Why hire a data analyst?
Data is piling up at the company's servers as there are more applications and digital interactions taking place in our increasingly digitized world. Companies around the world are hiring more data analysts to help them extract insights from the data they own, typically for the purpose of:
- Optimizing existing processes and practices
- Identifying opportunities for growth and cost reduction
- Reporting on progress and KPI’s across teams
- Building data pipelines to support applications
For a data analyst, leveraging his/her skill sets in mathematics, programming, and business domain can help a company become more competitive in its market space. Though a company may hire data analysts with very different titles serving a diversity of job functions.
What are the different types of data analysts?
Types of data analysts a company may hire include but are not limited to
- Marketing Analyst
- Sales Operations Analyst
- Financial Analyst
- Supply Chain Analyst
- IT System Analyst
- Corporate Strategy Analyst
- Legal Analyst
- HR Analyst
These job titles not only suggest distinctive job function domains but also the tools they use.
For a Marketing Analyst, their job includes
1. helping a company understand which products to sell & market
2. improving marketing budgets and channel effectiveness in terms of ROI.
3. collecting and processing data from customers, and competitors
4. forecasting revenue and marketing-related expenses
This means that a marketing analyst would need four pieces of core data: a. customer data b. competition data c. advertising data d. sales data. Therefore, they would likely need to use Customer Relation Management (CRM), Robotic Processing Automation (RPA), Extract Load and Transfer (ETL), and Business Intelligence (BI) tools for a lot of data that needs to be extracted, analyzed, automated and reported.
It really depends on the institution that one works for, a financial analyst at a Private Equity fund may only need to use proprietary company software for sourcing deals. A quantitative hedge fund analyst may need to use Python, R, C++, Java…for implementing algorithmic trading strategies. A financial analyst at a commercial bank or insurance firm may use statistical modeling tools for risk modeling. A tax and duties analyst at a corporation may need to juggle between government/regulatory tools and in-house software.
In general, finance is perhaps one of the most data-intensive industries for analysts. Due to the sensitivity of data involved, it is also one of the fields where very customized proprietary tools thrive.
Supply Chain Analyst
Supply Chain is becoming increasingly prevalent. There are a lot of logistics software that tracks productions, inventory, deliveries, transactions, and more. These systems typically need to be connected and unified into a centralized supply chain picture on which optimized decisions can be made. Data modeling tools are then used to build resiliency, sustainability, and agility into the company’s supply chain. Due to the enormous amount of data involved and its real-time decision-making nature, supply chain management is becoming increasingly data-driven and tech-enabled.
Sales Operations Analyst
A Sales Operations Analyst improves a company’s sales processes. They drive sales strategies, enhance sales efficiency, map territories, design compensation packages, and report on sales metrics.
This means that they heavily rely on customer and transaction data. CRM is the primary data source. They also need to connect with other functional teams such as Finance, Customer Service and Supply Chain to gather support data. Often this involves using data integration tools such as RPA and ETL to automate data flows. Reporting KPIs for higher level visibility is also very important for a Sales Operation Analyst.
What tools do analysts use?
Based on our research on over 3000+ analysts across industries, analysts with different titles and job functions tend to use many different tools. The sales and marketing analysts for example require more automation tools and proprietary databases while the financial, supply chain and IT analysts rely more heavily on data modeling tools. There is one tool however that all analysts use, and that is Business Intelligence tools for reporting.
The table below summarizes our findings on general trends in toolset used by different functional analysts:
What are data analysts doing differently in 2021 and onwards?
With an ever-increasing amount of data, there has been an explosive number of new powerful tools for analysts. This trend will only continue and it is inevitable that analysts will play more important roles as they are becoming more capable and versatile.
For example, a Marketing Analyst in the future may be able to switch from a completely analytical role to a more product & engineering-focused role since they already have a complete data stack on their toolbelt while not having to code. This means they can deliver a feature such as a Product Announcement landing page directly to a consumer’s hand. Whether it be for a marketing, or testing purpose, they can deliver more value for their company and clients while leveraging their marketing data more effectively.
For a supply chain or financial analyst, the future is ever bright. Although they work traditional “back-office” jobs and perhaps do not yet access the most modernized software products and solutions, they deal with some of the largest datasets by design. There is no doubt that more niche and powerful tools are in development and coming their way. These analysts’ modeling capabilities should get increasingly more powerful as their industries generate more and more data. The power of A.I. should benefit them and the companies they work for significantly.
There’re many other types of analysts that can easily be added to this list. We’re building a data hub for analysts of all natures to get data connected, transformed, and reported in a unified system. Feel free to try it out.
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