What is BI?
Business Intelligence (BI) is a computer based technique to identified, extracting (*1) and analyzing business data. For example senior management of an industry can inspect sales revenue by products and/or departments, or by associated costs and incomes. BI technologies provide historical, current and predictive views of business operations. So, management can take some strategic or operation decision easily.
BI is used for reporting, online analytical processing, data mining, process mining, complex event processing, business performance management, benchmarking, text mining and productive analysis .By using BI, management can monitor objectives from high level , understand what is happening, why is happening and can take necessary steps why the objectives are not full filled. Business intelligence aims to support better business decision-making. Thus a BI system can be called a decisions support system (DSS).
Before going to launch any product, company need to understand of market trend. Company uses BI to analyze market data and understand which product or which business is suitable for the current time in the market. In a word, BI gives you right information, right time in right format.
Suppose a software company wants to develop an ERP. Before going to develop an ERP, company’s business executive need to better understand of its development cost. Potentiality of it’s sells volume. Management need to understand that will it protect margins. To understand the above factors they have to have consolidated views of business. BI provides this visual and consolidates view. From this reports, company can take necessary action and can take decision whether they should develop an ERP or not.
Now suppose your ERP is developing and your management can see the current situation of the project. They have no time to see all the documents. They want to view the current status of the project from the top. This may be dashboard, BI provides this. So management can understand whether the product is developing cost effective way or not. BI can suggest corrective action against the data.
Sample sales Dashboard:
Before implementing a BI solution, it is worth taking different factors into consideration before proceeding. According to Kimball et al., these are the three critical areas that you need to assess within your organization before getting ready to do a BI project.
- The level of commitment and sponsorship of the project from senior management.
- The level of business need for creating a BI implementation.
- The amount and quality of business data available. Data can be gathered from Enterprise, department or individual.
How BI works?
BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes.
-Data warehousing and BI
Often BI applications use data gathered from a data warehouse or data mart. However, not all data warehouses are used for business intelligence, nor do all business intelligence applications require a data warehouse. In order to distinguish between concepts of business intelligence and data warehouses, Forrester Research often defines business intelligence in one of two ways:
Boarder Definition: “Business Intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making.” When using this definition, business intelligence also includes technologies such as data integration, data quality, data warehousing, master data management, text and content analytics, and many others that the market sometimes lumps into the Information Management segment. Therefore, Forrester refers to data preparation and data usage as two separate, but closely linked segments of the business intelligence architectural stack.
Forrester defines the latter, narrower business intelligence market as “referring to just the top layers of the BI architectural stack such as reporting, analytics and dashboards.”
-Business intelligence and business analytics
Thomas Davenport has argued that business intelligence should be divided into querying, reporting, OLAP, an “alerts” tool, and business analytics. In this definition, business analytics is the subset of BI based on statistics, prediction, and optimization.
Business Intelligence can be applied to the following business purposes (MARCKM), in order to drive business value:
MARCKM means – Measurement, Analytics, Reporting/Enterprise Reporting, Collaboration/Collaboration Platform, and Knowledge Management.
In addition to above, Business Intelligence also can provide a pro-active approach, such as ALARM function to alert immediately to end-user. There are many types of alerts, for example if some business value exceeds the threshold value the color of that amount in the report will turn RED and the Business Analyst is alerted. Sometimes an alert mail will be sent to the user as well. This end to end process requires data governance, which should be handled by the expert.
Semi-structured or unstructured data
Businesses collect a huge amount of valuable information. These information included in the form of e-mails, memos, notes from call-centers, news, user groups, chats, reports, web-pages, presentations, image-files, video-files, and marketing material and news.
BI uses both structured and unstructured data, but the former is easy to search, and the latter contains a large quantity of the information needed for analysis and decision making. It is very difficult to identify which information is in unstructured data. Unstructured and semi-structured data have different meanings depending on their context. In the context of relational database systems, it refers to data that cannot be stored in columns and rows. It must be stored in a BLOB(binary large object), a catch-all data type available in most relational database management systems. Since it is difficult to search information from unstructured data so, what will organization do to extract information from unstructured data?
Metadata is only way to search information from unstructured data. Metadata is actually data about data. Metadata can include information such as author and time of creation. This metadata can be stored in a database. So, it is easy to search by this metadata. To solve problems with search ability and assessment of data, it is necessary to know something about the content. It is more useful would be metadata about the actual content – e.g. summaries, topics, people or companies mentioned.
There are many challenges to develop BI with semi-structured and structured data. Those are:
-Accessing unstructured data because it is stored in a variety of format.
-There is no standard terminology.
-Volume of data is so high
-Search ability of unstructured data is not easy.
Many BI tools are available now. The information delivery model is given below.
Data can be collected from different source.ETL (Extract, transform and load) is responsible to collect these data. Then these data are kept in data warehouse by ETL. Data warehouse can be sub divided into Data marts. OLAP provides these data to the BI tools users by OLAP Cubes. BI tools display these result to the users.
Many BI tools are available now. Most of the organization follows-
- Reporting and querying software: tools that extract, sort, summarize, and present selected data
- Data mining
- Data warehousing
- Decision Engineering
- Process Mining
- Business Performance management
- Local Information System
Microsoft introduced a new BI tool name-Dashboard. Dashboard is a visual display of the most important information needed to achieve one or more objectives which fits in a single computer screen so it can be monitored at a glance – Stephen Few, Information Dashboard design.
Microsoft BI Solution – 2007 architecture.
Data extraction is the act or process of retrieving data out of (usually unstructured or poorly structured) data source for further data processing or data storage. The import into the intermediate extracting system is thus usually followed by data transformation and possibly the addition of metadata prior to export to another stage in the data workflow. Usually, the term data extraction is applied when (experimental) data is first imported into a computer from primary sources, like measuring recording devices. Today’s electronic devices will usually present a electrical connector (e.g. USB) through which ‘raw data” can be streamed into a personal computer.