Collaborative Data Management – Need of the hour!

Well the topic may seem like a pretty old concept, yet a vital one in the age of Big Data, Mobile BI and the Hadoops! As per FIMA 2012 benchmark report Data Quality (DQ) still remains as the topmost priority in data management strategy:

‘What gets measured improves!’ But often Data Quality (DQ) initiative is a reactive strategy as opposed to being a pro-active one; consider the impact bad data could have in a financial reporting scenario – brand tarnish, loss of investor confidence.

But are the business users aware of DQ issue? A research report by ‘The Data Warehousing Institute’, suggested that more that 80% of the business managers surveyed believed that the business data was fine, but just half of their technical counterparts agreed on the same!!! Having recognized this disparity, it would be a good idea to match the dimensions of data and the business problem created due to lack of data quality.

Data Quality Dimensions – IT Perspective

 

• Data Accuracy – the degree to which data reflects the real world
• Data Completeness – inclusion of all relevant attributes of data
• Data Consistency –  uniformity of data  across the enterprise
• Data Timeliness – Is the data up-to-date?
• Data Audit ability – Is the data reliable?

 

Business Problems – Due to Lack of Data Quality

Department/End-Users	Business Challenges	Data Quality Dimension*
Human Resources	The actual employee performance as reviewed by the manager is not in sync with the HR database, Inaccurate employee classification based on government classification groups – minorities, differently abled	Data consistency, accuracy
Marketing	Print and mailing costs associated with sending duplicate copies of promotional messages to the same customer/prospect, or sending it to the wrong address/email	Data timeliness
Customer Service	Extra call support minutes due to incomplete data with regards to customer and poorly-defined metadata for knowledge base	Data completeness
Sales	Lost sales due to lack of proper customer purchase/contact information that paralysis the organization from performing behavioral analytics	Data consistency, timeliness
‘C’ Level	Reports that drive top management decision making are not in sync with the actual operational data, getting a 360o view of the enterprise	Data consistency
Cross Functional	Sales and financial reports are not in sync with each other – typically data silos	Data consistency, audit ability
Procurement	The procurement level of commodities are different from the requirement of production resulting in excess/insufficient inventory	Data consistency, accuracy
Sales Channel	There are different representations of the same product across ecommerce sites, kiosks, stores and the product names/codes in these channels are different from those in the warehouse system. This results in delays/wrong items being shipped to the customer	Data consistency, accuracy

*Just a perspective, there could be other dimensions causing these issues too

As it is evident, data is not just an IT issue but a business issue too and requires a ‘Collaborative Data Management’ approach (including business and IT) towards ensuring quality data. The solution is multifold starting from planning, execution and sustaining a data quality strategy. Aspects such as data profiling, MDM, data governance are vital guards that helps to analyze data, get first-hand information on its quality and to maintain its quality on an on-going basis.

Collaborative Data Management – Approach

Key steps in Collaborative Data Management would be to:

• Define and measure metrics for data with business team
• Assess existing data for the metrics – carry out a profiling exercise with IT team
• Implement data quality measures as a joint team
• Enforce a data quality fire wall (MDM) to ensure correct data enters the information ecosystem as a governance process
• Institute Data Governance and Stewardship programs to make data quality a routine and stable practice at a strategic level

This approach would ensure that the data ecosystem within a company is distilled as it involves business and IT users from each department at all hierarchy.

Thanks for reading, would appreciate your thoughts.

 

Collaborative Data Management – Need of the hour!

Well the topic may seem like a pretty old concept, yet a vital one in the age of Big Data, Mobile BI and the Hadoops! As per FIMA 2012 benchmark report Data Quality (DQ) still remains as the topmost priority in data management strategy:

‘What gets measured improves!’ But often Data Quality (DQ) initiative is a reactive strategy as opposed to being a pro-active one; consider the impact bad data could have in a financial reporting scenario – brand tarnish, loss of investor confidence.

But are the business users aware of DQ issue? A research report by ‘The Data Warehousing Institute’, suggested that more that 80% of the business managers surveyed believed that the business data was fine, but just half of their technical counterparts agreed on the same!!! Having recognized this disparity, it would be a good idea to match the dimensions of data and the business problem created due to lack of data quality.

Data Quality Dimensions – IT Perspective

 

• Data Accuracy – the degree to which data reflects the real world
• Data Completeness – inclusion of all relevant attributes of data
• Data Consistency –  uniformity of data  across the enterprise
• Data Timeliness – Is the data up-to-date?
• Data Audit ability – Is the data reliable?

