Data Analytics: The Future of Today’s Digital World

Data analytics is the future of today’s digital world. You probably have heard this statement a lot but might not know the full extent of what it means. If you wish to switch your career to data analytics or want to understand the scope of this field, it’s the right time to do so.

Currently, an estimated 97% of businesses are investing in big data and artificial intelligence (AI) to drive their decision-making processes. In fact, 90% of the world’s data has been generated in just the last two years, showing the exponential rise of data collection and its critical role in today’s economy.

What Does Data Analytics Mean?

Many industry giants tend to collect data from their audiences. This data is collected in raw form, which often is not in a format that can benefit the business. This is where data analytics services become important. A professional data analysis team gives meaning to the data collected.

What is data analytics? The term data analytics refers to collecting and transforming raw data into valuable insights that help make actionable business decisions. The simplest data analytics definition is that it is the entire process that starts from extracting, organizing, analyzing, and ends with transforming the data from numbers to coherent information. When a data analyst performs the whole process, they then give suggestions to the company regarding what it should do next.

Data analytics involves interpreting large amounts of data, also called “big data,” and smaller data like company records.

The Role of Data Analytics

The role of data analytics somehow relates to business intelligence (BI), but with an exception. Where BI focuses on analyzing historical data, data analytics consulting services help predict the future of a business.

What Are the Benefits of Data Analytics?

The objectives of data analytics services have no limit. Benefits depend on how the business uses the collected data. Generally, data analytics benefits businesses with:

1. Better Decision-Making.
   Accurate data analytics consulting services allow for better decision-making. Business analysts can predict their potential sales when they have an entire track of their sales growth, past performance, and future market trends.

2. More Effective Marketing.
   Data analytics services can help businesses optimize their marketing efforts by focusing on product, price, placement, and promotion. This ensures enhanced customer service and improved marketing campaign outcomes.

3. Operational Efficiency.
   With insights from data analytics, businesses can identify flaws in operations and improve them for better productivity and cost-effectiveness.

4. Simplifying and Sharing Data.
   Data analytics consulting services help simplify complex data patterns, enabling better collaboration across departments.

What Are the Most Common Types of Data Analytics?

Data analytics is divided into four basic types:

1. Descriptive Analytics.

This type involves examining data to find out “what happened with a certain variable.” The variables can be revenue, cost, sales, demographic data, etc. Descriptive analytics uses visualized forms of data, such as bar charts, line graphs, tables, or pie charts, to find patterns and trends. For instance, what happened with the cost of inventory in August?

2. Diagnostic Analytics.

This type of data analytics explains “why did it happen” and “how did it happen” regarding a particular data trend. It is an advanced analytics form characterized by data discovery, data mining, and correlations. For instance, why did the cost of inventory increase in August and not in September?

3. Predictive Analytics.

Predictive analysis is all about making estimations or forecasting the future of the variable’s behavior. It also includes suggesting measurable actions to tackle those behavioral changes. In other words, it is the type of data analytics that uses data, statistical algorithms, and machine learning techniques to predict future events based on historical data. For instance, will a new store in XYZ city generate more sales, or should the business opt for another location?

4. Prescriptive Analytics.

It is a type of advanced analytics that interprets data to find answers to questions like, “what needs to be done?” or, “what can we do to make a particular thing happen?” It is characterized by graph analysis, complex event processing, simulation, neural networks, recommendation engines, etc. Simply, it deals with the actions that need to be taken to solve a problem. For instance, what can a business do to overcome the previous month’s loss, since discounts have not benefited as much?

What Are Some Examples of Data Analytics?

Data analytics helps organizations improve their operations, reduce costs, and make informed decisions. Here are three examples of big names of the industry that have used data analytics and boosted their growth

Example 1: Walmart

One of the biggest global retailers, Walmart, began using big data analytics way before it got popular. The company uses data mining to uncover the patterns of consumer behavior and their personal interests. For example, data analysis helps them suggest products to users based on which products were previously bought together by other users.

As a result, Walmart experienced a significant boost in the conversion rate of customers over the past few years. The company is continuously speeding up its big data analysis processes to provide consumers with the best e-commerce technologies and deliver excellent customer experiences.

Walmart is currently using Hadoop and NoSQL technologies to access internal customers to real-time data gathered from different data sources.

Example 2: Mint.com

Mint.com provides free personal finance management services that tell users where their money goes. The user has to enter their personal spending data, and the service generates useful insights about their spending. The company succeeded in finding one million new members in just six months via reverse engineering.

Reverse engineering is analyzing and gaining information from an existing process or structure, which is then used to re-establish a similar structure. 

Example 3: Marketo

Marketo is a leading software company that develops and sells marketing automation software through marketing analytics. The company uses Google Analytics, one of the leading data analytics tools, to collect customers’ data and segment them based on their personal interests and demographic data.

With the effective use of data analytics, Marketo experienced a boost in both conversions and engagement. Its conversions increased by 10x in 2015.

Also Read: The Evolution of Travel: From Traditional to Tech-Driven Solutions

What Do Data Analysts Do?

Generally, a data analyst is responsible for:

• Data mining, extracting and reorganizing the data in a readable format for humans and machines.

• Setting up the infrastructure to process, optimize, and analyze data. It also includes the ETL data function—which refers to extract, transform, and load.

• Establishing and maintaining a data warehouse for easy access and effective data management.

