A Data Analyst is an individual who pull data from various sources, analyze, manipulate and use visualization tools to present data to help organizations make better business decisions. Etiquette of a data analyst are data mining expectations of an analyst towards big data.
1.Plan for a messy data
Data usually are available in large amounts both structured and unstructured data. An analyst should prepare for removal of NULL data in whatever format data is provided (text file, spreadsheet etc).
2. Ask questions
Do not assume data should be in a certain format. Asking questions from data consumer helps the analyst represent data (bar, pie chart) or save data in formats (.xlsx and .txt) needed.
3. Data Knowledge
An Analyst should have a knowledge of what data undergoing analysis. A little knowledge of what the data does, how it is used and what problem is resolved by getting the information needed is very necessary.
4. Strategic Thinking
Having a robust mind on how data work and issues surrounding the use of misrepresented data, should make an analyst strategic and analytical in handling big data.
5. Build a collaborative platform
A collaborative platform helps in data analysis. The use of integrated tools and processes that help in data mining, making data approachable and often help in resolving big data challenges. For example, a platform where business data can be pulled into data sources, manipulated and visualized with integrated analytical tools.
6. Create a defined, measurable objective for each project.
Data analysts should have defined objectives for mining data such as what, how and when.
7. Cross check data
A data analyst must have the ability to cross check data with original source. Here, errors are uncovered from ETL processes or raw data itself. Plots and descriptive statistics are helpful in spotting issues.
8. Simplify data to increase chances of success
Most Data analysts and scientists know that simpler solutions are generally better solutions. Why? Because they have fewer moving parts that can break and there’s less likelihood of model over fitting. When simplifying, consider the predictors, but also the target variable… ask, “Can it be simplified as well?”
9. Automate Data
If you run reports weekly, bi-weekly and monthly, you should automate the processes for running such reports. This not only improve reporting speed but saves time for repetition of report preparation process.
10. Take a step wise approach
The most successful organizations start by solving a known problem in a new way rather than making the common mistake of trying to tackle unknown problems with unknown data. The next step is to solve the same problem with new data, and then move on to solving new problems with new data. “The organizations taking a step-wise approach have the highest likelihood to achieve success,” says Chastain.
There are a lot of approach to big data analytics, but success starts with a solid strategy. We hope you can use the above listed insights for your approach towards data.