Data Analysts collect, organize, and interpret data and information to create actionable insights for companies. To accomplish this, a Data Analyst must collect large amounts of data, sift through it, and assemble key sets of data based on the organization’s desired metrics or goals.
Data Analysts then often transform those key datasets into dashboards for different departments within the organization, presenting their insights in ways that can be used to inform activities and decision-making.
What Is a Data Analyst?
Data Analysts collect and analyze large amounts of data and information to find trends and actionable insights.
A good Data Analyst will understand users’ needs and how to identify opportunities, use data to evaluate products and processes to maximize efficiency, track and analyze campaigns to improve performance, and generally help companies make data-driven decisions that ultimately lead to a boost in business revenue.
Data Analysts work in everything from political campaign management and finance to mining and epidemiology. They also tend to work with colleagues across a variety of disciplines. To give an example: tracking the conversion rates of website visitors into customers yields data that lets a Digital Marketer follow a potential customer from their arrival at a landing page all the way through to them purchasing a product. Seeing what happens at each step helps the Marketer understand what content is working and why. That’s why the work of Data Analysts is so important throughout an organization.
As far as education, most Data Analysts have a bachelor’s degree, but not necessarily in a related field. Data analytics courses are an increasingly popular training option for professionals who want to pick up the necessary technical data skills and experience to step into entry-level Data Analyst jobs.
Data Analyst Responsibilities
Data Analyst responsibilities will vary wildly from industry to industry, company to company, so the answer to the question “What does a Data Analyst do?” will depend largely on the specific field and job role. Still, the diverse range of Data Analyst job responsibilities can be generalized by four fundamental categories: understanding the data, analyzing the data, building and managing databases, and communicating the data to others.
Generally speaking, you will expect to see some or all of the following responsibilities in a typical Data Analyst job description:
As a Data Analyst, you will be responsible for researching your company and your industry to identify opportunities for growth and areas for improved efficiency and productivity.
Data Analysts must gather data requirements, beginning with determining what you hope to accomplish and arriving at a clear sense of what information you need.
Data Analysts must collect data and information from primary and secondary sources, either from existing sources or by developing new channels for information gathering. Data Analysts may be responsible for developing a data collection system for an organization or other analysts.
Data Analysts must reformat data for consistency, removing duplicate entries and null sets, and so on. In very large datasets, this task is too onerous to complete by hand and requires the use of purpose-built tools and software.
You will have to create and apply algorithms to run automation tools, allowing you to understand, interpret, and reach solid conclusions about what the data shows.
Another requirement for Data Analysts is to model and analyze data to identify patterns and trends and interpret their meaning.
Once data analysis is complete, Data Analysts present their findings to other members of the organization, digested and packaged in a way they can easily grasp. This can include using data visualization software to create visualizations or dashboards for other members of the organization to help them quickly visualize data on other own.
What Tools Do Data Analysts Use?
Data Analysts use a number of different tools to collect and analyze data, and then to visualize and present insights for non-data professionals to understand. The most popular tools for Data Analysts include:
Excel is one of the most widely used statistical tools for Data Analysts. According to the most recent BrainStation Digital Skills Survey, 66 percent of data professionals cited it as their most-used tool, down from 81 percent a year prior. There are a number of potential reasons for the reduction in the number of Excel users, including the potential for accidental data loss in spreadsheets, as well as the inability to share data and information in real-time.
Next to Excel, SQL is the tool most used by Data Analysts. SQL, the industry-standard query language used in database management, is routinely used by 48 percent of respondents, so learning SQL should be a priority for aspiring Data Analysts.
Learning Python, the most widely used tool for statistical programming, is also a must for Data Analysts. The big-data number-cruncher Python is used by 45 percent of Data Analyst respondents on a regular basis (coming in next at 15 percent is the R programming language, another important language for data analytics).
Spark is the dominant processing framework — used to write applications across a range of operators for large-scale data processing — with 43 percent of professionals naming it their framework of choice; Hadoop, in second, claims a further 24 percent.
Tableau is by far the most widely used visualization tool for data analytics, with 60 percent of Data Analysts using it. Other popular visualization tools include Matplotlib and ggplot2—Python and R’s respective visualization packages.
Do Data Analysts Code?
Some Data Analysts do have to code as part of their day-to-day work, but coding skills are not typically required for jobs in data analysis. That said, while basic data analysis functions can be performed without writing code — interpreting Google Analytics data, for example — the rise of big data has added a layer of technical difficulty to the Data Analyst’s role, which means coding is now much more likely to come up and having those technical skills will certainly be valued if you do want to become a Data Analyst.
It is also worth noting that coding skills are typically required for Data Scientists. Programming languages like Python, R, and SQL have many important applications in data science, so any Data Analyst who eventually has ambitions to become a Data Scientist should look to learn to code even if they aren’t required for their current job in data analytics. And although it will vary from job to job, programming skills could help you earn a higher Data Analyst salary.
