Developing A Phd Research Topic For Your Research: How Data Driven Decision-Making Helps?

According to UGC report, every year approximately 77,000 scholars are enrolling for PhD research programs in India. but only 1/3^rd of them successfully complete the doctorate every year (Marg, 2015). This result clearly indicates the difficulty and standard of evaluation of this program, so it is  necessary that the scholar thoroughly analyse and select a good PhD research topic before plunging into the research work. There are many methodologies and techniques introduced over a period for effective selection and decision making in various field. Data Driven Decision Making which is also referred as Data Based Decision Making is a modern method which was found to be effective and efficient for strategic and systematic development and decision making. So, this article summarizes the framework, effectiveness, benefits of DDDM in selection of Research topics for the PhD scholars.  

Keywords: Dissertation Topic Selection, Best Research Thesis topic, computing Dissertation topic selection, PhD statistics topic selection support.

Data Driven Decision Making

Due to spontaneous growth and development in the modern technology, demand for a strong decision-making method arises. To tackle this issue, Data Driven Decision-Making method was introduced. This DDDM works in such a way that every decision made is backed up by the acquired data rather than making decisions purely based on observation. This method processes the data and predicts the course of action before committing to it. When compared to intuitive and gut decisions, DDDM is safe, strategic, and systematic. This modern method plays a vital role in various sectors such as business firm, management sectors, educational institutions, research units etc (Wohlstetter, Datnow, & Park, 2008).

The above figure illustrates the basic guideline for DDDM method, and these are the follows steps to be followed for effective selection and precise decision making (Lam, 2018).  

• Collect: All relevant data, raw primary source information and the secondary research is carried out and recorded in this phase. 
• Analyse: Simple quantitative and qualitative analysis is carried out to find links and similarities between each data source.
• Validate: Referring the acquired data with various data source to find its nature and predict the course of action.
• Act:  Based on the results obtained from the previous steps, a proper data driven decision is made for future use.

Framework of Data Driven Decision Making

This section elaborates the framework of data driven decision making exclusively for a PhD research scholar. The basic idea of the framework is mostly common for any sector and this consist of five detailed steps to achieve the desired goal (Babarskaite & Truuverk, 2019; Li, Wang, Sun, Zhang, & Chen, 2019).

1. Problem statement
2. Critical analysis
3. In-depth insight
4. Test phase
5. Results

Benefits of Data Driven Decision Making for PhD Research Scholars:

• Objective process: the results or decisions made by this method is purely based on the data and statistics obtained during the literature survey phase. The decision is not influenced by opinion, gut feeling etc. So, the scholar has evidential data to support every legitimate decision and action carried out throughout the project (Improving Schools With Data, 2020).
• Facilitates greater control: since the scholar collects and analyses all the viable data and resources for the project during the initially phase. The scholar exactly knows whether it is possible to complete the research work or not. So, by employing this method the scholar can select the research topic effectively and save a lot of time.
• Promotes transparency: this method purely relies on the available data and information in finalising a decision.  So, there is no room for manipulation in this method. Thus, DDDM is considered as effective and transparent method used for decision making (Heilig, 2014).
• Increases Agility:  the scholar exactly knows all the opportunities and outcomes well before the experimentally analysis phase. So, this method neatly sketches down all the opportunities to the scholar and better insights towards the outcome of the project.
• Builds confidence and consistency: since the scholar has a clear understanding and in-depth insight over the topic and the decisions made. This results in better performance, consistency, and confidence. In addition to it, this method is found to be less time consuming and more efficient when compared to other conventional methods (Softjourn, 2020).

Successful outcomes of Data Driven Decision Making in various sector:

1. Education

According to the Filderman, Toste, Didion, Peng, and Clemens, (2018) journal article  on DBDM in reading interventions, clearly indicates the effectiveness of this method in education sector. Since the central idea of DDDM is oriented towards individual performance data and development, this method is found to be successful in educational institutes. The scholarly literature review reveals the list of struggling readers from grades k-12 in an educational institute using data-based decision making and the teachers were able to find an alternative teaching technique to help the listed students. With the help of DDDM, teachers can easily monitor the individual performance of all the students (Marsh, Pane, & Hamilton, 2006).

