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Data Science Platform Market Size 2025-2029

The data science platform market size is forecast to increase by USD 763.9 million at a CAGR of 40.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the integration of artificial intelligence (AI) and machine learning (ML). This enhancement enables more advanced data analysis and prediction capabilities, making data science platforms an essential tool for businesses seeking to gain insights from their data. Another trend shaping the market is the emergence of containerization and microservices in platforms. This development offers increased flexibility and scalability, allowing organizations to efficiently manage their projects. 
However, the use of platforms also presents challenges, particularly In the area of data privacy and security. Ensuring the protection of sensitive data is crucial for businesses, and platforms must provide strong security measures to mitigate risks. In summary, the market is witnessing substantial growth due to the integration of AI and ML technologies, containerization, and microservices, while data privacy and security remain key challenges.

What will be the Size of the Data Science Platform Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing demand for advanced data analysis capabilities in various industries. Cloud-based solutions are gaining popularity as they offer scalability, flexibility, and cost savings. The market encompasses the entire project life cycle, from data acquisition and preparation to model development, training, and distribution. Big data, IoT, multimedia, machine data, consumer data, and business data are prime sources fueling this market's expansion. Unstructured data, previously challenging to process, is now being effectively managed through tools and software. Relational databases and machine learning models are integral components of platforms, enabling data exploration, preprocessing, and visualization.
Moreover, Artificial intelligence (AI) and machine learning (ML) technologies are essential for handling complex workflows, including data cleaning, model development, and model distribution. Data scientists benefit from these platforms by streamlining their tasks, improving productivity, and ensuring accurate and efficient model training. The market is expected to continue its growth trajectory as businesses increasingly recognize the value of data-driven insights.

How is this Data Science Platform Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud


Component

  Platform
  Services


End-user

  BFSI
  Retail and e-commerce
  Manufacturing
  Media and entertainment
  Others


Sector

  Large enterprises
  SMEs


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC

    China
    India
    Japan


  South America

    Brazil


  Middle East and Africa

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

On-premises deployment is a traditional method for implementing technology solutions within an organization. This approach involves purchasing software with a one-time license fee and a service contract. On-premises solutions offer enhanced security, as they keep user credentials and data within the company's premises. They can be customized to meet specific business requirements, allowing for quick adaptation. On-premises deployment eliminates the need for third-party providers to manage and secure data, ensuring data privacy and confidentiality. Additionally, it enables rapid and easy data access, and keeps IP addresses and data confidential. This deployment model is particularly beneficial for businesses dealing with sensitive data, such as those in manufacturing and large enterprises. While cloud-based solutions offer flexibility and cost savings, on-premises deployment remains a popular choice for organizations prioritizing data security and control.

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The on-premises segment was valued at USD 38.70 million in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 48% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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This statistic displays the various applications of data analytics and mining across procurement processes, according to chief procurement officers (CPOs) worldwide, as of 2017. Fifty-seven percent of the CPOs asked agreed that data analytics and mining had been applied to intelligent and advanced analytics for negotiations, and 40 percent of them indicated data analytics and mining had been applied to supplier portfolio optimization processes.

Process Analytics Market size was valued at USD 1864.26 Million in 2023 and is projected to reach USD 46769.11 Million by 2030, growing at a CAGR of 49.60% during the forecast period 2024-2030.

