Tuesday 16 October 2012

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.

 

Transitioning to a New World – An Analytical Perspective

Recently, I had the opportunity to speak at the Silicon India Business Intelligence Conference. The topic I chose for the discussion was focused on providing the BI & Analytics perspective for companies transitioning to a new world. 

The gist of my presentation is given below:

1)      First, established the fact that the world indeed is changing by showing some statistics:

  • Data Deluge: Amount of digital data created in the world right now stands at 7 Zettabytes per annum (1 Zettabyte = 1 Trillion Terabytes)
  • Social Media: Facebook has touched 1 Billion users which makes it the 3rd largest country in the world
  • Cloud: Tremendous amount of cloud infrastructure is being created
  • Mobility: There are 4.7 billion mobile subscribers which covers 65% of world population

2)      Enterprises face a very different marketplace due to the profound changes taking place in the way people buy, sell, interact with one another, spend their leisure time etc.

3)      To ensure that BI can help business navigate the new normal, there are 3 key focus areas.

  • Remove Bottlenecks – Give business what they want
  • Enhance Intelligence
  • End to End Visibility by strengthening the fundamentals

For each of the 3 areas mentioned above, I gave some specific examples of the trends in the BI space.

1)      For Removing Bottlenecks, the impact of in-memory and columnar databases were elaborated.

2)      For enhancing intelligence, working with unstructured data and using big data techniques were discussed.

3)      For the 3rd point, the focus was on strengthening the fundamentals in the BI landscape.

Please do check out my complete presentation at http://bit.ly/VLDDfF and let me know your views.

Thanks for reading.