Business Intelligence

an overview

Nur Cahyo Wibowo, SKom, Mkom Komputer & Masyarakat Progdi Sistem Informasi UPNVJT

What is Business?

   An organization that provides goods and services to others who want or need them. (Univ. of Minnesota) An economic system in which goods and services are exchanged for one another or money. (Business Dictionary) Kegiatan usaha yang terorganisasi untuk menghasilkan barang atau jasa guna memenuhi kebutuhan konsumen dan bertujuan menghasilkan profit (laba).

The Nature of Intelligence

         Learn from experience including by trial & error.

Apply knowledge acquired from experience to another situation.

Handle complex situations.

Solve problems when important information is missing is the essence of decision making.

Determining what is important.

The ability to reason and think.

Reacting quickly and correctly to a new situation.

Understand and interpret visual images.

Being creative and imaginative.

What is AI?

Artificial Intelligence systems include people, procedures, hardware, software, data and knowledge needed to develop computer systems and machines that demonstrate characteristics of intelligence [Ralph Stair].

So, What is BI?

   IT-enabled business decision making based on simple to complex data analysis processes (BI) applications are decision support tools that enable real-time, interactive access, analysis and manipulation of mission-critical corporate information.

Technically: • Database development and administration • Data mining • Data queries and report writing • Data analytics and simulations • Benchmarking of business performance • Dashboards • Decision support systems

Business Intelligence Systems

  The purpose of a business intelligence (BI) system is to provide the right information, to the right user, at the right time.

BI systems help users accomplish their goals and objectives by producing insights that lead to actions.

Source: www.kairon.com

Business Intelligence

ETL tools Data Warehouse Marketing Data Mart Finance Data Mart Distribution Data Mart BI Pivot Table OLAP Reports

Tecnologies Supporting BI

        Database systems and database integration Data warehousing, data stores and data marts Enterprise resource planning (ERP) systems Query and report writing technologies Data mining and analytics tools Decision support systems Customer relation management software Product lifecycle and supply chain management systems

Business Intelligence Tools

   Tools for searching business data in an attempt to find patterns is called business intelligence (BI)

tools.

The processing of data is simple: Data are sorted and grouped and simple totals and averages are calculated.

Reporting tools are used to address questions like:  What has happened in the past?

  What is the current situation?

How does the current situation compare to the past?

BI Tools Cont.

   Data-mining tools process data using statistical techniques, many of which are sophisticated and mathematically complex.

Data mining involves searching for patterns and relationships among data.

In most cases, data-mining tools are used to make predictions.  For example, we can use one form of analysis to compute the probability that a customer will default on a loan.

Data, Information, Knowledge

   Data • Items that are the most elementary descriptions of things, events, activities, and transactions • May be internal or external Information • Organized data that has meaning and value Knowledge • Processed data or information that conveys understanding or learning applicable to a problem or activity

Why Data Warehousing?

Konsumen mana yg memiliki margin tinggi/rendah?

Siapa saja konsumen saya dan produk apa saja yang mereka beli?

Jalur distribusi apa yang paling efektif?

Promosi produk apa yang paling berpengaruh terhadap penghasilan perusahaan?

Konsumen mana yang senang mengikuti berbagai kompetisi perusahaan? Apa dampak/pengaruh produk/layanan baru terhadap penghasilan perusahaan dan margin?

Data Warehouses and Data Marts

    Basic reports and simple OLAP analyses can be made directly from operational data.

Many organizations choose to extract operational data into facilities called data warehouses and data marts, both of which are facilities that prepare, store, and manage data specifically for data mining and other analyses.

Programs read operational data and extract, clean, and prepare that data for BI processing.

The prepared data are stored in a data-warehouse database using data-warehouse DBMS, which can be different from the organization’s operational DBMS.

Data Warehouses Versus Data Marts

    A data mart is a data collection, smaller than the data warehouse, that addresses a particular component or functional area of the business.

The data warehouse is like the distributor in the supply chain and the data mart is like the retail store in the supply chain.

Users in the data mart obtain data that pertain to a particular business function from the data warehouse.

It is expensive to create, staff, and operate data warehouses and data marts.

 

On-Line Analytical Processing (OLAP)

Literally, On-Line Analytical Processing. Designates a category of applications and technologies that allow the collection, storage, manipulation and reproduction of multidimensional data, with the goal of analysis.

Example: http://perso.wanadoo.fr/bernard.lupin/english

OLTP VS OLAP

Reporting Systems

  The purpose of a reporting system is to create meaningful information from disparate data sources and to deliver that information to the proper user on a timely basis.

Reporting systems generate information from data as a result of four operations: • Filtering data • Sorting data • Grouping data • Making simple calculations on the data

Digital Dashboard Example

Dashboard

Data Mining

   The application of statistical techniques to find patterns and relationships among data and to classify and predict.

Data mining represents a convergence of disciplines.

Data-mining techniques emerged from statistics and mathematics and from artificial intelligence and machine-learning fields in computer science.

Strategic, Tactical & Functional Benefits of Business Intelligence

Referensi

     David Kroenke, Business Intelligence and Knowledge Management Chapter 9, Prentice Hall 2007.

Anonym, Business Intelligence, Bellevue College.

Turban, dkk, Decision Support Systems and Intelligent Systems Chapter 5, Seventh Edition, Prentice Hall 2005.

Henry Yan, Business Intelligence, ISRC Technology Briefing October 26, 2006.

Hanim MA, Intro to Data Warehouse.