6 Ways to be a Big Data Superstar

The most required skill in professional environment is big data and analytics skills. Every organization deals with huge amount of data on regular basis.  There are other technical skill areas in demand include programming languages such as Python, C++, and Java, machine learning and AI experience, competence in quantitative analysis, data mining, and SQL/NoSQL databases and algorithm development. These are just the technical skill sets that are needed. In order to be a big data digital star and influencer. 

The 6 must-have skills:

1.Know the ins and outs of your company 
One needs to understand their company’s product lines, Processes, revenue sources, BI data, financial and sales reports and other strategic goals. This is very essential to bridge the gap between the IT and data science and the end user overall.

2.Knowledge on business process engineering 
Big data technologies like analytics, machine learning, IoT, robotic process automation, and AI are disruptive to businesses. These technologies disrupt because they impact established business processes that must be redesigned, and this means users must be retrained.
Too often, IT and even the end business inserts new technologies into business processes without evaluating how existing processes and workers will be affected. 
This can lead to the rejection of a project that could have been successful if it had been properly inserted and tested in a new business process before the process was put in place. You need to be able to work with technologists and end users so technology that adds to a business process improves the process and makes work easier.

3.Collaborate and command collaboration
Big data technology insertion and business process reengineering depend on a healthy collaboration between end users who are familiar with the business process flow and technologists who are providing the new technology to be used in the business process.
A rising digital star must lead by example, so you need to be a selfless collaborator who does everything possible to make the project a success. You must also be able to inspire others to enthusiastically collaborate so the team can create great business processes that leverage some of the outstanding big data technologies that are available. 

4.Follow-up on big data projects
One of the best ways to gain experience with big data projects is to follow up on implemented big data projects; this enables you to see what's going well and what can be improved. You can apply this knowledge in future projects. 
Plus, following up on projects after implementation tells customers that you care about their systems and work environment, and it paves the way for great user cooperation and collaboration in your next project together.

5.Adhere to compliance and governance
Big data champions always pencil in project time for compliance and governance conformance and QA checkout. It is never an option to skip this step.

6.Maintain data quality 
One of the reasons digital big data projects fail is because of poor data quality. Most IT and business users know this, but they also know that cleaning the data--especially if some of the clean-up must be manual - is tedious work that gets in the way of other projects. 

The result is the data cleansing step is not done as thoroughly as it should be, and this leads to major risks. A poor business decision might be made because the data it was based on was poor. A project can be cancelled because the data was poor, even if the algorithms are right. Digital big data champions always insist on quality data. 

By haripriya.krishnakum Krishnakumar | 03 Dec 2019 | 0 Comments