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.

Measurement Category or Stage:

Leadership understanding and attitude

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

Quality Organization status

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

Data quality problem handling

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

Cost of information quality

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

Quality Improvement

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

Company posture

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.