Data analytics helps organizations in many ways, such as by making operations more efficient, giving customers more personalized experiences, reducing risks, and making security better. Businesses and industries need it to understand their customers, their likes and dislikes, etc. Based on the result, productivity can be improved. This article aims to explain data analytics, its uses, and its capabilities.
What is data analytics?
Data analytics is a part of data science that focuses on specific goals in analyzing data to draw meaningful conclusions. For example, this goal could be to explain the current situation, understand the reasons for the problem, predict the future, or determine how best to solve the problem.
In data analytics, professionals collect, prepare, and interpret data. Finally, develop your model and test your conclusions. A business analytics team can consist of data engineers who create pipelines for analytics. This pipeline keeps the process of data analysis running smoothly.
What are the capabilities of data analytics?
There is an abundance of data in today’s world, and analyzing its significance is a challenge. Companies collect data from their customers and examine it to improve their business. When you have large amounts of data, you can use big data analytics to gather insights. For example, in the healthcare industry, scientists use data analytics in research to find new, more efficient drugs. In the same way, companies in the finance industry can look at how users spend their money to stop fraud and identity theft. Nowadays, with machine learning technology advancements, the analysis process has become faster.
What is the process of data analytics?
The data analytics process begins with collecting data. Businesses collect data from various applications, and data scientists analyze it. Data can be in various formats, so the first step is to convert them into one.
The next step is data cleansing: fixing duplicates and errors and making data consistent. It also organizes the data according to each company’s objectives. Data scientists analyze data models using programming languages such as Python, Scala, and R. These models are then tested on partial data sets several times before being run on full data. In the final step, the results are communicated to various authorities. This can be done using multiple charts and infographics.
What are the types of data analytics?
There are four types of data analytics:
- Descriptive: This is where you analyze your data to understand what happened over a period of time. This is the most basic form of analysis; typically this is done to communicate the company’s status to shareholders. For example, what were the business products demographics?
- Diagnostic: In this type of analytics, there is a need to understand the reason. For example, you might want to know why your products did not appeal to a particular demographic.
- Predictive: When we try to predict the state of a statistical system, it is predictive analytics. You probably want to know your company’s profits will be over the next 10 years. This analysis uses the conclusions drawn from the previous two conclusions to predict the future.
- Prescriptive: Analyze data to determine the appropriate course of action to achieve a desired outcome. For example, how to sell products to demographics that were previously uninterested.
You can also segregate data analysis based on method. Exploratory data analysis, for example, seeks to discover relationships between various types of data. Then they try to draw conclusions from it. However, our confirmatory data analysis uses statistical models to support these conclusions.
Data analytics is either qualitative or quantitative. Qualitative data analysis uses unquantified data, such as sounds and images. Quantitative data analysis is the traditional analysis of data containing numerical values.
What are the benefits of data analytics?
Below are some of the many benefits that are offered by data analytics.
Personalized Customer Experience
Businesses can provide personalized customer experiences by analyzing consumer data. Analyze data and create customer profiles that can be used in targeted marketing campaigns. For example, predictive analytics can help you know which products to sell during the checkout process.
Improved Decision Making
Data analytics can guide enterprises to minimize losses. Use predictive analytics to understand the business changes and prescriptive analytics to know how to respond to them. For example, businesses can predict how customers will react to certain types of products. Additionally, prescriptive analytics can be used to determine marketing tactics. This is comparable to business analytics.
Streamlined Operations
By looking at the supply chain and figuring out where the bottlenecks are, data analytics can help make operations more efficient. For example, if a vendor is statistically unlikely to meet your needs, you can substitute another vendor.
Improved Risk Management
Data analytics are all about prediction, so they help reduce risk. For example, you can analyze market conditions to understand where to invest. Or you can look up crime rates in different locations for different products to find the best place to open your store. Businesses can also run automated analysis programs to generate recommendations.
Enhanced Security
By examining the data, you can understand the root causes of past security breaches. It helps design security to avoid future cyberattacks. For example, data can indicate under what conditions these attacks typically occur. Then you can configure your security system to respond appropriately under certain conditions.
What is the impact of data analytics?
Data analytics tools have had a huge impact on our world. The first use of these tools was in the financial industry. They used credit card transaction patterns to identify fraud and identity theft. This data also tells us about the risks of lending money to users. Companies have started using clickstream analytics to sell their products. The navigation and page view patterns are used in these analytics.
