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Why do we need real time data analytics: Analogy to project management lifecycle

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With the advent Internet and ever changing data, the landscape and the value of data has changed to a great extent than it was 25 years ago. In today’s world the quicker a company can take action, the more impact they can have on the outcome. With stronger competition, every company in every industry needs to be more alert than ever before. The importance of data over its lifecycle hence has become a real important factor in decision making. While the older method of accumulating data, analyzing it and then taking a business decision (across a time period of a few weeks) is still practiced, we do not need to look further than software industries to see a change in practices that we should be adopting.

A decade ago, most IT companies preferred to follow the Waterfall model of project management. However today most modern companies follow Agile model where continuous engagement is required. Agile methodology gives a team the ability to act quickly in case any change is needed and hence eventually make the final product more stable and suitable. Continuous iteration process also squashes the bugs that would otherwise plague the product if it were developed through Waterfall model. While both the model has its pros and cons, Agile model provides more dynamism to the whole approach.

Waterfall Model
Waterfall Model

Agile Project management Model
Agile Model

The need of the hour can be simplified by drawing an analogy to the project management methodologies. The continuous process of product development is extremely important. In the same manner, it is important to provide input key inputs for the requirement phase. That is only possible if one gathers information from market and other sources. Hence the data that we feed into our software development process has a different value as the time passes

The value and importance of data along its life time is depicted in the chart below (credit: Sachin Sinha).
Data value vs Cost comparison
Data value vs Cost comparison

On a brief note, the data we gather keeps loosing its value on decisions as time passes. On the other hand if one had to make a decision on the stored data, with passing time more storage will be required. For a given business and considering that the daily volume of data collected remains constant, a team that analyses their data every month would require storage capacity higher than a team that analyses their data every week.

As our software development methodologies have changed to accommodate the faster development requirements and quicker change management, our business models and modus operandi are also changing to accommodate faster decision-making and to stay competitive in the market. Any business should seriously consider analyzing their data in real time to gain insights that could give them an edge over their competitors.

“To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of” – Ronald Fisher

As we navigate around the worlds of agile/scrum, unit economics and nanotechnologies, one thing becomes certain. Continuous iteration and management at unit level will be necessary to stay ahead of the curve in any industry in the near future. In terms of data, every second will count when we think of impactful data analytics.

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