A Denodo virtualization project typically classifies the
project duties of the primary implementation team into four Primary roles.
Denodo Data Virtualization Project Roles
Data Virtualization Architect
Denodo Platform Administrator
Data Virtualization Developer
Denodo Platform Java Programmer
Data Virtualization Internal Support Team
Project Team Member Alignment
While the denodo project is grouped into security permissions and a set of duties, it is import to note that the assignment of the roles can be very dynamic as to their assignment among project team members. Which team member who performs a given role can change the lifecycle of a denodo project. One team member may hold more than one role at any given time or acquire or lose roles based on the needs of the project.
virtualization Project Roles Duties
The knowledge, responsibilities, and duties of a denodo data
virtualization architect, include:
A Deep understanding of denodo security features
and data governance
Define and document5 best practices for users,
roles, and security permissions.
Have a strong understanding of enterprise
Defines data virtualization architecture and
Guides the definition and documentation of the
virtual data model, including, delivery modes, data sources, data combination,
The knowledge, responsibilities, and duties of a Denodo Platform
Denodo Platform Installation and maintenance, such as,
Installs denodo platform servers
Defines denodo platform update and upgrade policies
Creates, edits, and removes environments, clusters, and servs
Manages denodo licenses
Defines denodo platform backup policies
Defines procedures for artifact promotion between environments
Denodo platform configuration and management, such as,
Configures denodo platform server ports
Platform memory configuration and Java Virtual Machine (VM) options
Set the maximum number of concurrent requests
Set up database configuration
Specific cache server
Authentication configuration for users connecting to denodo platform (e.g., LDAP)
Secures (SSL) communications connections of denodo components
Provides connectivity credentials details for clients tools/applications (JDBC, ODBC,,,etc.)
Configuration of resources.
Setup Version Control System (VCS) configuration for denodo
Creates new Virtual Databases
Create Users, roles, and assigns privileges/roles.
Execute diagnostics and monitoring operations, analyzes logs and identifies potentials issues
Manages load balances variables
The Data Virtualization Developer role is divided into the
the knowledge, responsibilities, and duties of a Denodo Data
Virtualization Developer, by sub-role, Include:
The denodo data engineer’s duties include:
Implements the virtual data model construction
Importing data sources and creating base views,
Creating derived views applying combinations and
transformations to the datasets
Writes documentation, defines testing to eliminate
development errors before code promotion to other environments
The denodo business developer’s duties include:
Creates business vies for a specific business
area from derived and/or interface views
Implements data services delivery
The denodo application developer’s duties include:
Creates reporting vies from business views for
reports and or datasets frequently consumed by users
Denodo Platform Java
The Denodo Platform Java Programmer role is an optional,
specialized, role, which:
Creates custom denodo components, such as data sources, stored procedures, and VDP/iTPilot functions.
Implements custom filters in data routines
Tests and debugs any custom components using Denodo4e
Internal Support Team
The denodo data virtualization internal support team’s duties
Access to and knowledge of the use and trouble
of developed solutions
Tools and procedures to manage and support
project users and developers
Denodo provides some general Virtual Dataport naming
convention recommendations and guidance.
First, there is the general guidance for basic Virtual Dataport object
types and, secondly, more detail naming guidance recommends.
You can’t manage what you don’t measure is an old management adage that has been used for many years and while most attribute it to Peter Drucker, some claim that the quote was first used by Dr. W. Edwards Deming, although it is a bone of contention whether or not the quote is used in the correct context.
Irrespective of who said it first, I have always agreed with the principle. Coming from a corporate background where this is one of the management principles often used, I was surprised to learn that there are those that strongly disagree with the statement. This group argues that there are many things being managed at work that aren’t measurable, from the confidence we instill in a new, young manager, to the quality of new hires.
The argument is made that quantity is easy to measure, i.e., how much salespeople sell, how many leads marketing creates, or how many phone calls telemarketing makes, but that quality can’t be measured, i.e., excellent customer service, good technical support, or what differentiates a good consultant from a great one.
What to measure
Many organizations use Key Performance Indicators (KPIs) at multiple levels to measure their success at reaching targets, and will then manage the factors influencing the KPI to get it to where they want it to be. A KPI is a value that is measured and shows how effective a company is in reaching key business goals.
