some power users
Business users;
Analysis
Predefined and regular reporting, based on
predefined model
Free-form exploration, ad hoc analytics
Insight delivery
Distribution and notifications via scheduled
reports or portal; passive collection and use of
data (reporting driven).
Sharing and collaboration, storytelling,
open APIs
Notable Trends/Features of the Modern BI
•
Self-service BI/Analytics: Business led, IT enabled
•
Cloud BI and analytics: cloud computing is regarded as an ideal platform to provide business intelligence applications as it serves as a repository
for structured and unstructured data.
•
Other notable trends and developments – please do some research yourself
–
Embedded analytics: use of reporting and analytic capabilities directly in business applications http://www.gartner.com/it-glossary/embedded-analytics/
–
Augmented analytics and natural language processing: uses machine-learning automation to supplement human intelligence across the entire analytics
life-cycle.
–
Search driven analytics: (aka clickless analytics) aims to build a report and charts on the fly, using web search style.
•
Incorporating natural language processing
•
A quick intro: https://www.youtube.com/watch?v=868-pR-cxZo
–
Location intelligence http://sandhill.com/article/iot-and-the-growing-use-of-location-features-in-business-intelligence-software/
–
Expanding application areas at all levels: in more extensive application areas, e.g. sports, disease, network traffic, etc.
•
Capability specific trends (we will discuss these trends with the modules focusing on each component)
–
Data lake
–
Advanced analytics (machine learning, deep learning, AI, etc.)
–
Collaborative BI
–
Mobile BI: https://bi-survey.com/mobile-bi
–
Visual BI or visual analytics Visual oriented, - http://www.perceptualedge.com visual-based data discovery capabilities
–
In-memory processing (in-memory OLAP): emerging technology for processing of data stored in an in-memory database. http://www.bi-dw.info/in-
memory-olap.htm
–
New data gathering techniques and technologies. New data sources and capability to capture more data. From passive collection and use of data
(reporting driven) to proactive generation of data (business development driven)
–
Variety of visual medium and UI
•
More trends
–
https://bi-survey.com/top-business-intelligence-trends
–
http://www.zdnet.com/article/is-the-business-intelligence-market-finally-maturing/
–
https://www.slideshare.net/TableauSoftware/top-10-business-intelligence-trends-for-2017
–
https://www.mrc-productivity.com/blog/2019/01/5-business-intelligence-trends-to-watch-in-2019/
–
https://www.gartner.com/smarterwithgartner/gartner-top-10-data-and-analytics-trends-for-2021
46
Self-Service BI
•
[A solution for] end users designing and deploying their own reports and analyses within an approved and
supported architecture and tools portfolio.
–
http://www.gartner.com/it-glossary/self-service-business-intelligence
•
Key features
– Shifting focus from IT back to the user: enables all kinds of users with varied skill levels to autonomously execute full-
spectrum analytic workflows. These users include traditional power users, data professionals or data scientists, managers
and business analysts.
– A more distributed and collaborative environment.
–
The process is more flexible and agile, and it responds to user needs quickly. Supporting ad hoc analytic needs, hence more
interactive and explorative.
– Self-service BI tools still have fundamental BI components and provide BI capabilities, but they are more integrated (in one
software package) than separated.
–
Independent but very often work with enterprise systems.
– Good for individuals or non-corporate environments.
•
Different levels of self-service
– Started from client-oriented report building and data visualizations, and eventually extended to analysis models, and finally
to data discovery, preparation, and cleanse.
–
https://www.eckerson.com/articles/part-2-one-size-does-not-fit-all-customizing-self-service-analytics-for-business-users
•
Dashboards, reporting, end-user self-service, and advanced visualization are the top four most important
technologies and initiatives strategic to BI in 2018.
