Tips on leading the data quality program


The most important thing to understand about data quality is that the senior management needs to lead it for it to be implemented widely. It is up to the middle managers to get it started through their own individual initiative. It is possible for most of them to be able to bring on the needed changes as long as it is within their own chains of command. But since middle managers do not have the kind of influence needed for the data quality program to penetrate into the entire system of the organization, they need help from elsewhere.

There is no doubt a lot of factors at play when it comes to a great data quality program in an organization but what is most important is that the breadth and seniority of the managers who lead the effort are brought into play. The more senior and the broader the manager is, the better his influence plays on the program’s reach.

An organization is going to do very well with data quality if it can cause improvement in the management of its data. Data quality forces people to act and think differently. When the leadership is powerful, senior and broad, the chances of failure decreases on its own.

A great way of providing necessary leadership is by organizing a data council. If that is not possible, one can also go with an already existing council, such as the operations department of an organization. The best way to go about it would be to put the chief executive as head of the data council or the equivalent.

Councils have the following responsibilities:
  • Leading the data quality effort
  • Deploying management responsibilities for data
  • Supporting information chain and supplier management
  • Managing the “data culture”
  • Ensuring that data quality efforts are adequately funded

Roles and Responsibilities of Senior Management

Lead the data quality program

1) Formulate business case

  • Most important issues/opportunities relevant to data (i.e., cost reduction, customer satisfaction, competitive advantage, etc.)
  • Expected returns

2) Formulate and promulgate quality policy

  • Role of data quality to organization’s strategy
  • Managerial responsibilities
  • Targets for continuous improvement
  • Contribution to merit rating

3) Select major dimensions of data quality

  • In customers’ eyes (accuracy, timeliness, relevancy, etc.)
  • With respect to the competition
  • Cost of poor data quality (i.e., error detection and correction)

4) Communication of all of the above to important stakeholders, including key customers, employees, etc.

Support information chain and supplier management

1) Identify the most important processes and suppliers

2) Invest information chain and supplier managers with the needed authority

3) Establish the “project system” including machinery for

  • Soliciting project nominations
  • Selecting projects
  • Charting project teams
  • Selecting the team (leaders, members, facilitators, etc.)
  • Supporting project team
  • Reviewing results
  • Celebrating success
  • Ensuring the improvements are sustained
Advancing the data culture

1) Advancing the concept of data “as business assets”

2) Leading change management improvement

3) Motivating continuous improvement

4) Resolving issues as they occur

5) Ensuring that the training program is in place

  • Data curriculum
    • Process and supplier management
    • Planning, control, and improvement processes
    • Problem solving, team building, group dynamics
  • Style of training
Ensuring adequate funding

1) For training

2) For data quality staff

In some respects, it may be even more difficult to lead a quality program for data. After all, data are intangible, and bad data seem to strike like viruses. Further, it is so easy to confuse data or information with the supporting technology.


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