Programming Languages for Data Analytics
When it comes to data analysis, writing code doesn’t typically mean building an original piece of software from scratch. It’s much more likely to involve deploying packaged algorithms in statistical programming languages such as R, Python, and SQL, or writing scripts to transform data and generate visualizations.
A big, unwieldy dataset can’t yield the information a Data Analyst needs until the Analyst defines how that information should be manipulated. This is typically something determined on an as-needed basis, as it depends on the parameters of the dataset and the information required. All this makes the process more complicated than simply clicking a button—producing these queries does constitute a form of coding, in the fundamental sense of “generating a unique set of instructions for the computer to execute.”
Data Analysts are also frequently involved in data visualization, using languages like R to create high-quality interactive maps, charts, and more. Several software packages make it possible to develop highly sophisticated (and unique) visualizations—and any time you step off the beaten path, trying to extract something from data beyond a surface-level abstract, you’re likely looking at writing code.
What Is Data Analytics?
Data analytics describes any process that helps to clean, summarize, transform, and model data to discover new and meaningful patterns that can be used to inform decision-making. Once the sole dominion of titans like Facebook and Google, data analysis is now accessible to almost anyone.
Every company has access to data, whether it’s as simple as budget and sales figures or more complex data systems like a vast enterprise data warehouse. And even companies with relatively little data now have the potential to gather more—by optimizing their website, for example, or taking advantage of the analytics tools already built into their social media platforms.
In fact, the sheer quantity of raw digital data available today enables us to gain insights that would’ve been impossible even just a few years ago. But raw data, on its own, can’t tell us much. Before it can yield new information, it needs to be analyzed.
Types of Data Analysis
There are many types of data analysis and each of these kinds of data analysis will comprise several steps, beginning with determining what data is needed, collecting data, data cleaning and analysis, and ultimately, data interpretation and, potentially, visualization.
These are a few of the most prominent data analysis types:
Sometimes referred to as data mining, text analysis looks for patterns in large sets of written information—the results of customer feedback surveys, for instance, or social media posts. Text analysis can help extract information about key issues hidden inside unwieldy datasets.
Statistical analysis can look at both the overarching characteristics of a numerical dataset as well as meaningful samples or subsets of it. Descriptive statistics offer an overview of data to reveal potential trends (such as growth charts), while inferential statistics look for significant correlations between data points.
Diagnostic analytics drills deeper into the insights gained from statistical analysis to determine causes—to understand why correlations exist. If customers all seem to abandon their purchases at the same point in the checkout process, for example, or if rates of illness correlate to other factors, diagnostic analysis can help identify the reason why.
Predictive analytics is where figures are extrapolated or projected beyond the parameters of the existing dataset to help forecast future outcomes.
Prescriptive analytics draw on the total insights gained and uses them to determine the best course of action in a given situation.
Benefits of Data Analytics
The benefits of data analytics are many, as data analysis is important in business to help your organization identify and define problems, and to organize and interpret data sets to provide actionable insights and solutions. In addition, the relative speed and ease with which data can now be leveraged means that virtually every organization can optimize their operations and investments, allowing them to:
- Make informed decisions
- Set realistic targets
- Predict consumer behavior
There is a wide range of enterprise-level analytics tools available to businesses at virtually every scale. Google Analytics is a great example. The basic tools are free, and their insights can be used to recalibrate and dramatically improve the performance of your business website.
Data Analytics in Marketing
Data analytics can help businesses make marketing decisions by compiling data from across an organization. Marketing analytics gives organizations the opportunity to track the performance of various digital marketing campaigns, initiatives, and investments in context, allowing it to make better business decisions.
Data analysis can also allow your organization to dig deeper into each business function and department, ensuring you’re maximizing ROI.
Let’s take a closer look at how data analytics and digital marketing work together:
Social media marketing
What is your target audience talking about? How do they interact with your company? Do they share or like the content your business is posting? Coupling your social media presence with good data analysis can extract more value from your online profile.
Using analytics, email can double as an information-gathering tool by helping you determine which subject lines get the most opens, and what kinds of messages see the highest rates of success. You can also test which times of day or days of the week your audience is most likely to see and open emails. Data analytics ensures that you aren’t just sending your messages out into the universe and hoping for the best; it gives you the information you need to continuously improve.
Data can help define your target audience, including understanding where they are and how to reach them. Rather than wasting advertising dollars to reach a broad audience, data analytics allows you to more precisely target your ad spending to tailor your content to the right people for maximum impact on a leaner budget.
The benefits of analytics aren’t limited to marketing. Tracking expenditures over time, for example, can provide insight into where your organization’s biggest expenses are—and provide clues on how to run a more efficient operation. Anywhere data can be gathered, analyzed, and compared against key performance indicators, it can be used to effect change and improve outcomes.
Data Analyst vs. Data Scientist
How to Become a Data Analyst
Kickstart Your Data Career
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