Data Analysis

Carbon Digital Service for Data Analysis

Organizations in all industries increasingly rely on data to make critical business decisions—which new products to develop, new markets to enter, new investments to make, and new (or existing) customers to target. They also use data to identify inefficiencies and other business problems that need to be addressed.

In these organizations, the job of the data analyst is to assign a numerical value to these important business functions so performance can be assessed and compared over time. But the job involves more than just looking at numbers: An analyst also needs to know how to use data to enable an organization to make more informed decisions.

What is SAS?

Statistical Analysis Systems (SAS) is a company that has a suite of products that facilitate the extensive data science and data analysis required for medium to large sided businesses. While this isn't the only tool that we utilize (i.e., NoSQL, SQL, Python, etc.), SAS products are well suited for big data analysis.

What are Analytics?

Analytics brings together theory and practice to identify and communicate data-driven insights that allow managers, stakeholders, and other executives in an organization to make more informed decisions. Experienced data analysts consider their work in a larger context, within their organization and in consideration of various external factors. Analysts are also able to account for the competitive environment, internal and external business interests, and the absence of certain data sets in the data-based recommendations that they make to stakeholders.

Types of Data Analytics

Four types of data analytics build on each other to bring increasing value to an organization.

  • Descriptive analytics examines what happened in the past: Monthly revenue, quarterly sales, yearly website traffic, and so on. These types of findings allow an organization to spot trends.
  • Diagnostic analytics considers why something happened by comparing descriptive data sets to identify dependencies and patterns. This helps an organization determine the cause of a positive or negative outcome.
  • Predictive analytics seeks to determine likely outcomes by detecting tendencies in descriptive and diagnostic analyses. This allows an organization to take proactive action—like reaching out to a customer who is unlikely to renew a contract, for example.
  • Prescriptive analytics attempts to identify what business action to take. While this type of analysis brings significant value in the ability to address potential problems or stay ahead of industry trends, it often requires the use of complex algorithms and advanced technology such as machine learning.

What does Data Analysis look like?

To the question, it will vary depending on the extent to which a business has adopted data-driven decision-making practices. Generally speaking, though, our responsibilities typically include the following:

  • Designing and maintaining data systems and databases; this includes fixing coding errors and other data-related problems.
  • Mining data from primary and secondary sources, then reorganizing said data in a format that can be easily read by either human or machine.
  • Using statistical tools to interpret data sets, paying particular attention to trends and patterns that could be valuable for diagnostic and predictive analytics efforts.
  • Demonstrating the significance of their work in the context of local, national, and global trends that impact both their organization and industry.
  • Preparing reports for executive leadership that effectively communicate trends, patterns, and predictions using relevant data.
  • Collaborating with programmers, engineers, and organizational leaders to identify opportunities for process improvements, recommend system modifications, and develop policies for data governance.
  • Creating appropriate documentation that allows stakeholders to understand the steps of the data analysis process and duplicate or replicate the analysis if necessary.

Data Analysis vs. Data Science vs. Business Analysis

The difference in what a data analyst does as compared to a business analyst or a data scientist comes down to how the three roles use data.

  • Data Analyst: Serves as a gatekeeper for an organization’s data so stakeholders can understand data and use it to make strategic business decisions. It is a technical role that requires an undergraduate degree or master’s degree in analytics, computer modeling, science, or math.
  • Business Analyst: Serves in a strategic role focused on using the information that a data analyst uncovers to identify problems and propose solutions. These analysts typically earn a degree in a major such as business administration, economics, or finance.
  • Data Scientist: Takes the data visualizations created by data analysts a step further, sifting through the data to identify weaknesses, trends, or opportunities for an organization. This role also requires a background in math or computer science, along with some study or insight into human behavior to help make informed predictions.
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