• Using statistical analysis tools to interpret data patterns and trends that could be valuable for diagnostic and predictive analysis.

• Simplifying complex statistical models for informed business analytics. 

• Answering queries regarding the analyzed data and giving useful insights.

• Cleaning data and adjusting it to ensure quality for easy data management.

• Collaborating with programmers, business executives, and engineers to detect potential opportunities for improvements and develop policies.

• Preparing reports to provide useful information to business analysts.

All in all, data analysts give business-critical insights about its performance and help it make informed decisions. This is why it’s one of the highest-paying employment opportunities in the U.S.—the average salary of a data analyst starts from $54,000 and can even surpass the $100,000 mark for those working in top-tier companies or who have years of experience. 

What Is the Process of Data Analytics?

The typical data analytics procedure consists of five different steps.

Step 1: Collection

Before collecting data, you first need to understand what types of data you require. Once you do that, you can begin data collection based on your requirements. 

The data can be quantitative and qualitative and can be collected through various software such as customer relationship management (CRM) and enterprise resource planning (ERP). Such software can give you internal data, but if you want external data, you can rely on census bureaus, Google public data, application programming interfaces (APIs), and third-party sites.

Data analysts collect qualitative data from in-person observations. However, they usually use quantitative data more commonly than qualitative data.

Step 2: Cleaning

The next step is to clean the data collected. The initial data sets or raw data usually have missing or incomplete information. Some also have repetitive data, which can affect the conclusions significantly. 

The data analyst checks for such mistakes and ensures that the essential metrics, such as mean, median, mode, and range, seem understandable. Data scientists often have to convert the data set into a readable format using data analytics tools.

Step 3: Processing

The third step is to process and verify the data. The data analyst converts raw data into a usable form through parsing and encoding. In this step, the data is further processed to identify any errors or issues, which are fixed through the company protocols designed specifically for the purpose. 

Step 4: Analysis

The data set then goes through a thorough analysis. The data analyst examines different aspects of the data through tools opted by the company. In this step, the statistical importance of the results is also checked. 

Step 5: Data Visualization

In the final step, data scientists compile the results into multiple forms to present the data in its simplest and most understandable form. Usually, they develop data visualizations, such as pie charts, graphs, etc. In addition, most companies establish internal dashboards that monitor important key performance indicators (KPIs) through line graphs or charts.

Common Data Analytics Techniques

Analysts usually choose the ideal data analytics method following their data analysis objectives. These techniques help them verify their hypotheses and spot a specific pattern in the data.

• Regression analysis. It is a statistical data analytics method that focuses on correlations and connections between different variables.

• Factor analysis. The factor analysis helps the data analyst to spot a defensible structure or pattern underlying a specific data set. 

• Cohort analysis. It is the technique used to understand data grouping by certain characteristics.

• Cluster analysis. Cluster analysis helps analysts verify that the variables in one group resemble each other more than the variables present in other groups.

• Time series analysis. Time series analysis forecasts the future pattern or behavior of data that can be easily organized over a certain period, like stock market prices.

• Text analytics. Also known as text mining, text analytics helps analysts find specific patterns or information during the analysis of emails, open-ended survey responses, or websites.

What Are the Requirements To Become a Data Analyst?

A data analyst needs to have some expertise to implement the above-discussed techniques correctly. Without this certain set of hard and soft skills, they can’t read the data properly. So, to become a data analyst, you need to have the following data skills: 

• Mathematical and statistical analysis skills. Since a data analysis job is all about dealing with numbers, the data analyst needs mathematical skills at a professional level. If not a pro, at least they must have the ability to process and analyze a certain set of data. 

• Know-how of programming languages. Data analysis is not just about numbers. Data analysts also have to use different programming languages to process and make the data understandable for others. These programming languages include SQL, Oracle, and Python, which are not that hard to learn over time.    

• An analytical mindset. The data analysis process doesn’t just stop at transforming raw data into meaningful information. Instead, data analysts also need to dig deeper into the data and give valuable suggestions for good business decisions. Of course, it can’t be done without analytical thinking.  

• Problem-solving skills. Data analysts use important tools and diverse techniques to interpret the data. Realizing which tools or techniques should be used in certain situations make a data analyst stand out from the others. As we know, the data analytics definition also includes answering a question and solving essential business challenges. All these key functions can only be performed when a data analyst has keen problem-solving skills.  

• Communication skills. Data analysts stay in continual contact with business analysts and stakeholders. They have to share and present their valuable insights to these individuals, which can only be done with excellent communication skills. 

What Does a Career in Data Analytics Look Like?

Believe it or not, about 2.5 quintillion bytes of data are produced daily, and these figures are going to surge in the upcoming years. Considering the importance of data collection and the internet of things in this digital world, there is no better career than data analytics. Firms need business professionals who can make their collected data valuable. 

The high demand for data analysts has also made their salaries attractive. Apart from that, data analysts also get the opportunity to work and collaborate with upper management and stakeholders. This could help them get into managerial positions. 

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Conclusion

Data analytics is revolutionizing industries worldwide, enabling better decision-making, effective marketing, and operational efficiency. With a projected 26% growth in data science and analytics jobs by 2026 and a booming global market, the field offers lucrative career opportunities. As businesses increasingly rely on data-driven strategies, the demand for skilled professionals in this domain will only rise. Now is the perfect time to embrace this dynamic career path and harness the power of data analytics to shape the future.