Data Analytics is a field that draws from a range of technologies, tools, and skills that help Analysts manage quantitative and qualitative data. The goal of the data analytics process is to help individuals discover patterns, trends, and correlations within the data that can provide insights for the business so it can simplify operations, increase revenue, and discover new opportunities for growth. An important aspect of receiving professional data analytics training to set yourself up for a career change is learning what fields and industries that training will help you find work in. Each industry will have different requirements and expectations for prospective employees, and you should consider these expectations when you start your training. This is true for students who have an idea of what kind of work they want to do and for students who aren’t yet sure what path they want to take after learning data analytics skills.
What is Data Analytics?
Data analytics is a broad term that describes the various techniques used to analyze raw, unprocessed data so that relevant insights and trends can be extracted. Organizations then use this information to make more informed, data-driven decisions. The data analytics process includes gathering data, organizing them, and performing statistical analysis on these numbers. Once the analysis is complete, the Data Analyst offers predictions that can inform the company’s next steps.
Each day, an estimated 2.5 quintillion bytes of data is created. This number is expected to grow as more organizations embrace technological advances and expand their online presence. However, until these data are analyzed, they are just numbers. A Data Analyst’s job involves combining computer programming, math, and statistics to interpret these numbers and transform them into actionable insights that they can share with various stakeholders in their organization.
Read more about why you should learn data analytics.
What Can You Do with Data Analytics Skills?
The field of data analytics has many real-world applications across industries and professions. At the core of the data analytics process is the goal of using data to make better decisions. Companies rely on the data's insights to make more informed business decisions, which can help their organization grow, increase revenue, or better serve employees and customers.
Data analytics can help with marketing campaigns by eliminating the bulk of the guesswork in planning, creating content, and developing products. It also provides an overview of customers, which can help organizations better meet their needs. By better understanding your audience and what they want, targeting them with more pointed marketing initiatives and campaigns is easier.
Another widespread use of data analytics is to improve customer service. When data is analyzed, it reveals important insights about customers, which makes it possible to offer them more personalized service. Data can provide information on how customers prefer to communicate, what interests they have, what concerns they hold, and what products they look at most often. In addition, having data stored in a central location allows an organization’s customer service team to be on the same page as their marketing and sales teams.
Another use of data analytics is to improve the efficiency of operations within an organization. Data can reveal weaknesses in operations, as well as opportunities to streamline various processes. This not only helps the business perform more efficiently but can save money over time. For example, if you have a clearer idea of what your target audience is looking for, you won’t need to waste time and resources designing ads or writing content that doesn’t directly target their interests. This leads to less wasted money, as well as better campaign results.
Common Industries That Use Data Analytics
Data is all around us. The average person creates an estimated 1.7MB of data each second. Those who have the training to work with big data can learn from these numbers and apply the insights they find to help their organization reduce costs or improve operations. That’s why data professionals are currently in high demand across organizations and industries. Companies that applied big data solutions were able to increase profit by 8%. For this reason, over 97% of organizations currently invest in AI and big data solutions.
The following sections will explore some of the common industries that use Data Analysts, as well as the role these professionals play for their employers.
Data analytics is becoming more commonly used in pharma and healthcare. Advanced cloud technologies and big data have applications for improving customer satisfaction and medical services. The healthcare industry benefits in many ways from the improvements new technology brings. New products like remote patient monitoring devices and wearable trackers are two products that were created to monitor patient data. Advances in data analytics also offer healthcare providers a more comprehensive understanding of their patients, such as whether they’re taking their medicine as prescribed or adhering to their recommended treatment plan. Gathering data on patient health patterns offers Physicians a detailed understanding of patients’ current well-being and can also help medical practitioners offer better care and shorter waiting times.
Healthcare Analytics is a broad field with many subsets. Medical Statisticians are involved with clinical trials and academic research. They create studies, then gather and analyze clinical data. Health Information Managers organize and protect patient data. Healthcare Operations Analysts study how a medical institution like a hospital or clinic operates internally. Those who work as Healthcare Data Engineers are tasked with overseeing a healthcare institution’s data infrastructure, such as the data storage systems and vast databases in which patient data is stored.
Regardless of the field of healthcare in which data is collected and analyzed, one of the four following categories of analytics is used:
- Descriptive analytics draws from a patient’s historical data to find trends and benchmarks.
- Prescriptive analytics uses machine learning techniques to suggest strategies.
- Predictive analytics works with modeling and forecasting to predict the likelihood of certain health events occurring in the future.
- Discovery analytics applies machine learning technology to study clinical data. This field of data analytics seeks to unearth any data patterns that can provide useful insights for the organization.
Data Analytics is playing an increasing role in the function of government institutions. The government performs various kinds of analysis, such as health research, financial market analysis, environmental protection research, fraud detection, and energy exploration. It relies on big data to offer better services and increase overall satisfaction across fields of public service. For example, the FDA applies data science and analytics techniques to find and study food-related illnesses and diseases.
