10 Must-Know Data Operations for Every Business Analyst

6 min read
1/22/24 10:47 AM

Are you ready to take your business analysis skills to the next level? To truly excel in this field, you must master a wide range of essential data operations. Whether you're an aspiring analyst or a seasoned professional looking for skill enhancement, we've got you covered. This blog post will unveil the top 10 must-know skills that every business analyst should possess.

Data operation #1 Create data

Every business analyst must know the first essential data operation is how to create data. Data creation is the process of generating new, relevant data that can be used for analysis and decision-making purposes. It involves collecting, organizing, and inputting information in a structured format to ensure accuracy and consistency.

There are various methods and techniques for creating data, depending on the type of information needed and the purpose it serves. Some common approaches include manual data entry, automated data collection tools, and programming languages like SQL or Python.

Before embarking on creating new data, a business analyst should clearly understand what type of information is required for their analysis or project. This will help determine the most suitable method for gathering the necessary data.

For instance, if a company wants to capture its sales data, a business analyst may use details about sales transactions such as the customer who purchases, the items that were sold, the time of the purchase, the number of items sold, tax on sales, etc. Data validation techniques should also be applied during this stage to check for errors or inconsistencies in the collected information. This could involve cross-checking with other sources or performing random audits on a sample data set.

Data operation #2: Retrieve data

One of the most important skills for a business analyst is retrieving data available in a system. This involves knowing where to find the relevant data and how to retrieve it. It is crucial to have a good understanding of the company's data infrastructure to locate the necessary information efficiently.

Once you have identified where the data is located, you need to know how to extract it. This can be done through various techniques, such as using SQL queries on databases.

Data operation #3 Update data

Updating data involves making changes or additions to existing data sets. It may seem simple, but requires attention to detail, critical thinking, and technical proficiency. Here are some important aspects of updating data that every business analyst should know:

  1. Understanding the Data: Before updating any data, it is crucial to have a thorough understanding of the existing dataset. This includes knowing what type of data is being updated, its source, its purpose, and how it relates to other datasets within the organization. This knowledge will help identify potential issues or inconsistencies during the update process.
  2. Choosing the Right Tools: With advancements in technology, numerous tools are now available for updating and managing data. As a business analyst, it is essential to be familiar with these tools and choose the one that best suits your organization's needs. Some popular options include SQL databases for more complex updates.
  3. Maintaining Accuracy: It is vital to always ensure accuracy when updating data. Even small errors can have significant consequences on decision-making processes within an organization. Consider using validation checks or automated scripts to avoid mistakes or inaccuracies when updating large datasets manually.
  4. Incorporating Data Governance Principles: Data governance refers to the management framework used by organizations to ensure high-quality standards while handling their data assets effectively. As a business analyst responsible for updating data regularly, you must adhere to these principles while making any changes or additions.
  5. Documenting Changes: Documenting all changes made during an update process is critical in maintaining transparency and accountability within an organization's dataset management system. This includes recording the date, time, and nature of changes made and any challenges faced during the update process.

Data operation #4 Delete data

The first step in the data deletion process is identifying which data needs deletion. This requires a thorough understanding of the business's goals and objectives, as well as an understanding of the type of data being collected. Not all data may be relevant or useful for achieving specific business objectives, so it is important for analysts to have a clear understanding of what should be kept and what can be discarded.

Once the unnecessary data has been identified, it is crucial to consider any potential risks associated with deleting this information. Business analysts must carefully assess the impact of the deleted data on other systems or processes within the organization. This includes considering any dependencies between different datasets and ensuring that deleting one data set does not negatively affect another.

Data operation #5 Import data

Importing data involves transferring raw or processed information from one system or format to another. It can include gathering data from various sources such as databases, spreadsheets, web applications, text files, and more. This imported data is then used for further analysis or combined with existing datasets to understand the business problem at hand comprehensively.

Data operation #6 Export data

The importance of exporting data cannot be overstated. It allows analysts to access raw or processed data from various sources such as databases, spreadsheets, or web-based applications. This allows for deeper analysis and insights into the business operations. Exported data can also be used for reporting, creating visualizations, or sharing information with stakeholders.

Several methods for exporting data depend on the source and destination systems. One common method is using a query language such as SQL (Structured Query Language) to extract specific datasets from databases. This requires knowledge of database structures and querying techniques. Another method is using tools or software that allow for exporting large datasets in various file formats such as CSV (Comma Separated Values), Excel, or JSON (JavaScript Object Notation). Additionally, some web-based applications have built-in features that allow users to export reports or dashboards directly.

Data operation #7 Report data

Reporting data involves creating visual representations of complex data sets to communicate findings and insights to stakeholders effectively. It is an essential skill for any business analyst as it allows them to effectively communicate their analysis and recommendations clearly and concisely.

The first step in reporting data is understanding what type of report will best convey the information at hand. There are various types of reports, such as dashboards, charts, graphs, tables, and written summaries. Each type has its purpose, and depending on the audience or objective of the report, a different format may be more suitable. 

Next, it is important to choose the right visualization tools to create these reports. Popular tools include Microsoft Excel, Tableau, Power BI, and Google Data Studio. These tools have advanced features that allow for interactive and dynamic reporting, which can greatly enhance the presentation of data.

Data operation #8 Alert data

Alert data refers to notifications or warnings that are triggered when certain predefined conditions are met. These conditions can vary depending on the nature of the business and its goals but generally include anomalies, trends, thresholds, or patterns in the data. The purpose of alert data is to identify potential issues or opportunities within the dataset that require immediate attention.

One of the main responsibilities of a business analyst is to ensure that all relevant stakeholders have access to accurate and timely information. This includes identifying potential risks or opportunities impacting the organization's performance. By utilizing alert data operations, analysts can proactively detect any anomalies or shifts in trends that could affect key metrics such as sales, revenue, or customer satisfaction.

Data operation #9 Create dimensions for data

Dimensions are the characteristics or attributes that help describe and organize data. They provide a framework for understanding and analyzing data meaningfully, allowing businesses to gain valuable insights and make informed decisions.

The first step in creating dimensions for data is identifying the key dimensions that need to be included. This can vary depending on the specific business needs, but some common dimensions include time, location, product, customer, and sales channel. These dimensions act as categories or groupings that can be used to segment and analyze data.

After defining dimensions, it's important to determine how they relate to each other. This is known as creating hierarchies within dimensions. Hierarchies help establish relationships between different levels of detail within a given dimension. For instance, in the time dimension hierarchy could be defined as day → week → month → quarter → year.

Data operation #10 Create data for the dimensions

Data operation #10 involves creating data for the dimensions. Dimensions are essential to any data analysis, as they provide context and structure to the raw data. As a business analyst, it is crucial to understand how to create and manage dimensions effectively.

Once you have identified the necessary dimensions, you can start collecting information from different sources. This may involve extrapolating data from databases.

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