Data science is broadening, and so its branches all across the world. But there includes plenty of challenges that hinders the data scientist when dealing with data. So, let us walk through some major obstacles that are faced by the data scientists.

Data Cleansing

Big Data has started to cost companies over 25% of the possible revenue as cleansing of bad data is taking away operating expenses. So, working with the datasets filled with anomalies and inconsistencies is every scientist’s nightmare. The dirty data will lead to the dirty results. And data scientists have to work with the terabytes of data so just imagine their plight if they need to spend most of their time only sanitizing their data even starting the analysis.

Data Quality

Deep learning and machine learning algorithms will beat human intelligence. The algorithms are quite exemplary in learning to do what they’re taught to do and problem happens when the data given is curated poorly. Thus, data quality is very important and scientist will have a difficult task to curate this data.

Data security challenge

Data security is one big issue today. As data is been extracted through many social media, interconnected channels, and other nodes, there’s high vulnerability of the hacker attacks. Because of the confidentiality data element, Data scientists actually are facing some obstacles in the data usage, extraction, building models and algorithms. Process of getting consent from the users is causing one major delay in the turnaround time & cost overruns.

Identifying the main Issue

Hardest challenge that is faced by the data scientist when examining the real time problem will be identifying the main issue. They not just need to understand their data but also have to make this readable for common man. Insights from this analysis must remove major hiccups and glitches in the business.

Here are 5 components to look at:

  • Data Privacy, Unavailability and Veracity: These challenges generally revolved over the data itself, which includes how “dirty” this is, availability and privacy issues.
  • Insights Never Used in the Decision Making: Such challenges include the company politics, inability to integrate the study findings in decision-making procedures and lack of support.
  • Lack of Funds: The challenges on lack of funding generally impact what organization will purchase with external data sources, domain expertise and data science talent.
  • Wrong Questions Asked: The challenges are about difficulty to maintain the expectations about an impact of the data science projects as well as not having right question to answer and clear direction with an available data.
  • Limitations of the tools to scale or deploy: The challenges in such category are linked to tools used to extract the insights, deploy models and scaling solutions to full database.

Summary

The data scientists do experience many challenges in the machine learning and data science pursuits. The data professionals experience different types of challenges. Most common machine learning and data science challenges included lack of the data science talent, dirty data, lack of support and clear direction and question.

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