Business analytics

1. Business intelligence techniques
UNIT 2: Business intelligence techniques, data
warehousing, data mining and techniques,
OLAP, business intelligence system & software.

3. Business intelligence analyst
• The worldwide economy has taken a significant knock in
recent months, and businesses that have managed to
endure are now searching for methods to use
technological breakthroughs to advance.
• A business intelligence strategy is a roadmap that aims to
assist businesses in measuring their performance and
improving it through architecture and solutions.
• Business intelligence analyst abilities are at the forefront
of BI plans, especially planning.

4. Basic Understanding of BI
• Business intelligence (BI) software collects business data and transforms it
into practical insights that allow businesses to make educated business
judgments.
• BI tools allow businesses to access and analyze data through reports, graphs,
dashboards, charts, summaries, and maps to develop a BI strategy.
• A business intelligence strategy builds a roadmap for applying data in any
organization.
• PLAN- As by simply adopting the appropriate technology and building a
software platform won’t guarantee a profit.

5. Basic Understanding of BI
To develop a plan, we need clarity of the following 3 things;
1.How will you use the software platform?
2.What data will you manage for analysis?
3.And how will you enable your staff to make informed, data-driven decisions?

6. • A business intelligence strategy can help a firm profit from actionable insights.
• Eg Access to sales performance benchmarks, human resources salary projections,
and ensuring your shipping department understands what to ship each day
• A planned approach that includes discovery, planning, and measured execution
leads to success.
• Business intelligence strategies may help think through all the elements of putting
up business intelligence technology and executing everything( from planning to
objectives to personnel to ensuring that the new solution is a success).
• (BIS)It answers each area of how a firm utilizes data and each step in implementing
a business intelligence tool.

7. Business intelligence techniques
• Business Intelligence is concerned with assisting in decision-making.
• BI tools are frequently referred to as Decision Support Systems (DSS) or fact-
based support systems because they provide business users with the
technology to analyze their data and extract knowledge.

8. Business intelligence techniques
• Business Intelligence tools usually access the data in a data warehouse.
• A data warehouse already contains data from numerous production systems
within the organization,
• It is cleansed, consolidated, conformed, and stored in one location.
• BI applications may focus on analyzing the information (various business
intelligence techniques).

9. Data visualization
• Data is stored as a set or matrix of figures, it is accurate but tough to
understand.
• For eg Are sales increasing, decreasing, or staying the same?
• Analyzing several dimensions of information at once becomes much more
difficult.
• As a result, data visualization in charts is an easy approach to grasping how
to interpret the data immediately.

10. Data Mining
• Data mining is examining huge amounts of data to detect relevant patterns and
rules using automated or semi-automatic means.
• a corporate data warehouse possesses an enormous quantity.
• Discovering facts that may influence business decisions is very important.
• Database researchers employ data mining approaches to reveal hidden patterns
and relationships in the data.
• Knowledge discovery in databases comprises all of the steps involved in
transforming raw data into useful information with any necessary selections,
transformations, sub-sampling, and selection of the proper way for transformation.

11. Multi-Cloud
• Following the outbreak of the pandemic and the national lockdown that
ensued, many businesses worldwide began utilizing cloud technologies in
their operations.
• The advent of cloud technology has had a significant effect on many
organizations. Even after the limitations are lifted, companies still prefer to
work over the internet because of its ease of use and accessibility.
• Thanks to its low cost and easy-to-use features, even R&D projects are being
transferred to the cloud.

12. Reporting
• BI technologies help business users design, schedule, and generate
performance, sales, reconciliation, and savings reports.
• BI technology-generated reports efficiently gather and present information to
aid management, planning, and decision-making.
• Once the report is built, it may automatically be sent to a specified distribution
list in the proper format with current/weekly/monthly data.

13. Time-series Analysis Including (Predictive Techniques)
• Every data warehouse and business data is time-based. Product sales, calls,
hospitalizations, and so on are just a few examples of this.
• It’s critical to show how users’ behavior has evolved regarding product
relationships or sales contract modifications due to marketing campaigns.
• Future trends or outcomes may be forecast based on previous data.

14. Online Analytical Processing (OLAP)
• OLAP (Online Analytical Processing) is a fundamental business intelligence
approach that solves analytical issues with multiple dimensions.
• The multi-dimensional nature of OLAP allows users to examine data concerns
from various perspectives, which provides flexibility in dealing with problems.
• They can find latent problems by looking at things from different angles.
Budgeting, CRM data analysis, and financial prediction are examples of tasks
that can be done using OLAP

15. Extraction-Transaction-Loading (ETL)
• Extraction-Transaction-Loading (ETL) is a specialized business intelligence
approach that orchestrates data processing.
• It extracts data from storage and converts it to the processor before loading it into
the business intelligence system.
• They’re commonly used as a transaction tool, which transforms data from numerous
sources into data warehouses.
• The data is then filtered and moderated by ETL to meet the demands of the
business.
• It improves the quality level by loading it into end targets such as databases or data
warehouses, called quality verification.

16. Statistical Analysis
• Data analysis begins with the mathematical underpinnings used to assess the
significance and trustworthiness of observed connections.
• Distribution analysis and confidence intervals (for example, changes in user
behaviors, etc.) are impressive features.
• The technique of using statistics to establish and evaluate outcomes from
data mining is known as statistical analysis.

17. Given the enormous amount of Business Intelligence software
solutions available, narrowing down the right one for your business can
be a tedious process. How does a business start implementing this
software? One way to start is by looking at systems that are popular
among peers, because those products are the ones that are most
likely to stay constantly maintained and upgraded.