Setting a KPI and measuring a specific value is however not always as straightforward as it might seem. To set a KPI, the underlying business objective needs to be properly understood. In one example, a department manager’s KPI included the volume of sales, measured in dollars. In an effort to improve sales, the manager decided to change the remuneration of her sales reps from a fixed salary to a small, basic salary plus commission on sales made. The idea behind this was to incentivize the work, which would lead to increased sales. In the early months after implementing the change, the sales made by account reps did indeed increase dramatically. The CFO then however discovered that the profit margin on those increased sales was substantially lower than the minimum the company expected. The sales reps were discounting the product to increase sales, resulting in a high commission, but the net effect was that the company made less profit.
It is critical that the company’s objectives are clearly understood by all parties and that a suitable metric is measured to check if the objective is being met.
Can quality be measured?
Those arguing that quality, such as excellent customer service, or good technical support can’t be measured, often express the view that the only way that a company can determine how good their service or support is, is by asking the customer. I agree with that statement, but when you do that, aren’t you measuring these aspects? If 50% of your customers feel that your service and support is good, that is a measure against which you can manage and improve those objectives.
The same can be done for any qualitative metric. It merely becomes a question of what is appropriate to measure, and how to obtain those metrics. Qualitative measures often have to be done indirectly, i.e., you need to measure indirect results rather than direct ones.
The role of Business Intelligence
With the sheer volume of data available across the business, and with much of it residing in different systems, it becomes very difficult to extract the relevant metrics to measure and improve. This is where Business intelligence or BI comes in.
BI utilizes computer-based techniques to spot, extract, and analyze business data, including things like sales, marketing, and production in order to make substantial improvements. Business Intelligence uses data already collected in the business. It is able to utilize data from such diverse sources as website analytics, accounting systems, customer relationship management (CRM) and email management systems.
A Business Intelligence system can automatically use and analyze all the information from these applications in real-time. This enables companies to quickly see, manage and improve their performance. BI goes further than simply measuring performance so that it can be improved, but also helps identify weaknesses in the company.
When an organization grows to the point where huge volumes of data are involved, analytics are used to examine large and varied data sets to uncover correlations, hidden patterns, customer preferences, and market trends; so, organizations can make more-informed business decisions.
Both BI and big data analytics can hugely benefit Organization & Planning within any business. If you have all this information, irrespective of how exactly it was obtained or measured, managing the direction you want to go becomes an informed decision that can be planned for, rather than a guessing game based on ‘gut feel.’
A crucial element that is required in today’s fast-moving world is an organization’s ability to respond rapidly to changes in both the external and internal environment. This is known as Business agility, and it is not possible to do if the business does not measure what is going on inside and around it, and then manages accordingly.
When analyzing individual column data, at its most foundational level, column data can be classified by their fundamental use/characteristics. Granted, when you start rolling up the structure into multiple columns, table structure and table relationship, then other classifications/behaviors, such as keys (primary and foreign), indexes, and distribution come into play. However, many times when working with existing data sets it is essential to understand the nature the existing data to begin the modeling and information governance process.
Column Data Classification
Generally, individual columns can be classified into the classifications:
Identifier — A column/field which is unique to a row and/or can identify related data (e.g., Person ID, National identifier, ). Basically, think primary key and/or foreign key.
Indicator — A column/field, often called a Flag, that has a binary condition (e.g., True or False, Yes or No, Female or Male, Active or Inactive). Frequently used to identify compliance with complex with a specific business rule.
Code — A column/field that has a distinct and defined set of values, often abbreviated (e.g., State Code, Currency Code)
Temporal — A column/field that contains some type date, timestamp, time, interval, or numeric duration data
Quantity — A column/field that contains a numeric value (decimals, integers, etc.) and is not classified as an Identifier or Code (e.g., Price, Amount, Asset Value, Count)
Text — A column/field that contains alphanumeric values, possibly long text, and is not classified as an Identifier or Code (e.g., Name, Address, Long Description, Short Description)
Large Object (LOB)– A column/field that contains data traditional long text fields or binary data like graphics. The large objects can be broadly classified as Character Large Objects (CLOBs), Binary Large Objects (BLOBs), and Double-Byte Character Large Object (DBCLOB or NCLOB).
A Common Data Model (CDM) is a share data structure designed to provide well-formed and standardized data structures within an industry (e.g. medical, Insurance, etc.) or business channel (e.g. Human resource management, Asset Management, etc.), which can be applied to provide organizations a consistent unified view of business information. These common models can be leveraged as accelerators by organizations form the foundation for their information, including SOA interchanges, Mashup, data vitalization, Enterprise Data Model (EDM), business intelligence (BI), and/or to standardize their data models to improve meta data management and data integration practices.