Data analytics may seem like a foreign language to some. However, even though the individual tasks involved in data analysis vary depending on the organization, their benefits are straightforward.
Companies using extensive data analysis beat the competition time and time again. In fact, as a Toward Data Science survey shows, 54% of all businesses need business intelligence (BI) tools and more predictive analysis. But why? What are the benefits of data analytics?
Data analytics brings businesses continued success because it gives them actionable insight into how they can improve their business model. Customer data plays a crucial role in identifying issues and implementing solutions, thereby enhancing customer satisfaction and operational efficiency. It also shows the most effective products for customer needs and reveals areas to cut operational costs.
Fully Accountable is a team of financial analysts who can bring these benefits to your company. Our data analysts offer business intelligence that improves your bottom line and improves your cash flow. When you choose Fully Accountable, you’ll access extensive data analysis that achieves measurable, sustained growth.
What Is “Data Analytics?”
Let’s start by explaining data analytics as a field and how you can use it to your company’s advantage. At its most basic level, data analytics deals with discerning meaning from statistics and disorganized information. It’s essentially a data-centric approach to your financial audit.
When you can explore, understand, project, and strategize using abstract data, you can communicate valuable insights using that data. These analysts can interpret anything. From numbers (quantitative data) to sounds, images, and words (qualitative data), they draw conclusions that affect your bottom line and strategy.
Data analysts start with the raw data — disorganized data without context. They then take the raw data and derive helpful information, essentially stabilizing a once unstable system. To analyze this data, analysts must first collect, clean, and organize it.
Data analysis incorporates multiple metrics to make comprehensive decisions. Some of these advanced metrics include statistics, programming, and visualization. Nowadays, tools can automate many of these metrics. However, data analysts still need the tangible and intangible technical skills required to analyze the data despite the increased automation. Utilizing data analysis tools and knowing how to analyze data are crucial skills for deriving insights and making informed decisions.
Why Do Data Analytics Matter?
Two simple facts make data and business analytics necessary. The first critical component of data analytics is the decision-making power it gives companies.
The second is the data’s evidence-based nature. It gives teams the ability to make decisions rooted in empirical data. It’s important to note that data analysis isn’t always fully accurate, but it can help predict future trends and draw conclusions. Statistical analysis plays a crucial role in making data-driven decisions by modeling trends from gathered data and utilizing tools like Python and R.
Data analytics offers companies the ability to do the following:
- Proactively adapt to changing needs.
- Mitigate risk and fraud.
- Consistently deliver new products to your target audience.
- Further personalize their services to their customers.
Without data analytics, businesses essentially operate on instinct rather than tangible evidence.
While analytics is great for business, it has broader applications that extend far beyond the boardroom. For example, you can use data analytics to improve patient care and apply it to agriculture to help feed large populations.
What Does a Data Analyst Do?
Now that you understand what data analytics is, it’s important to examine data analysts’ role in organizations. The primary function of a data analyst is to convert raw data into actionable insights. After analyzing the raw data, data analysts should solve specific problems or questions that lead to actionable insights. Once the analyst solves these problems, they should provide evidence to their superiors to plan for the future accordingly.
You should associate the following responsibilities with data analysts:
- Developing and implementing databases and data collection systems
- Working closely with management to identify critical metrics, KPIs, and business needs
- Primary and secondary data collection
- Filter and clean data
- Identify and analyze trends and patterns in complex data sets
- Visualize and present findings to stakeholders
- Build and customize reports
- Develop and maintain dashboards
- Create and maintain documentation regarding data models, measures, and infrastructure as they develop
- Developing and managing relational databases to store and analyze large volumes of data
The responsibilities mentioned above examine the data analyst role from a broad perspective. To understand the role more, you need to look at some specific tasks data analysts undertake.
Data Analyst vs. Data Scientist
While both data analysts and data scientists play crucial roles in the field of data analysis, their responsibilities and skill sets differ significantly. Understanding these differences can help organizations effectively utilize their expertise.
- Scope of Work: A data analyst typically focuses on specific projects or tasks, analyzing data to inform business decisions. They work on tasks such as data cleaning, trend analysis, and report generation. In contrast, a data scientist works on more complex projects that require advanced data analysis techniques. They develop and implement models, conduct exploratory data analysis, and create algorithms to solve complex problems.
- Technical Skills: Data analysts possess technical skills in data analysis software and statistical methods. They are proficient in tools like Excel, SQL, and data visualization software. On the other hand, data scientists have advanced technical skills in programming languages such as Python and R. They also have expertise in machine learning, data visualization, and data analysis techniques, enabling them to handle large datasets and develop predictive models.
- Business Acumen: Data analysts often have strong business acumen and can effectively communicate complex data insights to non-technical stakeholders. They bridge the gap between data and business, ensuring that insights are actionable and aligned with business goals. Data scientists, while also capable of communicating insights, may focus more on the technical aspects of data analysis. Their work often involves developing new methodologies and pushing the boundaries of what data can reveal.
In summary, data analysts and data scientists bring complementary skills to an organization. Data analysts provide valuable insights that inform day-to-day business decisions, while data scientists drive innovation and develop advanced analytical solutions. Together, they enable organizations to harness the full potential of their data, making data-driven decisions that drive business growth.
Data Analyst Task and Processes
Define the Question
Data analysts need to understand the problem they’re trying to solve. Without this knowledge, raw data stays raw data.
For example, your company wants to boost revenue, and the executives want to accomplish this by releasing a new product line. Your data analysts spend loads of time and resources analyzing which products fit best for the new launch.
