Data science has been on a growing spree over the last few years. With an investment amounting to billions of dollars, data science is all set to challenge the way we have been treating and analyzing data in the past. 

It would not be wrong to state that data has changed our relationship with different types of fields like education, healthcare and infrastructure to mention a few.

In spite of the numerous success stories in the field of data science, it has been observed that organizations aren't reaping quick results in this domain. 

The reason for this is perhaps the failure to derive valuable insights from data. This means that there is a lack of coordination between various stakeholders in the pipeline which halts the process of data analytics from reaching its climax.

Issues in the data pipeline

A survey by Kaggle concluded that there were still a large number of issues which were faced by data scientists in Malaysia in the present times. 

The most prominent ones according to this survey included last mile issues, ambiguous results and lack of persuasion power of data scientists.

Data presentation and the art of persuasion

In the past, there have been several organisations which have focused on data visualisation and art of persuasion. 

In addition to this, training sessions are organised to oversimplify the Data Analytics story so that data can be presented to the receiver in the most lucid manner possible. 

Ranging from online businesses to startups, a lot of investment has been done on the programs to communicate tangible results from data in different types of language to reach the widest possible audience.

There have also been several lacunae in various elements of new technologies and this divide would run deeper if data scientists do not address it in near future. 

Numerous methods which are in the pipeline need to be adapted for effective visualization techniques including pie charts,  graphical interpretations, heat maps, histogram and polar maps.

The communication mismatch

Several entrepreneurs have realised the value of data which can help them to arrive at the greatest analysis possible from the underlying facts. 

The problem, however, is that data scientists around the globe shy away from making their work intelligible to the people at the helm of affairs. 

This creates problems at the micro and macro levels. For instance, it is difficult for a person having a business development background to effectively understand statistical curves and advanced algorithms. 

This is where big data scientists need to give up on their obsession of complex data models and try to formulate processes and systems which are comprehensible to a wide range of audience. 

Such design and development strategies need to be conceived which can help both entrepreneurs and the researchers.

Concluding remarks

Data presentation to a wide range of audience is both an art as well as science. Not only do entrepreneurs need to catch up with the latest research but scientists also need to find ways by which communication resonates with the audience. 

That will make the science of data analytics successful in the times to come.

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