–
https://www.forbes.com/sites/louiscolumbus/2018/06/08/the-state-of-business-intelligence-2018/#b2fca2878289
•
Tools and market
– Best self-service tools: https://www.pcmag.com/picks/the-best-self-service-business-intelligence-bi-tools
–
The global self-service business intelligence market to grow from USD 3963.04 million in 2016 to USD 10992.96 million by
2023, at a CAGR of 15.69%. http://www.nbc-2.com/story/38414064/global-self-service-business-intelligence-market-2018-
size-share-growth-trends-type-application-analysis-and-forecast-by-2023
47
IT Support in Self-Service BI
• The goal of self-service BI
– NOT to eliminate the need for IT
– Instead, to put data and results in the user’s hands and reduce the
burden on the IT department.
•
“Self-service BI does remove much of the reporting burden from
the IT department. The IT department must control the data and
the user access. They’re responsible for keeping the data clean,
and ensuring that users can only access data they’re authorized
to see. The self-service BI tool only acts as a doorway for users
to access the IT-controlled data.”
– https://www.mrc-productivity.com/blog/2015/08/6-common-
misconceptions-of-self-service-bi/
•
IT’s role
– Data management and governance, including security, access
control, data quality and accuracy, compliance, etc.
– Technical support for the systems and platforms, especially cloud
based
48
A Self-Service BI Architecture
49
Technology Insight for Modern Business Intelligence and Analytics Platforms
Gartner Report, October 2015
Cloud BI
• Cloud Business Intelligence (BI) hosts and provides systems and applications
through network (Internet).
•
It has been a trend as cloud computing, especially application level (software as
a service), has been commonly accepted.
– Customer Relationship Management (CRM) applications (Salesforce), online file collaboration and storage
(Dropbox, Box) and help desk software (UserVoice, Zendesk). This trend includes business intelligence tools
embracing the agility and accessibility of the Cloud.
• Cloud analytics is a service that runs data analysis and business intelligence
operations in a public or private cloud. Cloud analytics companies help
enterprises scale quickly by reducing the costs and administrative burden of on-
premises hardware.
• Cloud for analytics types:
• Public cloud — Storage and data processing is publicly accessible on multi-tenant
architecture that shares IT systems but not data.
• Private cloud — Accessible only to one company and acts as an extension of the
company’s IT infrastructure. Used when data privacy and security is paramount.
• Hybrid cloud — A combination of public and private clouds and most effective when only a
small amount of sensitive data needs to be in a private cloud.
• MSIT offers a course on cloud analytics (IT 7143).
50
Market, Career, Education, and
Resources
51
BI Market
• Commoditization and consolidation of multiple technologies
– Forrester no longer sees reporting and querying, online analytical processing (OLAP), data
visualization, dashboards, data exploration, and location analytics as separate market
categories within BI. Rather, most enterprise BI platforms now provide these capabilities.
– The same commoditization is happening in the cloud and mobile BI as most leading
vendors now build their platforms on cloud-based multi-tenant architecture or offer a cloud
version in addition to an on-premises one. Similarly mobile BI is now simply a feature of
most BI platforms.
– https://www.zdnet.com/article/is-the-business-intelligence-market-finally-maturing/
• Company has been going through consolidation
– Many smaller products that target specific functional areas are consolidated into major
BI/analytics suites and platforms.
Major vendors
https://www.appsruntheworld.com/
top-10-analytics-and-bi-software-
vendors-and-market-forecast/
o 2007 Hyperion Solutions $3.3bn  Oracle
o 2008 Business Objects  SAP 6.8B
o 2008 Cognos  IBM 5B
o 2019 Tableau  SalesForce 15.7B
o 2019 Looker  Google 2.6b
o 2021 Information Builders  TIBCO
o 2022 Yellowfin  Idera
o 2022 Dundas BI  InsightSoftware
Vendor Positioning
53
Gartner Magic Quadrant for Analytics and Business
Intelligence and Platforms 2023 also see
https://powerbi.microsoft.com/en-us/blog/microsoft-named-
a-leader-in-the-2023-gartner-magic-quadrant-for-analytics-
and-bi-platforms/
BARC https://bi-
survey.com/business-
intelligence-software-
comparison
G2 Grid for Analytics Platforms
https://www.g2.com/categories/
business-intelligence-platforms
In 2019 Gartner started to
put “analytics” before “BI”.
