Introduction
Throughout the world, organizations are trying to discover the value that data can offer. In this article, we look at the primary factors challenging the productivity of data scientists and discuss potential solutions. Check out our data science course syllabus.
Challenges Faced by Data Scientists and How They Should Overcome
Here are the challenges and solutions, step-by-step:
Finding the data
The most frequent problem faced by data scientists is still finding the “right” data, which has a direct effect on their capacity to create robust models.
Challenge 1: But why is it so difficult to locate data?
- The majority of businesses gather vast amounts of data without first figuring out who would actually use it and how.
- This is motivated by the availability of inexpensive storage as well as a concern of losing out on important insights that could be gained from it.
- Organizations wind up collecting pointless data as a result of this data-collection frenzy, which detracts from actionability.
- Because of this, it is more difficult for data users to locate the data assets that are pertinent to the business plan.
Solution: Companies must make sure they gather pertinent data that will be used.
- Understanding precisely what must be measured to inform decision-making is crucial for this, and different businesses have different requirements.
Challenge 2: It can be challenging for data scientists to locate the appropriate asset because data is dispersed across several sources.
Solution: Putting all the information in one location is part of the solution.
- Because of this, many companies use data warehouses to keep track of data from many sources.
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Obtaining the Data
Accessing the latter is the next barrier for data scientists after they have found the correct table. Data scientists are finding it more difficult to access datasets due to security and regulatory concerns.
Cyberattacks have increased in frequency as businesses move to cloud data management. This has resulted in two significant problems:
- These attacks are increasingly threatening confidential data.
- Businesses are now subject to stricter regulations as a result of cyberattacks.
- Data scientists are consequently having a difficult time obtaining permission to utilize the data, which significantly slows down their jobs.
- Even worse is being denied access to a dataset.
Solutions: Organizations must balance ensuring rigorous adherence to data protection regulations like GDPR and maintaining data security while granting the necessary access to the data to the appropriate parties.
- Either costly fines and time-consuming audits or the inability to effectively leverage data will result from failing to meet one of these two goals.
- Data catalogs ensure that the appropriate individuals have access to the data they require while streamlining the regulatory compliance process.
- This is mostly accomplished by access management tools, which allow you to grant or restrict access to tables based on employee status with a single click.
- Data scientists will be able to access the datasets they require with ease in this manner.
Understanding the Data
Data scientists should be able to work their magic and create potent predictive models as soon as they locate and gain access to a particular table.
Challenge: They typically spend absurdly long periods sitting and asking themselves questions like these:
- What is meant by the column name ‘CT21’?
- To whom may I address this?
- Why do so many values appear to be missing?
Getting an answer is difficult, even if these inquiries are straightforward.
Finding someone who understands the meaning of the column name you are looking for is like trying to find a needle in a haystack since databases are not owned by organizations.
Solutions:
Once more, documenting data assets can help your organization’s data scientists avoid devoting excessive time to these fundamental inquiries.
- Your data scientists’ productivity will increase dramatically if you can provide a written definition for each column in each table in your data warehouse.
- It is quicker than allowing unrecorded assets to wander about your company while ineffective data scientists spend 80% of their time attempting to identify them.
- When you specify a single column in a database, the definition is propagated to all other columns with the same name in other tables due to automation features found in modern data documentation systems.
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Data Cleaning Challenge
Challenge: Data cleansing, outlier removal, variable encoding, and other laborious tasks are all part of this time-consuming process.
- Models must be based on clean, high-quality data, even if data preprocessing is frequently regarded as the worst aspect of a data scientist’s work.
- Incorrect patterns are learned by machine learning models, which eventually result in inaccurate predictions.
- How can data scientists ensure that only high-quality data is used for training machine learning models while spending less time preprocessing data?
Solution:
Using augmented analytics is one way to solve the problem.
- It is enhancing the way data scientists preprocess data by using technologies like AI and machine learning to help with data preparation.
- This makes it possible for some data cleansing tasks to be automated, which can save data scientists a lot of time without sacrificing productivity.
Delivering the findings to non-technical stakeholders
Challenge: Since the ultimate objective of data science is to direct and enhance organizational decision-making, the work of data scientists is intended to be completely linked with business strategy.
This communication with non-technical stakeholders is difficult for two reasons:
- It might be challenging for data scientists to convert their results into understandable business insights because they frequently have a technical background.
- Most businesses have poorly defined business terms and KPIs.
- For example, everyone is aware of the general components of a company’s return on investment (ROI), but rarely are all departments in agreement on the precise formula used to calculate it.
- In the end, there are as many definitions of ROI as there are personnel who compute it.
The same is true for other business terminologies and KPIs. Because of this, data scientists find it even more difficult to comprehend and articulate the significance of their work to particular KPIs.
Solution: They can use ideas like “data storytelling” to give their analysis and visuals a compelling story.
- Using a data catalog is a smart method to create a single source of truth for your business terminology and KPIs.
- This approach guarantees that everyone agrees on the essential definitions for your company.
Conclusion
Gathering pertinent data, centralizing data assets, documenting your tables, and explicitly defining business terminology and KPIs are all simple best practices that will significantly increase your data team’s productivity and reduce aggravation. Hone your skills with our data science courses in Chennai.