 

Business Problems – Due to Lack of Data Quality

Department/End-Users	Business Challenges	Data Quality Dimension*
Human Resources	The actual employee performance as reviewed by the manager is not in sync with the HR database, Inaccurate employee classification based on government classification groups – minorities, differently abled	Data consistency, accuracy
Marketing	Print and mailing costs associated with sending duplicate copies of promotional messages to the same customer/prospect, or sending it to the wrong address/email	Data timeliness
Customer Service	Extra call support minutes due to incomplete data with regards to customer and poorly-defined metadata for knowledge base	Data completeness
Sales	Lost sales due to lack of proper customer purchase/contact information that paralysis the organization from performing behavioral analytics	Data consistency, timeliness
‘C’ Level	Reports that drive top management decision making are not in sync with the actual operational data, getting a 360o view of the enterprise	Data consistency
Cross Functional	Sales and financial reports are not in sync with each other – typically data silos	Data consistency, audit ability
Procurement	The procurement level of commodities are different from the requirement of production resulting in excess/insufficient inventory	Data consistency, accuracy
Sales Channel	There are different representations of the same product across ecommerce sites, kiosks, stores and the product names/codes in these channels are different from those in the warehouse system. This results in delays/wrong items being shipped to the customer	Data consistency, accuracy

*Just a perspective, there could be other dimensions causing these issues too

As it is evident, data is not just an IT issue but a business issue too and requires a ‘Collaborative Data Management’ approach (including business and IT) towards ensuring quality data. The solution is multifold starting from planning, execution and sustaining a data quality strategy. Aspects such as data profiling, MDM, data governance are vital guards that helps to analyze data, get first-hand information on its quality and to maintain its quality on an on-going basis.

Collaborative Data Management – Approach

Key steps in Collaborative Data Management would be to:

• Define and measure metrics for data with business team
• Assess existing data for the metrics – carry out a profiling exercise with IT team
• Implement data quality measures as a joint team
• Enforce a data quality fire wall (MDM) to ensure correct data enters the information ecosystem as a governance process
• Institute Data Governance and Stewardship programs to make data quality a routine and stable practice at a strategic level

This approach would ensure that the data ecosystem within a company is distilled as it involves business and IT users from each department at all hierarchy.

Thanks for reading, would appreciate your thoughts.

 

Collaborative Data Management – Need of the hour!

Well the topic may seem like a pretty old concept, yet a vital one in the age of Big Data, Mobile BI and the Hadoops! As per FIMA 2012 benchmark report Data Quality (DQ) still remains as the topmost priority in data management strategy:

‘What gets measured improves!’ But often Data Quality (DQ) initiative is a reactive strategy as opposed to being a pro-active one; consider the impact bad data could have in a financial reporting scenario – brand tarnish, loss of investor confidence.

But are the business users aware of DQ issue? A research report by ‘The Data Warehousing Institute’, suggested that more that 80% of the business managers surveyed believed that the business data was fine, but just half of their technical counterparts agreed on the same!!! Having recognized this disparity, it would be a good idea to match the dimensions of data and the business problem created due to lack of data quality.

Data Quality Dimensions – IT Perspective

 

• Data Accuracy – the degree to which data reflects the real world
• Data Completeness – inclusion of all relevant attributes of data
• Data Consistency –  uniformity of data  across the enterprise
• Data Timeliness – Is the data up-to-date?
• Data Audit ability – Is the data reliable?

 

Business Problems – Due to Lack of Data Quality

Department/End-Users	Business Challenges	Data Quality Dimension*
Human Resources	The actual employee performance as reviewed by the manager is not in sync with the HR database, Inaccurate employee classification based on government classification groups – minorities, differently abled	Data consistency, accuracy
Marketing	Print and mailing costs associated with sending duplicate copies of promotional messages to the same customer/prospect, or sending it to the wrong address/email	Data timeliness
Customer Service	Extra call support minutes due to incomplete data with regards to customer and poorly-defined metadata for knowledge base	Data completeness
Sales	Lost sales due to lack of proper customer purchase/contact information that paralysis the organization from performing behavioral analytics	Data consistency, timeliness
‘C’ Level	Reports that drive top management decision making are not in sync with the actual operational data, getting a 360o view of the enterprise	Data consistency
Cross Functional	Sales and financial reports are not in sync with each other – typically data silos	Data consistency, audit ability
Procurement	The procurement level of commodities are different from the requirement of production resulting in excess/insufficient inventory	Data consistency, accuracy
Sales Channel	There are different representations of the same product across ecommerce sites, kiosks, stores and the product names/codes in these channels are different from those in the warehouse system. This results in delays/wrong items being shipped to the customer	Data consistency, accuracy

