Business intelligence environments involve collecting data from various sources, transforming and organizing it using tools like ETL, and storing it in data warehouses or marts. This data is then analyzed using OLAP and reporting tools to provide useful information for business decisions. Setting up an effective BI environment requires understanding business requirements, defining processes, determining data needs, integrating data sources, and selecting appropriate tools and techniques. Careful planning and skilled people are needed to ensure the BI environment supports organizational goals.

1. Business Intelligence
Environment

2. Implementing BI is a long process and it requires a lot of analysis and
investment. A typical BI environment involves business models, data
models, data sources, ETL, tools needed to transform and organize the
data into useful information, target data warehouse, data marts, OLAP
analysis and reporting tools.
Setting up a business intelligence environment not only rely on tools,
techniques and processes, it also requires skilled business people to
carefully drive these in the right direction.

3. Business intelligence environment
Care should be taken in -
• understanding the business requirements,
• setting up the targets,
• analysing and defining the various processes associated with these,
determining what kind of data needed to be analysed,
• determining the source and target for that data,
• defining how to integrate that data for BI analysis and
• determining and gathering the tools and techniques to achieve this
goal.

4. Six Elements in the Business Intelligence Environment
1.Data from the business environment
2.Business intelligence infrastructure
3.Business analytics toolset
4.Managerial users and methods
5.Delivery platform–MIS, DSS, ESS
6.User interface

6. Business Intelligence environment

7. • The term business model refers to a company's plan for making a
profit. It identifies the products or services the business plans to sell,
its identified target market, and any anticipated expenses.
• Data modeling (data modelling) is the process of creating a data
model for the data to be stored in a database. This data model is a
conceptual representation of Data objects, the associations between
different data objects, and the rules.
• There are three types of data modeling techniques for business
intelligence: Conceptual, Logical, and Physical.

8. • Conceptual data modeling
examines the business's
operations, intending to
create a model with the
most important parts
(such as describing a
store's order system).
Essentially, this data
model defines what data
the system will contain.
Example: Entity-
Relationship Diagrams.

9. • Logical data modeling examines
business functions (e.g.
manufacturing, shipping),
intending to create a model
describing how each operation
works within the whole
company. It also defines how a
system should be implemented,
by mapping out technical rules
and data structures. Example:
Data Flow Diagram, Data Vaults.

10. • Physical data modeling examines
how the database will actually
be implemented, intending to
model how the databases,
applications, and features will
interact with each other. Here,
the actual database is created;
the schema structure is
developed, refined, and tested.
Data models generated should
support key business operations.
Example: Data Matrices.

12. A relational database is a collection of data items with pre-defined relationships between
them. These items are organized as a set of tables with columns and rows. Tables are used
to hold information about the objects to be represented in the database.

13. In business intelligence, an ETL tool extracts data from one or
more data-sources, transforms it and cleanses it to be optimized
for reporting and analysis, and loads it into a data store or data
warehouse. ETL stands for extract, transform, and load.

14. OLAP (Online Analytical Processing) is the technology behind many Business
Intelligence (BI) applications. OLAP (for online analytical processing) is software
for performing multidimensional analysis at high speeds on large volumes of data
from a data warehouse, data mart, or some other unified, centralized data store.

15. A business intelligence (BI) platform is technology that helps businesses
their data. It serves as the backbone
of a company’s business intelligence strategy, which is how a company
uses information to make better decisions.
The purpose of BI platforms is to help companies compete in today’s
data-saturated world by turning their data into a competitive
advantage.

16. Why are BI platforms important?
When implemented well, modern business intelligence platforms make
company’s data usable. Utilizing data more effectively has always been
the core purpose of business intelligence, but BI platforms make this
process much more efficient, resulting in a totally new relationship with
data.
In 2016, the International Data Group (IDG) found that the average
company managed 162.9 terabytes of data. Add that to Forrester’s
finding that “between 60% and 73% of all data within an enterprise
goes unused for analytics”.
Modern companies have a lot of data, but they can’t use it well.