Global Process Analytics Market Drivers

The market drivers for the Process Analytics Market can be influenced by various factors. These may include:

Initiatives for Digital Transformation: Businesses in a variety of industries are going through a digital transformation to increase production, customer happiness, and efficiency. Understanding and improving company processes is made easier with the aid of process analytics tools, which is essential for a successful digital transformation.
Growing Process Mining Adoption: Process mining solutions are becoming more and more well-liked since they facilitate the analysis of business processes using event logs. The need for process analytics solutions is being driven by these tools, which offer insights into process inefficiencies and bottlenecks.
Growing Requirement for Risk Management and Compliance: Process analytics adoption is being driven by regulatory regulations and the necessity of strong risk management frameworks in organisations. These solutions offer transparency and traceability in company processes, which aids in guaranteeing adherence to norms and regulations.
Technological Developments in Big Data and AI: Big data analytics and artificial intelligence (AI) are enhanced when combined with process analytics technologies, allowing for more precise and timely analysis. The development of technology is one of the main factors propelling the market.
Growth of Data-Driven: Judging Decision-making in business is becoming more and more dependent on data-driven insights. Process analytics solutions offer comprehensive insights into process performance and opportunities for enhancement, which helps to make better decisions.
Need for Enhanced Operational Effectiveness: Businesses are always searching for methods to cut expenses and increase operational effectiveness. Process optimisation and identification of inefficiencies result in improved resource usage and cost savings thanks to process analytics.
The expansion of cloud computing: Organisations may more easily implement and employ process analytics tools because to the scalability, flexibility, and cost advantages that come with adopting cloud-based solutions. The market for process analytics is being driven by the expansion of cloud computing.
Growing Intricacy of Business Procedures: Globalisation, mergers and acquisitions, changing market dynamics, and other factors are making business processes increasingly complicated, necessitating the use of sophisticated technologies for process analysis and management.
Improved Management of Customer Experience: Businesses are concentrating on enhancing the customer experience, and process analytics solutions aid in comprehending interactions and touchpoints with customers. This realisation facilitates process simplification for increased customer satisfaction.
Advantage of Competition: Businesses are using process analytics to improve productivity, acquire a competitive edge, and increase their ability to adapt to changes in the market.

During a survey carried out in 2024, roughly one in three marketing managers from France, Germany, and the United Kingdom stated that they based every marketing decision on data. Under 10 percent of respondents in all five surveyed countries said they struggled to incorporate data analytics into their decision-making process.

Google Data Analytics Capstone Project

Cyclistic Dataset

Case Study: How Does a Bike-Share Navigate Speedy Success?

Introduction

Welcome to the Cyclistic bike-share analysis case study! In this case study, you will perform many real-world tasks of a junior data analyst. You will work for a fictional company, Cyclistic, and meet different characters and team members. In order to answer the key business questions, you will follow the steps of the data analysis process: ask, prepare, process, analyze, share, and act. Along the way, the Case Study Roadmap tables — including guiding questions and key tasks — will help you stay on the right path.

Scenario

You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations. Characters and teams.

Cyclistic: A bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day.

Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels.

Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals — as well as how you, as a junior data analyst, can help Cyclistic achieve them.

Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.

Data overview

ride_id: It is a distinct identifier assigned to each individual ride. rideable_type: This column indicates the type of bikes used for each ride. started_at: This column denotes the timestamp when a particular ride began. ended_at: This column represents the timestamp when a specific ride concluded. start_station_name: This column contains the name of the station where the bike ride originated. start_station_id: This column represents the unique identifier for the station where the bike ride originated. end_station_name: This column contains the name of the station where the bike ride concluded. end_station_id: This column represents the unique identifier for the station where the bike ride concluded. start_lat: This column denotes the latitude coordinate of the starting point of the bike ride. start_lng: This column denotes the longitude coordinate of the starting point of the bike ride. end_lat: This column denotes the latitude coordinate of the ending point of the bike ride. end_lng: This column denotes the longitude coordinate of the ending point of the bike ride. member_casual: This column indicates whether the rider is a member or a casual user.

