Many companies have loads of data, but hardly any steering information.
Information Management can have many different meanings. Good definitions emphasize that people have information requirements in order to steer processes by using information technology.
Information Management is centered around People, Processes and IT.
eQTeam sees identifying, analyzing and controlling information requirements as core activities of Information Management. Besides these, other key aspects are: data origin, data lineage, authorization, compliance with regulations, and especially privacy-protection of employee and customer data.
Many decisions are often taken based on gut feeling stemming from past experiences. Informed decisions, however, can be very different from those purely based on a vague notion.
When identifying the need for steering information the question often arises: which steering information is needed to make a well informed decision?
What makes answering this question tedious, is that information requirements:
Many new requirements are based on the answers to earlier ones. Software like Business Intelligence tools are specialized in serving this continued need for new information.
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Once an information requirement is clear, and hence is known which information is needed to make a well informed decision, there are 2 next steps:
Describing requirements unequivocally is a tedious chore. Many people give different meanings to the same term. Or, one and the same fact can be denominated by several synonyms.
A task for Information Management is building a meta-information collection which stores: what is meant by which term, how calculations should be performed, and from which data sources information items are fed.
Data sources are often registration systems which are transaction oriented. Managing these are often denominated by the term Data Management.
Data is an important resource in an organization. When employees cannot dispose of business data, or when data get lost, these events will seriously disturb business processes.To mitigate damages there should be an integrated approach to data management, consisting of five elements:
When the term transaction is used, people mostly think of a financial transaction. The latter is however only a specific example of this term which refers to a number of actions that should be completed as a whole to achieve a status change of something.
So transferring an amount of money can only be successful when the amount is deduced from account A and added to account B. If the monetary registration system would not work correctly, money can be created or lost. The same applies to inventory and transport registration systems. Registering facts that do not result in a status change are irrelevant.
Systems which store transactions are called transaction systems. A very commonly used denomination is On-line Transactional Processsing or OLTP system. Many employees in IT will use the term and, even more so, its abbreviation without thinking. eQTeam prefers to call these, registration systems as registration is the main goal.
Nearly all companies have several systems to registrate their business data. And this leads to additional complexity. Not only data structures of underlying databases differ widely, also master data are often stored in duplicated forms.
Data is not equivalent to information. (See also: What is information?) Registration is about storing data in a database. Information is about acquiring and presenting datasets from one or more databases and interpretation of the results by using metadata.
Metadata explains the meaning of a certain data-element. In a certain table a number is stored. What number is it? Is it a monetary amount, a volume, or something else? Can all numbers be added? Etc.
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According to Gartner, the process to transform data into information and acquire knowledge by analysis and interpretation in order to benefit stategically. This area arose in the mid 1990's since many managers realized that in their company a lot of data was being stored, but very little steering information was available.
When Business Intelligence is regarded from a wider perspective it is the continuing process in which organizations gather and register data in a structured way before analyzing these and applying gained knowledge during decision-making processes to improve the company's performance.
A Data Warehouse (DWH) is a collection of datasets originating from one or several transaction systems (also known as registration systems) gathered to combine these into useful management information. Due to its American origin, the process of setting up a Data Warehouse has been turned into a gerund-like word called data warehousing. By now the term Data Warehouse mostly refers to the underlying datastructure meant for numerous Business Intelligence reporting tools to acquire information from.
Until about 15 years ago it was very common to store as much detail in a DWH as possible. Nowadays many countries have very strict privacy-laws, forcing a business to decide whether sensitive detailed information is really necessary for steering purposes. If so, each customer needs to be informed and approve the use of his/her personal data. Often this can be achieved by adding this use to the general terms and conditions and having customers agree to them prematurely.
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