The Data Analytics in Retail Industry is segmented by Application (Merchandising and Supply Chain Analytics, Social Media Analytics, Customer Analytics, Operational Intelligence, Other Applications), by Business Type (Small and Medium Enterprises, Large-scale Organizations), and Geography. The market size and forecasts are provided in terms of value (USD billion) for all the above segments.

The first step in any new digital experience optimization program is to build a strong understanding of the digital journey. The reason is pretty simple. Whether it’s a software registration experience or an ecommerce path to purchase, our goal is always to identify challenges and present a clear roadmap to address them. But we first […]

The report covers Global Supply Chain Big Data Analytics Market Size and it is segmented by Type (Solution, Service), End User (Retail, Manufacturing, Transportation and Logistics, Healthcare, Other End Users), and Geography (North America, Europe, Asia Pacific, Latin America, and Middle East and Africa). The market size and forecasts are provided in terms of value (USD) for all the above segments.

This statistic shows the size of the global big data analytics services market related to healthcare in 2016 and a forecast for 2025, by application. It is predicted that by 2025 the market for health-related financial analytics services using big data will increase to over 13 billion U.S. dollars.

The global visual analytics market is experiencing robust growth, projected to reach a substantial size with a Compound Annual Growth Rate (CAGR) of 17.9% between 2019 and 2033. This expansion is driven by several key factors. The increasing availability of big data necessitates effective tools for analysis and interpretation, fueling the demand for user-friendly visual analytics platforms. Businesses across various sectors are adopting these solutions to gain actionable insights from complex datasets, improving decision-making processes and fostering data-driven strategies. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of visual analytics tools, enabling more sophisticated analysis and predictive modeling. The integration of these technologies is leading to more intuitive and efficient data exploration, contributing significantly to market growth. Major players like IBM, Oracle, SAP, and Microsoft are actively investing in research and development, further driving innovation and competition within the market. The market segmentation is likely diverse, encompassing various deployment models (cloud, on-premise), industry verticals (finance, healthcare, retail), and functionality (data visualization, dashboards, predictive analytics). The competitive landscape is characterized by a mix of established players and emerging companies offering innovative solutions. While the market faces certain challenges, such as the need for skilled professionals to effectively utilize these tools and concerns regarding data security and privacy, the overall trajectory remains positive. The continued adoption of digital transformation initiatives across industries and the growing demand for data-driven insights are expected to propel the market towards significant expansion in the coming years. The robust CAGR indicates a promising future for the visual analytics market, highlighting its importance in the modern data-centric business environment.

Contains view count data for the top 20 pages each day on the Somerville MA city website dating back to 2020. Data is used in the City's dashboard which can be found at https://www.somervilledata.farm/.

Big Data Analysis Platform Market Outlook

The global market size for Big Data Analysis Platforms is projected to grow from USD 35.5 billion in 2023 to an impressive USD 110.7 billion by 2032, reflecting a CAGR of 13.5%. This substantial growth can be attributed to the increasing adoption of data-driven decision-making processes across various industries, the rapid proliferation of IoT devices, and the ever-growing volumes of data generated globally.

One of the primary growth factors for the Big Data Analysis Platform market is the escalating need for businesses to derive actionable insights from complex and voluminous datasets. With the advent of technologies such as artificial intelligence and machine learning, organizations are increasingly leveraging big data analytics to enhance their operational efficiency, customer experience, and competitiveness. The ability to process vast amounts of data quickly and accurately is proving to be a game-changer, enabling businesses to make more informed decisions, predict market trends, and optimize their supply chains.

Another significant driver is the rise of digital transformation initiatives across various sectors. Companies are increasingly adopting digital technologies to improve their business processes and meet changing customer expectations. Big Data Analysis Platforms are central to these initiatives, providing the necessary tools to analyze and interpret data from diverse sources, including social media, customer transactions, and sensor data. This trend is particularly pronounced in sectors such as retail, healthcare, and BFSI (banking, financial services, and insurance), where data analytics is crucial for personalizing customer experiences, managing risks, and improving operational efficiencies.

