Why Business Intelligence? 5 Business Purposes

Fundamentally, Business Intelligence (BI) can be applied for at least 5 business purposes:

• Measurement – to inform business managers and leaders about the progress towards certain business goals.
• Analytics – to arrive at more optimal decisions and to perform business knowledge discovery. This  typically involves techniques like data mining, process mining, statistical analysis, predictive analytics, predictive modeling, business process modeling, complex event processing and prescriptive analytics. 
• Reporting – to provide strategic reporting to serve the strategic management of a business, not operational reporting. Frequently involves data visualization, executive information system and OLAP. 
• Collaboration – to allow different parties or departments (inside or outside an organization to work together through techniques such as data sharing and electronic data interchange.
• Knowledge Management – to make the company data-driven through strategies and practices to identify, create, represent, distribute, and enable adoption of insights and experiences that are true business knowledge. Knowledge management leads to learning management and regulatory compliance.

Note that in all of these 5 areas, business intelligence can be implemented passive and more pro-active, such as when managers are warned or alerted via an alarm function. Such warnings can come  in many different shapes and forms. For example, if some business result is below a certain critical threshold value, the amount on a dashboard may be shown in BOLD, in RED, or even FLASHING to alert a decision maker. Sometimes an automatic alert (email) may be sent to one or more individuals.

DSS

Hi all,

I am new to the forum, also new to BI. I have been working for marketing research agency for quite sometime now i am trying into BI. As i understand Market Intelligence, Business Intelligence and Competitive Intelligence are 3 major components in Decision Support System, i want to know is there real serious correlation among these components. what i am trying to understand here is how far these field contribute to DSS of any corporation. It can be evaluate better by understanding contribution by BI vs. MI and BI vs. CI and MI vs. CI.

i appreciate your help!
thanks,
zabb

Scorecards and Dashboards For Contact Centre

Hi, I found a interesting Article:

Scorecards and Dashboards For Contact Centre:
According to Harvard Business Review, the Balanced Scorecard concept is the most influential management idea in the past 75 years. The performance of an organization is tracked against four key perspectives such as Finance, Customers, Internal Processes and finally Learning, Innovation and Growth. These perspectives are divided into multiple sets of Key Performance Indicators ( KPIs ) and grouped against these perspectives and further broken from top level to the employee level KPIs.

Companies are globally moving towards implementing enterprise wide organizational Balanced Scorecard. Simplicity, personalization and empowerment are the keys while building Agent Dashboards. These goals are achieved through the application of Business Intelligence ( BI ) concepts. A contact center performance is an aggregation of every agent performance.

An Agent should be able to customize and get the unified view of the aggregated metrics and track them on daily basis. How much he has achieved with respect to target? How his performance benchmarked with respect to overall call center performance. Is there performance improvement over a period of time? He can drill down and see which particular KPI is bringing his overall score down and he can see how he has or has not been able to improve his performance over a period of time.

A Supervisor should be able to see how agents reporting to him are performing. His Dashboard should show an aggregated score of all his agents and simultaneously showing tabular information of comparative scores of all agents working under him. He can drill down on agent score who are not performing well. He can see individual KPIs of agents and study whether there are improvements over a period of time under the respective KPI.

A Manager would like to see dashboards of aggregated scores of supervisors on weekly basis rather than daily basis. He can drill down till agent’s individual KPI level if he desires. A manager could also drill down against different channels and see the performance of different supervisors under different channels.

A Call Center Head would like to see the aggregated performance of his senior managers and across channels and will be interested to track performance against KPIs on monthly basis. He can drill down from manager level right up to the agent level and their individual KPIs to see where the actual pains are located.

A Quality Manager would like to see how agents are performing against quality related KPIs. He would further like to analyze poor performing KPIs against type of service requests, type of channel and may be against important set of clients so that specific training programs could be designed for specific sets of agents rather than a plane blanket approach where every one is trained for everything.

In all the performance metrics could be analyzed against multiple dimensions such as channels customers, type of services, location etc. Users down the line are empowered to analyze the data and able to get operational and strategic insights. Hundreds of variants of Dashboards and Reports could be created from a simple single user friendly interactive interface

All above could be simply achieved through web browser and single user interactive interface. Every person in the organization can personalize the dashboard or reports from this single user interface. The power of analysis is coupled into the reporting and dashboard. It is this Business Intelligence capability that creates the unique customer experience in getting the insights of business.

30 ways to use OLAP in business

An interesting article by Contour Components discloses 30 ways on where OLAP or multidimensional data analysis is applicable. OLAP is a specific way of financial and statistical data representation for executives, specialists and analysts. It is designed to aid in decision making and better information understanding. The main idea is to answer the user’s questions, arising at the work time, on-the-fly.

An OLAP system allows user to get into details and generalize, filter, sort and regroup data at the time of analysis. Intermediate and final totals are recalculated instantly.

The main data viewing and manipulation tool is the dynamic electronic worksheet. Its elements – columns and rows – are the manipulation controls. Moving rows and columns or clicking them user makes the system perform calculations and show data in different aspects.

Thus, user can produce lots of reports out of a single dataset on his own, without any interference with IT-specialists. This saves IT departments from continuous hard-coding of various kinds of reports and gives additional degree of freedom to executives and specialists for getting the essential information.

OLAP breaks data into two groups: facts (numbers, also called measures) and dimensions (descriptions). Facts are aggregated in a given slice by some algorithm while the user defines grouping and aggregation depth.

Also, an electronic worksheet can display data with a regular structure. OLAP is suitable everywhere, where a task of multifactor data analysis takes place. Generally, having a table filled with data, given that it contains at least one descriptive column and one or more data columns, OLAP can become an effective and convenient tool for analyzing such table and producing reports.

Read 30 ideas of using OLAP

Bestselling books on data warehousing and decision-making

What is BI really? Multiple Versions of the Truth

What is BI really? Here are some 'versions of the truth':
- According to a website BI is a broad category of Management Information Systems, applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better B. decisions.
An article by Peter Bochner and Jack Vaughan mentions a few more definitions:
- The process firms go through to gather, store and analyze data.
- A critical activity that helps companies to make faster, smarter decisions, as well as increase revenue, build customer loyalty, streamline operations, improve risk management and even enable previously impossible B. processes.
- A constantly evolving strategy, vision and architecture that continuously seeks to align an organization’s operations and direction with its strategic B. goals.
- The theory that the more you know about your customers and the B. problem you’re trying to solve, the better you’re able to solve it.
- According to Danny Siegel from Pfizer Inc an effective BI system provides corporations with “one version of the truth”.

Ain't it funny that there is no single version of the truth about what BI is? Perhaps one of you has an even better definition to share with us?

Eliminating the Guesswork in B. Decisions...

Read on a Business Intelligence Portal:
"Today, the biggest threat to businesses is a lack of information or even worse incomplete information. Without all the facts, decisions are made, and risks and opportunities are assessed based on anecdotal, incomplete or outdated information – otherwise known as guesswork".

I would argue that the opposite of this phrase is also true:
"Today, the biggest threat to businesses is too much information or even worse overcomplete information. Without this abundance of detailed facts, loads of useless and contradicting data, and poorly integrated information systems, strategic decision making and risk management could focus on the factors that matter and would be far less complicated and more effective".