Vendors/Products
•
Traditional big four: these are mega vendors that
provide complete solutions that cover full
spectrum of BI processes and related
applications.
– Microsoft: SQL Server, Power BI, SharePoint,
Excel https://www.microsoft.com/en-us/sql-server/
– SAP: SAP BusinessObjects BI, Lumira
https://www.sap.com/products/analytics/business-
intelligence-bi.html
–
IBM: Cognos, Watson
https://www.ibm.com/analytics/business-
intelligence/
– Oracle: Oracle BI 12c
https://www.oracle.com/solutions/business-
analytics/business-intelligence/
• Other notable vendors/products
– SAS: SAS Enterprise BI
https://www.sas.com/en_us/solutions/business-
intelligence.html
– Salesforce Tableau
– QlikTech
–
TIBCO
– Sissense
– Metabase
– Cloud based
• Google Looker Data Studio
• Amazon QuickSight
• mode.com
•
Zoho
– Open-source tools, including BIRT, Pentaho,
Metabase, etc.
•
https://blog.capterra.com/top-8-free-and-open-
source-business-intelligence-software/
•
Integrated solutions for a specific industry
–
https://www.datagear.com
• More other analytics/BI tools list
–
https://www.peerspot.com/categories/bi-business-
intelligence-tools
–
https://www.gartner.com/reviews/market/analytics-
business-intelligence-platforms
–
https://bi-survey.com/business-intelligence-software-
comparison
–
https://www.g2.com/categories/business-intelligence
–
https://www.datanyze.com/market-share/business-
intelligence--243
–
https://www.techradar.com/best/best-bi-tools
–
https://www.itcentralstation.com/categories/business
-intelligence-bi-tools
–
http://www.capterra.com/business-intelligence-
software/
–
https://www.pcmag.com/picks/the-best-self-service-
business-intelligence-bi-tools
– Others
•
https://www.softwareadvice.com/bi/
•
https://www.betterbuys.com/bi/reviews/
•
https://www.bitool.net/business-intelligence.html
54
BI/Analytics Careers
• Typical BI/Analytics positions
– BI analyst
– BI solution architect and integration specialist
– BI application developer and tester
– BI system support specialist
– Data warehouse specialist
– Database analyst, developer and tester
– Report/dashboard developer
• More about jobs and careers
– https://www.datapine.com/blog/bi-skills-for-business-intelligence-career/
– https://www.discoverdatascience.org/career-information/
– https://searchdatamanagement.techtarget.com/feature/Data-management-roles-Data-
architect-vs-data-engineer-others
– https://dzone.com/articles/five-data-tasks-that-keep-data-engineers-awake-at
– Data analyst: https://www.investopedia.com/articles/professionals/121515/data-analyst-
career-path-qualifications.asp
– https://blog.udacity.com/2014/12/data-analyst-vs-data-scientist-vs-data-engineer.html
Critical Knowledge and Skills
•
Three competencies
–
Technical, Business (management), Analytical
•
Technical knowledge
– Knowledge of database systems and data warehousing technologies
– Ability to manage database system integration, implementation and testing
– Ability to manage relational databases and create complex reports
– Knowledge and ability to implement data and information policies, security requirements, and state and federal regulations
– Knowledge of client tools used by business users
– Knowledge of data models
– Knowledge of programming tools used in analytics
•
Solution development and management
– Working with business and user requirements
– Capturing and documenting the business requirements for BI solution
–
Translating business requirements into technical requirements
– BI project lifecycle and management
•
Business and Customer Skills and Knowledge
– Effective communication and consultation with business users
– Understanding of the flow of information throughout the organization
– Ability to effectively communicate with and get support from technology and business specialists
– Ability to understand the use of data and information in each organizational units
– Ability to train business users in information management and interpretation
•
https://www.datapine.com/blog/bi-skills-for-business-intelligence-career/
Sample Roles (from real world job ads)
Business Intelligence Specialist
• Maintain or update business intelligence tools,
databases, dashboards, systems, or methods.
