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10 Challenges That Data Science Industry Still Faces!!

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Most of the companies are utilizing data and analytics for attaining tasks that were earlier thought impossible given the size and uneven distribution. It has become an important part of the overall working of most of the industries.

The adoption of data analytics has been increased, it comes due to its own challenges. Keeping with the data science forefront various companies, it has helped in solving various problems whether it is about finding the talent or solving challenges that are revolving around, hidden security risk and vulnerabilities.

In this article, we will talk about 10 such challenges that the data science companies are facing despite the breathtaking growth that has been witnessed with its adoption over the years.

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1. Hiring People With Relevant Skills:

Skills

This is the most faced challenge as most of the industries are struggling with the talent shortage. Many data analysts believe that actual challenge faced by Indian analytics industry is the lack of skills within the workforce. There are a significant amount of professionals that are not equipped with the relevant skills according to the business requirements. There is a need for people who can execute complex data science projects, having the right analytics skills and business domain knowledge.

Industries today are still grappling to build the right team while assuring the right infrastructure of hardware and software implementation. Lack of talent is there in the people who have the right mix of business and programming knowledge.

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2. Finding Right Data And Sizing:

Data Sizing

 

Having  ‘appropriate data’ is a common problem faced by industries and plays an important role in building the appropriate model. The volume and velocity of data are large enough, The biggest challenge is to make sense of it all to drive favourable decisions. It is crucial to capture and correct data to make a strong and perfect analytical model.

Industries need to know if they are equipped to make sense of large data and whether all the data points are going to be utilized. It is crucial to know what is critical and what needs to be measured in order to make organizational decisions. A lot of efforts, time and money are spent on storing and collecting data without determining how data will eventually be consumed and by whom.

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3. Consolidation of information:

Information

All companies have a huge amount of data that is mostly scattered. In such cases, information remains one of the major challenges as most organizations struggle with internal data systems. The companies are struggling with collecting data into a single preview to reap maximum benefits. It is crucial to have a proper view of data while enhancing the information with analytics-infused data elements.

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4. Teaching People About What Data Can Do For You:

Teaching

Data science has created many opportunities for young talents. Most of the data science leaders believe that it is the major challenges to tell people about what data can do for us. They have to adapt to ask the proper questions so that data can do things beyond counting, reporting, and aggregating numbers.

It is the biggest challenge to convince companies to move to a data-dedicated decision-making process. To overcome through this it is crucial to know the right use, highlighting the impact data analytics can have on their business.

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5. Identifying The Right Area To Invest:

Areas to invest

Most of the service providers do not consider it as an aspect, but it is crucial to know and engage main stakeholders and ensure that the right commitment is obtained from the side while defining analytics roadmap for them. This is important to have a project move in the right direction and deliver the right business impact.

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6. Right Storytelling:

Right Storytelling

Analytics is about dealing with tough and complex models that could be difficult for end users to understand. It requires great story narrating skills for a data analyst and team members to able to make the data and make it easy, understandable and to be able to finish how they can work together to be the best for Artificial Intelligence models at hand.

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7. Building Data Science Models:

Models

Data analysts team that is interested in creating the data science models but they might not be necessarily solving business problems. The whole process of acquisition of data science solutions to execution can be quite difficult and it is important to create the models that can solve the challenges in real-time. People should have a strong problem-solving capability to make this happen and again directs back to the point of recruiting the right talent.

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8. Recognizing Appropriate Analytics:

Analytics

Industries are still evolving, many data science experts believe that there are not a lot of use cases that actually exist out there. It has become difficult to identify correct data for the appropriate analytics use case. It has become tough for analysts to do all the things by themselves such as inspect the content and convince people to adopt it, especially if it is being done for the first time in an organization.

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9. Agility:

Agility

The data science functions are build in a way that allows limited interaction with the end business user. Analysts believe that for data science to be generating more meaningful ways to support a business, it needs to be more agile and in sync with business during the decision-making process.

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10. Knowing The Importance of Data Security:

Data Security

Data Science is all about managing a big amount of data and ensuring the security of data that companies are dealing with remains a big challenge. Industries need to work on privacy and making data as safe as possible from any wrong use.

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