Tag: Big Data Analytics

casino gaming industry

Why the Online Casino Industry Needs Big Data Analytics

Big data analytics is a top priority for businesses across industries and the online casino gaming industry is no exception. Today many firms look up to big data for better ways to save costs, whilst delivering a service of the highest quality. Since access to processing tools and data is becoming more affordable, big data analytics is being adopted by several businesses. One industry that has absorbed big data analytics is the global online casino gaming industry. Most of the online casinos are making use of advanced analytics methodologies to shape their marketing campaigns and reforming their products as per the consumer’s behavior.

The online casino gaming industry is said to reach an all-time high worth of approximately 87 billion USD by 2024, hence this is the best time to utilize consumer data available with them.

Here is how big data analytics is transforming the global online casino gaming industry:

casino gaming industry

Wonder why the casino gaming industry is relying on big data analytics for marketing? Request a free proposal to know more. 

What Big Data Analytics Can Reveal for Casino Gaming Industry


big data analytics in retail

Big Data Analytics in Retail: 3 Success Stories from the Front Lines

When it comes to leveraging big data analytics in retail, there are several factors and statistical techniques that determine success including the approach to data mining, predictive modeling, machine learning, and data modeling. Retailers that have successfully incorporated big data analytics into their core business operations have achieved higher success rates and better profits when compared to their peers.

In this article, our analytics experts have outlined three success stories from the front lines and have explained how leading retailers have used big data analytics in retail to drive improvements in business operations.

Big Data Analytics in Retail: Business Benefits


casino gaming industry

How to Build a Connected Big Data Ecosystem in 4 Simple Steps

We live in the big data era where tumultuous shifts are underway in BI, analytics, and data management, prompting enterprises to take a new perspective on creating a big data ecosystem. Also, owing to the ongoing developments, big data ecosystems are no longer the outmoded, insular systems contained within corporate walls. The ability of businesses to interpret data and act on insights can be augmented using a connected big data ecosystem that includes a complex network of applications, infrastructure, and advanced big data analytics tools that help capture and analyze data. As a result, enterprises are not just looking at revamping their big data ecosystem but are learning to integrate expertise and insight to drive growth and innovation.

Though the dynamism in the market and ever-evolving trends impact the big data ecosystem and its ability to generate comprehensive insights, our big data analytics experts have identified four crucial steps that form the basis of a connected data ecosystem.

Enterprises are looking to gain better insights by tapping into third-party data sets that not just offer new opportunities but pose several challenges. Request a FREE Proposal to learn how to tackle those using big data analytics!

big data ecosystem

The creation of a big data ecosystem might seem challenging, but it pays off by assisting teams to run smoothly with a holistic view of the business goals that they should achieve

Contact our experts for more insights on our big data analytics capabilities.

Step 1: Data Discovery & Repository Creation

The first step in building a big data ecosystem revolves around collecting data and analyzing their sources while creating a unified source of truth. In an urge to devise a connected big data ecosystem, businesses tend to gather information from disparate sources across the organization and then integrate domain-specific data pre-processing techniques & build big data and machine learning models to address critical issues. By adopting such an approach, businesses end up creating their version of a single source of truth and continue benchmarking results unaware of its impact on the business process. To avoid such issues, it’s essential to create a single source of truth that unifies domains and promotes collaboration to build and deliver a 360-degree view of the business goals.

Step 2: Centralized, Connected Big Data Ecosystem Design

Quantzig’s approach to devising a connected big data ecosystem depends on the level of analytical maturity within an organization. Before designing the centralized data ecosystem, our big data analytics experts suggest businesses must conduct an analytical maturity assessment that can offer insights on the right tools, the approach, and technology based on the business goals. Having worked with business leaders from different verticals, we do see a growing trend toward the deployment of a robust, organized big data ecosystem that can power the next wave of data-driven decision making at scale. By analyzing the analytics maturity of our clients and assessing the ideal characteristics of a data repository, we help prospects design a centralized big data ecosystem that aligns with their goals and helps fuel growth and profitability.

