Data Management

Data Management and Emergence of Data
Data management is the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and
enhance the value of data and information assets.
 Data is one of your organization’s most valuable resources. When fully leveraged, it will help your organization control costs, understand
your customers and the market and, ultimately, improve your bottom line. This takes your data beyond basic integration and turning it into
insightful and actionable information
 Data collection and processing features are managed by the DMS Service – a Windows service that runs unattended.
 DMS Service performs the following:
 Communications (telemetry) management ,configuration and management
 Data collection and storage to a database management system (DBMS)
 Data dissemination (DBMS, serial, TCP/IP, email, SMS)
 DMS Plug-ins enable clients to customize their software package based on the sensors used in their system and the type of information they
need to view from the acquired data.
 DMS includes two software applications for the presentation of acquired data: desktop application, a web application.
Types
Content Management Software
Content management software (CM) is used to collaboratively create, edit, review, index, search, translate, publish and archive various
types of digital media and electronic text.
Education Management Software
Education management software is used by teachers, students, and school administrators for organization and collaboration, and to
facilitate learning. Learn More about Education Management Software
Learning Management Systems (LMS)
Learning management systems (LMS) are software applications for delivering, tracking and managing training. They are used mainly by
educational institutions and corporate training departments
Career Management and Placement Services
Career management, development and placement services include consultants, businesses, organizations and employment agencies that
provide information and resources related to employment and career direction.
Thermal Management Design and Analysis Services
Thermal management design and analysis services perform tests and redesigns around thermal dissipation issues.

Facility Management Services
Facility management services perform building operations and maintenance, project management, subcontractor management,
energy management, budget planning, commissioning and de-commissioning services for buildings and facilities.
Marketing Resource Management Software
Marketing Resource Management Software automates the process of completing marketing work.
Document Management Software
Document management software (DM) enables organizations to create, capture, store, index, and retrieve information digitally.
Knowledge Management Software
Knowledge management software (KM) is used to manage the way that information is collected, stored, and retrieved.
Performance Management Software
Performance Management Software is used for reporting and analysis of tracking your Key Performance Indicators (KPIs), incident data and
other variables or a project, employee or enterprise.
Approaches to Data Management
Master data management (MDM), for example, is a comprehensive method of enabling an enterprise to link all of its critical data to one file, called
a master file, that provides a common point of reference. The effective management of corporate data has grown in importance as businesses
are subject to an increasing number of compliance regulations. Furthermore, the sheer volume of data that must be managed by organizations
has increased so markedly that it is sometimes referred to as big data.
Data Management - Book of Knowledge (DMBoK)
A team of data management professionals produced "The DAMA Guide to the Data Management Body of Knowledge" (DAMA-
DMBOK Guide), under the guidance of a DAMA-DMBOK Editorial Board. The publication was made available in April, 2009.
The “body of knowledge” about data management is quite large and constantly growing. It provides a “definitive introduction” to
data management and defines a standard industry view of data management functions, terminology and best practices, without
detailing specific methods and techniques. The DAMA-DMBOK is not a complete authority on any specific topic, but is on
source of information from widely recognized publications, articles and websites for further reading.
The figure below provides an overview of the major areas (bold) with some of the basics functions that described.

Information Management
Data Resource Management or Information Resources Management are terms that have been synonymous with organizations who manage data. But the
implications of the following questions within an organization are critical for the growth, stability, and delivery of business results: who gets what data and
who converts data into information; who balances the competing interests of leaders and followers; and who benefits from the stewardship (not the
ownership) of the data; and how does the choice of implementation of information technologies affect organizational survival. So, without a sound set of
principles, practices, tools, techniques, and decision criteria, the organization can be severely constrained in meeting its targeted goals. Data Management
provides the foundation to organization survival and information security.
Having an organization who focuses on information and data management helps to catalog, assess, validate, and determine the viability of the data
resource. Along with decision-making, managing of data is essentially for making good, reliable business decisions.
Increase in the Growth of Data
Changes in solid state electronics, communication infrastructure, miniaturization of computing devices will dynamically influence the growth of data. In the
data management world, there is discussion of structured (housed in files, databases, etc., where it is organized using an explicit structure ) compared to
unstructured data, such as: email, bitmap images/objects, or text which is not part of a database. Actually, the common nomenclature being used is
"unstructured" but really it has a very complex structure.
By analogy, data is like a book in the library. It’s great when you can go into a library, search the catalog to locate the book, go to the shelf, open the book
and find the information for which you were looking. Data in many forms is like the thousands of books in a library. Like a library book, data needs to be
cataloged so it can be properly accessed. This cataloguing function results in data about the data or data resource data (some call it metadata). Without
such data (the library card catalog), we won’t easily find our book and its content.
We have a similar example in the business environment. We create a spreadsheet that provides information about our products and their prices. We name
the spreadsheet abc.xls on our personal computer. We created it today (when) but, we do not provide any additional information about where the data
came from (it's source), the purpose for which we need it (reasons why), who else needs this information (either internally or externally), or how we actually
created the information (if calculations or special programs were used to complete the request for the data). The data has significant meaning since it is the
means by which we search, access, and provide data meaning to others. It helps to provide the overall context for the use of abc.xls.
Within the spreadsheet, we have captured other data. For each column, we have created a column name that describes the content of the column. For
example, customer name, customer number, order date, product name, product number, description, quantity that was sold and the price the customer paid
for it on that date. We also include the cost of the product to calculate the net profit made on the sale. Down the rows, we have listed each customer who
purchased the products.