Stair descent analysis has been typically limited to laboratory staircases of 4 or 5 steps. To date there has been no report of gait parameters during unconstrained stair descent outside of the laboratory, and few motion capture datasets are publicly available. We aim to collect a dataset and perform gait analysis for stair descent outside of the laboratory. We aim to measure basic kinematic and kinetic gait parameters and foot placement behavior. We present a public stair descent dataset from 101 unimpaired participants aged 18-35 on an unconstrained 13-step staircase collected using wearable sensors. The dataset consists of kinematics (full-body joint angle and position), kinetics (plantar normal forces, acceleration), and foot placement for 30,609 steps. This is the first quantitative observation of gait data from a large number (n = 101) of participants descending an unconstrained staircase outside of a laboratory. The dataset is a public resource for understanding typical stair descent.

In fiscal year 2021, over 107 thousand professionals were working in the field of data analysis in Japan. This number is expected to continue to grow consistently over the next years, since an increasing number of companies are using big data they have collected via smart devices, sensors and such to help their decision making process.

Scientific investigation is of value only insofar as relevant results are obtained and communicated, a task that requires organizing, evaluating, analysing and unambiguously communicating the significance of data. In this context, working with ecological data, reflecting the complexities and interactions of the natural world, can be a challenge. Recent innovations for statistical analysis of multifaceted interrelated data make obtaining more accurate and meaningful results possible, but key decisions of the analyses to use, and which components to present in a scientific paper or report, may be overwhelming. We offer a 10-step protocol to streamline analysis of data that will enhance understanding of the data, the statistical models and the results, and optimize communication with the reader with respect to both the procedure and the outcomes. The protocol takes the investigator from study design and organization of data (formulating relevant questions, visualizing data collection, data exploration, identifying dependency), through conducting analysis (presenting, fitting and validating the model) and presenting output (numerically and visually), to extending the model via simulation. Each step includes procedures to clarify aspects of the data that affect statistical analysis, as well as guidelines for written presentation. Steps are illustrated with examples using data from the literature. Following this protocol will reduce the organization, analysis and presentation of what may be an overwhelming information avalanche into sequential and, more to the point, manageable, steps. It provides guidelines for selecting optimal statistical tools to assess data relevance and significance, for choosing aspects of the analysis to include in a published report and for clearly communicating information.

High-throughput multiplexed protein quantification using mass spectrometry is steadily increasing in popularity, with the two major techniques being data-dependent acquisition (DDA) and targeted acquisition using selected reaction monitoring (SRM). However, both techniques involve extensive data processing, which can be performed by a multitude of different software solutions. Analysis of quantitative LC-MS/MS data is mainly performed in three major steps: processing of raw data, normalization, and statistical analysis. To evaluate the impact of data processing steps, we developed two new benchmark data sets, one each for DDA and SRM, with samples consisting of a long-range dilution series of synthetic peptides spiked in a total cell protein digest. The generated data were processed by eight different software workflows and three postprocessing steps. The results show that the choice of the raw data processing software and the postprocessing steps play an important role in the final outcome. Also, the linear dynamic range of the DDA data could be extended by an order of magnitude through feature alignment and a charge state merging algorithm proposed here. Furthermore, the benchmark data sets are made publicly available for further benchmarking and software developments.

This is the replication package for the analysis done in the paper "Evolution of statistical analysis in empirical software engineering research: Current state and steps forward" (DOI: https://doi.org/10.1016/j.jss.2019.07.002, preprint: https://arxiv.org/abs/1706.00933).

The package includes CSV files with data on statistical usage extracted from 5 journals in SE (EMSE, IST, JSS, TOSEM, TSE). The data was extracted from papers between 2001 - 2015. The package also contains forms, scripts and figures (generated using the scripts) used in the paper.