Looking for customized big data and machine learning solutions? Request a FREE demo to get a glimpse of our analytics platforms and the services we offer.

Step 3: Data Collation & Analysis

Mastering data collation and analysis is how businesses today avoid flying blind. Increasingly, this requires tapping into insights from external data sources and a growing number of businesses are doing so in pursuit of a competitive edge. However, the actual data analysis begins after designing the big data ecosystem. A robust data repository plays a vital role in helping businesses collect and analyze data from disparate sources. With the unrelenting pressures to innovate and grow, companies must consider enhancing their ability to collate, segment, and analyze data using a connected, evolving big data ecosystem. Through our advanced big data analytics solutions, our team of data scientists, evangelists, and analysts help clients unravel new insights from the data they possess.

Step 4: Insight Generation Using Big Data Analytics

The final step in creating a connected big data ecosystem revolves around insight generation and all the processes that help analyze data. During this phase, our analytics experts collaborate with representatives from various teams within your organization to communicate the finding and share personalized recommendations to meet your business goals and KPI’s. Once a strong foundation for insight generation is set, the processes can be duplicated to ensure your teams are abreast of the latest findings and can leverage new insights to inform decision making.

Want more insights on creating a big data ecosystem? Request for more information right away!

With the ongoing developments and technological advancements across industries, the big data ecosystem of businesses will continue to lag, leading to challenges in data analysis and insight generation. These challenges are poised to present major opportunities for those who can leverage big data analytics to meet the evolving market needs.

Big Data and Knowledge Management

Top Three Big Data Trends: Expert Predictions For 2020

The rapid advancements in technology have amplified data generation, opening doors for big data trends to enhance business processes and boost profitability.  The role of big data trends in enterprise decision making has been elevated to such an extent where analyzing data to extract insights has been proven to be invaluable to both large- and small-scale establishments. As we enter the next phase of big data evolution, businesses across industries are heading for a fundamental shift backed by the adoption of big data trends. In such a scenario, businesses must not only keep an eye on the emerging big data trends but must leverage them successfully to succeed in the long run.

We bring to you the top three big data trends that are set to change the business landscape in 2020 and beyond.

big data trends

With several years of expertise in the field of data and analytics, we help businesses manage their data sets at cost-effective rates by offering actionable insights into big data trends to help them take their business forward. Request a FREE proposal to learn how we can help you.

Top 3 Big Data Trends Set to Change the Business Landscape in 2020


Big Data

Big Data Challenges in the Media and Entertainment Industry

The media and entertainment industry is growing at an unprecedented rate, with companies finding it difficult to keep up with the pace. The challenges arise due to pressures to keep the costs down while trying to improve revenues. Further complications arise as the media consumption habits of customers are highly fragmented. The trend of one big company dominating the market is gradually fading away, providing an opportunity for small players to complete successfully. One of the most significant trend observed in the media industry is the shift in media platforms from traditional channels to online mediums. As a result, it paves the way for the media industry to implement big data and data analytics technology to gain more profound audience insights. With a new source of data available to the media companies each day, companies can efficiently understand customer needs and deliver content that pleases the audience. However, with the rapid adoption of digital technologies, the media industry does face a few big data challenges which slows down their progression. To fully realize the potential of digital Free demotechnologies, the companies should successfully tackle these big data challenges.

Big data challenges in the media industry

Data privacy concernsData Privacy

Numerous leaks of personal information and media have been making the headlines recently. Consequently, consumers are being more sensitive towards their data and are concerned on how their personal data is being used. Additionally, policymakers have also addressed their issues and have implemented regulations for businesses that handle personal data. Such big data challenges can pose problems when it comes to accumulating sufficient user data, without which accurate analysis cannot be performed. Regulations have also been in place for companies that broker personal data to media houses.

