Big data is a key resource for business: it is used in IT, retail, finance, healthcare, gaming, cybersports, telecom, and marketing. The coolest and most modern companies call themselves Data-Driven. They make strategic decisions based on data.
Here are three situations where businesses can benefit from a big data analyst:
Incomplete purchases. In an online store, users add items to the cart, but then leave the site without placing an order. A big data analyst first finds out at what point a user loses interest. For example, leaving the site when they see a complicated registration form. Then suggests and tests hypotheses to help retain the customer and drive the store to the desired outcome (checkout).
“Bad” debts. The bank wants to minimize the number of customers who don’t pay back loans. The analyst looks at what characteristics of the customer indicate whether they will make their payments on time. On this basis, the customer will be approved or not approved for a loan.
Checking the effectiveness of the design solution. The creators of a dating app want to understand how users react to the color of the button. A data analyst will test two prototypes: one part of users see a version with a blue button, and another part sees a version with a red button. In the end, it helps the interface designer decide which color the button will work best.
Qualitative data analysis can also:
- identify present and future customer needs;
- predict the demand for a product or service;
- estimate the probability of error in different actions;
- control the operation and wear and tear of equipment;
- manage logistics;
- Monitor the efficiency of employees.
All this helps the company learn more about itself, increase profits and reduce costs.
What knowledge and skills does a data analyst need?
Here’s a starter pack for the novice data analyst:
- Work with data using Google Sheets, Sublime, Excel;
- use at least one programming language to solve problems: Python or R;
- Write queries to SQL databases;
- implement reporting in BI-systems: Tableau, Power BI, Google Data Studio, etc;
- Have basic knowledge of statistics.
Depending on the area, specific tools can be added.