Moreover, the growing adoption of cloud computing is significantly influencing the market. Cloud-based Big Data Analysis Platforms offer several advantages over traditional on-premises solutions, including scalability, flexibility, and cost-effectiveness. Businesses of all sizes are increasingly turning to cloud-based analytics solutions to handle their data processing needs. The ability to scale up or down based on demand, coupled with reduced infrastructure costs, makes cloud-based solutions particularly appealing to small and medium-sized enterprises (SMEs) that may not have the resources to invest in extensive on-premises infrastructure.

Data Science and Machine-Learning Platforms play a pivotal role in the evolution of Big Data Analysis Platforms. These platforms provide the necessary tools and frameworks for processing and analyzing vast datasets, enabling organizations to uncover hidden patterns and insights. By integrating data science techniques with machine learning algorithms, businesses can automate the analysis process, leading to more accurate predictions and efficient decision-making. This integration is particularly beneficial in sectors such as finance and healthcare, where the ability to quickly analyze complex data can lead to significant competitive advantages. As the demand for data-driven insights continues to grow, the role of data science and machine-learning platforms in enhancing big data analytics capabilities is becoming increasingly critical.

From a regional perspective, North America currently holds the largest market share, driven by the presence of major technology companies, high adoption rates of advanced technologies, and substantial investments in data analytics infrastructure. Europe and the Asia Pacific regions are also experiencing significant growth, fueled by increasing digitalization efforts and the rising importance of data analytics in business strategy. The Asia Pacific region, in particular, is expected to witness the highest CAGR during the forecast period, propelled by rapid economic growth, a burgeoning middle class, and increasing internet and smartphone penetration.

Component Analysis

The Big Data Analysis Platform market can be broadly categorized into three components: Software, Hardware, and Services. The software segment includes analytics software, data management software, and visualization tools, which are crucial for analyzing and interpreting large datasets. This segment is expected to dominate the market due to the continuous advancements in analytics software and the increasing need for sophisticated data analysis tools. Analytics software enables organizations to process and analyze data from multiple sources,