Lack of financial muscle

One of the most significant big data challenges in the media industry is the lack of financial muscle for media start-ups and SMEs. While accounting for cost factors to implement data analytics, companies need to look at various factors including data storage costs, infrastructure costs, data processing costs, and human resource costs. Although it is relatively easy to start a new company producing content, games, or apps, it can be tough to scale up without significant investments. Thankfully, the advent of cloud storage and SaaS solutions have provided a way out for start-ups and SMEs.

Difficulty in talent acquisition

Talent Acquisition

There is a significant imbalance in the supply and demand of data scientists all across the world. The imbalance is mostly in terms of shortage of supply of analytics professionals. Furthermore, the demand for data scientists has been increasing exponentially, and there aren’t many professionals to fill the void. As a result, companies have to pay out hefty salaries to such professionals usually upwards of $100,000. The problem of talent acquisition is one of the key big data challenges in the media industry, which cries out for professionals in data journalism and product management.

Low penetration rates of high-speed broadband

A large part of the content delivered to the audience today is via online mediums. To facilitate this, high-speed broadband is a must. However, the penetration rates of high-speed broadband services haven’t been that impressive across the world as it is limited mostly to metro cities. Firstly, it reduces the potential customer base and then gathers data from only urban segment consumers. Analysts cannot gain insights into customers who lack access to high-speed broadband and thus resort to traditional mediums.

Piracy, copyrights, and account sharingAccount sharing

Piracy and copyright issues have been there in the media and entertainment industry for a long time. However, the advent of digital medium has created new big data challenges, the problem of account sharing. For the majority of video streaming sites, a large number of people gain access by sharing account information and passwords. Analysts will have a tough time performing analysis on customer preferences as they cannot pinpoint the demographic details of the user. Both the child and the adult may be using the same account, so an effective judgment is impossible on whether the child or the adult prefers a specific genre.

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big data analytics

Big Data Analytics Helped a Client to Ease Data Processing & Improve Service Efficiency by 12%

“Considering the unique needs of our business and the complexities of our data sets, Quantzig’s big data analytics experts did an outstanding job in laying an analytics roadmap.”

About the Client

The client is a leading mobile marketing automation solutions provider based out of Austria that measures mobile app engagement and provides granular, analytical insights to its customers. They also empower companies to send customized marketing messages to meet the unique needs of the end-users across multiple channels such as in-app, push, email, web, and other media.

The Business Challenge

In today’s economically uncertain era, many leading businesses have come to appreciate that the key to better decisions, more effective customer engagement, sharper competitive edge, hyper-efficient operations, and compelling product development is- Data. The challenges faced by business is not due to the shortage of raw materials, but due to the lack of domain expertise and analytical skills to turn the unstructured, huge volumes of “Big Data” into actionable insights.

Request a free brochure of our analytics solutions to gain comprehensive insights into our portfolio of big data analytics capabilities and learn how customized analytics solutions can help you gain a front-liner advantage

The marketing automation segment witnessed accelerating growth only after 2014, around the same time when mobile users first outnumbered desktop users. Today, unstructured online data sets have grown exponentially making it crucial for businesses to leverage analytical methodologies to analyze these data sets. Moreover, the ongoing data deluge signifies that marketers pursuing consumers need to deploy a way to closely understand the end-users of their applications. This is crucial because once they analyze consumers’ mobile behavior, they can hone their core mobile marketing competencies to match their requirements.

The client- a mobile marketing automation company (MMAC), needed a massively scalable big data analytics platform to inform its marketing-oriented customers about how well their mobile applications were engaging mass-market consumers. The company sought a ‘single version of truth’ platform that was also affordable and user-friendly for the application developers, typically marketers who weren’t necessarily data scientists.

The client’s challenges spanned three core areas including:

  • Velocity
  • Variety
  • Volume

Still unsure about how big data analytics solutions can help you drive profitable growth? Talk to our analytics experts for comprehenisve insights.