Now, most of us can relate to this spreadsheet since it is a typical example of business sales information. But it does raise some interesting questions.
What is a sale? Is it the day that the customer ordered it? Is it the day that we delivered it? Is it the day that the customer paid for it? So, when is a sale a
“sale”?
As we can see from this spreadsheet example, various interpretations and implications are made based upon the understanding of what the data
represents. If definitions of the data are not available, commonly understood terms may be misinterpreted by your employees and customers. Your
organization now has a data integrity problem, which is called "data chaos".
Stages of Data management
Without some framework for data and information quality, it is difficult (if not impossible) to manage and change your business. The following
framework defines stages of development of your data management activities. Six (6) measurement categories span the five (5) stages of
maturity.
MeasurementCategoryorStage:
Leadershipunderstandingandattitude
 Uncertain: No leadership understanding of the issue
 Awakening: Willing to invest time and money to investigate.
 Defined: Become knowledgeable and supportive of effort
 Managed: Take on a participative role
 Certainty: Information quality becomes a key company strategy
QualityOrganizationstatus
 Uncertain: Quality is built into software application and tools
 Awakening: Emphasis to correct bad data and metadata
 Defined: Formalize data quality organization
 Managed: Participates with CIO in management
 Certainty: Information and Data Quality is foremost concern
Dataqualityproblemhandling
 Uncertain: No formal process defined
 Awakening: Short-term team handle major problem
 Defined: Problems faced openly
 Managed: Proactive problem recognition of data quality issues
 Certainty: Most data quality problems prevented
Costofinformationquality
 Uncertain: Unknown
 Awakening: Reporting of some items
 Defined: Open Reporting of all items
 Managed: Improved savings drives new opportunities
 Certainty: Significant data quality cost savings achieved
QualityImprovement
 Uncertain: No data quality process
 Awakening: Short-term data quality effects observed

 Defined: Development as a key program/initiative
 Managed: Data Quality process becomes effective and efficient
 Certainty: Normal and continued process improvement
Companyposture
 Uncertain: Don't know why there is a Data Quality problem occurring
 Awakening: Some recognition of data quality problem
 Defined: Start to resolve major data quality problems
 Managed: Recognize that Data Error prevention is a key business operation
 Certainty: Know reasons for data quality problems
Remember data is the source of the enterprise knowledge. Measuring it has value -- just as valuable as measuring your business’ financial worth
because it creates value either by design or by default. By default is not acceptable in today’s marketplace in light of the changes in solid state
electronics, communication infrastructure, and the miniaturization of computing devices that will dynamically influence the exponential growth of
data!
Reason For Emergence of Data
 Increase in computational power as described by Moore’s law
 Number of internet enabled data generating devices; majorly known as M2M
 Falling cost of data storage devices. i.e. data is available to everybody virtually free or no cost
What is the Future of Data Management
The data management profession will definitely be impacted by current and future trends. Factors that are related to changing various
communications and computer technologies, the use of social media, and an organization's need to obtain and use quality information and data.
These factors will be manifested in the following:
 an exponential growth in data (i.e., big data).
 the mobile delivery of information (i.e., phone and tablet applications, etc.).
 the quality of the data for required informational needs (i.e., real-time access anywhere).
 various technology changes in mobile, storage, computing, and communications affecting data needs.
 organizational and personal needs to access and use high-quality data for decision-making.
There are other factors that will influence the need for organizations to organize, structure, relate, monitor, assess, deliver, and dispose of data as
needed. Let's examine some areas now.
The computer industry evolution will require tools and techniques to manage data and it will drive a cultural transition as well. The business
culture will change since business executives and professionals will make demands for the management of data. The current environment is full
of redundant, low-quality, disparate data affecting the information required for decision-making. The cultural transformation that will occur is that
business professionals will team up with data management professionals to focus on high-quality, non-redundant, business decision-making
data. The transformation will focus on the discipline of data management.
The discipline of data management will continue to demand expertise. Various roles and responsibilities include: Chief Data Manager or
Architect, Data Architects, Data Modelers, Data Stewards, Database Architects, and various data technicians. Each of these roles demand a
particular set of skills that may include: mathematics (like set theory), statistics, linguistics, logic, philosophy, inductive and deductive reasoning,
inter-personnel communications, writing, presentation skills, and a solid foundation in business fundamentals.

Summary of Trends
The availability of data from so many difference sources drives today's organizations to constantly pursue the latest data from reliable and
accurate sources. The implications of having data at our fingertips at anytime and anywhere is our reality. Data is captured from many sources:
databases, files, blogs, email, images, satellite, cameras, video, and other related sources. Mobile technology is changing the landscape for most
businesses because the speed of the delivery of data to these devices makes fact-based informed decisions much more suspect. Why?
As the current century unfolds, business professionals and data management professionals will partner to organize, structure, relate, monitor,
assess, deliver, and dispose data as needed by organizations as a matter of survival. The partnering efforts will drive the data management
profession to support a business asset management approach.

Data Management

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (12)

Similar to Data Management

Similar to Data Management (20)

Recently uploaded

Recently uploaded (20)

Data Management