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big data analytics for smart healthcare

3 Quantifiable Benefits of Big Data Analytics for Smart Healthcare Service Providers

Given the ubiquity of healthcare data generated by business processes within the healthcare sector, healthcare data analytics and big data analytics for smart healthcare play an increasingly important role in every aspect of healthcare decision making. In such an environment, the use of solutions such as big data analytics for smart healthcare is not only crucial to analyze data generated by smart health devices but also an essential source of patient and operational information that drives decision making in the healthcare services sector.

big data analytics for smart healthcare

Why are healthcare service providers embracing big data analytics for smart healthcare service improvements? Request a FREE proposal to find out.

However, until recently, making these benefits a reality by transmitting data obtained from smart health devices in an appropriate format to the downstream systems has been a significant stumbling block. But today a new breed of healthcare technology that focuses on the use of big data analytics for smart healthcare has transformed and made it much easier to process the data generated by medical information systems (MIS). For forward-thinking healthcare service providers, big data analytics for smart healthcare has turned out to be crucial in not only capturing device information digitally but also normalizing it, providing patient context and transmitting it to downstream applications that use it in a wide range of innovative ways.

When combined with insights from BI and data visualization tools, big data analytics for smart healthcare services can help healthcare service providers to enhance operational efficiency by offering real-time information that can support decisions and deliver actionable insights.

Healthcare service providers are actively looking to address their organizational challenges using big data analytics.

Learn why you should’nt overlook such challenges in today’s complex business environment.

Benefits of Big Data Analytics for Smart Healthcare Service Providers

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big data ecosystem

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.

fitness industry

3 Ways Fitness Industry is Driving Growth Using Data and Analytics | Quantzig

The health and fitness industry in the US is booming for all the good reasons- changing lifestyles, healthy food habits, rise in use of smart wearables and fitness trackers, and a well-informed, growing customer base. Due to many such factors promoting healthy lifestyles, those operating in the fitness industry have started witnessing an incremental growth in revenue along with an expanding customer base. To those in the business, this means a rise in unstructured data sets that hold valuable insights that could potentially transform business functions.

For many such reasons, advanced fitness analytics solutions that help analyze and effectively manage huge volumes of customer data sets have become an indispensable part of every business strategy. In this context, fitness data analytics solutions that leverage data obtained from smart wearables, social media platforms, fitness equipment, research reports, and customer feedback forms play a crucial role in improving business processes. However, what truly brings about a difference in business strategy and growth, lies in how businesses utilize these data sets to improve the overall experience of their customers.

Learn how deploying machine learning algorithms can help you drive growth in the fitness industry. Request a FREE proposal for comprehensive insights into our capabilities.

Fitness Industry Trends to Watch Out for in 2020

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big data analytics for smart healthcare

How Emerging MedTech Companies Can Succeed in the European Market

MedTech’ is one of Europe’s most diverse and innovative sectors that witnessed a high number of patents filed by emerging Medtech companies over the past few years. The rise in market entrants can also be witnessed in the influx of data sets from trade flows and employment statistics obtained from the Medtech companies in Europe. As the European MedTech market evolves, emerging medical device manufacturers and Medtech companies will need to adopt a flexible and evolving approach to launch new equipment and drive maximum value for patients and key stakeholders.

medtech companies

Establishing your presence in the European market amidst these challenges might seem like an uphill task. We can help you tackle these challenges to stay ahead of the curve. Request a FREE proposal to learn more.

Today, it’s increasingly clear that the MedTech industry is going through an unprecedented level of change across key markets. In Europe, the challenges revolve around budget pressures that are impacting market access and reimbursement. To tackle the growing challenges, payers and providers require evidence of value at the clinical and socio-economic levels to combat price changes and fluctuations in demands.

Amid these challenges, emerging Medtech companies must proactively address the issues curtailing profitability and growth. In such a scenario- How must the MedTech operating model change across the value chain from re-thinking business strategy, to operational efficiency, to distribution and support to ensure the future success of the industry in Europe?

Having worked with leading Medtech companies from various geographies, our analytics experts have witnessed a running battle between distributors and manufacturers for managing and controlling supply chain costs in the European market. As emerging MedTech companies consider how to be successful in the European markets, they must address two key questions:

  • How will we bring high-quality equipment to patients wherever in the world they may live?
  • How do we effectively manage the transformation of the traditional business models to one that meets the diverse needs of the emerging markets?

Looking for comprehensive insights on the European Medtech market? Get in touch with our analytics experts.

Our analytics experts have listed a few examples of how Medtech companies can drive measurable growth and incremental revenue by leveraging the power of analytics.

