Data collection is advantageous since its analysis yields superior insights leading to more customer conversions.
Data analytics identifies the most effective approach for client demands. Other uses of data analytics in business include fine-tuning overall corporate operations and identifying new opportunities.
Additionally, it contributes to organizations’ ongoing success by providing practical insights on enhancing business models.
As organizations have grown more data-driven, the significance of data analysis has increased—opening opportunities for data analyst roles. In many cases, the marketing department has the most need for a data analyst.
What is a data analyst?
A data analyst collects, processes, and analyzes big data that can be used to improve corporate decisions.
Every organization, regardless of size, creates and gathers data from:
- Consumer feedback
- Accounting reports
- Logistics
- Marketing research
A data analyst uses the gathered data to determine several metrics, including how to enhance customer experience, price new supplies, and cut transportation expenses.
Data analysts help marketers to design focused campaigns and make informed decisions to achieve a positive ROI.
In other words, a data analyst acts as a gatekeeper for an organization’s data and ensures that stakeholders can comprehend critical business reports.
Key responsibilities of a data analyst
Data analysts are responsible for data manipulation, data modeling, and reporting. Once you have determined who a data analyst is, it is essential to understand their tasks and duties.
To explain further, here are the key responsibilities of a data analyst.
Data mining and cleansing
The vital functions of a data analyst include data mining. Various data sources are mined and structured to extract new information, and these sources are constructed to boost the system’s efficiency.
On the other hand, initial data collection will frequently yield jumbled and missing numbers. Therefore, it is necessary to cleanse the obtained data and prepare it for further analysis.
Interpreting data trends
A data analyst employs a variety of tools and frameworks to identify trends and patterns in complicated datasets — to uncover hidden business insights.
In connection, the interpretation of data trends assists marketers in manipulating and summarizing information from the result of their market research. This is to provide comprehensive solutions to critical issues affecting the business’s operations.
Preparing summary reports
Data interpretation influences a firm’s direction, so a data analyst produces concise summary reports. This is to help the executive team make smart decisions in a timely manner.
Collaborating with different teams
Data analysts collaborate with various teams to oversee other necessary reports produced by the following departments:
- Management team
- Development team
- Data scientists
The significant purpose of the collaboration is to guarantee the appropriate execution of business needs and identify possibilities for process improvement.
5 skills to look for in a data analyst
In 2023, more and more companies will opt to utilize data to their advantage. Organizations need to look for a data analyst with the following skills that can contribute promising improvements in customer experience, cost reduction, and corporate success:
1. Domain expertise and degree
Data analysts assist organizations in making sense of the massive quantities of data they acquire.
As such, they must possess domain expertise and a relevant mathematics and computer science degree. Attaining relevant degrees helps data analysts become credible and reliable in their data analysis functions.
2. Knowledge of data analysis tools
A data analyst must be knowledgeable and competent with databases and data analysis technologies.
To assess patterns and create reliable insights, data analysts should be adept in developing SQL queries and processes and familiar with Microsoft Excel, Matlab, and IBM SPSS.
Other analysis tools a data analyst should know how to use are:
- Tableau
- RapidMiner
- KNIME
- Power BI
- Apache Spark
- QlikView
- Talend
- Splunk
3. Programming skills
Data gathered from sources cannot be analyzed without programming tools. Thus, a data analyst must be familiar with programming language tools like Python, MATLAB, and the statistical computer languages R and SAS in data analytics.
4. Understanding machine learning algorithms
A data analyst must know how to modify algorithmic programs and automate data analysis operations.
Employers should look for a data analyst with knowledge of probability distributions, hypothesis testing, regression analysis, and various classification and clustering techniques.
5. Familiarity with visualization tools
Since this is one of the key responsibilities, a data analyst must be able to generate various business reports using data visualization tools.
As mentioned, Tableau, QlikView, and Power BI are excellent additions to this year’s analytic tools. Organizations can implement a free learning program if a data analyst needs to gain experience or familiarity with the newest tools.
Data analysts must also possess excellent communication and presentation skills, enabling them to communicate their ideas using the tools to stakeholders and clients.
A data analyst assists business managers in reaching organizational objectives. To achieve this successfully, data analysts must satisfy the requirements specified by businesses.
Further, use the abovementioned skills as a guide when looking for the most qualified data analyst!
