In a recent article “The age of analytics: competing in a data-driven world”, Mckinsey Global Institute reports that data analytics is capable of changing the basis of competition and market position and only a few companies are capturing a fraction of value from analytics. The most significant barrier companies face in extracting value from data and analytics is many businesses struggle to incorporate data-driven insights into day-to-day business processes (MGI, 2016).

If your business has an ISO management system based, then the standard requires that the business analysis and evaluates appropriate data and information arising from monitoring and measurement.  Business owners / Managers need to make evidence-based business decisions based on the factual evidence obtained in their process. Data analysis will help you gain insight into your business process and enable you to make the correct decision.

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What is data analysis?

Data analysis is a process where data is inspected, cleaned, transformed and modelled with an intention to gain insights and discover useful information that will help in deriving conclusions and support decision-making.

 

What are the benefits of data analysis?

Numerous studies have outlined the benefits of data analysis, some of which are:

  • By analysing your data and information, businesses will be able to take quicker and more informed business decision with sufficient evidence or facts.
  • Provides access for business to gain a deeper understanding of customer requirements which in turn help business build relationship and gain customer satisfaction.
  • Increasing visibility and awareness of potential risks and helping manager take appropriate control measures.
  • During the change, data insights improve management’s flexibility and enhance capability to handle volatility and change.
  • Significantly help business reduce cost and increase process efficiency and productivity.

Popular ISO Standards such as ISO 9001:2015 (Quality Management), ISO 45001 (Health & Safety Management) to be released in March 2018, ISO 14001:2015 (Environment Management) requires the business to perform data analysis to evaluate the below areas:

  • Product and service conformance
  • Customer satisfaction levels
  • Performance and effectiveness of the management system
  • Effectiveness of planning
  • Performance of external service providers
  • Areas of opportunity in the management system
  • Risks and opportunities
  • OH&S incidents, accident, reports
  • Effectiveness of programs and initiatives

 

Examples:

#1 Customer satisfaction survey data will provide the management with critical insights about customer needs and expectation

#2 Product / Service Conformity to requirements: In order ensure the customer is satisfied, business should make sure its products and service conform to the requirements of the customer. The key things to monitor would include any critical items identified by the customer, legal or regulatory mandates imposed on the company, or information known by the company that is required to make the product or service work.

#3 Characteristics and trends, including risks: Trend analysis promotes the business to better handle ‘Black Swan’ events by taking appropriate preventive measures before the event can occur and the potential impact the business. Foresight is key to business survival and growth.  Data such as safety incidents, accidents and near misses will provide great understandings on the effectiveness of risk control in place and help business to improve effectiveness and handle risks better.

#4 Quality Tower as discussed by Utekhin (2008)

 

The Quality Tower(Levels of quality managements development) Objects of control Statistical Data
 tower Business context, stakeholder needs, Industrial etc. Risks Data about quality costs and losses accordance with business context and  other risks
Objectives of development Customer satisfaction monitoring results
Management system effectiveness Quality auditing results. Process effectiveness criteria
Process and resource conformity Technological process and resource parameters. Process and resources nonconformities
Conformance of product / Service Product / Service quality characteristics, Test results. Product / service nonconformities

 

References:

MGI 2016. The age of analytics: competingin a data-driven world. Mckinsey Global Institute.

UTEKHIN, G. „Use of statistical techniques in quality management systems‟.  The 8th International Conference “Reliability and statistics in transportation and communication-2008, 2008.

 

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