Do you have a business of your own that you’d like to enhance and expand? Or a product to develop? If you have a plan where to start from - that’s great. If not, you should do some analytics. Big Data Analytics can help with obtaining potential user data, processing it, cleansing it, and receiving a valuable output. And, predictive analytics can make predictions according to your past, present, and future business events. Wonder, how big data and predictive analytics work? Get to read this post now to answer this question.
Where does Big Data Analytics Stand on the Market today?
Big Data stands for raw and voluminous sets of information that become valuable in studies and analyses. The more new technologies we have the more capacity of data these technologies accumulate. So, it is of great importance to make use of these large amounts of information by analyzing them. And, this type of analysis requires specific tools and automation as humans cannot process big data manually, because it’s impossible. The process of automated big data analysis is called big data analytics.
Big Data Analytics is a vast complex of processes that analyze Big Data from different perspectives to make sure there are patterns and correlations, market trends, and customer preferences with the help of which organization can make the right business decisions. So, data analytics is one of the biggest priorities of all organizations in any industry there is. To learn more about how big data analytics impacts industries get to read our topic-related articles:
What are the benefits of Big Data Analytics?
Allied Market Research reports that the big data and business analytics market worldwide is forecasted to reach $420.98 billion by 2027 at a CAGR of 10.9% from 2020 to 2027. And it’s no wonder, as organizations can benefit from using big data analytics software and tools and make data-driven decisions to improve business outcomes. The most common improvements might include efficient marketing, new revenue, customer personalization, and improved effectiveness of operations that could lead a business to the top among its competitors.
Among the potential benefits of Big Data Analytics you can find:
- A quick analysis of large amounts of data that exist in different sources, formats, and types
- Ability to make fast and better decisions to strategize more effectively, improving areas of strategic decision-making such as supply chain, and operations
- Possibility to save costs savings, with the help of effectively optimized business processes
- Deeper knowledge of customer needs, behavior and emotions, that positively impact marketing insights and give extensive information for further product development
- More efficiently conducted strategies of risk management that are extracted from large samples of data
Analytics Insight reports 10 Big data analytics technologies to look up to in 2021. These are:
- Apache Hadoop, a software library that uses simple programming models to carry out distributed processing of large data sets across clusters of computers.
- MongoDB, a distributed document-based database with the main purpose to assist modern application developers in the development and work with the cloud.
- R, a free-of-charge software environment created for statistical computing and graphics.
- Tableau, a visual analytics platform that helps to see and understand data that can solve potential problems.
- Cassandra, an open-source NoSQL database manages large amounts of data with a significant speed, and with the highest performance possible.
- Qlik, an end-to-end, multi-cloud data integration analytics solution that turns raw data into valuable insights that might close all the informational gaps.
- Splunk, a data analytics platform with extra security, observability, IT operations, and everything that includes data processing.
- ElasticSearch, a distributed, analytics engine with RESTful search that can address the increasing quantity of use cases.
- Knime, a software that creates and produces data science with an easy and intuitive environment, enabling big data stakeholders to focus on their core input.
- RapidMiner, an end-to-end transparent data science platform that is integrated and optimized for building ML models, design these models using a visual workflow designer or automated modeling, and deploy ML models turning them into beneficial actions.
How does Big Data Analytics Work?
Mainly, Big Data Analytics makes use of 4 key processes as Bitnews Today states. These are collecting data, processing information, clearing out, and analyzing. Let’s look a bit closer at each of these processes.
Mobile records, customer feedback forms, mail threads received from the customers, survey reports, social media platforms, and mobile applications are the sources data analysts can collect specific information from. Different businesses try to make use of data collecting and extract all the valuable information there is to gain insight, advance, and prosper. Big Data analyzed from the older enlisted is quite chaotic - unstructured or semi-structured. Thus, this information is not readable without using specific tools.
After the data is collected, the next step to make use of it will require data storage in the data pool or warehouse. Being located in one convenient place allows analysts to organize, configure, and group parts and bits of big data to draw a larger picture per request that will be also more accurate with regards to final results.
To make sure the processed data analysts work with is complete and feasible, it has to be clean from duplications, watery inputs, system errors, and other sorts of deviations. So, this step allows to polish big data to receive more accurate results afterward.
This is the final step, where the raw data that was collected, processed, and cleansed can be analyzed with the possibility to extract the much-needed results. Here, you can use:
- Data Mining (helps extract useful and feasible data patterns)
- Artificial intelligence (using human-like thinking to explore and extract deep data analytics)
- Text mining (with the help of AI it is possible to get meaningful informational insights out of a pool of unstructured textual information)
- Machine Learning (using AI to make computers learn based on their past experiences)
- Predictive Analytics (great predictions and future insights for a business based on past and historical data analyses)
- Deep learning (analysis and extraction of huge and unmanaged data)
Although Big Data Analytics has numerous possibilities and methodologies to analyze data, let’s focus on predictive analytics and its manifestation in 2021.
Where does Predictive Analytics stand today?
According to IBM, big data predictive analytics belongs to advanced analytics. It is able to predict future results with the help of historical data, statistical modeling, data mining, and machine learning. Businesses use predictive analytics to understand their risks and opportunities with the help of the data patterns it is possible to predict.
