The world of data analytics can be overwhelming for young professionals just starting out in the industry. This is so due to the sheer rate of change which is a constant. It always seems like everyone needs to catch up on the latest offerings on the market. To put these concerns in perspective, the skills listed below are the basic skills for any young or seasoned professional to aim for.
1. Data extraction – This is the phase in which companies work with their clients to obtain the relevant data points that are required to perform the relevant analyzes for the client. For example, it is critical that the correct information is pulled from the customer’s ERP system as efficiently as possible because it is never a good practice to request the same data over and over again from the customer, as it would be a very frustrating experience for everyone involved. . Young professionals should try to familiarize themselves with the most commonly installed ERP systems, such as SAP and Oracle, in order to feel comfortable obtaining customer data efficiently.
2. Data cleansing or transformation – Once we have the relevant customer data, it is important that the data is transformed so that it can create the desired results from the analytical procedures designed. This stage may require the data to also be cleansed (ie formatted manually or through scripts depending on available tools) to make the data usable. The results of this stage are typically additional tables and exported files, which provide additional data points, that are calculated from the source files. The most recommended data transformation tools used in the industry are Audit Command Language, SAS, and SQL scripting.
3. Reports/Display – Everyone likes a story. This is the foundation of this stage of any data analysis delivery process. Once all the relevant data points are available, they can be imported into tools like Tableau, Spotfire, or MS Excel to create dashboards. Dashboards are nothing more than a collection of graphs, the purpose of which is to highlight a certain point that emerged from the analysis performed on the raw data files.
Granted, it’s mostly the people who work in the data transformation stage who feel the heat from the people in the other two stages. That’s why it’s critical that all work done is documented in records, if possible, to keep track of initial requirements and any iterative changes that occur. For example, audit command language scripts are build logs, which can be provided as documentation of all steps taken to transform raw data files into team members working to extract the data and build final dashboards.
These conceptual skills should be the basis for any young professional to choose the tools they want to learn in the long term. In a fast-paced industry, these concepts are the only constant factors around which the entire industry revolves.