The extraction tool mentioned in the paper is available in dockerhub via: https://hub.docker.com/r/robertfeldt/sept

Hello! Welcome to the Capstone project I have completed to earn my Data Analytics certificate through Google. I chose to complete this case study through RStudio desktop. The reason I did this is that R is the primary new concept I learned throughout this course. I wanted to embrace my curiosity and learn more about R through this project. In the beginning of this report I will provide the scenario of the case study I was given. After this I will walk you through my Data Analysis process based on the steps I learned in this course:

1. Ask
2. Prepare
3. Process
4. Analyze
5. Share
6. Act

The data I used for this analysis comes from this FitBit data set: https://www.kaggle.com/datasets/arashnic/fitbit

" This dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. "

Data analysis can be accurate and reliable only if the underlying assumptions of the used statistical method are validated. Any violations of these assumptions can change the outcomes and conclusions of the analysis. In this study, we developed Smart Data Analysis V2 (SDA-V2), an interactive and user-friendly web application, to assist users with limited statistical knowledge in data analysis, and it can be freely accessed at https://jularatchumnaul.shinyapps.io/SDA-V2/. SDA-V2 automatically explores and visualizes data, examines the underlying assumptions associated with the parametric test, and selects an appropriate statistical method for the given data. Furthermore, SDA-V2 can assess the quality of research instruments and determine the minimum sample size required for a meaningful study. However, while SDA-V2 is a valuable tool for simplifying statistical analysis, it does not replace the need for a fundamental understanding of statistical principles. Researchers are encouraged to combine their expertise with the software’s capabilities to achieve the most accurate and credible results.

Workshop given at the 1st Linguistics Studies Biennial Conference (LSBC 2024), Kuwait University, Kuwait City, Kuwait.

Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative for reducing process-based sources of variation and extreme biological outliers. Without this step, statistical results can be biased. Additionally, liquid chromatography–MS proteomics data present inherent challenges due to large amounts of missing data that require special consideration during statistical analysis. While a number of R packages exist to address these challenges individually, there is no single R package that addresses all of them. We present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data analysis (EDA), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quantitative and qualitative statistical test, 19 proteins whose statistical significance would have been missed by a quantitative test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.

The role of Data Science and AI for predicting the decline of professionals in the recruitment process: augmenting decision-making in human resources management Features Description: Declined: Variable to be predict, where value 0 means that the candi- date continued in the recruit- ment process until the hiring, and value 1 implies the candi- date’s declination from recruit- ment process. ValueClient: The total amount the customer plan to pay by the hired candidate. The value 0 means that client yet did not define a value to pay the candidate. Values must be greater than or equal to 0. ExtraCost: Extra cost the customer has to pay to hire the candidate. Values must be greater than or equal to 0. ValueResources: Requested value by the candidate to work. The value 0 means that the candidate did not request a salary amount yet an this value will be negotiate later. Values must be greater than or equal to 0. Net: The difference between the “ValueClient”, yearly taxes and “ValueResources”. Negative values mean that the amount the client plans to pay the candidate has not yet been defined and is still open for negotiation. DaysOnContact: Number of days that the candidate is in the “Contact” step of the recruitment process. Values must be greater than or equal to 0. DaysOnInterview: Number of days that the candidate is in the “Interview” step of the recruitment process. Values must be greater than or equal to 0. DaysOnSendCV: Number of days that the candidate is in the “Send CV” step of the recruitment process. Values must be greater than or equal to 0. DaysOnReturn: Number of days that the candidate is in the “Return” step of the recruitment process. Values must be greater than or equal to 0. DaysOnCSchedule: Number of days that the candidate is in the “C. Schedule” step of the recruitment process. Values must be greater than or equal to 0. DaysOnCRealized: Number of days that the candidate is in the “C. Realized” step of the recruitment process. Values must be greater than or equal to 0. ProcessDuration: Duration of entire recruitment process in days. Values must be greater than or equal to 0

Recent technological advances have made it possible to carry out high-throughput metabonomics studies using gas chromatography coupled with time-of-flight mass spectrometry. Large volumes of data are produced from these studies and there is a pressing need for algorithms that can efficiently process and analyze data in a high-throughput fashion as well. We present an Automated Data Analysis Pipeline (ADAP) that has been developed for this purpose. ADAP consists of peak detection, deconvolution, peak alignment, and library search. It allows data to flow seamlessly through the analysis steps without any human intervention and features two novel algorithms in the analysis. Specifically, clustering is successfully applied in deconvolution to resolve coeluting compounds that are very common in complex samples and a two-phase alignment process has been implemented to enhance alignment accuracy. ADAP is written in standard C++ and R and uses parallel computing via Message Passing Interface for fast peak detection and deconvolution. ADAP has been applied to analyze both mixed standards samples and serum samples and identified and quantified metabolites successfully. ADAP is available at http://www.du-lab.org.