• Marketing and Management

Walmart, an American multinational corporation which operates nearly 11,484 hypermarkets across the global with approx. 2.2 million employees used this process for emergency merchandise during Hurricane Frances in 2004, as The NY Times reported. The analysts dogged the customer history and the record of past purchases to figure out the type of merchandise to be stocked before the storm.

            Thus, Walmart made great profit by anticipating demand in the year 2004. This incident is a solid proof for the importance of DDDM in marketing sector (Durcevic, 2019).

• Aviation Industry

The competition and transaction involved in aviation industry is comparatively larger than the other industries. So Southwest Airlines executives studied the customer data to gain larger perspective of customers requirement and addition of new service which would be popular as well as profitable. In course the times, the airline observed and analysed their costumer’s online behaviours and their needs which resulted in exemplary level of customer experience.

As the result of DDDM, Southwest airlines has its own customer base and standard which reflected a steady growth in the subsequent years (Durcevic, 2019).

Conclusion

Data plays a vital role in determining the overall performance of the organisation. So, it is highly important to make use of the potential data for the development. It is conclusive that data driven decision making helps the PhD scholar in developing and selecting a good research topic for his/her research work. Moreover, the framework of this system helps the scholar to critical analysis and making decisions solemnly based on analysed data. So, this results in clear objective, greater control, promotes transparency, builds confidence and consistency. Hence, DDDM methodology is found to be effective and high beneficial irrespective of the sectors.   

Reference

Babarskaite, S., & Truuverk, K. (2019). Data-Driven Decision Making Framework And Its Application In Estonian Startup Scene (Estonian Business School). Retrieved from https://www.invicta.ee/wp-content/uploads/2019/07/Kristiina_Truuverk_Sigita_Babarskaite.pdf

Durcevic, S. (2019). Why Data Driven Decision Making is Your Path To Business Success. Retrieved July 3, 2020, from Business Intelligence website: https://www.datapine.com/blog/data-driven-decision-making-in-businesses/

Filderman, M. J., Toste, J. R., Didion, L. A., Peng, P., & Clemens, N. H. (2018). Data-based decision making in reading interventions: a synthesis and meta-analysis of the effects for struggling readers. The Journal of Special Education, 52(3), 174–187. Retrieved from https://journals.sagepub.com/doi/abs/10.1177/0022466918790001

Heilig, C. (2014). Data driven decision making: data interplay within a high school district (Rowan University). Retrieved from http://rdw.rowan.edu/cgi/viewcontent.cgi?article=1537&context=etd

Improving Schools With Data. (2020). The Importance of Data-Based Decision Making. Retrieved from https://us.corwin.com/sites/default/files/upm-assets/25562_book_item_25562.pdf

Lam, C. (2018). More Than a Feeling: Applying a Data-Driven Framework in the Technical and Professional Communication Team Project. IEEE Transactions on Professional Communication, 61(4), 409–427. Retrieved from https://ieeexplore.ieee.org/abstract/document/8490738/

Li, Q., Wang, P., Sun, Y., Zhang, Y., & Chen, C. (2019). Data-driven decision making in graduate students’ research topic selection. Aslib Journal of Information Management, 71(5), 657–676. https://doi.org/10.1108/AJIM-01-2019-0019

Marg, B. S. Z. (2015). Annual Report 2014-15. Retrieved from https://www.ugc.ac.in/pdfnews/2465555_Annual-Report-2014-15.pdf

Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven decision making in education: Evidence from recent RAND research. Retrieved from Rand Corporation website: https://www.rand.org/pubs/occasional_papers/OP170/

Softjourn. (2020). Data-Driven Decision Making. Retrieved July 3, 2020, from Softjourn website: https://softjourn.com/blog/article/data-driven-decision-making

Wohlstetter, P., Datnow, A., & Park, V. (2008). Creating a system for data-driven decision-making: Applying the principal-agent framework. School Effectiveness and School Improvement, 19(3), 239–259. Retrieved from https://www.tandfonline.com/doi/abs/10.1080/09243450802246376