What is Data Analytics?

Data analytics analyzes or simplifies high-velocity computational data and draws helpful conclusions/results using specialized computer systems. The data to be analyzed is in raw form, and these computer systems help analysts decode, transform, and organize this data to come up with identifiable patterns and conclusions. 

Data analytics is performed by skilled professionals with strong expertise in statistics, known as data scientists. These analysts discover patterns in the data to improve performance, increase efficiency, and help organizations and businesses boost their success. 


Steps Involved in Data Analytics 

Data scientists work with data in various ways by cracking down the process into different steps, among which data mining, data management, statistical analysis, and data presentation are the primary steps. 

  • Data Mining 

Data mining is the most intensive step in data analytics. It involves analyzing the unstructured data sources and extracting data. This includes raw sensor data, large complex databases, and written texts from which the data is extracted, transformed, loaded, and converted into a useful and manageable format.


  • Data Management 

Data Management involves designing and implementing databases for easy access to the results-driven by data mining: the most common creation and management of SQL, NoSQL, and Non-relational databases. 


  • Statistical Analysis 

Statistical Analysis is an important step in creating meaningful insights from the collected data. In this step, data is analyzed using statistics and machine learning techniques. R or Python are used as essential statistical programming languages. For advanced Analysis, open-source libraries and packages are used. 


  • Data Presentation 

In this step, the prepared insights are shared with the stakeholders. Data visualization is used as an important tool to present the data. Data is complemented with compelling visualizations that help the managers and executives understand the importance of the data insights


Data Analytics Technologies; The Core to Process 

Data analytics uses various techniques and technologies to provide valuable insights and formats. However, some of the most important technologies that render new analytics so strong are as follows; 


Machine Learning/Deep Learning 

Machine learning/deep learning includes algorithms that help create insights into raw data. It is a subset of artificial intelligence and facilitates applications to collect data, read it, and anticipate results without having someone intervene to program the system to get the outcome explicitly. 


Data Integration Tools

Data integration tools/solutions like Apache Hive, Apache Spark, MongoDB, Hadoop, Apache Pig, Amazon EMR, MapReduce, and Couchbase are powerful and allow businesses to streamline the data. 


Microsoft Excel 

Microsoft Excel is one of the top tools that help data analysts to learn about data. To calculate margins, represent data visually, create flexible Data Aggregation, and calculate other common ratios. It is one of the most affordable tools for data analytics. Revenue patterns, market trends, operations, and many others can be analyzed through this tool. 


R or Python-Statistical Programming

R and Python are easy-to-read syntaxes that give analysts a smoother learning curve. R has a steeper learning curve initially, but it provides enormously valuable insight into the data collected for the Analysis. 


Types of Data Analytics 

Data analytics has four types, namely; 


  • Descriptive Analytics 

The process of analyzing and interpreting the historical data to understand various changes that occurred in business over this period. This process helps analysts draw comparisons by using a range of historic data. The product of this process is most commonly year-over-year pricing changes, the number of users, month-over-month growth, and total revenue per subscriber. These measures describe all the instances and changes in a business during a set period. 


Limitation: The limitation to this type of data analytics is that it does not clarify if anything is wrong or right and gives very little clarification. 


  • Diagnostic Analytics 

Diagnostic analytics involves studying the data to determine the causes of correlations and trends between variables. It is the next step after descriptive analytics is done manually. This type is used to examine market trends, explain customer behavior, identify technical issues, and improve company culture. It helps analysts know why something has happened at this time. 


  • Predictive Analytics 

Predictive analytics uses modeling techniques and statistics to make future predictions about the outcomes and performance of the business. It uses advanced analytics techniques like data mining, modeling, statistics, machine learning, and artificial intelligence to look at the historical and current data patterns and determine if they are likely to emerge again. 

It thus turns out to become the most significant device for businesses and investors in helping them adjust where they use their resources to take advantage of all the possible future events. It helps businesses to improve operational efficiencies and reduce risks. 