Not only does data analytics play a large role in how the U.S. government currently operates, but it is expected to continue to improve its operations as data technology continues to evolve. The State Department continues to improve operations and find smarter ways to transform insights into actions, which has benefits for diplomatic endeavors, as well as foreign policy. These improvements don’t have to be large-scale initiatives; simple data is also valuable to the government. Basic questions pertaining to how many employees work at an agency or how many vehicles the agency has provided helpful information that can be quickly gathered and used. During the COVID-19 pandemic, data analytics allowed local governments to share data with one another in ways that had not previously been used. This helped them track how and where the virus spread and distribute vaccines. Currently, data analytics has many applications for streamlining any process that involves tracking or research.
The retail industry revolves around creating a positive customer experience and profitable margins. Within this industry, there is constant competition to succeed and beat out competitors. Both providers and stores use data to learn more about what products are selling, when they’re selling, and how they’re selling. The analytics process allows them to measure variables pertaining to inventory volumes, consumer demand, sales, and supply chain. Many Retailers are turning to data analytics to anticipate what customers want so they can be the first to offer these products and services. The more those in retail know about how to use the insights big data provides, the better they’ll be able to compete with others in the market and stay on top.
An estimated 95% of retail stores currently apply some form of data analytics to their operations. Retailers use data analytics in many ways to improve customers’ experience and increase profit. Data analytics helps them improve marketing initiatives by personalizing them to customer experience. It allows them to streamline supply chain management. Those with a background in data analytics also apply their skills to managing prices so that sales can be maximized.
All types of manufacturing industries, such as gas, oil, minerals, and metals, continue to create a large volume of data. Not only is there a large amount of data being generated from these industries, but much of it is hard to handle due to its complexity. This means it’s vital for data analytics. This is why those who work in the manufacturing industry are digitizing and investing significant sums in predictive analytics tools.
Organizations find value in all kinds of data, such as graphical information, temporal data, IoT records, text, and graphics. Using real-time data helps enhance various services and products. Machine learning and data visualization tools such as Tableau allow companies to increase the efficiency and the quality of their products. The data that’s gathered allows manufacturers to know how much production they will need to meet market demand, which can help the business perform better as a whole. In addition, manufacturing data helps organizations project how great of a demand there will be for a specific product. The company can then use these forecasts to manufacture the needed volume of products or to reduce production downtime.
Big data technologies have many applications for the banking sector. The biggest banks and financial markets rely on cutting-edge data tools and software like Microsoft Power BI and Splunk to perform tasks like staying current on trade analytics in high-frequency trading, predictive analytics, sentiment measurement, and pre-trade decision-support analytics. In addition, the data analytics process and solutions also help financial organizations with anti-money laundering initiatives, risk analysis, fraud mitigation, risk management, and demand enterprise.
Data analytics tools also help security and banking agencies stay on top of any fluctuations in the financial market. Stock exchanges, for example, use natural language processors and network analytics to follow and monitor any illegal trade practices in the stock market to help stop them. Investment banks use risk models to reduce a portfolio’s risk and help it perform better. These models are used across fields so that financial institutions can articulate how risky an investment is, what is likely to transpire, and the costs associated with mitigating risk factors. Financial institutions also perform data analysis to evaluate the risk of loans they offer customers. This requires gathering customer data, such as credit scores, outstanding credit card debts, and the amounts owed on cards. Credit risk analysis also involves reviewing historical data to determine how credit-worthy an individual is, as well as the risk of lending them money.
Learn Data Analytics Skills with Noble Desktop
There’s never been a better time to study data analytics. If you’re interested in learning more about this popular field, Noble Desktop has you covered. Noble offers several data analytics classes for beginners and those with a background in data who want to master specific or advanced tools and skills.
Noble’s Data Analytics Certificate is a project-based course designed to teach students how to collect, wrangle, analyze, and visualize data. Participants complete several real-world projects using predictive and prescriptive analytics to prepare them for careers as Business Analysts or Data Analysts. This course provides all students with one-on-one mentoring, as well as the option of a free course retake to revisit the material.
In addition, Noble’s SQL Bootcamp is a great learning option for learners interested in studying SQL to help with their data journey. This 18-hour class teaches participants how to use PostgreSQL to find information in databases. Those who enroll will learn how to write SQL queries, aggregate data, join tables, and filter results.
For students who want to work with Tableau for data analytics and visualization, Noble’s Tableau Bootcamp is a solid choice. This class is taught by an expert instructor and is available both in-person in Manhattan and in the live online format for those who wish to study remotely. This course takes students on a learning journey, starting with the basics of data visualization. Those enrolled also work with Tableau Public and its various tools for analysis and visualization. By course completion, participants will be able to analyze, filter, structure, and visualize data and publish the results. All students have the option of a free course retake.
- Because more data is being created each day than ever before, the demand for data professionals is high across industries. These individuals provide their organizations with the ability to extract valuable insights from large datasets, then apply these findings so that the business can improve its operations and make more money. By applying descriptive, predictive, prescriptive, and discovery analytics, organizations can better understand their current financial situation, as well as how certain investments or policy changes can affect their bottom line in the future.
- Data analytics is used in a range of fields, such as healthcare, government, banking, manufacturing, and retail. The majority of all organizations now rely on data analytics to stay competitive and make a profit.
- The tools of the data analytics process help organizations evaluate and mitigate risk, increase profits, improve customer service, and make accurate projections about what may transpire for an organization in the future.
- Enrolling in one of Noble Desktop’s in-person or live online data analytics courses is a great way to learn more about what professions work with data and the tools they use to do so.