However, after analyzing the data, your analysts discover the problem isn’t with your products. It’s your flawed sales process and low customer satisfaction. With this insight, your team realizes it can boost revenue at a fraction of the price.
Data analysts understand the importance of probing every angle of the question they’re trying to solve. They need to bring unbiased advice to their superiors and use the correct metrics to reach a decision.
Collect Data
After the data analyst understands the question, they need to uncover the raw data that leads them to the best answer. In some cases, this includes quantitative data, and in others, it includes qualitative data.
The data analyst’s job is to understand how to obtain the required data. The next step is to clean the data, whether the data is collected and analyzed with social media monitoring, website analytics, or online tracking.
Data Cleaning
When your data analyst first collects data, it will still be in raw form. This means it will still have errors and need organization. You can use custom algorithms, generic software, and exploratory analyses to clean the data for analysis.
Data cleaning tasks entail removing errors, duplicates, and outliers, eradicating unwanted data, structuring data in valuable ways, and filling gaps. After cleaning the data, the data analyst needs to validate the data.
Conducting Analysis
After cleaning and validating the dataset, the analyst should be ready to analyze the data. There are different types of quantitative data analysis:
- Descriptive analysis– Analyses what happened based on the data.
- Diagnostic analysis– Examines why things happened based on the data.
- Predictive analysis– Answers what will happen in the future based on the data.
- Prescriptive analysis– Details the best course of action based on the data.
Analysts should decide which type of analysis to use based on the question they’re trying to answer. For example, if they are trying to answer a company’s projected third-quarter revenue, they should use predictive analytics.
Communicate Results
After carrying out analysis and drawing conclusions, the data analyst should communicate the results to their superiors. Presenting data to seniors should include a visual representation of the data. Visualizing the data involves creating interactive dashboards, documents, reports, and presentations.
Clearly communicating data results is one of the most critical responsibilities of a data analyst. Without clear data communication, conflict can arise, and there will be discrepancies in the best actions for your company.
What Skills Do Data Analysts Need?
High-quality data analysts need particular data analyst skills to succeed in their jobs. The following attributes are critical for data analysts. Mastering these data analyst skills is essential for becoming a successful data analyst.
Math and Statistics
Data analysts need to have math and statistical skills. Data analysts can either have an undergraduate degree in math, statistics, or computing. However, while qualifications often dictate a person’s skill level, they aren’t always the most accurate measurement of a candidate’s ability. As long as data analysts have a solid foundation in math, they should be sufficient for the role.
Programming Skills
Additionally, data analysts must understand how to adjust algorithmic programs and how to automate data analytic tasks. They should be familiar with Python or MATLAB, and statistical computing languages such as R and SAS are all popular in data analytics.
Database Knowledge
In addition to programming languages, data analysts need knowledge of database warehousing software, such as Hive and Spark, and database query languages, such as SQL.
Excel Skills
Excel is a valuable tool for communicating interpreted data in a readable format. Data analysts can also use it for complex calculations.
Data Visualization Skills
One of the most important skills in data analysis is visualizing data with charts and graphs. Visualization skills help other company members interpret patterns, correlations, and trends. Analysts should understand how to create plots in Python and other tables and charts in Microsoft Excel to do so.
Machine Learning Knowledge
Junior-level data analysts don’t need to be experts in machine learning. However, machine learning is a critical component for many data analytic tasks. Data analysts should be familiar with the theory of supervised learning vs. unsupervised learning.
Why Do Companies Need Data Analysts? – Data Analysis Benefits
Evidence-Based Decisions
Why is data important for your business? The best answer is how data analysts impact the decision-making process. Decision-makers in businesses should have access to key data that makes big decisions easier. For larger organizations, decision-making can take weeks or months. Even with smaller organizations, high-quality data analysts can streamline the decision-making process and improve a company’s agility.
In a Deloitte Analytics Advantage Report, 49% of the survey respondents asserted the most significant benefit of using data analytics is that it improves the decision-making process.
Testing and Retesting
Without data analysis, your company hasn’t the faintest clue as to whether the decisions they make land on work. If you don’t use data to measure your strategy success, how can you expect to make decisions based on fact instead of opinion and instinct? And if you can’t test your decisions, how can you analyze the success of your decisions?
Improved Target Market Reach
Data analysis helps determine which forms of advertising reach your customers best and their impact on your customer’s buying experience. These data types enable you to understand advertising methods and predict those with the most significant impact on your target audience.
New Innovations
Data analysis helps companies stay one step ahead of future mishaps. They can examine consumer behavior and devise pragmatic strategies that address those behaviors. With these innovations, companies gain an edge over competitors that aren’t using data analytics. After creating these innovations, companies can patent them and reap additional benefits.
Cutting Operation Costs
If you have data analytics ingrained in your business, you can continuously cut costs that aren’t serving your bottom line. Data analytics gives you insight into which business sectors need the most resource allocation and which sectors deserve their resources pulled. Data analysis makes all of your company’s actions precise and meaningful. This helps you avoid aimless business strategies and eliminate activities that add no value to your business.
What a Data Analyst Does and Why Every Organization Needs One
By this point, you know the importance of data analytics, but if you need a refresher, here it is: data analytics helps your business strategize for future financial obstacles and cut operational costs. They streamline your production and help you reach your target markets more efficiently. However, data analysts need specialized mathematics, statistics, and programming expertise. This training brings these benefits to fruition, but finding these experts can be challenging.
At Fully Accountable, we aren’t just accountants. Our team of experts understands the metrics and software that bring you the best results. By using our outsourced financial services, your business receives real-time results and the opportunity to increase your cash flow.
Contact us today to learn how we can streamline your business model.
