How To Leverage Big Data Efficiently For Driving Profits

Nearly 2.5 quintillion bytes of data is being created every day. That’s a huge number. It is so because of the growing digital world. People post daily, comment and share so many things on the internet. All this escalate to widespread data.

According to Bill Schmarzo, the chief technology officer of the Big Data Practice of EMC Global Services, Big data used to refer to storing all the data possible. However, the techniques and tools now used to collect and make use of data has made a major change in data landscape. 

All we want to profit from it

Despite owing big data programs, if you are not successful enough to drive profits and enhance efficiency, then it is of no use. For that, you need to ensure and understand that what kind of people are responsible for it to turn a maximum profit through it. Also, if you are treating Big Data only as a technical matter.

Data is valuable if data science is capable enough to analyze the variables and metrics in data that might be better predictors of performance. Majority of the companies only care about driving profits instead of the what they have accomplished. The aim is to gather right data and, analyze and identify it so that it can meet your needs. And further, use this analysis to streamline business operations and improve efficiency.

Let’s have an example, If you have big data which can be analyzed in terms of economics, you should hire a person who understands economics and is from the same background. A good choice is appointing a chief data monetization officer rather than a chief data officer, said Bill Schmarzo. While chief data officers usually are from IT background but a Monetization officer can offer extra things. A chief data monetization officer can give different results. An economic background of CDMO is important and also if he can examine a company's Big Data processes by focusing on monetizing the digital assets.

You may also read : Big Data Trends That Dominates In 2018

Appointing a right person for the right job

The job profiles of data scientist and big data developers come under highest paying skills in the tech world. But when you assign wrong people for the particular job, it is not going to be productive for your company. Even the data scientist might not be valuable for you if you have put him in the wrong job.The reason for shedding a light into it is, most companies make data scientist do the work of data engineering. It is considered a huge blunder because these two are different skill sets. So companies who fathom the difference between two positions can create efficiency in big data operations.

Data engineer is the one who is at the helm of acquiring, cleansing and putting data in order. Whereas Data scientist is responsible for drawing meaningful insights data and choosing the right neural network to process and analyze it. So, mixing up the two job roles is impetuous, according to Schmarzo. Ultimately, for becoming an expert in a demanded skill set is not an easy endeavor.People who are involved in ETL can be taught skills to become data engineers. But for turning them into data scientists requires learning a new set of skills and data tools.

To cope with the inadequacy of the employees in the tech field, you need to retrain workers. For bearing up with limited budgets and high-incomes, enterprises can train their existing workers who are more experienced, with desired and required skill set for reaping benefits.