Data is one of the most important commodities that could be more valuable in the first digital era than oil. These days, businesses are rapidly generating and profiting from a vast amount of data that lets them obtain a great deal of value for their business development. This vast volume of knowledge is not always adequate to provide actionable perspectives organisations pursue.
The role of data professionals will continue to grow in importance as companies begin to completely focus on the utilisation of their internal data properties and analyse the convergence of hundreds of third party data sources.
Today, all sorts of companies hire data analysts to make sense of the increasing volume and variety of data they produce and gather. Wringing actionable responses out of details has become a vital business capability. Big data is gathered from all sorts of companies and they continue to use it to build or optimise decisions. B2B and B2C trade, health care, retail and marketing firms in diverse fields all use data analytics to optimize operations and maximise revenues. The rise of big data has added a layer of technical difficulty to the data analyst’s role, which means coding is now much more likely to come up.
Learning how to code is a time investment task however, it’s an investment worth making that could not only save you time but save on effort too.
A data analyst’s role typically surrounds the following steps: Obtaining data, cleaning data, analysing data and visualising data.
Data is now obtained through a variety of sources. Forbes reports that humans create 2.5 quintillion bytes of data daily. Large datasets give rise to a variety of data quality issues. These issues can be anything ranging from duplicate or missing datasets and values, inconsistent data, misentered data or even outdated data, obtaining data from multiple datasets, pulling data they need from each one can be quite tedious.
Programming alternatives: With the help of querying languages such as SQL, obtaining data for an organisation becomes a significantly simpler task.
After obtaining the necessary data and compiling it in one location, the data needs to be cleaned. Labelling errors, minor spelling mistakes and other minute errors can cause major problems along the road making analysis more long winded and manually cleaning data is a time consuming exercise.
Programming alternatives: Data analysts can use Python and R to easily clean data and organise it in a much more efficiently formatted dataset that can aid analysis.
Once a dataset is cleaned and uniformly formatted, it is ready to be analysed. Data analysis involves extracting useful insights from a dataset and is critical, spreadsheets provide certain basic functions however, there are many limitations to them, there are only so many rows and columns per spreadsheet. So when you run out of rows/columns, you’re forced to move to a new tab or a new file. While it’s debatable that needing that many rows or columns of data is unlikely in most circumstances, there are cases where datasets grow over time and eventually the spreadsheet will not be able to contain all of that data, especially when you’re dealing with large amounts of data over time.
Programming alternatives: When it comes to data analysis, data analysts could consider moving away from spreadsheets and adding R and Python to their repertoire. Python is ubiquitous within the data science community and R is another language that can help expedite and streamline the data analysis process.
Visualising the results of data analysis helps data analysts convey the importance of the findings in a succinct and digestible manner. This can be done utilising visual tools such as graphs and charts allowing a broader audience to understand a data analyst’s work. Visualising large datasets is simpler when utilising programming languages compared to spreadsheets.
Programming alternatives: Python is a commonly used language which also contains packages such as seaborn and prettyplotlib that can help create stunning visuals. R is another program that can be utilised to showcase data in the form of dashboards which provide a much more visual representation compared to tables and charts.