Data Driven Decision Making for Non Data Experts

Y. Navatej1, Dr. T. Ravindar1

1School of Technology, Woxsen University, Hyderabad, India

Executive Summary

Data driven decision making (DDDM) is a method of decision making that ensures that an organization learns to use data to make informed decisions, leading to improvement in productivity, profitability, and innovation as well as less bias and uncertainty. In this white paper we summarized the results of scientific research publications and authoritative studies and presented them in the form of frameworks, metrics, case studies, and practical guidance, tailored specifically for non-data professionals. With the help of illustrative examples (numerous tables, charts, and images), as well as clear writing, it will explain to the reader how to develop data literacy, how to eliminate obstacles, and how to successfully apply DDDM in a variety of circumstances.
 

Key words: Data expert, Data driven decision, Retail inventory, Healthcare operations, Preventive maintenance

1. Introduction

Conventional decisions are frequently focused on intuition, but with the explosive increase in the amount of data and analysis tools, there is a need to employ a formal process. DDDM entails data aggregation, verification, extraction, and interpretation on which strategic and operational decisions can be based [1, 2]. Those who are not data experts can engage in data programs by using non-programmer models and common sense technologies, and build a collaborative, data-driven culture where data is based.

1. Adoption and Impact Trends
   1. Speedy Implementation in the Manufacturing Sector

The percentage of manufacturers in the U.S. using DDDM increased almost threefold in 2005-2010, to 30 per cent, owing to breakthroughs in information technology and management strategies ostensibly associated with productivity improvements [1, 3].

A line chart that indicates the tripling of the DDD adoption rate observed among the manufacturing plants in the US between 2005 and 2010

1. 1. Sectoral Improvements in Performance

According to empirical research, sector specific returns are observed: manufacturing productivity benefited 3%, the productivity of the banking sector improved 9%-10%, and the value of businesses in the leading data-related companies grew 3%-5% [2, 4].

Bar chart on the performances and improvement in sectors due to decisions made using data

1. Core Components of a Data Driven Culture
   1. Governance/leadership

It is very important to have executive sponsorship. The top leaders should support the data programs, establish governance rules, and invest in data management, with ownership and accountability considered explicit over the data assets [5].

1. 1. Data accessibility and Data quality

Two aspects of data access need relevant data accessibility and quality to be considered. Decision making is heavily dependent on high quality data with reliable information and it is smart for companies to maintain organized data catalogues, set clear data standards, and regularly review their data for consistent results [6].

1. 1. Training and Data Literacy

The essential skill is data literacy, that is the skills to understand, analyse, and communicate data. Training needs to be focused on the data collection, validation, data analysis methods, and data interpretation via such tools as spreadsheets, BI platforms, and visualization software [7, 8].

1. 1. Decision-making processes as a collective group

Cross functional teams comprising domain experts and data experts improve the generation of insights through open communication, collective problem resolution, and frequent data reviews find their way through dashboards that are shared [9].

1. There are a number of barriers to its implementation.

With apparent advantages, there are barriers to the application of the theory in organizations:

The solution to these barriers is the development of a data first mentality, minimal data workflows, and prioritization of the key metrics in terms of alignment with the business objectives [10, 11].

A bar chart representing the challenges that organizations face during adoption of data driven decision making

1. Practical Framework to Non Data Specialists

Step 1: Setting the scene for the decision: Provide objectives, scope, and key performance measures for example, increase conversion on the site by discovering drop off points that attract a lot of traffic [2].

Step 2: Data Collection and Data Validation: Data has to be collected from internal sources, third parties, public repositories, and check for completeness and accuracy by quickly looking at missing values and outliers [6].

Step 3: Analysing and interpreting the data: Browse for the trends and correlations using spreadsheet functions and BI dashboards and present it visually by thinking of bar charts, line graphs, and heat maps as ways of easily learning [9].

Step 4: Decide, Decide, and Watch: Convert insights into effective actions that have precise responsibilities and schedules and analyze the results according to specific measures, going through repeated cycles of data, and always strive to make it better [2].

1. Case Studies
   1. Retail Inventory Optimization

Walmart made use of past sales and weather data to forecast demand, thus eliminating stockouts by 16 percent and saving $1.29 billion yearly [12].

1. 1. Efficiency in the Healthcare Operations

The predictive analytics model developed by The Cleveland Clinic was able to decrease emergency department boarding times by 38 percent and length of stay by 0.5 days per patient [12].

1. 1. Preventive Maintenance in Manufacturing

At Siemens, digital twins and analytics based on sensors helped to increase the quality of production towards 99,9996 percent, and efficiency grew by 30 percent [12].

1. Future Directions

New trends are to have AI embedded to provide automated advice (decision intelligence), to democratize analytics by using low code tools, and to use edge analytics to make decisions in real time at the local level. The more intuitive tools developed, the more a non-data expert will be able to stimulate innovation in every possible area [4, 11].

Conclusion

Types of people who are crucial in the democratization of data are non data experts and when business teams understand the core principles, follow clear frameworks, and work well together, they can use data driven decision making to boost performance, reduce bias, and build a culture that values continuous learning. Data literacy and taking on a data driven approach help organizations operate amidst complexity and achieve sustained competitive advantage.

References

1.T. H. Davenport and J. G. Harris, Competing on Analytics: The New Science of Winning, Boston, MA, USA: Harvard Business Review Press, 2007.

2.A. McAfee and E. Brynjolfsson, “Big data: The management revolution,” Harvard Business Review, vol. 90, no. 10, pp. 61–67, Oct. 2012.

3.S. LaValle, E. Lesser, R. Shockley, M. S. Hopkins, and N. Kruschwitz, “Big data, analytics and the path from insights to value,” MIT Sloan Management Review, vol. 52, no. 2, pp. 21–31, Winter 2011.

4.E. Brynjolfsson and K. McElheran, “The rapid adoption of data-driven decision-making,” American Economic Review, vol. 106, no. 5, pp. 133–139, May 2016.

5.E. Brynjolfsson and L. M. Hitt, “Beyond computation: Information technology, organizational transformation and business performance,” Journal of Economic Perspectives, vol. 14, no. 4, pp. 23–48, Fall 2000.

6.E. Brynjolfsson, K. McElheran, et al., “Data-driven decision making and predictive analytics in

U.S. manufacturing,” Emerging Science & Technology Journal, Jul. 6, 2019.

1. EPBL, “A review of data-driven decision making in engineering management,” Engineering Science & Technology Journal, 2024.
2. Strim Group, “Competing on Analytics summary,” STRIMgroup, 2005.
3. MIT Sloan Initiative for Digital Economy, “Data-driven decision making in action,” MIT Sloan Initiative for Digital Economy Brief, 2016.
4. Harvard Business Review Ideacast, “Competing on Analytics,” podcast, 2007.

11.U.S. Census Bureau CES Working Papers, “Data driven decision making in U.S. manufacturing,” CES-WP-16-06, 2016.

12.OpenICPSR, “Replication data for: The rapid adoption of data driven decision-making,” OpenICPSR, 2019.