17. Business intelligence platforms solve this problem by helping companies
manage and understand what’s going on with those terabytes of data. From
there, data takes on new importance by helping companies -
• make a better product by understanding customer behavior,
• serve customers better by learning who they are and what they need, and
• run a more efficient business by identifying potential issues before they
become larger problems.
With the right BI platform, all of this can happen, which provides an advantage
that no competitor can replicate, because decisions are fast, accurate, and
(most importantly) based on your company’s data — not anyone else’s.

18. What does BI platform do?
A business intelligence platform helps companies to
. The goal is to develop a data-driven
culture that provides every employee with the ability to identify and
act on insights.
1. Gather Data
According to IDG, companies use an average of 400 data sources across
their organization to feed their BI capabilities. BI platforms should
gather data from all of those sources into one place in order to
understand and visualize what’s happening.
• Integrate many data sources. BI platforms should integrate multiple
databases, data warehouses and CSV files.

19. • Clean data and manage data quality. According to BI-Survey.com,
data quality management (DQ management) tops the list of BI trends
gaining importance. Disparate data sets need to be made usable and
intelligible, and BI platforms should help maintain that quality and
speed up the process of cleaning poor-quality data.
• Blend data. Imported and cleaned data is ready to be blended
together into one functioning data set. BI platforms makes it simple
to blend data and gain a complete view across all data sources.
• Ensure compliance and security. BI platforms helps manage the
company's data in a secure and compliant way.

20. 2. Understand Data
With mountains of data stored into and accessible from one place. A BI platform
will give the ability to sort through, organize, and query the data.
• Model and organize data. With all of the data centralized, company needs an
efficient way to organize it into relationships that work for the business. For
instance, companies can use features like data stores, which can be thought of
as customized and curated mini-databases, to solve the specific needs of
marketing team.
• Query data. Every user of the BI platform has an easy way of querying the
data to get the answers they need quickly.
• Perform exploratory data analysis (EDA). BI platforms also allows power
users, such as data analysts and scientists, to go as deep as they need to in
order to test hypotheses.

21. 3. Visualize Data
It’s not enough for company's data to be understandable — it has to
be usable. BI platforms should make it easy to collaborate and act on
the insights of company's team after you gather and understand it.
• Share the findings with dynamic dashboards. After modeling,
cleaning, and querying the data, BI platforms should make it easy to
create custom, dynamic dashboards to visualize what’s happening
with company's data in real time. Bonus points if one can manipulate
and interact with these dashboards without coding knowledge.
• Collaborate and communicate. Many people across the organization
will use BI platform, so the ability to collaborate and comment within
the platform and even within dashboards is vitally important. Setting
up custom alerts will also keep leaders, teams, and even the entire
company informed of important developments.

22. 3. Work with other technology. BI platforms are just one important
part of company's tech stack, which means it needs to work well with
all of the other tools being use. That includes where it gets data from
and where you send your dashboards to.

23. There are lots of debates going on among decision makers in business to choose the type
of business intelligence they should adopt. When managers have a good idea of what they
wish to analyze sales figures or customer satisfaction stats but they are not aware of the
end results then most preferred tool is strategic business intelligence. When decision
makers stand on the option they “don’t know” strategic business intelligence is adopted.
There are instances in a business organization where managers deliver anticipated
information in such situations operational business intelligence is the best preferred over
strategic intelligence. This is mainly because operational intelligence emphasizes on
standard tasks that employees need to complete. Consider an example of operational
intelligence where an account manager in an organization makes an entry of a new order
for the customer. Most of the organization would like the account manager to know
intelligence about the customer’s credit status and whether he has any overdue invoices.