The US marketing analytics market, a significant segment of the global industry, is experiencing robust growth, fueled by the increasing adoption of data-driven decision-making across various sectors. The market's substantial size, estimated at $X billion in 2025 (assuming a proportional share of the global market based on US economic influence and digital marketing maturity), is projected to expand at a Compound Annual Growth Rate (CAGR) exceeding 12.73% through 2033. This growth is driven by several key factors. Firstly, the proliferation of digital channels and the resulting explosion of marketing data necessitate sophisticated analytics solutions for effective campaign management and ROI optimization. Secondly, the rising adoption of cloud-based marketing analytics platforms offers scalability, cost-efficiency, and enhanced accessibility for businesses of all sizes. Thirdly, the increasing demand for personalized marketing experiences pushes businesses to leverage advanced analytics to understand customer behavior and preferences, leading to targeted campaigns and improved customer engagement. Furthermore, the burgeoning need for real-time data insights to rapidly respond to market changes and optimize marketing strategies further contributes to this growth. The US market's segmentation mirrors global trends, with cloud deployment dominating due to its inherent advantages. Key application areas include online marketing, email marketing, and social media marketing, reflecting the omnipresence of these channels. Major end-user sectors like retail, BFSI (Banking, Financial Services, and Insurance), and technology are leading adopters, leveraging analytics to improve customer acquisition, retention, and profitability. While the competitive landscape is crowded with established players like IBM, Microsoft, Salesforce, and Adobe, the market also presents opportunities for specialized niche players focusing on specific industry verticals or advanced analytical techniques. The continued innovation in areas like artificial intelligence (AI), machine learning (ML), and predictive analytics will likely shape future market growth, particularly in areas like customer journey mapping and predictive modeling for marketing campaign optimization. The US market's robust growth trajectory suggests significant investment opportunities and underscores the critical role of marketing analytics in the ongoing digital transformation across various industries. Recent developments include: June 2023 - Moody’s Corporation and Microsoft have announced a new partnership to deliver next-generation data, analytics, research, collaboration, and risk solutions for financial services and global knowledge workers. Built on a combination of Moody’s robust data and analytical capabilities and the power and scale of Microsoft Azure OpenAI Service, the partnership creates innovative offerings that enhance insights into corporate intelligence and risk assessment, powered by Microsoft AI and anchored by Moody’s proprietary data, analytics, and research., July 2022 - Neustar, a TransUnion company, announced a partnership with integrated data platform Adverity to allow marketers to connect all their data effortlessly to boost marketing and brand effectiveness. To better optimize marketing spending and boost return on investment (ROI), marketers need a comprehensive data strategy as data-driven marketing becomes more complex. Through this relationship, companies and agencies can more accurately assess the marketing effectiveness of various online and offline platforms, such as the walled garden and television ecosystems., December 2022 - Vi Labs, an Enterprise-AI for digital health, acquired Motus Consumer Insights, a member acquisition analytics, site selection, and marketing BI firm. Through the acquisition, Vi's robust AI-powered customer engagement and retention solution will be combined with the premier platforms for customer acquisition and site selection in the market. Vi's mission to use the power of data and AI to support people living active and healthy lifestyles worldwide is only accelerated by this deal.. Key drivers for this market are: Increase in Social Media Channels, Increasing Need to Utilize Marketing Budgets for an Effective ROI; Adoption of Cloud Technology and Big Data. Potential restraints include: Increase in Social Media Channels, Increasing Need to Utilize Marketing Budgets for an Effective ROI; Adoption of Cloud Technology and Big Data. Notable trends are: Adoption of Cloud Technology and Big Data is Expected to Drive the Market Growth.

The global marketing data analysis software market is projected to grow from XXX million in 2023 to XXX million by 2033, with a CAGR of XX% during the forecast period. The growth of the market is attributed to the increasing adoption of data-driven marketing strategies by businesses to improve their customer engagement and sales performance. Additionally, the growing popularity of cloud-based software solutions and the availability of advanced analytical tools are driving the market growth. The market is segmented based on application, type, company, and region. The retail and e-commerce segment holds the largest market share due to the high demand for data analysis in the industry. The website analysis software segment is expected to witness significant growth during the forecast period due to the increasing need for businesses to track and analyze website traffic and behavior. The North American region dominates the market, followed by Europe and Asia Pacific. The key players in the market are HubSpot, Semrush, Looker Data Sciences (Google), Insider, LeadsRx, SharpSpring, OWOX BI, and Whatagraph BV.

The Big Data Analytics in Banking Market is Segmented by Type of Solutions (Data Discovery and Visualization (DDV) and Advanced Analytics (AA)), and Geography (North America, Europe, Asia-Pacific, Latin America, Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD Million) for all the Above Segments.

The capstone was completed in Power Bi. Due to restrictions on sharing, I've made a powerpoint of the report that demonstrates the data in use and the insight gained from the research.

dailyActivity_merged contains a summary of daily activity such as total distance, intensities (i.e., very active, sedentary), and total minutes in intensities.

There is a discrepancy between the total distance and the sum of VeryActiveDistance, ModeratelyActiveDistance, LightActiveDistance and SedentaryActiveDistance. With an average of 5.489702122 miles in tracker distance, this can be off on average up to .077053 miles or 370 feet.

1.6% (15/940) of tracker distances listed do not match total distance. I will need clarification between total distance and tracker distance. For my report, I will be using total distance.

Aggregated daily data does not contain null values. No assumptions need to be made based on this.

98/940 records are <= 500 feet. 77/98 have a total of 0 steps and the remaining data is 0. A filter has been added to void records where total steps are <= 500.

