Data Analytics Overview

What is Data Analytics?

Data analytics can be defined as examining data through various algorithms and applications and making inferences about the information it contains in the light of these examinations. Data science is a multidisciplinary field. Data analytics, which emerged from data science and has become one of the most critical sciences of today, also has a very comprehensive field of study.

The “big data” that emerged in the century we live in, that is, data analytics used in the analysis of big data, is actually a field that helps many different sectors. Technologies or campaigns developed using data analytics have become the developments and trends that touch our lives the most.

It would be wrong to say that the only benefit of data analytics is for business transactions. Data analytics also helps to reveal the truth or falsehood by checking the theories and hypotheses made in the scientific field.

In data science, there are many ways to analyze data. Once we get an idea of what data analytics is, let’s take a look at the types of data analytics.

The data analytics process consists of several steps:

The first step is to determine the data requirements or how the data is grouped. Data can be broken down by age, demographic, income or gender. Data values can be numeric or split by category.
The second step in data analytics is the data collection process. This can be done through a variety of sources, such as computers, online resources, cameras, environmental resources, or staff.
Data should be organized in such a way that it can be analyzed after it is collected. Data organization can take place in the form of a spreadsheet or other software that can receive statistical data.
The data is then cleaned before analysis. This indicates that it has been cleaned and checked to make sure there are no duplications or errors and are not missing. This step helps to correct errors before moving on to a data analyst to analyze.

[Important: Data analytics focuses on drawing conclusions based on what the analyst already knows.]

Kaynak: SmartData Collective

Big Data Analytics

Big data analytics is the use of advanced analytical techniques against very large, diverse datasets from different sources and containing structured, semi-structured and unstructured data of varying sizes from terabytes to zetabytes.

Big data is a term for datasets whose size or type is beyond the ability of traditional relational databases to capture, manage and process data with low latency. Big data has one or more of the following characteristics: high volume, high velocity, or high diversity. Artificial intelligence, mobile, social, and the Internet of Things (IoT) are driving data complexity with new forms and sources of data. For example, big data; The majority come from sensors, devices, video/audio, networks, log files, transactional applications, web and social media, rendered in real time and at massive scale.

Analysis of big data enables analysts, researchers and business users to make better and faster decisions using data that was previously inaccessible or unavailable. Businesses can use advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing to gain new insights independently of or in conjunction with existing corporate data.

Data Analytics Types

In data analytics, questions are asked in certain patterns in order to analyze the information and achieve the desired result. As a result of different questions asked, data analytics is diversified and divided into different methods. It is possible to examine the types of data analytics under 4 headings. These types are:

1. Descriptive Analytics

Descriptive analytics answers the question “what happened?”. Descriptive analytics combine raw data from multiple data sources to provide valuable historical insights. However, these findings indicate that something is wrong or right without explaining why. That’s why data consultants don’t recommend that highly data-driven companies settle for descriptive analytics alone. They also recommend combining descriptive analytics with other types of data analytics.

2. Diagnostic Analytics

Diagnostic analytics is the process of examining data to understand the cause, event, or why something happened. Techniques such as drill-down, data exploration, data mining, and correlations are often used. The main question answered in this data type is “why did it happen?”.

Diagnostic analytics helps answer why something happened. It draws conclusions through exploration and warnings, as well as inquiry and drill-down phases. Query and drillthroughs are used to get more details from a report.

Discovery and alerts report a potential problem before it occurs. For example, you can use diagnostic analytics to “discover” the most qualified candidate for a new position at your company and information about him.

3. Predictive Analytics

Predictive analytics is where you begin by predicting “potential future outcomes” and turning the results of your descriptive and diagnostic analyzes into actionable concepts for decision making.

While predicting the future results, predictive analytics does not make a judgment as to whether the situation involving an answer to a question like “Will a new competitor join the market?” is likely to occur. Instead, it allows us to get a result such as “Our sales may decrease by 20% in 2020 based on data on a competitor joining the market”. Aim; is to identify a trend, correlation, causality, or probability.

With predictive analytics, a data analyst uses quantitative analysis of the dataset and gets results, often referred to as predictive modelling. This is a broader approach aimed at characterizing predictions, examining models for their accuracy. Machine learning algorithms, classification models, and regression models are part of the predictive analytics field.

4. Prescriptive Analytics

Prescriptive analytics builds on predictive analytics by helping to identify recommended (predicted) actions based on desired potential (predicted) outcomes. Prescriptive analytics help organizations achieve their business goals. Prescriptive analytics models continually “learn” through feedback mechanisms to continually analyze action and event relationships and suggest the most appropriate solution. By simulating the solution, it can review all key performance criteria to ensure the result hits the correct metric targets before anything is implemented.

In prescriptive analytics, artificial intelligence, machine learning, organizational goals, limitations set by the organization, and many other factors are often used as helpers to reach the right inferences.

After answering the question “What is data analytics?”, we also examined the types of data analytics. We have already mentioned that although data analytics is still in its development phase, it is used in many fields today. Now let’s look at the usage areas of data analytics.

By reading the next part of our article, you can get detailed information about the areas where data analytics is used the most and how it is used.

Data Analytics Usage Areas

Data analytics has numerous uses. From online shopping, hi-tech industries to government, everyone is involved in decision making, budgeting, planning, etc. uses data analytics to help them with issues. Data analytics is used in various fields such as:

1. Transportation

Data analytics can be applied to help improve transportation systems and the intelligence around them. The predictive method of analysis helps find transportation problems such as traffic or network congestion. For example, during the wedding season or holiday season, transportation facilities are prepared to accommodate large numbers of travelers traveling from one place to another using forecasting tools and techniques.

2. Logistics and Delivery

There are many logistics companies that use data analytics to manage their overall operations. Using data analytics applications, you can determine the best shipping routes, approximate delivery times, and also track the real-time status of shipped goods using GPS trackers. Data analytics is one of the most important factors that make online shopping easier and more preferred.

3. Web search or Internet Web Results

Web search engines like Yahoo, Bing, Duckduckgo, Google use a set of data to answer you when you search for data. Every time you hit the search button, search engines use data analysis algorithms to deliver the best searched results within a limited time frame. When we search for any information, the resulting dataset is obtained through data analytics.

The searched data is considered a keyword and all relevant information is presented in an easy-to-understand order. For example, when you search for a product on Amazon, it will still appear on your social media profiles or provide you with the details of the product to convince you of that product.

4. Manufacturing

Data analytics helps manufacturing industries maintain their overall work through specific tools such as forecast analysis, regression analysis, budgeting. Based on data collected and analyzed from demand samples, the unit can calculate the number of products that need to be produced, and similarly increases operating capacity and profitability in many other operations.

5. Security

Data analytics provides organizations with the highest level of security. Security Analytics is a way to deal with online protection focused on examining information to enable proactive security efforts. No organization can predict the future, especially when it comes to security hazards. However, thanks to security investigation devices that can examine security events, it is possible to detect the danger or make predictions about it.

6. Education

Data analytics applications in education is the most needed field of science in the current scenario. It is used mostly in adaptive learning, innovations, adaptive content etc. It is among the tasks of data analytics to facilitate understanding and learning, and to predict, diversify, research and elaboration of information about students and their special situations due to the conditions in which learning takes place.

7. Health

Data analytics applications in healthcare can be used to channel huge metrics of information in seconds to discover treatment options or responses to a variety of diseases. This not only provides precise adjustments based on recorded data, but can also provide accurate answers for exceptional concerns for specific patients.

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https://www.scnsoft.com/blog/4-types-of-data-analytics