24. Strategic Business Intelligence
Strategic Business Intelligence also known as auto-delivered intelligence is often
associated with reporting from an analytical data source or data warehouse.
Basically, strategic business intelligence improves a business process by analyzing a
predetermined set of data relevant to that process and provides historical context
of data. In addition, strategic intelligence provides the base for forecasting, goal-
setting, planning and direction. Strategic business intelligence needs to be
delivered in an interactive manner, enabling the manager to present his views on
data in different ways. Also, strategic business intelligence emphasizes on its output
on a graphical display such as charts and graphs to represent trends, opportunities
and problem areas. Strategic business intelligence converges on four important
parameters:
• Collection and storage of data
• Optimisation of data for analysis
• Identification of crucial business drivers through past data records
• Seeking answers to key business questions

25. Operational Business Intelligence
Operational business intelligence is associated with the transactional or operational data
source and is consistent with reporting data during organizational processes. In general,
operational business intelligence provides time-sensitive, relevant information to
operations managers, business professionals, and front-line, customer-facing employees to
support daily work processes. Also if the data retrieved from the analysis directly supports
or helps complete operational tasks, then the intelligence is operational in nature. Since
operational business intelligence is task oriented there is less need of charts and graphs.
Consider an example informing a staff member in an organization regarding information
on client’s credit or on over dues. In such a scenario graphical representation won’t hold
good but a brief message will solve the problem.
Hence communication methods and devices play a vital role in operational business
intelligence. Thus, operational business intelligence comprises multiple delivery methods
like instant message, email, dashboard and Twitter. The output from an operational
business intelligence include invoices, schedules, shipping documents, receipts and
financial statements.

26. Multiplicity of Business Intelligence Tools
Multiplicity of Business intelligence is forming a wide group of
application programs and technologies for collecting, accumulating,
analysing, and giving access to data from a variety of data sources. It
provides project clients with consistent and appropriate information
and analysis for better decision making. This technology is a collection
of software applications for examining an organisation’s unprocessed
data for intelligent decision making for the success of an organisation.

27. Modern Business Intelligence
Modern BI addresses the new business reality with Self-Serve Tools
- to satisfy the needs of an average business user with sophisticated
features in an easy-to-use integrated BI environment.
- offers mainstream tools with access and flexibility so that business
users can produce reports and analysis on real time basis
- share data with other users to make decisions and optimize business
results.
- BI dashboards are more flexible and support personalization.

28. - They are no longer restricted to formats and delivery designed by IT. -
- Disparate data sources are integrated into a uniform view, so users
can drag and drop data and interact with the tools.
- The design of modern BI tools recognizes the reality of time-sensitive,
rapidly changing business markets and competitive positioning and
provides more options and flexibility to support data popularity, Social
BI and power users within the organization.
- Every business understands the value of objective metrics and
accurate analysis and the modern BI environment is designed to
support these goals at every level of the organization.

29. Enterprise Business Intelligence
The deployment of BI throughout a large corporation. Larger, more complex
companies create more data and require more extensive and sophisticated
business intelligence platforms. Enterprise BI helps these organizations increase
productivity and efficiency.
Enterprise business intelligence platforms provide a higher capacity for data
management and analytics. Using data to create a holistic view of the business
can lead to critical efficiencies. When data collection and analytics remains
project- or team-focused, essential information gets siloed, and companies risk
inefficiencies, redundancies, and potential mission conflicts between teams.
Companies can improve performance with an enterprise approach to BI. This
approach involves aligning business, data, and analytics strategies and
leveraging resources and expertise.

30. Enterprise business intelligence at coca cola bottling plant allowed to
bring together up to 200 million lines of data from 100 different
systems into one dashboard, replacing a daily 45-minute manual
process. The new platform serves the needs of multiple audiences and
roles, from leadership dashboards that focus on strategy and growth to
field sales dashboards with customer portfolios and sales quota
tracking. Users can quickly answer questions like: Are products
delivered on time? Are projects staying within budget? How is this
salesperson performing?

31. Information worker
Information Workers are individuals who work with information instead
the physical objects of labor. Information workers are individuals who
create, manage, share, receive and use information in the course of
their daily work, including those who act and react to information.
The information worker is a label placed on individuals that primarily
work with information and data. Information workers perform non-
routine, cognitive, or creative work that often requires both structured
and unstructured information inputs from multiple sources.