I removed 5/12/2016 due to lack of sufficient user data.

dailyActivity_merged contains the same calories as dailyCalories_merged when using activity date and ID as a primary key.

dailyActivity_merged does not contain the same calories as hourlyCalories_merged when summing the calories per day in the hourly table.

PseudoData contains mock data I created for users. Pseudo names were created for the ID's to make data relatable for the audience. Teams were generated in the event the analysis discussed this possibility.

heartrate_seconds_merged contains heart rate value every 15 seconds over time.

I removed 5/12/2016 due to lack of sufficient user data. Events were averaged to the nearest hour. The windows function lag() was used to find time between events to determine usage. The visuals will show lag, or time when the device is not used, if it's greater than the total charge time, 2 hours.

hourlyCalories_merged contains calories per hour per ID. The Date and Time were separated into two columns.

Global Smart Grid Data Analytics Market size is set to expand from $ 7.07 Billion in 2023 to $ 21.07 Billion by 2032, with an anticipated CAGR of around 12.9% from 2024 to 2032.

Question Paper Solutions of chapter Introduction to Data Analytics of Data Analytics Skills for Managers, 5th Semester , Bachelor in Business Administration 2020 - 2021

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Data analytics by size class of enterprise

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.

The data used for analysis can come from many different sources and be presented in various formats. Data science is an essential part of many industries today, given the massive amounts of data that are produced, and is one of the most debated topics in IT circles.

The Data Mining Tools Market size was valued at USD 1.01 USD billion in 2023 and is projected to reach USD 1.99 USD billion by 2032, exhibiting a CAGR of 10.2 % during the forecast period. The growing adoption of data-driven decision-making and the increasing need for business intelligence are major factors driving market growth. Data mining refers to filtering, sorting, and classifying data from larger datasets to reveal subtle patterns and relationships, which helps enterprises identify and solve complex business problems through data analysis. Data mining software tools and techniques allow organizations to foresee future market trends and make business-critical decisions at crucial times. Data mining is an essential component of data science that employs advanced data analytics to derive insightful information from large volumes of data. Businesses rely heavily on data mining to undertake analytics initiatives in the organizational setup. The analyzed data sourced from data mining is used for varied analytics and business intelligence (BI) applications, which consider real-time data analysis along with some historical pieces of information. Recent developments include: May 2023 – WiMi Hologram Cloud Inc. introduced a new data interaction system developed by combining neural network technology and data mining. Using real-time interaction, the system can offer reliable and safe information transmission., May 2023 – U.S. Data Mining Group, Inc., operating in bitcoin mining site, announced a hosting contract to deploy 150,000 bitcoins in partnership with major companies such as TeslaWatt, Sphere 3D, Marathon Digital, and more. The company is offering industry turn-key solutions for curtailment, accounting, and customer relations., April 2023 – Artificial intelligence and single-cell biotech analytics firm, One Biosciences, launched a single cell data mining algorithm called ‘MAYA’. The algorithm is for cancer patients to detect therapeutic vulnerabilities., May 2022 – Europe-based Solarisbank, a banking-as-a-service provider, announced its partnership with Snowflake to boost its cloud data strategy. Using the advanced cloud infrastructure, the company can enhance data mining efficiency and strengthen its banking position.. Key drivers for this market are: Increasing Focus on Customer Satisfaction to Drive Market Growth. Potential restraints include: Requirement of Skilled Technical Resources Likely to Hamper Market Growth. Notable trends are: Incorporation of Data Mining and Machine Learning Solutions to Propel Market Growth.