Predictive analytics also belongs to big data and data science. Today, businesses use transactional database data, equipment log files, images, video, sensors, and other data sources to gain insights. You can extract information from this data with the help of deep learning and machine learning algorithms. What can you get out of extracting data? You will see patterns in the scope of data and will be able to forecast future events. For example, the algorithm approach includes linear and nonlinear regressions, neural networks, support vector machines, and decision trees.
Predictive analytics is most helpful in such industries as Banking, Healthcare, Human Resources, Marketing and Sales, Retail, and Supply Chain. By 2022 the market forecast promises Predictive Analytics $11 billion in annual revenue as more and more businesses make use of predictive analytics big data techniques for almost everything: from fraud detection to medical diagnosis, according to the Statista report.
What are the types of Predictive Analytics?
Generally, there are three types of predictive analytics businesses can apply to:
- predictive modeling
- descriptive modeling
- decision-making modeling
Predictive modeling needs statistical data to be able to predict the outcomes. The main goal of predictive modeling is to make sure that similar units in different samples have similar performance or vice versa. For example, you can predict your customer’s behavior and credit risk with the help of predictive modeling.
Descriptive modeling tends to classify customers into groups to describe certain relationships within a dataset. So, as a result, you get a summary of different relationships between customers and products, e.g. product preferences accounting for age, status, gender, etc.
Decision-making modeling shows a clear relationship between elements in a decision. These may be the data, the decision, and the forecasted results. The relationship between elements can potentially predict future results, increase the probability of the needed outcomes and decrease the others.
What are the benefits of Predictive analytics?
According to the G2 catalog, there are the main 8 benefits any business can obtain with the help of predictive analytics. So, applying to predictive analytics can:
- Make you more competitive on the market
- Open up new product (service) opportunities
- Optimize performance of the product (service)
- Gain insights according to customers’ preferences
- Decrease cost waste and risk occurrence
- Address problems immediately
- Meet user demand 100%
- Improve collaboration
The other potential benefits of predictive analytics are in the detection of:
- possible fraud to be warned against it
- pattern consistency so they could be improved
- Illegal actions that can be prevented
- marketing campaign flaws that can be optimized
- сustomer purchase preferences to know about
- planning workforce pluses that can be enhances
- churn rate that can be analyzed
- сompetitor progress that can be analyzed
How Predictive Analytics Works?
To make use of predictive analytics, every business should be driven by a business goal. For instance, the goal might be in cost reduction, time optimization, and elimination of wastes. The goal can be supported with the help of one of the predictive analytics models to process an abundance of data and receive results that were desired initially.
Based on the explanation above, let’s define some basic steps of applying to predictive analytics. For instance, to predict the sales revenue it is obligatory to:
Step 1. Take data from multiple sources, especially the ones with product sales data, marketing budgets, and the national gross domestic product (GDP) value.
Step 2. Cleanse the data from any unnecessary constituents and accumulate it or group it according to similar data type.
Step 3. Create a predictive model. E.g. neural networks can be applied to revenue forecasting.
Step 4. Develop the model into the environment of production and make it accessible through other apps.
Big Data Analytics and Predictive Analytics Comparison
Big data and predictive analytics sound similar in some cases, but they are definitely not. So, let’s look closer at predictive analytics and big data comparison to understand what’s different.
How the world is using big data for predictive analysis?
To predict future events, predictive analytics identifies patterns of Big Data that are meaningful. Predictive analytics can be applied to unknown data in the present, past, and future. Predictive analytics using big data is capable to provide valuable business intelligence.
To make an impact, predictive analytics in big data has several working models. These are:
This model looks like a tree, where the branches of the tree indicate available choices, and individual leaves denote a decision. This model is simple to use and can save you time in urgent decision-making, predicting the best outcome in a short time.
This model is used in statistical analysis, where you have large sets of data and need to determine certain patterns. Also, there should be a linear relationship between the inputs. The model makes up a formula, which shows the concrete relationship between all the inputs found in the dataset.
This model imitates human brain work in a way. It deals with complex data relationships applying to AI and pattern recognition. Having a problem with voluminous data that requires understanding the relationships between inputs and outputs or a need to predict events makes this model a helpful tool to use.
Industries and projects that Utilize Predictive Analytics
Industries that have voluminous data to analyze are actively using big data for predictive analysis. These industries are:
- Health Care
- Public Transportation
Projects that are the most suitable for using big data and predictive analysis are:
- Big Data for cybersecurity
- Health status prediction
- Anomaly detection in cloud servers
- Recruitment for Big Data job profiles
- Malicious user detection in Big Data collection
- Tourist behavior analysis
- Credit Scoring
- Electricity price forecasting
And the list can go further. Predictive analytics is applicable to almost anything and everywhere.
Inoxoft provides Data Science and Big Data Analytics services that can help you enclose valuable insights from data and apply effective solutions on strategic, operational, and tactical levels.
Inoxoft offers expertise in
- Natural Language Processing
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If you search for custom Big Data services or Data Science services, contact our expert now. Inoxoft offers consultations with all the details and comprehensive answers to help you achieve your goals as soon as possible.
Data analytics big data and predictive analytics can both help you advance your business with the help of consumer data sets that are being analyzed, grouped, and cleansed for all the irrelevant information. Predictive analysis on big data allows businesses to look ahead into the future and grow basing on past and present experiences and pre-planned future direction.
Published by Nazar Kvartalnyi