•
Provide technical support for existing reports,
dashboards, or other tools.
•
Create BItools or systems, including design of
related databases, spreadsheets, or outputs.
57
Business Intelligence Analyst
•
Technical skill requirements
– Works with business users to obtain data requirements
for new analytic applications, design conceptual and
logical models for the data warehouse and/or data mart.
– Develops processes for capturing and maintaining
metadata from all data warehousing components.
•
Business skills requirements
–
Transform data into analytical insight and desire to
leverage the best technique to arrive at the right answer.
– Generate standard or custom reports summarizing
business, financial, or economic data for review by
executives, managers, clients, and other stakeholders.
– Analyze competitive market strategies through analysis
of related product, market, or share trends.
– Collect business intelligence data from available
industry reports, public information, field reports, or
purchased sources.
– Maintain library of model documents, templates, or other
reusable knowledge assets.
Business Intelligence Developer
•
Business Intelligence Developer is responsible for
designing and developing Business Intelligence
solutions for the enterprise.
•
Key functions include designing, developing, testing,
debugging, and documenting extract, transform, load
(ETL) data processes and data analysis reporting for
enterprise-wide data warehouse implementations.
•
Responsibilities include:
– working closely with business and technical
teams to understand, document, design and
code ETL processes;
– working closely with business teams to
understand, document and design and code data
analysis and reporting needs;
–
translating source mapping documents and
reporting requirements into dimensional data
models;
–
designing, developing, testing, optimizing and
deploying server integration packages and
stored procedures to perform all ETL related
functions;
–
develop data cubes, reports, data extracts,
dashboards or scorecards based on business
requirements.
•
The Business Intelligence Report Developer is
responsible for developing, deploying and supporting
reports, report applications, data warehouses and
business intelligence systems.
BI jobs in Atlanta
https://www.dice.com/jobs?q=BI&l=Atlanta%2C+Ga+
Metro+Area
BI/Analytics Local Resources
• BI/Analytics Education at KSU
– MSIT/BSIT - Graduate Certificate in Data Analytics and Intelligent
Technology https://msit.kennesaw.edu/future-students/program-
requirements.php, which includes my
•
IT 7113 Data Visualization https://idi.kennesaw.edu/it7113
•
IT 7123 Business Intelligence https://idi.kennesaw.edu/it7123/
– BSIT - the new concentration on “data analytics and technology”, including
•
IT 3703 Intro to data analytics and technology
•
IT 4713 Business Intelligence http://jackzheng.net/teaching/it4713/
– Other departments
• School of Data Science https://datascience.kennesaw.edu
•
IS 8935 Business Intelligence
• Certificate in High Performance Cluster Computing
http://ccse.kennesaw.edu/cs/programs/cert-hpcc.php
– Lecture notes on BI and Data Visualization
• https://www.edocr.com/user/jgzheng
• Local organizations and events
– https://www.meetup.com/Atlanta-Society-for-Business-Intelligence/
– https://www.meetup.com/Atlanta-Microsoft-Business-Intelligence-Users/
58
Core Readings
• BI
– Business intelligence in the enterprise https://www.techtarget.com/searchbusinessanalytics/Ultimate-
guide-to-business-intelligence-in-the-enterprise
– A quick, conceptual, and practical introduction of BI by Jared Hillam (Intricity), from a traditional
perspective: http://www.youtube.com/watch?v=LFnewuBsYiY
– BI intro video by LearnItFirst (focused more on the traditional BI; there are some good points which I do
agree): https://www.youtube.com/watch?v=LhZX0MAYKp8
• Comparison
– Distinguishing Analytics, Business Intelligence, Data Science (classic reading):