The dataset includes YouTube trending videos statistics for Mediterranean countries on 2022-11-07. It contains 15 columns and it's related to 19 countries:

IT - Italy ES - Spain GR - Greece HR - Croatia TR - Turkey AL - Albania DZ - Algeria EG - Egypt LY - Lybia TN - Tunisia MA - Morocco IL - Israel ME - Montenegro LB - Lebanon FR - France BA - Bosnia and Herzegovina MT - Malta SI - Slovenia CY - Cyprus

SY - Syria

The columns are, instead, the following:

country: where is the country in which the video was published. video_id: video identification number. Each video has one. You can find it clicking on a video with the right button and selecting 'stats for nerds'. title: title of the video. publishedAt: publication date of the video. channelId: identification number of the channel who published the video. channelTitle: name of the channel who published the video. categoryId: identification number category of the video. Each number corresponds to a certain category. For example, 10 corresponds to 'music' category. Check here for the complete list. trending_date: trending date of the video. tags: tags present in the video. view_count: view count of the video. comment_count: number of comments in the video. thumbnail_link: the link of the image that appears before clicking the video. -comments_disabled: tells if the comments are disabled or not for a certain video. -ratings_disabled: tells if the rating is disabled or not for that video. -description: description below the video. Inspiration You can perform an exploratory data analysis of the dataset, working with Pandas or Numpy (if you use Python) or other data analysis libraries; and you can practice to run queries using SQL or the Pandas functions. Also, it's possible to analyze the titles, the tags and the description of the videos to search for relevant information. Remember to upvote if you found the dataset useful :).

License

CC0

Original Data Source: YouTube Trending Videos of the Day

The Alternative Data Vendor market is experiencing robust growth, driven by the increasing demand for non-traditional data sources to enhance investment strategies and business decision-making. The market's expansion is fueled by the proliferation of digital data, advancements in data analytics, and a growing need for more comprehensive and nuanced insights across various sectors. The BFSI (Banking, Financial Services, and Insurance) sector remains a significant driver, leveraging alternative data for credit scoring, fraud detection, and risk management. However, growth is also witnessed in industrial, IT and telecommunications, and retail and logistics sectors as businesses seek competitive advantages through data-driven decision-making. The diverse types of alternative data, including credit card transactions, web data, sentiment analysis, and public data, cater to a wide range of applications. While data privacy and regulatory concerns pose challenges, the market is overcoming these hurdles through robust data anonymization and compliance strategies. The competitive landscape features both established players like S&P Global and Bloomberg, along with emerging technology-driven companies, fostering innovation and market expansion. We project a steady compound annual growth rate (CAGR) resulting in a substantial market expansion over the next decade. This growth is expected to be distributed across regions, with North America and Europe maintaining leading positions due to early adoption and developed data infrastructure. The forecast period from 2025 to 2033 anticipates continued market expansion, propelled by factors such as increasing data availability from IoT devices, refined analytical techniques, and expanding applications across new sectors. The market's segmentation by application and data type is expected to further evolve, with niche players focusing on specific data sets and industries. This specialized approach allows for deeper insights and catering to specific client needs. Geographic expansion will continue, with growth in Asia-Pacific particularly driven by the increasing adoption of digital technologies and expanding economic activity. Strategic partnerships and mergers and acquisitions will likely shape the competitive landscape, fostering consolidation and further innovation in alternative data solutions. Despite challenges related to data quality, security, and ethical considerations, the overall outlook for the Alternative Data Vendor market remains highly positive, with substantial growth opportunities over the long term.

Market Size and Growth: The website visitor tracking software market is projected to reach USD XX million by 2033, expanding at a CAGR of XX% from 2025 to 2033. The market is driven by the increasing adoption of digital marketing and analytics, as businesses seek to understand their website visitors' behavior and optimize their marketing campaigns. The growing demand for data privacy and compliance regulations is also fueling market growth. Industry Trends and Dynamics: The website visitor tracking software market is experiencing several trends, including the rise of cloud-based solutions, the integration of artificial intelligence (AI) and machine learning (ML) for enhanced data analysis, and the increased focus on personalization and customer segmentation. Key players in the market include Visitor Queue, Crazy Egg, VWO Insights, Leadfeeder, and Google Analytics, among others. The competitive landscape is characterized by strategic partnerships, acquisitions, and product innovations. Regional markets are also witnessing significant growth, particularly in North America, Europe, and Asia Pacific, as businesses across these regions embrace digital transformation and customer-centric strategies.