A data driven approach to quality improvement

Service Knowledge Base hosted a session on Data-Driven Quality Improvement Process on 7th January with Amit Choudhary, Senior Advisor, Data Analytics Solution Expert, KPMG.

Data-driven quality has replaced customer experience-based quality. While organizations may initially lose customers in this transition process, they will have made a foundation for acquiring a larger number of customers in the long run. The essence of data-driven process improvement can be summed up in this sentence.

Data-driven decision-making is best achieved by using the Question, Plan, Collect, Analyze, Recommend method that ultimately converts data into insights that lead to the formulation of action plans, such as improving service quality, product quality, customer satisfaction, etc.

Data-driven project management has the advantage of bringing about plenty of cost optimization. A data-driven approach can help study the impact of a project internally and externally to the organization, thereby improving project risk management.

Leveraging data can help leverage the strategy by dynamically updating short-term plans that contribute to the overall long-term strategy. An organization that uses data will be more agile and faster since it will be driven by data. An analysis of the project at different stages can enhance business performance by being data-driven.

A data-driven organization can be formed by combining Descriptive Analytics (what happened? ), Diagnostic Analytics (why did it happen? ), Predictive Analytics (what will happen?), and Prescriptive Analytics (how do we make it happen?)

There were case studies presented by the speaker on four different sectors: telecom, retail, manufacturing, and financial services. This provided a better understanding of the importance of improving business process quality through analytics.

Regression & classification models, Hypothesis testing, Simulation, Visualization & Hybrid ML are some useful techniques for building a data-driven quality organization.

Minitab, Excel with plug-ins, Crystal Ball, and Power BI / Tableau are the tools.

An organization that follows a bottom-to-top approach will find it easier to obtain quality data.

Cloud platforms are becoming the future of data storage by switching from 'Data Bases' to 'Virtual Data Lakes'

Certifications that working professionals should focus on to upskill themselves are generally categorized into three levels

-        For leaders and decision makers, pursue AI and ML Framework for Quality, Agility in Quality Framework, Awareness of entire data analytics framework

-        At the mid managerial or at executional level, choose business statistics, visualization (Tableau, PowerBI) and natural language processing (NLP).

-        For technical staff, they should be skilled in Python, R, Click View, SQL, and MongoDB

Data-driven decision-making needs to be made accountable by leaders within organizations. Business Leaders should go all out and learn as much about data as possible, concluded the speaker.