Animal ecologists often collect hierarchically-structured data and analyze these with linear mixed-effects models. Specific complications arise when the effect sizes of covariates vary on multiple levels (e.g., within vs among subjects). Mean-centering of covariates within subjects offers a useful approach in such situations, but is not without problems. A statistical model represents a hypothesis about the underlying biological process. Mean-centering within clusters assumes that the lower level responses (e.g. within subjects) depend on the deviation from the subject mean (relative) rather than on absolute values of the covariate. This may or may not be biologically realistic. We show that mismatch between the nature of the generating (i.e., biological) process and the form of the statistical analysis produce major conceptual and operational challenges for empiricists. We explored the consequences of mismatches by simulating data with three response-generating processes differing in the source of correlation between a covariate and the response. These data were then analyzed by three different analysis equations. We asked how robustly different analysis equations estimate key parameters of interest and under which circumstances biases arise. Mismatches between generating and analytical equations created several intractable problems for estimating key parameters. The most widely misestimated parameter was the among-subject variance in response. We found that no single analysis equation was robust in estimating all parameters generated by all equations. Importantly, even when response-generating and analysis equations matched mathematically, bias in some parameters arose when sampling across the range of the covariate was limited. Our results have general implications for how we collect and analyze data. They also remind us more generally that conclusions from statistical analysis of data are conditional on a hypothesis, sometimes implicit, for the process(es) that generated the attributes we measure. We discuss strategies for real data analysis in face of uncertainty about the underlying biological process. Methods All data were generated through simulations, so included with this submission are a Read Me file containing general descriptions of data files, a code file that contains R code for the simulations and analysis data files (which will generate new datasets with the same parameters) and the analyzed results in the data files archived here. These data files form the basis for all results presented in the published paper. The code file (in R markdown) has more detailed descriptions of each file of analyzed results.

Data Prep Market size was valued at USD 4.02 Billion in 2024 and is projected to reach USD 16.12 Billion by 2031, growing at a CAGR of 19% from 2024 to 2031.

Global Data Prep Market Drivers

Increasing Demand for Data Analytics: Businesses across all industries are increasingly relying on data-driven decision-making, necessitating the need for clean, reliable, and useful information. This rising reliance on data increases the demand for better data preparation technologies, which are required to transform raw data into meaningful insights.
Growing Volume and Complexity of Data: The increase in data generation continues unabated, with information streaming in from a variety of sources. This data frequently lacks consistency or organization, therefore effective data preparation is critical for accurate analysis. To assure quality and coherence while dealing with such a large and complicated data landscape, powerful technologies are required.
Increased Use of Self-Service Data Preparation Tools: User-friendly, self-service data preparation solutions are gaining popularity because they enable non-technical users to access, clean, and prepare data. independently. This democratizes data access, decreases reliance on IT departments, and speeds up the data analysis process, making data-driven insights more available to all business units.
Integration of AI and ML: Advanced data preparation technologies are progressively using AI and machine learning capabilities to improve their effectiveness. These technologies automate repetitive activities, detect data quality issues, and recommend data transformations, increasing productivity and accuracy. The use of AI and ML streamlines the data preparation process, making it faster and more reliable.
Regulatory Compliance Requirements: Many businesses are subject to tight regulations governing data security and privacy. Data preparation technologies play an important role in ensuring that data meets these compliance requirements. By giving functions that help manage and protect sensitive information these technologies help firms negotiate complex regulatory climates.
Cloud-based Data Management: The transition to cloud-based data storage and analytics platforms needs data preparation solutions that can work smoothly with cloud-based data sources. These solutions must be able to integrate with a variety of cloud settings to assist effective data administration and preparation while also supporting modern data infrastructure.