*Just a perspective, there could be other dimensions causing these issues too

As it is evident, data is not just an IT issue but a business issue too and requires a ‘Collaborative Data Management’ approach (including business and IT) towards ensuring quality data. The solution is multifold starting from planning, execution and sustaining a data quality strategy. Aspects such as data profiling, MDM, data governance are vital guards that helps to analyze data, get first-hand information on its quality and to maintain its quality on an on-going basis.

Collaborative Data Management – Approach

Key steps in Collaborative Data Management would be to:

• Define and measure metrics for data with business team
• Assess existing data for the metrics – carry out a profiling exercise with IT team
• Implement data quality measures as a joint team
• Enforce a data quality fire wall (MDM) to ensure correct data enters the information ecosystem as a governance process
• Institute Data Governance and Stewardship programs to make data quality a routine and stable practice at a strategic level

This approach would ensure that the data ecosystem within a company is distilled as it involves business and IT users from each department at all hierarchy.

Thanks for reading, would appreciate your thoughts.

 

BusinessObjects Administration – Custom Access Levels

Hi BOOglers,

Another interesting feature in business objects, Custom access level is going to be the topic of discussion for this blog. Please note the custom access levels are introduced only from Business Objects 3.0 onwards.

As you all know Access levels are groups of rights that users frequently need. They allow administrators to set common security levels quickly and uniformly rather than individual rights to be set one by one. Business Objects comes with several predefined access levels. Beginning with View and ending with Full Control, each access level builds upon the rights granted by the previous level. We can also create and customize your own access levels. This will greatly reduce administrative and maintenance costs associated with security.

Predefined Access Levels

Below four are list of predefined access levels and associated list of right(s).

Access level	Description	Rights involved
View	If set on the folder level, a principal can view the folder, objects within the folder, and each object’s generated instances.		View objects View document instances
Schedule	A principal can generate instances by scheduling an object to run against a specified data source once or on a recurring basis. The principal can view, delete, and pause the scheduling of instances that they own.		View access level rights, plus: Schedule the document to run Print the report’s data Edit objects that the user owns
View On Demand	A principal can refresh data on demand against a data source.		Schedule access level rights, plus: Refresh the report’s data
Full Control	A principal has full administrative Control of the object.		All available rights

* Principle refer to User group or User

Access levels in CMC

Advanced rights

Icon	Rights option	Description
	Granted	The right is granted to a principal.
	Denied	The right is denied to a principal.
	Not Specified	The right is unspecified for a principal. By default, rights set to Not Specified are denied.
	Apply to Object	The right applies to the object. This option becomes available when you click Granted or Denied.
	Apply to Sub Object	The right applies to sub-objects. This option becomes available when you click Granted or Denied.

Custom Access Levels

Consider a situation in which an administrator must manage two groups, Marketing managers and Marketing employees. Both groups need to access ten reports in the Business Objects Enterprise system, but Marketing managers require more rights than marketing employees. The predefined access levels do not meet the needs of either group. Instead of adding groups to each report as principals and modifying their rights in ten different places, the administrator can create two new access levels, Marketing Managers and Marketing Employees. The administrator then adds both groups as principals to the reports and assigns the groups their respective access levels. When rights need to be modified, the administrator can modify the access levels. Because the access levels apply to both groups across all ten reports, the rights those groups have to the reports are automatically updated.

We can create a new custom access level either start from the scratch or copy the existing access levels. We can also add/remove set of rights from existing custom access level from the existing custom access level.

Right Click on the Custom Access level and Select Included Rights.

And you will get the screen like below. Select the appropriate rights as per the requirement,

Then click OK to complete.

Finally you can assign the Custom Access level against each User group/User on a particular folder.

Administrator will get the best benefits out of this because they will get-rid of the traditional rights assignment using Advanced rights option. Also It is easy to manage the rights when they are grouped together.

Feel free to leave your comments. Thanks for reading! 

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