Data, information and knowledge – these three terms are used frequently day-to-day.
The main difference between the three is the level of abstraction. Data is the least abstract concept, followed by information and then knowledge. The length of the River Ganges is a piece of data; a book about the River Ganges regarding the states of India it flows through, the cities that lie on its banks and the reasons it is considered holy by many people is information; and a report detailing how to utilize the river water for irrigation is a matter of knowledge.
Raw data refers to a collection of numbers, characters, images and other outputs.
The heart of any information system – and the foundation of a business enterprise – is its stored data and the mechanism by which the data is retrieved and presented. The system’s goal is to provide accurate information at the right time to the right person at the right place, and at the right cost.
A View of Today’s Business Intelligence Environment
For many companies, there is lots of room for improvement in the business intelligence environment (see Figure 1). Data is often stored redundantly, and it’s lacking accuracy, consistency and timeliness. Most systems are not integrated, and data searches are often complex. Additionally, the value of information is hidden in multiple, disparate data sources and, as such, has not yet been fully realized. Even if the data was improved, accessibility channels to the data are limited.
Another problem with the data is the question of ownership. Data is generated by the applications that support the organization’s business processes. Metrics are defined using this data, but no ownership is assigned to the metrics. As a result, no user group is held responsible for ensuring accuracy and timeliness. All too often, the IT organization becomes the data owner by default, since it defines and supports the metrics, which doesn’t reflect reality.
People and computers constantly produce and collect large amounts of data. One must be able to access, integrate and manage all this data in order for it to be transformed into something useful for everyone. The goal is to use BI to run the business more intelligently. In today’s competitive and unpredictable world, the quality and timeliness of an organization’s BI can determine not only the difference between profit and loss, but also the difference between survival and bankruptcy.
Various Faces of BI Applications
BI applications need to be part of a cross-organizational culture as opposed to departmental decision support silos. This cross-organizational approach needs to ensure information consolidation, integration and integrity. Done well, it can result in BI applications and operational analytic applications that provide the following benefits inside the firewall:
- Analytic power with a comprehensible interface in the hands of every user.
- Access to disparate data sources.
- Support for key performance indicators for each job through a dashboard.
- Interactivity at every user level.
- Content delivery, in the form of information, on many different media, including mobile phones.
The data should be available in a variety of formats, including metrics, KPIs, dimensions, performance scorecards and dashboards. Here are the distinctions among these terms:
- Metric: A metric connotes any measurement.
- KPIs: A KPI is a measure of importance that one can act upon. Organizations may have many measures of importance to the business but only a few KPIs. They are often represented as a percentage or a ratio of the actual figure.
- Dimensions: Dimensions are business objects, such as a customer, a product, etc.
- Performance scorecards: Scorecards are a method of measuring activities and outcomes for effective performance management. They are measured against the stated KPIs, and they enable executives, as well as employees at all levels, to measure their performance as it corresponds to specific goals and objectives.
- Dashboards: Enterprise performance dashboards are based on meaningful and well-defined KPIs. The major components of a dashboard are KPIs and scorecards, and they can vary by industry, business function, type of decision-maker and the level of skills and tools used. Effective dashboards can be very powerful in driving action. They should enable business users to consolidate their KPIs on a single page for continuous and effective monitoring, as well as drill-down on individual KPIs to answer specific business questions. The dashboard interface should be designed such that business users can create custom dashboard pages without any IT support.
Challenges with Varied Data Sources
One of the challenges in building cross-organizational BI applications is merging data from different types of data sources. There are three major types of data sources: operational, private and external.
Operational data: Online transaction processing, batch and high-volume transaction output systems provide internal operational data on a variety of subjects, including financial, logistics, sales, order entry, personnel, billing and research and engineering.
Private data: Internal departmental data usually comes from the desktops and workstations of business analysts, knowledge workers, statisticians and managers. Examples include product analysis spreadsheets, regional product usage spreadsheets and prospective customer databases.
External data: Organizations often purchase external data from vendors that specialize in collecting industry-specific information available in the public domain, such as health care statistics, customer profile information, customer catalog ordering habits and customer credit reports. External data is usually clustered around the following categories:
- Sales and marketing data: Vendor sales and market shares.
- Credit data: Credit ratings, business viability assessments.
- Competitive data: Products, services, prices, sales promotions, mergers, takeovers.
- Industry data: Technology trends, marketing trends, management science, trade information.
- Economic data: Currency fluctuations, political indicators, interest rate movements, stock and bond prices, income groups, consumer behavior.
- Demographic data: Age profiles, population density.
- Commodity data: Raw material prices.
- Psychometric data: Consumer profiling.
- Meteorological data: Weather conditions, rainfall, temperature (especially for agricultural and travel industries).
Turning Data and Information into Knowledge
Integrating, transforming and presenting data from various sources is a slow and tedious process. For that reason, most companies turn to automated ways of accomplishing this.
Ideally, organizations should use an automated system that acts as a data analyzer to break down data and provide a visualization of trends. Users should be able to add, modify and change the order of dimensions and measures. Aided by the appropriate software, raw data should be transferred into nuggets of information, leading to knowledge that will result in effective, actionable decisions.
The software accessing the data should produce information and, ultimately, provide knowledge. Data should be represented in the form of lists, charts and matrices. Software design should be Web-based and be able to integrate any data source and document, including applications, data warehouses/data marts, real-time data feeds, high-volume transaction outputs and document print streams.
Most organizations today need better comprehension of their data. They need to find individuals in the organization who can identify important data and ensure its accuracy and consistency. They also need to develop analysts who can help executives set targets for the value of some of the data, which will produce measures and KPIs.
It is important for these analysts to have an enterprise-wide mindset. Additionally, they should adopt software that is user-friendly, so that changes with dimensions, measures and KPIs can be made easily. And there should be very little need for training to make use of the information generated.
Organizations today are data-rich and information-starved. They need to develop staff and invest in tools and systems that enable them to turn data into digestible information, resulting in even better knowledge.
This is the third in a series of articles by Shaku Atre. Click on the titles to read the other recent articles: "Who in the World Uses Only Words and Numbers in Reports?"; "Who in the World Wants to Stay Locked Up?"; "Who in the World Doesn't Want to Reach for the Clouds?"; "Who in the World Wouldn’t Want a Collaborative BI Architecture?"; "Who in the World Needs a Data Warehouse?"; "Who in the World Wouldn’t Want to Evaluate BI Products?"; "Who in the World Needs a Hard Drive?"; and "Who in the World Wants to Just Be Structured?"
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