Challenges of Data Science Technology

Challenges of Data Science Technology

  • Identification of the issue
  • Getting Access to the Right Information
  • Cleaning up the data consists of the following steps
  • Professionals are in short supply
  • Identifying the Problem
  • Data Integrity
  • Quantity of data
  • Various Data Sources

1. Data Preparation

Data scientists spend around 80% of their time cleaning and preparing information before utilising it for analysis in order to improve its quality — that is, to make it accurate and consistent.. However, 57 percent of them believe it to be the most difficult aspect of their work, describing it as time-consuming and monotonous. On a daily basis, they must process terabytes of data across different formats, sources, functions, and platforms while keeping a track of their operations to avoid repetition.

2. A variety of data sources

More data sources will be needed by data scientists to make meaningful judgments as businesses continue to use various sorts of applications and technologies and generate various forms of data. This method necessitates manual data entry and time-consuming data searching, which results in mistakes, repetitions, and, ultimately, incorrect judgments.

3. Data Protection

  1. 2. Cyberattacks are becoming more prevalent as companies migrate to cloud data management. This has resulted in two significant issues:
  2. Confidential information is at risk.
  3. 4. As a result of recurrent intrusions, regulatory norms have changed, lengthening the data permission and use processes, further aggravating the data scientists' dissatisfaction.

4. Recognizing The Business Issue

Data scientists must first completely grasp the business challenge before doing data analysis and developing solutions. Most data scientists approach this in a mechanical manner, diving directly into analysing data sets without first determining the business problem and purpose.

5.Effective Non-Technical Stakeholder Communication

Data scientists must be able to communicate successfully with corporate leaders who may not be aware of the intricacies and technical language involved in their job. If the CEO, stakeholder, or customer is unable to comprehend their models, their solutions are unlikely to be implemented.

6.Involvement of Data Engineers

Data scientists and data engineers are frequently found working on the same projects in organisations. This necessitates efficient communication between them in order to get the greatest results. However, their goals and procedures are frequently incompatible, resulting in misunderstanding and stifling information transfer.

7. Undefined KPIs And Metrics

Management teams' lack of awareness of data science leads to unreasonable expectations of data scientists, which has an impact on their performance. Data scientists are supposed to come up with a magic bullet that will fix all of the company's issues. This is very counterproductive.

Therefore, every business should have:

  1. Data scientists must use well-defined criteria to assess the correctness of their analyses.
  2. Appropriate business KPIs to evaluate the analysis' influence on the company