https://www.dataversity.net/distinguishing-analytics-business-intelligence-data-science/
– Data Analyst vs Data Engineer vs Data Scientist: Skills, Responsibilities, Salary
https://www.edureka.co/blog/data-analyst-vs-data-engineer-vs-data-scientist/ - from some job and career
perspectives - with a nice video https://www.youtube.com/watch?v=ioZNNfxXXqo)
• BI processes and capability
– What is Business Intelligence? A Guide | Sigma Computing:
https://www.sigmacomputing.com/resources/learn/what-is-business-intelligence
•
The BI industry
– Gartner Magic Quadrant for Analytics and Business Intelligence Platforms 2023: see a promotional
summary here (follow the blog to get a full report copy - requires a sign-up but it’s free)
https://powerbi.microsoft.com/en-us/blog/microsoft-named-a-leader-in-the-2023-gartner-magic-quadrant-
for-analytics-and-bi-platforms/
59
Additional Good General Resources
•
A Brief History of Decision Support Systems by D.J. Power:
http://dssresources.com/history/dsshistory.html
•
An Overview of (traditional) BI Technology from CACM (premium
magazine from ACM): http://cacm.acm.org/magazines/2011/8/114953-
an-overview-of-business-intelligence-technology/fulltext
•
http://wps.prenhall.com/wps/media/objects/2519/2580469/addit_chmatl
/TURBMC04_0131854615App.pdf
•
Advanced Analytics and Business Intelligence:
https://www.youtube.com/watch?v=oNNk9-tmsZY
•
History of BI (casual video with wacky visuals):
https://www.youtube.com/watch?v=_1y5jBESLPE
•
More about jobs and careers
–
https://www.datapine.com/blog/bi-skills-for-business-intelligence-
career/
–
https://www.discoverdatascience.org/career-information/
–
https://searchdatamanagement.techtarget.com/feature/Data-
management-roles-Data-architect-vs-data-engineer-others
–
https://dzone.com/articles/five-data-tasks-that-keep-data-engineers-
awake-at
–
Data analyst:
https://www.investopedia.com/articles/professionals/121515/data-
analyst-career-path-qualifications.asp
–
https://blog.udacity.com/2014/12/data-analyst-vs-data-scientist-vs-
data-engineer.html
•
Industry experts and influencers
–
Howard Dresner: http://dresneradvisory.com
–
Wayne Eckerson: https://www.eckerson.com/blogs/the-new-bi-leader
–
Gregory Piatetsky: http://www.kdnuggets.com
–
Ralph Kimball
•
General BI resource web sites
–
BI and DW resource directory: http://www.bi-dw.info
–
BeyeNetwork: http://www.b-eye-network.com
–
https://solutionsreview.com/business-intelligence/
–
DSS Resources: http://dssresources.com/
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ACM techpack: http://techpack.acm.org/bi/
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http://blog.capterra.com/learn-about-business-intelligence-resources/
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https://www.itprotoday.com/business-intelligence
•
General learning resources
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https://www.1keydata.com/datawarehousing/datawarehouse.html
•
Organizations, communities, and events
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BI Bake Off https://powerbi.microsoft.com/en-us/blog/tag/bi-bake-off/
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Dataversity: http://www.dataversity.net/
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The Data Warehousing Institute: http://tdwi.org
•
Paid industry reports: you may get some free reprints from some
vendors after registration.
–
Gartner annual report on “Magic Quadrant for Analytics and Business
Intelligence Platforms”
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Gartner report “Technology Insight for Modern Analytics and Business
Intelligence Platforms”
–
The Forrester Wave™: Enterprise BI Platforms (two versions, one for
on-premise and one for cloud)
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Forrester Playbook:
https://www.forrester.com/playbook/The+InsightsDriven+Business+Play
book/-/E-PLA940
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