  • Traditional outcome-driven business models no longer suffice when it comes to supporting the evolving needs of Medtech companies that revolve around improving revenues and preserving margins. New analytical approaches are quickly evolving across the industry to optimize everything from inventory to sales, minimize supply chain disruptions, attract new customers and retain existing customers, and expand market footprint based on outcomes and economic value. 
  • To sustain a competitive edge in a competitive marketplace, Medtech companies must invest in the right analytics tools and solutions to bring about an organization-wide impact. The developments in this industry have paved the way for major transformations that have impacted every aspect of conducting business, as such it is no more a question of when, but how businesses can drive outcomes using advanced analytics.
  • Medtech companies can also use predictive modeling and advanced analytics techniques to foresee threats and identify factors that impact business performance. Collaborating with analytics service providers can help Medtech companies to leverage data science and advanced analytics to improvise business models and jump-start the internal skill-building journey.

Request a FREE demo to get a glimpse into our analytics solutions, platforms, and the methodologies adopted by our data scientists and data analytics experts to resolve the complex challenges faced by our clients.

big data trends

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

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conversational ai

Conversational AI: The Next Big Thing in Customer Experience Management in Retail

Looking back at the journey of humans since the first interaction with machines it’s obvious that we’ve come a long way. Tapes and punch cards have now been replaced with mouse clicks and touch screen displays. After analyzing the developments in today’s world, our experts believe that we’re back to the basics or the first principles of human interaction through human-like conversations, commonly known as ‘Conversational AI’. Though Conversational AI turned out to be a huge leap forward for businesses across industries, retailers all over the globe were the ones who achieved maximum success by leveraging conversational AI to improve customer experience.

Today, businesses from different verticals are fast catching up with this trend and are leveraging conversational AI to enhance all aspects of their business processes. Also, as the need for a coordinated business strategy gains prominence globally, conversational AI is increasingly becoming essential for enterprises looking to establish a strong market presence. From speech-enabled interfaces that improve customer experience, through intelligent virtual assistants and chatbots for customer service, to human-like digital assistants to drive online sales, conversational AI has transformed customer experience across channels and different business verticals.

Wonder why businesses across verticals are keen on strengthening their conversational AI and machine learning capabilities? We’ve got you covered. Request a FREE proposal for expert insights.

 

Key Challenges Facing Retail

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food delivery companies

Why Online Food Delivery Companies Bet Big on AI and Machine Learning – A CXO’s Guide

food delivery companies

After several years of being confined to technology labs and the pages of sci-fi books, today artificial intelligence (AI) and big data have become the dominant focal point for businesses across industries. Barely a day passes by without new magazine and paper articles, blog entries, and tweets about such advancements in the field of AI and machine learningHaving said that, it’s not very surprising that AI and machine learning in the food and beverage industry have played a crucial role in the rapid developments that have taken place over the past few years.

Wonder why food delivery companies are keen on strengthening their AI and machine learning capabilities? We’ve got you covered. Request a FREE proposal for expert insights.

How Food Delivery Companies are Improving Performance Using AI and Machine Learning

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merchandising analytics

How Merchandising Analytics Helps European Retailers to Redefine Customer Experiences | Quantzig

The holy grail of modern retail business is to maintain an exceptional understanding of the consumer buying behavior, decision cycle, and path to purchase. Lack of complete insight into consumers buying behavior and journey leads to a decrease in customer satisfaction and loyalty. The European retail market is also facing a similar challenge. With the substantial increase in consumer data, it has become difficult for retailers in the European market to analyze the plethora of data and extract meaningful insights.

Quantzig’s merchandising analytics solutions help businesses to optimize merchandise assortments and store-front layouts. Request a FREE proposal today to gain comprehensive insights.

European retail players are increasingly leveraging merchandising analytics solutions to understand consumers’ purchasing behavior and deliver a consistent experience to customers. It also plays a crucial role in increasing ROI and improving outcomes.Re Furthermore, merchandising analytics is paving way for European retailers to improve enterprise-wide visibility, enabling actions that improve the product, placement, pricing, and promotion performance. In this article, our analytics experts have highlighted a few ways in which merchandising analytics is helping retailers to respond to customer needs at the right time and place resulting in improved customer loyalty and experience.

Merchandising analytics can offer actionable insights into visual merchandising displays and category planning to enable retailers to work smartly. Want to know how? Talk to our analytics experts now.


How Can Merchandising Analytics Improve Customer Experiences?

By optimizing in-store operations

Merchandising analytics solutions can help the retailer to analyze data obtained from the Internet of Things (IoT) devices and assists sales personnel to take necessary action in case of unexpected events. Also, it can help in optimizing store labor efficiency by capturing data of store personnel, including their in-store tasks and activities. Furthermore, this data can be used to create an optimized labor schedule.

Request a FREE demo to find out how our visual merchandising solutions can help you to optimize your promotional activities that are targeted at customer segments based on their buying behavior.