Global Manufacturing Analytics Market size was valued at USD 10.44 Billion in 2024 and is projected to reach USD 44.76 Billion by 2031, growing at a CAGR of 22.01% from 2024 to 2031.

Global Manufacturing Analytics Market Drivers

Growing Adoption of Industrial Internet of Things (IIoT): As more sensors and connected devices are used in manufacturing processes, massive volumes of data are generated. This increases the demand for analytics solutions in order to extract useful insights from the data.

Demand for Operational Efficiency: In order to increase output, cut expenses, and minimize downtime, manufacturers strive to improve their operations. Real-time operational data analysis is made possible by analytics systems, which promote proactive decision-making and process enhancements.

Growing Complexity in production Processes: With numerous steps, variables, and dependencies, modern production processes are getting more and more complicated. These intricate processes can be analyzed and optimized with the help of analytics technologies to increase productivity and quality.

Emphasis on Predictive Maintenance: To reduce downtime and prevent equipment breakdowns, manufacturers are implementing predictive maintenance procedures. By using machine learning algorithms to evaluate equipment data and forecast maintenance requirements, manufacturing analytics systems can optimize maintenance schedules and minimize unscheduled downtime.

Quality Control and Compliance Requirements: The use of analytics solutions in manufacturing is influenced by strict quality control guidelines and legal compliance obligations. Manufacturers may ensure compliance with quality standards and laws by using these technologies to monitor and evaluate product quality metrics in real-time.

Demand for Supply Chain Optimization: In an effort to increase productivity, save expenses, and boost customer happiness, manufacturers are putting more and more emphasis on supply chain optimization. Analytics tools give manufacturers insight into the workings of their supply chains, allowing them to spot bottlenecks, maximize inventory, and enhance logistical procedures.

Technological Developments in Big Data and Analytics: The production of analytics solutions is becoming more innovative due to advances in machine learning, artificial intelligence, and big data analytics. Thanks to these developments, manufacturers can now analyze massive amounts of data in real time, derive insights that can be put into practice, and improve their operations continuously.

ABSTRACT A lot amounts of data i.e information that related to make wonders with work is called as 'BIG DATA' Last two decades big data treated as a special interest and had a lot potentiality because of hidden features in it. To generate, store, and analyze big data with an aim to improve the services they provide in multiple no of small & large scale industries. As we are considering the health care industry for this big data is providing multiple opportunities like records of patients, inflow & outflow of the hospitals. It also generates a significant portion of big data relevant to public healthcare in biomedical research. In order to derive meaningful information analysis & proper management of data is required. In the haystack seeking solution in big data will be quickly analyzable just like finding a needle. in big data analysis various challenges associated with each step of handling big data surpassed by using high-end computing solutions. for improving public health healthcare providers provide relevant solutions & to systematically generate and analyze big data requirements to be fully loaded with efficient infrastructure. in big data can change the game by opening new avenues for modern healthcare with an efficient management, analysis, and interpretation. vigorous instructions are given by the various industries like public sectors followed by healthcare for the betterment of services and as well as financial upgrades. by taking the revolution in healthcare industry we can accommodate personnel medicine included by therapies in strong integration manner. Keywords: Healthcare, Biomedical Research, Big Data Analytics, Internet of Things, Personalized Medicine, Quantum Computing Cite this Article: Krishnachaitanya.Katkam and Harsh Lohiya, Patient Centric Management Analysis and Future Prospects in Big Data Healthcare, International Journal of Computer Engineering and Technology (IJCET), 13(3), 2022, pp. 76-86.