The demand for data scientists and data practitioners continues to increase.
One of the main reasons data scientists are hired is to develop algorithms and build machine learning models for organizations. However, most of the time, that is not the case.
Data practitioners spend 80% of their valuable time finding, cleaning, and organizing the data, leaving only 20% to actually perform analysis on it – which is the most enjoyable part of the role.
This continues to be an 80/20 rule, also known as the Pareto principle, in data science as the amount of data available has increased exponentially. More often than not, data scientists spend hours preparing and cleaning the data to produce a report for stakeholders, only to find out they were looking for something else or didn’t understand the analysis enough to act on it.
Preparing and Analyzing Data
One of the main issues data professionals often see is the organizational structure.
Data scientists often perform their work in silos, which can create issues with the workloads and increase the risk of error.
Research shows 62% of data analysts depend on others within their organization to perform certain steps in the analytics process. This lack of cooperation slows down the analysis process and delays reports that need to be generated to move the analysis forward.
Here are common hurdles data scientists run into when preparing the data for analysis:
- White spaces
- Null values
- Non-identical duplicates
- Unrecognizable characters
- Currency and unit conversions
And with more data available, data professionals see more problems. Each data set comes with a unique set of challenges that must be taken care of before moving forward in the analysis.
Additionally, data wrangling greatly depends on:
- Which data source is used
- The number of sources
- The amount of data
- The task itself
- Nature of data (distribution, missing value, etc.)
Furthermore, data scientists work with stringent deadlines that may compromise the quality of the work from excellent to “good enough.” For example, if a dataset for a time-sensitive project takes longer than expected to collect and clean the data, it may be outdated before the finalization of the analysis. That is why it’s important for organizations to prioritize the business needs: what needs to be resolved immediately and what can wait.
Overcoming the Pitfalls
Data enhances business operations and the structure of an organization. Having one central source of truth is vital for data scientists as they are also in charge of the data governance, ensuring the data is secured and private.
It doesn’t only help data professionals with what they need, it accelerates the analysis and gives them the confidence to use any given data set without having to stop and ensure it’s updated and clean.
Data catalogs are a metadata management system and helps data analysts find the data they need and provide the necessary information to evaluate if it can be sustainable to use. There are a number of benefits to leverage data catalogs, including:
- Data governance optimization
- Data quality consistency
- Data efficiency improvement
- Risk of error reduction
Looking Forward
Data scientists play an essential role in organizations by pushing forward innovation. The most important step is to make the data accessible to everyone in the organization and easy to use. Data that is not used or cannot be used doesn’t have any value.
In other words, creating a data-driven culture is vital for companies. Data-driven organizations view data as a core business asset essential to business growth and success – it’s not just something that is nice to have.
Additionally, when a business is data-driven, staff have access to clean, high-quality data that can be easily accessed to perform their daily work, helping accelerate the process.
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