Digital data has been around for a long time. It is snowballing, doubling every two to three years. With time, it has changed the way we live. We rely on digital data more than ever, and our day-to-day activities depend on how we use it. According to a Forbes article, in 2020, about 1.7 megabytes of new information was created every second. Our future is where this data goes and grows.
Businesses use data every day and for various purposes - customer research, create profitable advertising campaigns and improve their approaches. Data sure has many advantages, but without analyzing it, there is no use to access them. Here's where data analytics comes in. With proper tools and resources, it can help businesses grow.
What is Data Analytics article will dig deep into data analytics - what it means, its evolution, the types, the process, and ultimately, what its future is.
Let’s look into the topics that will be discussed in this article:
As data is becoming more prominent by the minute, organizations are becoming data-driven, which means adopting methods to collect more data. This data is then sorted, stored, and then analyzed to derive logical and valuable information. Data analytics makes the process possible.
Data analytics is the process that refers to deriving valuable insights and information from data using quantitative and qualitative methods. It helps businesses and even in science - researchers use it to verify their theories, for example.
What kind of data can a company collect? There are three primary kinds.
First-party data: The data a company collects about its customers.
Second-party data: The data a company gets from a known organization that collected it originally.
Aggregated data: The data a company buys from a marketplace.
So, now that you know what Data Analytics is, let's cover a brief evolution of it.
Data analytics has become the next big thing in both large companies and small startups. The process of data analytics has evolved. Let's take a journey through the evolution of data analytics.
Data Analytics and Statistics: Statistics has a pretty long history. Like, for example, taxation, governments carried out planning activities for the creation of censuses. It was possible with the use of statistics. Data analytics stemmed from statistics, which analyzed the obtained data.
Data Analysis and Computing: Technology advancements were game-changers to how businesses adopted data analytics. In 1890, Herman Hollerith invented the "Tabulating Machine" to reduce the time taken to create the Census. This machine was highly useful in finishing the 1890 US Census in only 18 months.
Data mining: Data mining got introduced in the 1990s, which is a process that discovers patterns in large data files. When data analytics moved from traditional methods to more modern means, you could obtain more positive results.
Google Web Search: When the Google search engine came into the picture, big data could be analyzed and processed quickly. It played an essential part in the evolution of Data Analytics because the search engine was more automated, scalable, and high-performing.
Data Processing: Today, Python & R are the leading technologies in data analytics. They are open-sourced and are capable of integrating with big data platforms and visualization tools. Businesses prefer R when the primary goal is exploratory analysis or modeling. At the same time, enterprises prefer Python to develop applications that have an embedded analytics engine.
Predictive Modeling: Some advanced data analytics techniques that the data scientists and organizations are using are: Random Forests, Matrix Factorization, TensorFlow, Simulated Annealing, etc.
Visualization: Many organizations are adopting more open-source technologies for their business. Few examples are D3 and Angular. This decision relies on several factors like cost, customization options, visual appeal, and interactivity.
There are three main types of data analytics: descriptive, diagnostic, predictive, and prescriptive. Each has its own set of goals and roles in the data analytics process.
Descriptive analytics answers the "what" questions in the data analytics process. It helps stakeholders understand large datasets by summarizing them. The descriptive analysis tracks the organization's past performance. It includes the following steps:
Diagnostic analytics answers the "why" questions in the data analytics process. It analyzes the results from the descriptive analysis and then further evaluates it to find the cause. The diagnostic analysis process takes place in three steps:
Identifying any unexpected changes in the data
Data related to the changes is collected.
Statistical techniques help find relationships and trends related to the changes.
The purpose of predictive analytics is to answer questions about the future of the data analytics process. The past data identifies the trends. The techniques used in the process include statistical and machine learning techniques. A few of them are neural networks, decision trees, and regression.
Prescriptive analysis helps businesses make well-informed decisions and predict the analytics. This type of data analytics uses machine learning strategies that are capable of finding patterns in large datasets.
Data analytics combines statistics, IT, and business. The main objective of a data analyst is to discover patterns in data. By doing this, the efficiency and performance of an organization improve.
We can explain the role of data analytics with the following points:
What makes data analytics different from data analysis? Let's find out.
Here are the six key differences between them both:
Structure: Data analysis includes defining data, investigation, cleaning, transforming the data to give meaningful results. Data analytics generally has a collection of data and investigation.
Key idea: While data analysis is a specialized form of data analytics, on the other hand, data analytics is a 'general' form of analytics.
Tools: The available data analysis tools are Google Fusion Tables, NodeXL, Tableau Public, etc. The available tools for data analytics are Python, SAS, Apache Spark, Excel, etc.
Purpose: Data analysis can be used for descriptive analysis, exploratory analysis, predictive analysis, etc. On the other hand, data analytics is used to find customer patterns, market trends, hidden patterns, etc.
The data analytics process takes place in the following steps:
The first step is data collection. Data scientists identify the required information first and then work with the other data engineers and IT specialists to assemble and categorize it. Data integration routines combine the data from different sources and convert them into a standard format. It is then loaded into an analytics system.
In this next step, data quality issues are fixed. It includes running data profiling and data cleansing tasks. After, data governance policies are applied to make sure the data follows corporate standards.
In this step, the data scientist builds an analytics model using predictive modeling tools. The model is "trained," which means its accuracy is tested, revised, and tested again. Finally, you can run the model in production mode.
The data analytics process’s last step includes communicating the results obtained from the analytical model to end-users. Charts and infographics are used to make this step easier.
[ Related Article: Data Analyst Interview Questions and Answers ]
In simple words, big data analytics evaluate large data sets that contain different types of data. Hence, the name - Big Data. It helps identify hidden patterns in the data, market trends, customer preferences and demands, and other useful information. Big data analytics allows businesses to do well-informed companies so they can reap profits for their organization.
Now that we have understood the critical ideas of data analytics let's move on to the field’s career prospects and future.
This question depends on the type of organization and its decision-making process. But traditionally, the primary responsibilities of a data analyst typically include:
To get a job as a data analyst, you need to have these skills.
According to Zippia, the average salary for an entry-level data analyst is $54,000. He/she should have at least a bachelor's degree. With an experience of at least 2-4 years, this salary can rise to $70,000 in the USA.
Data analytics is applied in the following top areas:
It will be no surprise that data analytics tools will evolve rapidly in the coming years. According to recent studies, the field will have one of the fastest-growing rates. Here's what we can expect how the future of data analytics can look like:
More companies will adopt data analytics. What makes this field extremely useful is that businesses can retrieve data and create helpful reports without understanding the underlying algorithms. Data analytics increases the efficiency of the organization, and more companies will continue to adopt data analytics.
It will be a challenge to hire more data specialists. Even today, there is a shortage of skilled data analytics and scientists in the industry. Experts in the fields need to plan correctly to address that challenge by creating funding for training or creating programs to help the candidates.
There will be growth in machine learning. Machine learning and artificial intelligence (AI) strategies and tools will become more advanced. Businesses can adopt these strategies to create new products and services with new increased value.
As data keeps growing, it will be challenging to manage it. Finding a way to manage large sets of company data continually will become a challenge. New threats of security issues, privacy, time, and resources problems will arise, and solutions need to be found to address these challenges.
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Today, data analytics is driving some of the significant companies to success. In a competitive world, gaining insights from extensive data contributes to ultimate growth. So, in summary, companies taking advantage of the benefits of data analytics and its advancements certainly stand out from the crowd without a doubt.
In this article, we have covered all the major concepts under data analytics. We hope you have understood its importance by now and how it helps businesses grow to become successful.