By focusing on customer centric assortments

With the increasing complexities in the European retail market due to growing political complications, and consumer expectations, it has become difficult for companies to know exactly what combination of product, place, and price will motivate consumers to buy more often. Merchandising analytics solutions are allowing retailers to develop customer-centric assortments that make it easier for customers to choose their merchandise when shopping in-store and online. Also, it is empowering sales teams with valuable consumer insights that help them perfectly align merchandise offerings with customers, markets, channels, and locations.

Gaining behavioral insights

In the customer-centric world, it has become essential for retail businesses to have the ability to capture and use consumer data optimally. With the use of merchandising analytics, businesses can analyze customer-buying histories and behavioral data to understand the typical purchase pathway of their potential customer segments. This can further help them prioritize specific customer outcomes such as reduced churn and improved cross-sell.

To learn more about the role of merchandising analytics in transforming retail business operations, Request for more information right away!

medical device industry

Three Applications of AI in the Medical Device Industry

Traditionally, players in the medical device industry have focused on delivering value through manufacturing and innovation alone. But this can no longer suffice as pressure on the medical industry has increased by three-folds owing to the foundational shifts in the business models. To succeed in the new normal those operating in the medical device industry will have to move out of their conventional roles and focus on integrating AI with their offerings to offer holistic solutions.

Today AI and analytics have become pervasive throughout the healthcare value chain, enhancing the ability of businesses to perform sophisticated data analysis to achieve improvements in numerous healthcare areas right from R&D to product development. Today, artificial intelligence plays a crucial role in managing patient data, streamlining medical diagnostics, and in enhancing the R&D efforts within the medical device industry. Moreover, with AI being analogous to human learning and decision-making, it has entrusted the ability to learn without the use of rule-based programming. This, in turn, has proved to be beneficial in empowering medical device industry players to model and gauge the impact of such transformations at a mind-boggling speed that has not been possible ever before.

With developments in AI and machine learning creating new opportunties for established and emerging companies across industries, it’s crucial for businesses to explore its true potential to succeed in the long-run. Request a FREE proposal to learn how we can help.

Three Ways AI is Transforming the Medical Device Industry

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marketing analytics

Everything Marketers Need to Know Before Implementing Marketing Analytics

Analytics has influenced all possible industries and business functions. Great importance has been placed on marketing analytics in recent times as it allows marketers to optimize all aspects of their marketing strategy. Today, digital marketing generates a lot of data that includes transaction data, online data, social media data, customer feedback, and customer data. The data collected can be used for further analysis to understand the customers and deliver favorable customer experiences. Marketing analytics can help decision-makers, right from the start by assisting in market segmentation, target marketing, and product positioning. But there are a few things that marketers must keep in mind while applying marketing analytics in business operations.

Our marketing analytics solutions can help you reduce maverick spend on marketing campaigns, identify irrelevant marketing channels, and improve marketing mix strategies. Request a free brochure of our analytics solutions right away to gain better insights!

MARKETING ANALYTICS BLOG

Tips for Marketers Before Implementing Marketing Analytics

Devise a smart marketing strategy

One of the primary steps for any organization to undertake before delving into marketing analytics tools is to devise an effective marketing strategy. There are many tools available in the digital marketplace that allows companies to construct tailor-made campaigns that revolve around their product and help brands speak highly about themselves. These tools are primarily based on the search engine audience sentiments that revolve around popular keywords, trending hashtags, and market trends among many others. They allow marketers to analyze and evaluate various aspects such as incoming leads, conversions, and website traffic. If marketers incorporate this workflow, key insights can be easily derived at the end of the campaign. These valuable insights can be applied to the next campaign or can be used adaptively if the campaign is long-term or recurring.

Talk to our analytics experts and learn how we can help you integrate data across all marketing channels and combine it into a common marketing view.

Data collection and segregation

Today marketers have access to troves of data sets generated by disparate sources. Marketers can convert these data into meaningful insights using marketing analytics. But to do so successfully, they must ensure that they have analyzed and collated data elements generated from all the sources which were involved in a marketing campaign. Data from social media, emails, POS machines, call centers, beacons, and Wi-Fi need to be collated and safeguarded. These data sets are unstructured and in different formats. This data can later be structured to get a clear understanding of how customers are shopping today.

Using integrated artificial intelligence and powerful statistical techniques, our analytics solutions can help you identify the right  marketing mix, to optimize your marketing spend. . Gain limited-time complimentary access to our analytics platform today.

Choose the right data visualization tools

The data generated must prove useful for the marketing teams and stakeholders if you are to gain meaningful insights from it. The key to getting this right is to choose the most appropriate data visualizations, which can help identify patterns and interpret the data. Thus, it is important to choose marketing analytics tools that allow you to choose or customize your visualizations instead of using default charts for displaying data.

Learn more about how marketing analytics solutions can help you create better predictions and faster learning curves than traditional rule-based methods.

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