Solution Offered and Value Delivered

As frontrunners among big data analytics solution providers, we exhibit proven big data analytics capabilities in successfully handling the entire lifecycle of big data implementation including deployment, development, maintenance, and support. Having worked on advanced technologies and big data analytics tools that are leading the big data ecosystem, our analytics experts poses the capability to develop big data analytics frameworks that address all the functional components including data provisioning, data management and data consumption. 

We adopted a comprehensive three-step approach to big data analytics that offered a 360-degree view of consumers interactions with mobile apps. The insights also enabled the client to choose what changes might boost usage, increase business, and retain consumers – and improve the ROI of their marketing investments. Also, by deploying visually interactive big data analytics dashboards we offered in-depth insights tailored specifically to each app developer and its offerings.

Gain limited time complimentary access to our analytics platform to learn how big data models and statistical techniques can help you make better business decisions

Phase 1

The first phase of this big data analytics case study revolved around data cleansing, data aggregation, and data analysis. A detailed analysis of customer data obtained from millions of smartphones helped the client to gain comprehensive insights into the demands of the end-users.

Phase 2

The second phase of the big data analytics engagement focused on analyzing and translating user behavior across a broad range of mobile apps. It also involved analyzing data on time-series information, sequential action, geo-location, and others across all types of applications and mobile devices.

Phase 3

The final phase of this big data analytics engagement revolved around data visualization and dashboarding to help the client analyze user paths while making in-app purchases.


Business Outcome

With the help of our big data analytics solutions, the mobile marketing automation solutions provider was able to improve their service efficiency by 12% and expand their capabilities, beyond supplying its customers with aggregated data about users of their applications. Data visualization and the devised big data analytics framework enabled the client to track and gauge customer engagement rates. Our big data analytics solutions also offered insights on how they could improve the UI of their applications to improve customer engagement.

Would you like to learn more about our big data analytics capabilities? 

clickstream data

Clickstream Data Analysis: Cost-Effective Approach for Businesses to Succeed in 2020 and Beyond

What is Clickstream Data?

Clickstream data refers to the data generated by the users when they perform any activity or when they navigate over a web application. It comprises valuable information for businesses that can help them quantify user’s behavior and get an idea of how effective their website is at driving sales. Also, with the help of clickstream data, businesses can understand the user experience, based on their navigation patterns. Furthermore, by analyzing clickstream data businesses can predict which page customers are likely to visit next, improve their marketing strategy and come up with better recommendations.

Talk to our analytics experts to know how our big data analytics solutions can help you visualize online visitor interactions through online channels.

CLICKSTREAM DATA ANALYTICSBenefits of Clickstream Data Analysis for Businesses 

Click path optimization

Clickstream data analysis can guide businesses in website traffic analysis that can further help them in tracking the path the user takes while navigating through their website. As a result, businesses can gauge metrics that affect user experience such as the number of pages visited, page loading timings, the amount of data transmitted, and frequency of users.. Furthermore, by gaining such useful insights through clickstream data analytics, businesses can optimize the click path by making minor changes to the website to reduce bounce rates and increase conversions.

Our clickstream data analytics solutions enable businesses to track KPI’s and gain insights into customer buying behavior. Request a FREE proposal to gain in-depth insights into our analytics solutions.

Market basket analysis

Clickstream data analysis can pave the way for market basket analysis that can give them a better understanding of aggregate customer purchasing behavior. Also, by analyzing market basket, businesses can discover common interests of customers and common paths they take to arrive at the purchasing decision. Such valuable information can help businesses to determine the most productive path a site user can take for researching and buying a product.

Quantzig’s real-time data monitoring and clickstream data analytics solutions can help you observe user activity and determine the impact of your marketing campaigns. Request a FREE demo below to know more.

