Marketers are drowning in data. Today, the issue is not a lack of data but rather the highly sophisticated process of data analysis that is required to gain actionable data insight. The vast majority of data is unstructured, posing challenges in creating a cohesive picture of customers and business processes. Marketer involved in data analysis must continually hone six specific skills to keep up with changes in data analysis, meet business objectives and serve customers’ needs.
To learn more, check out the infographic below created by the University of Maryland’s Robert H. Smith School of Business.
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<p style="clear:both;margin-bottom:20px;"><a href="https://onlinebusiness.umd.edu/blog/6-data-analyst-skills-for-the-modern-marketer-to-master/" rel="noreferrer" target="_blank"><img src="https://s3.amazonaws.com/utep-uploads/wp-content/uploads/UMD/2020/05/05083055/UMD-MSBA-2019-IG-2-of-2-5-Data-Analyst-Skills-for-the-Modern-Marketer-to-Master-final.png" alt="How developing strong data analysis skills can help a company optimize marketing insight." style="max-width:100%;" /></a></p><p style="clear:both;margin-bottom:20px;"><a href="https://onlinebusiness.umd.edu/msba/" rel="noreferrer" target="_blank">University of Maryland Online MS in Business Analytics</a></p>
Laying the Groundwork
Before data can be analyzed, it must be gathered from a variety of sources and organized into a manageable format. In addition, data analysts must understand the business and possess strong communication skills before diving into the data analysis process.
The Data Analysis Process
Data analysis starts with data transformation, which is the process of gathering data from various sources and in different formats and organizing it in one uniformly formatted location so it can be analyzed. This enables the process of data science, which consists of algorithms, predictive analytics, and predictive modeling. This three-pronged process leads to data visualization to tell the story behind the data, API integration to meld data collection and dissemination in an organization, and data quality assurance to make sure the process yields consistently accurate results.
Data Analyst Skill 1: Business Acumen
Business acumen involves understanding how businesses operate. This is represented by knowledge of business strategy, market positioning, competitive advantage, business operations, and business weaknesses and threats. This is important because it builds value into the analyst’s role, improves an individual’s understanding of company data, ability to identify early warning signs, and capacity to determine whom to reach out to with questions.
Data Analyst Skill 2: Communication
When sharing data insights, it’s important to consider the medium serving as the basis for communication, the audience receiving the communication, and the time frame surrounding the communication. This skill matters because it promotes effective communication across an entire organization, and it also helps key decision-makers informed.
A Deep Dive into Data Analyst Skills
To manage complex data analysis software and conduct rigorous tests, data analysts must have strong critical thinking and technical skills.
Data Analyst Skill 3: Critical Thinking
Critical thinking requires diving deeper into the data to complete a task. This skill enables an individual to consider the significance of outliers, use visual analytics to investigate anomalies, and look at the data from different angles. This skill matters because it not only allows people to recognize personal biases, but it also helps them create stronger arguments.
Data Analyst Skill 4: Technical Skills
Technical skills are crucial because they involve keeping up with technological developments. Strong technological skills require and understanding of how data touchpoints and systems are connected. This understanding can be grasped through a series of key questions, such as where the data is stored, how was the data transformed, how the data was collected, and who will be reviewing data insights. This skill matters because it allows individuals to understand the context of data, which could lead to the extraction of meaningful insights.
There are plenty of data analytics tools and software data analysts can use in conjunction with strong technical skills. These include Mixpanel, Adwords Performance Grader, Formismo, Crazyegg, and BuzzSumo.
The Finishing Touches
Once the data analysis is complete, data visualization and presentation are key steps in sharing insights with stakeholders and decision-makers. These require skill sets as well as specific tools and software.
Data Analyst Skill 5: Data Visualization
Data visualization involves using the data to create a picture to better gain insight. This can help an individual to better understand which data types fits the data. It can also make it easier to highlight key points and to provide context when presenting the data. Data visualization matters because it allows data analysts to frame the story correctly, to visually access large amounts of data, inform decision-making, and improve lead generation and customer retention.
Data Analyst Skill 6: Presentation
As the term suggests, presentation skills allow individuals to present information to a live audience effectively. Specifically, it enables data analysts to focus on key points, present information in a logical order, anticipate follow-up questions, understand how to navigate interactive dashboards, and be aware of what interests the intended audience. This competency matters because it not only allows the data analyst to build credibility with stakeholders, but also to maintain their personal brand within the organization.
There are several data visualization tools and software on the market data analysts can use to use their presentation skills effectively. These include Tableau, Piotiy, ArcGIS, Google Charge, and Infogr.am.
Conclusion: A Core Competency for The Future
Data analysts will be in demand as organizations continue to rely on data insights to drive revenue and meet business objectives. Competitive business leaders know that success in business is tied to effective and actionable data analysis.