In the healthcare industry, data mining is used to assess the effectiveness of treatments. It also allows the industry to allocate funds to the most appropriate issues. Moreover, diagnostic procedures can greatly benefit from such analyses. For example, large amounts of data can be analyzed to understand symptom patterns. The results can be applied to accelerate drug discovery.
Mobile network operators in the telecommunications business use data analytics to keep customers from switching networks. CRMs also use data analytics to improve relationships with customers and give call centers the information they need. IoT devices deliver massive amounts of data every hour. These systems could use these analytical tools to look at how you use your device so that they can give you a more personalized experience.
Top case studies of data analytics
Walmart
Walmart is a massive company with over 10 thousand stores and over 5,000 retail units serving over 100 million customers. Therefore, the data available from Walmart’s operations is enormous. In addition, “Data Cafe” is used for data analysis. It is a place where more than 40 petabytes of data can be modeled, changed, and seen. They use nearly 100 million keywords and other modern tools to learn about their customers and find out how to make their business better. In conclusion, data analytics helps Walmart manage its massive supply chain and deliver the right shopping experience every time.
Amazon
Amazon is another large company with over 1,000,000,000 gigabytes of data on over 1,400,000 servers. We provide eCommerce, cloud computing, and artificial intelligence services and also leverage analytical tools. For example, Amazon analyzes customer purchase data to help users make purchasing decisions. These products are priced according to predictive analytics models. Finally, it uses machine learning algorithms to prevent fraudulent transactions.
FAQs
1. What are data analytics applications and their types?
Data analytics applications include analyzing:
- customer data for a shopping experience personalized to them
- credit card transactions to prevent fraud
- patient histories for diagnostics and drug discovery
- company data to minimize the cost
- transportation data to optimize the logistics of a delivery
- using geographical data to isolate areas with high crime rates
- allocating internet bandwidth according to their usage.
Its types are
- Descriptive: Basic analytics for understanding the situation
- Diagnostic: Analytics for knowing the reason for problems
- Predictive: For determining the future conditions
- Prescriptive: Analytics for understanding the best course of action.
2. What data analytics tools are used the most?
There are many tools; some are specific to a purpose, and others are general-purpose programming languages. For example, Tableau is business intelligence (BI) software. However, R and Python are programming languages. The most commonly used data analytics tools are:
- R & Python: Programming languages popular for data analytics
- Microsoft Excel: Spreadsheet processor with data analytics, workbook sharing, and real time data analytics
- Tableau: Business intelligence tool for analyzing and visualizing data
- Rapid Miner: Platform for data analytics, building ML models, and deployment
- KNIME: Konstanz Information Miner, a free and open source platform
- Power BI: A Microsoft product for business analytics
3. What is the difference between data science and data analytics?
Data science and data analytics both deal with analyzing data and drawing conclusions. However, data analytics is a subset of data science.
In data analytics, you look for specific answers to the problems that data science has shown you. Data science tries to understand meaningful relationships in data and ask new questions. It includes mathematics, statistics, computer science, information science, ML, and AI.
Data science concepts include data inference, predictive modeling, and ML algorithms to get business insights. But data analytics mainly uses statistics, mathematics, and statistical analysis. In addition, they try to communicate complex analysis in simple language.
4. What is big data analytics?
“Big data” refers to information so complex and large that you cannot analyze it using traditional data processing techniques. It is challenging to capture, store, analyze, visualize, and update big data. The four concepts of big data analytics are volume, variety, velocity, and veracity.
“Volume” refers to how much; “variety” refers to what kind; “velocity” refers to how quick; and “veracity” refers to how useful it is. The sources of big data include AI, IoT, mobile networks, etc. Big Data analytics enables organizations to discover valuable insights in order to provide better service.
5. How are data science and artificial intelligence linked?
Data science and artificial intelligence have different goals. Data science looks at data to find patterns and insights, while artificial intelligence tries to copy human intelligence and make it better. However, both have one connection: data. In data science, you analyze data; in artificial intelligence, you teach computers how to analyze data. Data science gives insights, and AI delivers actions. For big data analytics, you have to use both data science and AI because of the large quantity and complex nature of the data. AI applications also include creating smart assistance, chatbots, and social media monitoring tools.