Predictive analytical models ate used for weather forecasts, customer service, voice to text mobile phone messaging translation, creating video games, and investment portfolio development. 


  • Prescriptive Analytics  

Prescriptive analytics help analysts prescribe what move to make next to eliminate the future risks or take full advantage of a promising trend that is most likely to come in the future. It also uses advanced tools similar to business rules, machine learning, and algorithms, thus being the most modern and manageable technique. It uses these techniques to consider all the relevant factors and yields recommendations for the next steps. 

It is used for making important investment decisions, lead scoring, content curation through algorithms recommendations, banking sector for fraud detection, product management (development and improvement), and marketing sectors for email automation. 


Limitation: While these algorithms can provide advanced and data-informed recommendations, they still can’t replace human discernment. It is, therefore, important that you do not rely completely on the decisions and strategies that it informs and use your judgment to provide guard rails and context to algorithms outputs.  

Importance of Data Analytics 

The importance of data analytics has been recognized in all industries. Below are some points that highlight the importance of the process; 

  1. It helps businesses optimize their achievements and boost their success by glimpsing opportunities before time. 
  2. It helps companies to avoid any future risks and lower the rate of dereliction they can encounter otherwise. 
  3. Business analytics helps businesses make important decisions, as it helps them know their customer’s desires and fulfill their expectations. This way, the businesses easily reach for better and new services and products.
  4. It prevents businesses from unwanted expenses by helping them collect huge data that is important in specifying improved and profitable manners of doing business. 
  5. It also enables knowledge discovery capabilities. Providing enough understanding of the current state of business and setting a solid foundation to predict future outcomes helps businesses compete better. 
  6. Data analytics makes their marketing campaigns more customer-oriented by helping businesses know what their customers want before time. It thus increases brand loyalty and saves money needed to be spent otherwise on convincing a customer to make the purchase. 
  7. It helps companies to improve their overall operational efficiency. Helping them know which actions/operations have yielded the best overall results under certain conditions, which areas are error-prone, and which are needed to be improved. 


Applications of Data Analytics

Data analytics has its branches in many industries. It is an important aspect that helps businesses improve performance and efficiency. It plays an important role in banking and financial sectors as it helps them predict market trends and assess risks. It reveals credit scores that affect everyone in determining lending risks. It also detects and prevents fraud from improving efficiency for financial institutions. 

Data analytics and statistics have helped scientists in scientific research by using advanced analytic techniques. These techniques find trends in complex systems. 


Additionally, it helps improve our world by providing important information for healthcare, environmental protection, and crime prevention. Moreover, the way data analytics is revolutionizing the healthcare sector is obvious through its help in predicting patient outcomes, improving diagnostic techniques, and allocating funding efficiently. Pharmaceutical companies use data analytics for drug discoveries through machine learning, understanding the market for drugs, and predicting their sales. 

Alongside machine learning, data analytics is also used in internet of things (IoT) devices like Nest thermostat (that tracks movement and temperature to regulate cooling and heating) to help analysts predict people’s behavior. This is an improved and efficient way of creating advanced home automation that adapts to people’s way of living. 


Data analytics has been used in various industries for over many years now and is thought to be developed and intervene more in every new and modern field. Using advanced algorithms and tools helps analysts derive some important conclusions that help businesses/companies of all types. 

Proper management and using judgments to add context and meaning to the data can take the future world by surprise. Greater improvements, increased success, and reduced business risks are what it will continue to add to the world. However, it also contributes to getting some new inventions that can change the world’s course for the better. 


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  3. Maruti Techlabs, (2017). 10 Key Technologies that enable Big Data Analytics for businesses. Maruti Techlabs. https://towardsdatascience.com/10-key-technologies-that-enable-big-data-analytics-for-businesses-d82703891e2f
  4. Scott W. O’Connor, (2020). 7 Must-Have Skills For Data Analysts. Northeastern University Graduate Program. https://www.northeastern.edu/graduate/blog/data-analyst-skills/