EIM Intro - Information Lifecycle

8/9/2019 EIM Intro - Information Lifecycle

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RFCorsello

Research

Foundation

Enterprise Information ManagemInformation Lifecycle


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Introduction

Information Management is a complex subject covering all aspemanaging information within a given domain or organization

Sharing between domains and organizations is a large need that bradditional complexity

Information Management requires the understanding of the thrbasic states of information

Data raw values

Information data under a given context

Knowledge the emergence of understanding from information


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Information StFrom Data to Kno


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Data

Data is: Raw values, such as 5 or 5 dollars

Collections of values, such as a spreadsheet file

Data does not imply format or structure

Data itself is the value, not the storage

Data may be structured or formatted in any way

Any specific format MAY provide context

Data within an appropriate context becomes information

Once removed from a context, or in an irrelevant context is once again data

Data may be relevant in many contexts, which is the cornerstone for sharing


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Information

Information is:

Data under a relevant context

Context must be relevant to the data

Context must also be relevant to the observer

A concept information does not physically differ from data

Information implies an ability to gain understanding

But only if the information is available

Information must be relevant to the context of the observer

Data must also be relevant to the context of the observer to be more than simply data

Knowledge arises from information

Facts are information known to be correct or true

Information is data with relevant context

Data is any value or set of values


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Knowledge

Knowledge is:

The theoretical or practical understanding of a subject

Facts and information

Awareness or familiarity gained by experience of a fact or situation

Knowledge arises from information

Facts are information known to be correct or true

Poor context for information may greatly impact the ability to derive knowledge

Information implies an ability to gain understanding

But only if the information is available

Knowledge results in the synthesis of information

Possibly just in the mind

Tools for content management enable the creation of context

Linking between contexts and content provides additional information

Knowledge management is more appropriately information management

It is the users that translate knowledge into information within the systems


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Data to Knowledge

From an Information Technology perspective

All knowledge is represented as information

All information is represented as data

Structure of the data as stored provides context

Derivation of information from data comes from how the data is accessed

The access methods and data structures form the context

Knowledge is represented as new information generate from existing information

One user creates information that a second user accesses

The second user has gained knowledge from the first

If software were able to take action based upon the information, that software would also have gained knan intelligent agent

Data is what is managed in a computer system

The contexts are also represented in a computer as data


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Information LifecFrom Capture to D


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The Lifecycle

The information lifecycle is the processes by which data comes existence, is managed over time and eventually is discarded.

There are generally four basic states of the information lifecycle

Creation, collection or capture

Distribution, use and access

Maintenance, update or change

Disposition, archival or destruction


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Creation

The creation of data is the process of generating and storing da For some data, the entire lifecycle may be outside of IT systems

as paper records.

The data creation phase is broken into three primary areas:

Capture

Assessment and Approval

Ingestion


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CreaCapture, Assessment, In


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Capture

Data capture can be divided into four primary categories:

Continual or telemetry, where data is automatically generated and fed into ainformation repository, such as surveillance cameras

Bulk, or offline where data is collected and aggregated, then fed in bulk toinformation repository

Manual, which is the traditional human process of collecting data a single at a time

Derived, or automated generation, where data is created by performing com

on other data. This includes activities such as: Models and simulations

Statistical analysis

Interpolation or smoothing


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Assessment and Approval

The assessment process involves the evaluation of captured data to ensure it meets pre-defined criterion for acceptance

The two primary parts to the assessment process are:

Quality Assurance (QA)

Quality Control (QC)

QA is the set of practices that are performed to:

Ensure data will meet acceptance criteria prior to being created. This involves activities such as:

maintenance and calibration of instruments

usage guidance for proper instruments.

Evaluate collected data to enhance quality assurance activities for future collections.

Evaluate QC practices and results to ensure quality criteria are met.

QC is the set of practices that:

Ensures the data within a repository will meet or exceed quality criterion

Prevent poor quality data from making it into the public repositories

The assessment and approval stage ensures only the created data meeting quality and acceptance criteria are available to futu

Poor assessment and approval practices result in poor quality data being available


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Ingestion

Accepted data is loaded into the appropriate business repositories The process of ingesting data may involve transformations to match the

destination format

Transformation is a common requirement for automated collection me

To maintain a full and verifiable chain of custody

Raw data is kept in addition to the transformed data

For space savings, raw data are often archived to an offline store

Once data is ingested, it is available for consumption

It is not uncommon for the entire creation process to be automateda single system


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Distribution and Use

Use of data within a repository is the primary purpose for the d

existence

Data use is considered in several ways:

Discoverability

Accessibility

Usability


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Distribution andDiscoverable, Accessible, Available


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Discoverable

Once data is within a repository is may be used

In order to use data it must be discovered by a potential user

Mechanisms to facilitate the location of data are discovery mechanisms

If data cannot be found, it cannot be used

Discoverability is key in the storage of data and the availability of that storaguser system

If a user must search in multiple locations to find data, it is of marginal discoand use

For data to be discovered, the discovery data (metadata or catalog) must alsaccessible

A user interface is the central location for discovery to be exposed to a user


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Accessibility

Data must be accessed to provide value

The accessibility of data involves aspects such as:

Security

Logical location

Format

If data is secured so that potential users cannot access it, the value of the data is diminished to those us

In sensitive domains, this is expected and desired

Logical location further limits accessibility if the data is contained within a repository that cannot be acc Behind a firewall

Simply far away, then data transfer may take too long

Data in formats that are proprietary or poorly supported may not be accessible to the tools required

Overall, accessibility is a balancing act with security, need and cost


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Usability

Data in an unusable format given the available tools is unusable

If data must be processed prior to being used it is less usable

If processing time is long, data may become irrelevant before it is usable

Usability has subtle implications such as:

Scale

Temporal currency

Accuracy and precision

Low precision data cannot be used in a high precision analysis

Cost of data creation always a trade off against anticipated use

Collecting high-quality, high-precision data can always pay off if cost shared with users in need oflower-precision data

Redundant data collections are purely evil


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Maintenance and DisposNow that we have it, What do we do with it and How do we get


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Maintenance

Data that changes over time must be maintained

Data editing is subject to discovery, access and usage in addition to the need edits

In some scenarios, only the current values are relevant

In other scenarios, temporal changes are of greater significance than the current values

Editing scenarios affect and influence data management strategies.

The maintenance phase of the lifecycle includes:

The entire set of practices and processes governing data management and maintenance

Issues such as archival, availability, continuity of operations (COOP), fault-tolerance, perfand total costs

The maintenance phase is the longest lived part of the data lifecycle

All data uses occur within the maintenance phase


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Disposition

Disposition involves the processes and practices by which data is aged within repo

Disposition includes:

Archival or removal of old data

Segregation of history data from live data

Mechanisms for making segregated data available

It is common that disposition is driven by storage costs and legal mandates such a

SarbanesOxley (Sarb Ox / SOX)

ClingerCohen

Health Insurance Portability and Accountability Act (HIPAA)

Once data has been disposed it is no longer part of the information lifecycle

If the data is still available, such as historic data, it is not disposed


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EIM Intro - Information Lifecycle

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