Top 3 Skills to Develop for Data Analytics

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The data analytics world can be overwhelming for young professionals who are just starting out in the industry. This is so because of the sheer pace of change that is a constant. It always seems that everyone needs to catch up with the latest offering in the marketplace. In order to put these concerns in perspective, the skills listed below are the core skills for any young or seasoned professional to aim
1. Data Extraction - This is the phase where the firms work with their clients to obtain the relevant data points that are required to perform the relevant analytics for the client. For instance, it is paramount that the correct be extracted from the client's ERP system as efficiently as possible because it is never a good practice to request the same data over and over from the client as it would be a very frustrating experience for everyone involved. Young professionals should try to be familiar with the most commonly installed ERP systems like SAP & Oracle in order to be comfortable obtaining data efficiently from the clients.
2. Data Cleansing or Transformation - Once we have the relevant data from the client, it is important that the data should be transformed so that it can create the desired outputs from the designed analytics procedures. This stage may require data to be cleansed as well (i.e. formatted manually or via scripts depending on the available tools) so that the data is useable. The outputs from this stage are typically additional tables and exported files, which provide additional data points, which are calculated from the source files. The most recommended data transformation tools used in the industry are Audit Command Language, SAS & SQL scripting.
3. Reports/Visualisation - Everyone likes a story. This is the basis of this stage of any data analytics delivery process. Once all the relevant data points are available, it can be imported into tools like Tableau, Spotfire or MS Excel to create dashboards. Dashboards are nothing but a collection of graphs, which are aimed at making a certain point that has emerged from the analytics performed on the raw data files.
Granted, mostly it is the folks who work in the data transformation stage that feel the heat from folks in the both other stages. That is why it is critical that all the work done is documented in logs if possible to keep track of initial requirements and iterative changes that take place. For instance audit command language scripts are create logs, which can be provided as documentation for all the steps taken to transform the raw data files to team members working to extract the data and create final dashboards.
These conceptual skills should be the basis for any young professional to choose the tools that they wish to learn for the long run. In a fast paced industry, these concepts are the only constant factors around which, the whole industry revolves.
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