Next best product analysis

By leveraging clickstream data analytics, marketers can conduct the next best product analysis (NBP). With the help of this analysis, businesses can analyze what products customers prefer to buy together. This can further help them send real-time offers on such products to the customers to improve customer experience. Consequently, this can improve sales and revenue growth in the future.

Better customer segmentation

Clickstream data analysis offers in-depth insights into how individual customer segments behave. This can further help them to personalize customer experience at every touchpoint by analyzing customer behavior and interests in real-time. Additionally, customer segmentation at the granular level can establish another level of transparency and trust with customers and improve customer loyalty and retention.

To learn more about clickstream data analytics and its benefits, request for more information right away!

Big data analytics

Big Data Analytics Framework Empowered an Oil and Gas Company to Reduce Operational Costs by 37% Through Continuous Process Improvements

The client is a leading multinational oil and gas industry player, based out of Austria. The company boasts an impressive number of 6,000+ clients worldwide and employs over 10,000 employees. The oil and gas company wanted to devise a big data analytics framework that supports data acquisition, aggregation, transformation, and cleansing to create an enterprise data management platform that will serve as a single source of truth and help make crucial business decisions.

The Business Challenge

Modern big data analytics tools and cognitive technologies have been proven to be useful in maintaining and analyzing huge troves of data sets generated by businesses across industries. In the oil and gas industry, big data analytics helps improve data management, identify and map oil reservoirs, and optimize operational costs. With the generation of huge volumes of unstructured data sets from sensors, oil and gas companies have found themselves in a fix and are looking at capitalizing on new opportunities by leveraging advanced big data analytics solutions. Apart from offering actionable insights to improve decision making, big data analytics helps oil and gas companies to leverage big data to improve recovery rates, reduce environmental impacts, and avoid accidents.

Request a FREE proposal to find out how our big data analytics solutions can help your organization navigate its next. 

The current data management platform deployed by the client was expensive and offered limited scalability, due to which data was being underutilized and choked in their data storage systems curtailing its processing ability and giving rise to challenges such as high processing costs, limited analytical access to data, constrained EDW capacity, and inability to process unstructured datasets. To tackle these challenges, they wanted to leverage Quantzig’s big data analytics solutions and deploy a robust data management platform to effectively store and manage the growing complexities of the unstructured data sets. 

big data analytics

Would you like to know more about the benefits of big data? Get in touch with our experts right away!

Solutions Offered and Value Delivered

We adopted a comprehensive three-pronged approach to help the client tackle the challenges around data storage and data processing.

Phase 1

Partnering with the client’s data management team, we undertook a detailed industry assessment to understand their current big data analytics capabilities, scalability bottlenecks, and other factors hindering growth.

Phase 2

Our big data analytics team collaborated with the oil and gas client to devise an end-to-end big data analytics framework to tackle their challenges.

Phase 3

The third phase of this big data analytics engagement focused on deploying a big data platform to help them improve their analytical capabilities, understand the customer needs better, and generate more revenue opportunity.

Request a FREE demo and gain insights into the benefits of deploying a robust big data analytics framework. 

Our big data analytics solutions also empowered the client to:

  • Store, maintain, and process data as they moved through various stages of the data lifecycle
  • Achieve a 37% reduction in operational costs with continuous process improvements
  • Develop a big data analytics roadmap to scale business processes and analyze unstructured data sets
  • Demonstrate significant improvement over current ad-hoc analytics capability

Why Choose Quantzig as Your Big Data Analytics Solution Provider?

The staggering volumes of unstructured sensor data have given rise to several data management challenges in the oil and gas industry. To tackle these challenges, businesses must comprehend the pieces of information and extract actionable insights. Backed with powerful big data analytics tools and over a decade of experience in working with clients across industries, our big data analytics solutions can help you do just that by breaking down data silos to unearth valuable insights to act upon.

Request for more information to learn how we can help you tackle the complexities associated with ustructured data sets. 

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