Pain Points of a Data Analyst

Who is a Data Analyst?

Data analysts collect, process, and perform statistical analyses on data. Their goal is to discover how data can be used to answer questions and solve problems. Every business collects data to help the company make better decisions. A data analyst transforms this data into meaningful information which business stakeholders can understand and take actions upon. The problems solved by a data analyst range from developing a pricing strategy, reducing customer attrition, and improving customer satisfaction, to solving any issues faced by the company that are costly.

Since the ‘data analyst’ job is relatively new in the market, there is also a lot of confusion regarding the exact role. Moreover, there are other similar sounding job roles, such as data scientist and data engineer, which add to the confusion. Before diving deep into the challenges faced by a data analyst, let us look at the differences between these three roles.

 

Description of the different job roles

Data analystData scientist

Data engineer

  • Cleaning raw data
  • Using statistics to get a high overview of the data
  • Identify interesting trends in data
  • Creating visualizations and dashboards for easier decision making
  • Presenting results to clients or internal teams
  • Evaluating statistical models to uncover insights from data
  • Using machine learning for predictive algorithms
  • Testing and iterating on algorithms to improve accuracy of a model
  • Building visualizations for summarizing results from the advanced analytics
  • Constructing data pipelines for handling data at scale
  • Integrating new data sources into existing pipelines
  • Developing, testing, and maintaining databases and large-scale processing systems
  • Establishing the foundation which is used by data analysts and scientists

 

Now that we’ve taken a look at the differences in the job roles, let’s understand the challenges faced by a data analyst in an organization.

5 Pain Points of a Data Analyst

 

  1. Nature of the data

Data stored by businesses, in most of the cases, is never directly analyzable. It requires extensive cleaning before actually using it to generate insights. The first few questions you need to ask after looking at a dataset are: Does the data make sense? Does each column have the right values? — for example, does the name column contain only names and does the address column contain only addresses? Are there missing or junk values? etc. Only after confirming the validity of the data, can you go ahead with the analysis. Depending on the quality of the data, data cleaning may take a lot of time which is a major challenge for a data analyst.

 

  1. Analytics is an iterative process

Data analytics is a continuous and iterative process. Many businesses try to get a one-shot solution in a short period of time. However, in real-world problems, the problems evolve over a period of time and clarity is attained only as we perform the analysis. Constant communication is required between the business stakeholders and the data analysts to ensure a successful analytics project.

 

  1.  Non-consumption of results

The aim of any analytical project is not only to generate insights for the business, but also for the business to implement the recommendations and measure success. If any action is not taken on the results of the analysis, then the analysis is futile. A way to ensure consumption of results is to make sure the analytical roadmap does not deviate from the business objective. As mentioned in the previous point, a meeting cadence between the stakeholders and analysts is necessary for guiding the analytical work.

 

  1. Unsynchronized data sources

Analytics is holistic only when data is readily available and accessible. Different data sources should be linked with each other for data to be made usable. To enable this, data governance should be considered as an essential foundation in every organization. A lack of data connectivity can be a hurdle for data analysts as it leads to deficient insights and unactionable recommendations.

 

  1. Muddy business objectives

Most often, business problems are muddy as there is a lack of clarity regarding the objectives and outcomes of the analysis. Business problems usually start with a vague and high level objective such as increasing sales, reducing customer attrition, increasing member footfall, or improving customer satisfaction, etc. To ensure the efficacy of the analysis, this high level objective needs to be broken down into sub-problems for it to be worked on in an analytics project.

 

Conclusion

Data analyst is one of the hottest career options in today’s market as analytics has completely transformed how decisions are made in an organization. However, there are a lot of challenges to overcome for ensuring utilization of analytics. The objective of all analytical work is to improve the business in some way. The biggest gap however is that even though vast amounts of data are being collected and stored, actionable analytics is still scarce. This is what’s known as the utilization gap. To ensure analytics is utilitarian and impactful, business stakeholders and analysts should work hand in hand throughout the course of the project.

Reources:

1.https://www.dataquest.io/blog/data-analyst-data-scientist-data-engineer/

2.https://www.forbes.com/sites/christinemoorman/2013/03/17/the-utilization-gap-big-datas-biggest-challenge/#66df8d0f3563

 

Rohan Joseph

About Rohan Joseph

Practicing the dark arts of data science. I am currently pursuing Master's in Operations Research at Virginia Tech and working with Chartio to democratize analytics in every organization.