Data-driven decision management

 Data-driven decision management

“Without data, you’re just another person with an opinion.”

W. Edwards Deming

We currently live in a “smart” era, with smartphones, smartwatches, we even have smart water bottles. All of them recording our every move, sip, and clicks. We have a wide selection of applications, to register our thoughts, photos, and comments. All these constantly communicating with the users and a central system creating not just data but big data – volume, variety, and velocity.

But does a collection of data translate into information? 

Data-driven decision management is the effort to combine the data collected (observation), retrieve information and knowledge from it, and implement strategic business decisions. Data-driven decision-making has led to greater transparency and accountability at organizational levels making sure that decisions are made not using human intuitions or past or present fads but in a more objective and empirical fashion.

Phases of Data Driven Decision Management:

  1. Set objectives: The very first step would be to define your business objectives. This gives a clear problem statement allowing you to set goals and prioritize them at an operational level with the ability to track them quantitatively and qualitatively. Goals are elucidated into key performance indicators (KPIs) to be measured, that can be used to determine the success or failure of the metrics involved. Facts and goals form a continuous process, where goals help relevant facts to be analyzed and facts in turn help new goals to be set.  
data objective setting
  1. Build your data system: Now that the KPIs have been defined, the right data to be collected can be easily recognized. Building a robust and automated data system is a crucial step for every enterprise. The goal is to construct an infrastructure compatible with both the rate and the scale of the data generated by the system. Data collection to data presentation can be categorized in a 4-step process. 
data collection process
  • Data Collection: The data gathered can be classified as quantitative vs qualitative. Quantitative data are numeric in nature with measurements as result. Whereas qualitative data is non-numeric and more subjective in nature.
  • Data Storage: Because data comes in all forms like transactions, videos, files etc., they are classified as structured or unstructured data. Specific systems are used to store them according to the type of the data. 
  • Data transformation: The raw data from storage is transformed into a palatable form in this step. Data is segregated, organized, cleaned and summarized in line with the KPIs set allowing you to form observations quickly.
  • Data Presentation: The observations are presented in a visual form making easy to understand and more importantly quick to analyze by the business. The story that the data tells you has to be illustrated in a way that is clear to the business. The reports created have to be up to date giving the latest snapshot as close to the reality as possible.
  1. Draw Conclusions and taking action: For effective conclusions to be drawn its essential the data is presented to the right people at the right time in the right manner. You have to remember that information comes with an expiry date, hence the decision making is a time bound step. Conclusions put to use is action. An enterprises’ success relies on extracting actionable insights, weighing the options with respect to the broader business set objectives.
  1. Revise and Re-evaluate: Once carried out periodically, decision making has now turned into an iterative process. The adaptability of enterprise to the ever-changing needs of users is critical. Today’s fast paced data collection has made it possible to recognize changes in the user requirements quickly.  The advantage of data driven models are that it not only presents the known knowns and the known unknowns but also the unknown unknowns. This will give you modified goals along with new goals to be achieved to make more impact in the market. 
data decision management

Future of Data driven Decision management 

Although there are still many companies that haven’t fully shifted to the data-driven decision model, DDDM is evolving. The new kid in town is artificial intelligence i.e., AI-driven decision making. An argument can be made that AI will never fully replace the data-driven approach, as the human element is crucial in any business. But this will definitely alleviate the daily routine operational decisions, freeing up humans to concentrate on only the operations that truly matter. AI is less prone to human biases and can process much larger data without the excessive summarizations that humans might need. Utilizing AI and humans seems to be the future we need to embrace than choosing one or the other.

Conclusion

Data-driven decision management is no longer a nice-to-have feature, but a must to have feature. With data growing exponentially humans cannot be expected to take decisions without the support of data. To harness the full potential of DDDM, data has to be collected, processed, and presented in a systematic manner. Data not only gives you information on all the different numbers within your business but also is able to pick up on the nuances of human behavior, giving the business the best shot at growing. 

Author Bio:

Ragini Radhakrishnan is a Data Analytics specialist with a Masters’s degree in Bioinformatics from the University of South Florida. I make the data talk by building robust data systems. Being in the data field has made me extremely adaptable, now I never shy away from learning new technologies. I am a climate enthusiast and believe that every bit of effort counts. 

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