Data Analytics can provide organizations with an edge with benefits like streamlining operations, personalized experiences, risk mitigation and improved security. Businesses and industries need it to understand their customers, likes, dislikes, etc. And based on the result, they can improve their productivity. This article intends to tell you about data analytics, its uses, and its capabilities.
What is data analytics?
Data analytics is a part of data science where you focus on a specific goal while analyzing data to draw meaningful conclusions. For example, this goal could be to describe your current situation, understand the reason behind any problems, predict the future, or determine the best way to solve a problem.
In data analytics, professionals collect and prepare data. Then they interpret the data to conclude. Finally, they develop models and test the conclusions. The business analytics team may consist of data engineers to create the pipeline for the analysis. This pipeline ensures that the process of data analytics happens smoothly.
What are the capabilities of data analytics?
In the twenty-first century, there is an abundance of data. And it is a challenge to analyze it and understand its significance. Companies worldwide collect data from their customers, and they study it to improve their business. They do this with various analytical tools.
Wherever you have large amounts of data, you can use big data analytics to gather insights. For example, in health industries, scientists use data analytics in research to find new, more efficient drugs. Likewise, companies can explore users’ transaction patterns in the finance industry to prevent fraud and identity theft. And now, with machine learning technology advancements, the analysis process has become faster.
What is the process of data analytics?
The process of data analytics begins with gathering data. Businesses collect data from various applications, and then data scientists analyze them. The data may be in different formats. So the first step is to transform them into one.
The next step is data cleansing: fixing duplicates and errors and making data consistent. You also organize the data according to the particular business objective.
After that, data scientists analyze data models using programming languages like Python, Scala, R, etc. They then repeatedly test these models on partial data sets before running them with complete data.
The final step is to communicate the results to various authorities. And you can do this by using multiple charts and infographics.
What are the types of data analytics?
There are four basic types of data analytics:
- Descriptive: Here, you analyze data to understand what happened during a specific period. It is the most basic form of analysis; you usually do it to communicate your company’s status to shareholders. For example, what were the demographics of your business products?
- Diagnostic: In this type of analytics, you want to understand the reason for something. For example, you might want to know why your products did not appeal to a particular demographic.
- Predictive: If you are trying to predict the state of a statistical system, it is predictive analytics. You may want to know your company’s profits in the next ten years. This analysis uses the conclusions drawn from the previous two to project the future.
- Prescriptive: You will analyse the data to determine the appropriate action for the desired outcome. For example, how to sell your products to a demographic which previously didn’t show interest.
You can also separate data analysis based on their methodologies. For example, an exploratory data analysis tries to find relationships between various parts of data. Then they try to form conclusions based on them. However, a confirmatory data analysis uses statistical models to confirm these conclusions.
Data analytics can be either qualitative or quantitative. Qualitative data analytics uses data such as sounds and visuals without quantifying them. Quantitative data analytics is the traditional analysis of data that has numbers.
What are the benefits of data analytics?
Following are some of the many benefits offered by data analytics.
1. Personalized Customer Experience
Businesses can provide personalized customer experiences by analyzing consumer data. They can analyze the data, create customer profiles, and use them for targeted marketing campaigns. For example, predictive analytics lets you know which products to market during checkout.
2. Improved Decision Making
Data analytics can guide enterprises to minimise loss. You can use predictive analytics to understand the business changes and prescriptive analytics to know how to cope with them. For example, businesses can predict the reaction of customers to a particular type of product. Then they can use prescriptive analytics to determine how the marketing should be; this is similar to business analytics.
3. Streamlined Operations
Data analytics can improve operational efficiency by analyzing the supply chain and understanding the bottlenecks. For example, if a vendor is statistically unlikely to satisfy your needs, you can replace this vendor.
4. Improved Risk Management
As data analytics is all about prediction, it can help you mitigate risks. For example, you can analyze market conditions to understand where to invest. Or you can examine the crime rates of various places for various products and find the optimal places to open shops. Businesses can also run automated analytics programs to make recommendations.
5. Enhanced Security
You can understand the cause of past breaches by studying data. It will help you design your security to avoid future cyberattacks. For example, the data can show in what conditions these attacks usually happen. Then you can set your security systems to react accordingly under conditions.
What is the impact of data analytics?
Data analytics tools have hugely impacted our world; one of the earliest uses of these tools was by financial industries. They used credit card transaction patterns to identify frauds and identity thefts. This data can also tell us the risk of lending money to a user.
Businesses started using clickstream analysis to identify people who would buy their products. This analytics uses the navigation and pageview patterns.
In the health industry, data mining evaluates the effectiveness of treatments. It also allows the industry to allocate funding to the most appropriate problems. Additionally, diagnostic techniques can immensely benefit from such analysis. For example, you can analyse a large amount of data to understand patterns in symptoms. The result can be applied to improve the speed of discovering a new drug.
In the telecom industry, mobile network operators use data analytics to prevent customers from shifting their networks.
Companies also use data analytics on CRM to boost customer relationships and provide necessary information to call centers.
IoT devices provide large amounts of data every hour. These systems can use these analytical tools to analyse your interactions with the equipment to provide a customised experience.
Top case studies of data analytics
Walmart is a massive company with over ten thousand stores and over five thousand retail units serving over 100 million customers. Therefore, the data from Walmart’s operations is enormous. And to analyse the data, it uses its “Data Cafe.” It is a data analytics hub that can model, manipulate, and visualise over 40 petabytes of data. In addition, it uses around 100 million keywords and other modern tools to understand its customers and derive insights to improve its business. In conclusion, data analytics helps Walmart manage its massive supply chain and provide relevant shopping experiences.
Amazon is another massive company with over 1,000,000,000 gigabytes of data across over 1,400,000 servers. It provides eCommerce, cloud computing, and artificial intelligence services. It also leverages analytical tools. For example, Amazon analyses the customer purchase data and helps users decide what to buy. And the prices of these products are according to the predictive analytics models. Finally, it uses machine learning algorithms to prevent fraudulent transactions.
1. What are data analytics applications and their types?
Data analytics applications include analysing:
- customer data for a shopping experience personalised to them
- credit card transactions to prevent fraud
- patient histories for diagnostics and drug discovery
- company data to minimise 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 a Business Intelligence (BI) software. However, R and Python are programming languages. The most 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 analysing data and drawing conclusions. However, data analytics is a subset of data science.
In data analytics, you focus on specific solutions to the problems discovered by data science. 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 analyse it using traditional data processing techniques. It is challenging to capture, store, analyse, visualise, 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 helps organizations discover valuable insights to deliver better service.
5. How are data science and artificial intelligence linked?
Data science and artificial intelligence have different goals. Data science analyzes data to find patterns and insights; artificial intelligence tries to mimic and improve human intelligence. However, both have one connection: data. In data science, you analyse data; in artificial intelligence, you teach computers how to analyse 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.