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5 Challenges In A Data-Driven Project And How To Overcome Them


5 Challenges In A Data-Driven Project And How To Overcome Them


The term data-driven means to build tools and abilities in order to act on data. As a foundation of the data-driven project, a broad and deep understanding of the content, structure, quality issues, as well as necessary transformations along with appropriate tools and technological resources are required.



Tools and technologies keep evolving constantly. At the current scenario, one of the crucial tasks in an organisation while managing data is the analysis of numerous amount of data. Working with the data is not an easy task and can be time-consuming.

In this article, we list down 5 challenges which can be seen in a data-driven project and measures to avoid them.

1| Data Quality

The process of discovering data can is a crucial and fundamental task in a data-driven project. The approaches for quality of data can be discovered based on certain requirements, such as user-centred and other organisational frameworks.

How to Avoid

The methods such as data profiling and data exploration will help the analysers to investigate the quality of datasets as well as the implications of their use. The data quality cycle must be followed in order to make the best practice for improving and ensuring high data quality.

2| Data Integration

In general, the method of combining data from various sources and store it together to get a unified view is known as data integration. Inconsistent data in an organisation is likely to have data integration issues.

How To Avoid

There are several data integration platforms such as Talend, Adeptia, Actian, QlikView, etc.  which can be used to solve complex issues of data integration. These tools provide features for data integration such as automate and orchestrate transformations, build extensible frameworks, automate query performance optimisation, etc.

3| Dirty Data

Data which contains inaccurate information can be said as dirty data. To remove the dirty data from a dataset is virtually impossible. Depending on the severity of the errors, strategies to work with dirty data needs to be implemented. There are basically six types of dirty data, they are mentioned below

  • Inaccurate Data: In this case, the data can be technically correct but inaccurate for the organisation.
  • Incorrect Data: Incorrect data occurs when field values are created outside of the valid range of values.
  • Duplicate Data: Duplicate data may occur due to reasons such as repeated submissions, improper data joining, etc.
  • Inconsistent Data: Data redundancy is one of the main causes of inconsistent data.
  • Incomplete Data: This is due to the data with missing values.
  • Business Rule Violation: This type of data violates the business rule in an organisation.

How To Avoid

This challenge can be overcome when the organisations hire data management experts in order to cleanse, validate, replace, delete the raw and unstructured data. There are also data cleansing tools or data scrubbing tools such as TIBCO Clarity available in the market to clean the dirty data.

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4| Data Uncertainty

Reasons for data uncertainties can be ranged from measurement errors, processing errors, etc. Known and unknown errors, as well as uncertainties, should be expected when using real-world data. There are five common types of uncertainty and they are mentioned below:

  • Measurement Precision: Approximation leads to uncertainty.
  • Predictions: It can be projections of future events, which may or may not happen.
  • Inconsistency: Inconsistency between experts in a field or across datasets is an indication of uncertainty.
  • Incompleteness:  Incompleteness in datasets including missing data or data known to be erroneous also causes uncertainty.
  • Credibility:  Credibility of data or of the source of data is another type of uncertainty

How To Avoid

There are powerful uncertainty quantification and analytics software tools such as SmartUQ, UQlab, etc. which is used to reduce the time, expense, and uncertainty associated with simulations, testing, and analyzing complex systems.

5| Data Transformation

Raw data from various sources most often don’t work well together and thus it needs to be cleaned and normalised. Data Transformation can be said as the method of converting the data from one format to another in order to gain meaningful insights from the data. Data Transformation can also be known as ETL (Extract Transform Load) which helps in converting raw data source into a validated and clean form for gaining positive insights. Although the whole data can be transformed into a usable form, yet there remain some issues which can go wrong with the ETL project such as an increase in data velocity, time cost of fixing broken data connections, etc.

How To Avoid

There are various ETL tools such as Ketl, Jedox, etc. which can be used to extract data and store it in the proper format for the purpose of analysis.  

Bottom Line

With the emerging technologies, data-driven projects have become fundamental in the path of success for an organisation. Data is a valuable asset in an organisation which comes in various sizes. The road to get a successful data-driven project is to overcome these challenges as much as possible. There are numerous tools available nowadays in the market to extract valuable patterns from the unstructured data.



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