Data Management

Data Management

DATA MANAGEMENT

Data management includes the following

  • Identification of opportunities for data identification
  • Prioritization/classification of data in each of those opportunities
  • Identification of data to be managed
  • Planning for data management
  • Systematic arrangement for data collection
  • Data collation
  • Analysis
  • Identification and implementation of corrective and preventive actions

QMS clause No. 8, identifies Measurement, Analysis and Improvement and focuses on measurement of QMS and calls for determination of applicable methods, including statistical techniques. Data planning however starts from Cl. No. 4.1, when there is a need to monitor, measure, and analyze QMS processes. In addition, clause number 8.2.1, 8.2.2, 8.2.3, and 8.2.4 also need to be managed.

Records form a major chunk of data management. Records requirement are specified and needs to be addressed. Records provide evidence that the realization processes meet the QMS requirements. Hence, management of data from those records forms the basis of the improvements. Records could be in the form of sheer information and measurable data. Such measureable data management is what is needed in the Clause No. 8.

Data measurement

Records form the basis for data identification.

Below given are some of the areas of data measurement:

  • Process related- 5.6, 6.2.2, 6.3, 7.2, 7.3, 7.4.1, 7.4.2,7.4.3, 7.5.1,7.5.2, 7.5.3, 7.5.4,7.5.5, 7.6, 8.2.1, 8.2,2,8.2.3
  • Product/service related- 7.2, 7.3,7.4.3,7.5.1, 7.5.3, 8.2.4,

The first step is to segregate information and monitorable and measurable data from the records.

Data management includes systematic collection of data from the processes and product/service realization

Data collectionis done in such a manner, that they can be collated and analysed

Designing of various formats/templates/checklists/control charts to be used for data collection play an important role. Frequency of collection and method of recording data is planned to provide adequate information on the effectiveness of the control of the process/product/service.

A poorly designed format/template can defeat the purpose of data measurement, without providing required information. Once the data is collected, a collation matrix would help in consolidating them and help in prioritizing for subsequent analysis.

A collation Matrix would typically contain data on various parameters of each process being monitored, frequency of data collection, and the source of data collected.

Collation of data is done using a data matrix. The next step is prioritizing the parameters towards process improvements. Prioritization is based on the criticality of parameters/processes, expectations of customers, stakeholders, etc., The next step would be to carry out data analysis.

Data analysis consists of using various statistical tools. Statistical methods help to understand processes, to bring them under control, and then to improve them. If one is running a high-volume product process, Control Charts help in Pareto Chart and Cause-and-Effect chart are the most useful tools for others. When we are dealing with simple individual processes, the above can be applied effectively. However, in an organization where many processes are involved, it is better to deal with them at a global level, since there are multiple process owners.

Approach

A sensible and effective approach to carry out Data Analysis would be by using a Data Analysis Team (DAT) drawn for the specific purpose. The following steps are recommended.

  • As the Team Leader, Management Representative (MR) takes the initiative in forming this DAT. He selects the team members having good analytical skills and process knowledge in his/her area of work.
  • MR has to ensure that at least one member is represented by each process/function identified for improvement.
  • Brainstorming sessions would make the job easier for the MR.
  • The first step is to study the existing system, and identify potential causes of root causes and prepare a Ready Reckoner
  • The various heads under which these Root Causes are identified could be
  • Men
  • Machine
  • Measurement
  • Method/System
  • Materials
  • Documentation
  • One must realize that the various potential root causes fall under 80/20 rule- meaning that common causes are 80% and only 20% are special to an organization.
  • Some examples of potential root causes under each head are
  • Men- lack/less of communication skills, lack/less of commitment/attitude of personnel, lack of product knowledge, lack of product knowledge
  • Machine- improper machine/tools/dies/jigs setting, inadequate/improper machine maintenance, inadequate machine/equipment performance
  • Measurement- inadequate test equipment, improper recording, inadequate calibration
  • Method/system- inadequate monitoring/controlling of processes, inadequacy in operational planning, inadequate supplier control
  • Materials- improper choice of parts/components, material mix-up, alternative materials used
  • Documentation- inadequate process documentation, inadequate recording, non-availability of controlled documentation
  • Deployment of the ready-reckoner in completing the data analysis would be discussed in the next article. Meanwhile, initiate and go ahead with forming DAT. Any help, do not hesitate to contact. Good luck
Top View