Tag: Advanced Analytics Methods

cloud analytics

Cloud Analytics: A Guide to Enterprise Data Consolidation

What is Cloud Analytics?

Cloud analytics is an approach that involves the application of advanced algorithms to identify data patterns and extract valuable insights. Today companies are increasingly relying on data analysis to improve business operations and gain an edge over competitors. By leveraging cloud analytics approaches, companies can improve their decisions significantly and enhance business performance. Cloud analytics provides an indispensable platform that can help in scaling analytics capabilities and reduce the burden of management and implementation.

Cloud analytics can help organizations to improve their data governance capabilities by consolidating information from all communication channels and resources. Want to know how? Get in touch with our experts right away.

Components of Cloud Analytics

Data sources

Data sources are the original sources of data that comprise ERPs, social media data, CRM, and website usage data. Twitter sentiment data is an example of a cloud-based data source.

Data models

Cloud-based data models explain the relationship between various data points. They are created with structured data types.

Analytic models

Analytics-based models are mathematical models that are used to forecast business outcomes and drive change using data-driven and fact-based information.

Cloud analytics solutions can help companies to improve flexibility and serve their customers better by consolidating critical data. Request a FREE proposal to gain better insights.

Types of Cloud Analytics

Public cloud

Public clouds are operated by third-party cloud service providers and within a public cloud, all hardware, software, and other supporting infrastructure is managed by the cloud service providers.

Private cloud

A private cloud is a cloud computing resource that is used exclusively by a single organization. A private cloud is usually located on the company’s on-site datacenter. Also, some companies pay third-party service providers for hosting their private cloud. A private cloud facilitates the maintenance of the services and infrastructure on a private network.

Hybrid cloud

Hybrid clouds comprise both public and private clouds which are connected by technology that allows the sharing of data and applications between them. A hybrid cloud-based platform provides better flexibility and deployment options to the business by allowing data and applications to move between private and public clouds. Furthermore, this helps organizations to optimize existing security, compliance, and infrastructure.

Request a FREE demo below to know how our advanced analytics services can help your business.

Benefits of Cloud Analytics

Performance

Cloud analytics helps businesses to improve performances through fast and efficient computing hardware. This offers several benefits over a single corporate data center, including reduced network latency for applications and greater economies of scale.

A better understanding of the bottom line

Cloud analytics can transform every segment of an organization by giving access to human resources, marketing, and operations to the important financial information they need to execute the visual analysis. This can provide a better understanding of the bottom-line impact of their decisions.

Breaking down departmental silos

For any organization financial data is very important. Therefore, if business users outside the finance department have access to this data, it can help in improving the business functions. Cloud analytics provides the ability to query and analyze data from a single source. Furthermore, cloud analytics solutions help in sharing strategy and discipline across the core business operations.

Want to know more about the benefits of cloud analytics? Request for more information below.

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How Advanced Analytics is Changing the Business Landscape in 2019?

What is Advanced Analytics?

Advanced analytics describes data analysis that goes steps ahead than simple mathematical calculations such as sums and averages or sorting and filtering. Advanced analytics leverages statistical and mathematical formulas and algorithms to recognize patterns, generate new information, and to forecast outcomes and their respective probabilities. It can be applied to several cases, ranging from the prevention of contract termination, classic customer value and success forecasts, or sales, price, and demand forecasts to new tasks such as the machine failure prediction, monitoring of social media and evaluation, and predictive policing. Advanced analytics enables both innovation and optimization. It has the potential to improve existing processes.

Are you finding it difficult to make informed business decisions, while ensuring information privacy and security? Get in touch with our analysts who can complement you with a shared need to discover and build those capabilities and drive continuous market excellence.

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What are the Different Advanced Analytics Techniques?

In advanced analytics, “advanced” refers to quantitative methods such as algorithms, statistics, and stochastic processes. All the advanced analytics techniques are not predictive, but they are future-oriented as the core idea of the techniques is to support data-driven decisions in the future. Furthermore, the advanced analytics techniques can be categorized into three functions:

Descriptive analysis

The descriptive analysis focuses on understanding an underlying process or phenomenon. The descriptive analysis has the potential to answer questions such as, “Which products do consumers usually purchase together?” or “What are the typical characteristics of customers who tend to churn?”

Predictive analysis

The predictive analysis aims at studying hidden relationships between factors and outcomes. The data derived is then used to forecast or estimate an unknown value. For example, a predictive model will allow us to estimate how much revenue will be lost if temperatures drop 10 degrees or forecast which customers are going to churn.

Simulation and optimization

Simulation copies the operation and characteristics of a process and condenses the outcome whereas, optimization prioritizes the options of decision-based on a key performance index.

Are you struggling to manage the high volumes of data generated by high-tech sensors? Leveraging advanced analytics solutions may help. It has the potential to generate predictive insights that facilitate proactive business decisions and pre-emptive planning. Request a free proposal here to check out our portfolio of advanced analytics solutions.

 

Why Advanced Analytics Is Becoming So Important for Businesses? 

In this era of digitalization  new technologies and analytic methods are improving business operations and are helping businesses to maintain a competitive advantage in the following ways:

  • New big data technologies enable processing and analysis of large amounts of data and cost-effective storage
  • Modern and intuitive user interfaces allow user groups to derive informed insights and make better decisions
  • Advanced analytics software facilitates better analysis of collected data, relationships, and future events.

Today, modern technologies like predictive analytics accelerate and improve decisions and processes along the value chain. Therefore, you should carefully evaluate how advanced analytics can be used within your company in order to excel in the competitive landscape.

Key Benefits of Advanced Analytics

Detailed analysis helps in personalized marketing

Advanced analytics helps identify patterns from less aggregated datasets, such as at the store level or at an individual customer level. For example, advanced analytics can help businesses to capture the characteristics of every individual customer and offer him or her customized products by analyzing the pool of data. This eliminates the method of simply concluding the overall market demand of a customer segment and then offering the full audience’s favorite product. By leveraging big data analytics solutions businesses can significantly improve marketing effectiveness as well as customer satisfaction.

Better performing advanced analytics techniques improve accuracy

Most of the advanced data analytics techniques used today are more robust, faster, and accurate. For example, artificial neural networks greatly enhanced pattern recognition performance by generating a collection of functions that transform the input variables to approximate the required outcome. Predictive analytics and deep learning algorithms are applied to create more powerful hierarchies. Additionally, big data solutions allow machines to learn the complex business world in a much more flexible way. As a result, the prediction from analytical models is becoming more accurate and reliable.

Identify the level of risk in the implementation of the decision

By leveraging predictive analytics solutions, businesses can gain a confidence level for their prediction and select models based on performance measures. Furthermore, it helps companies by conveying the level of risk involved in the recommended action. Additionally, advanced analytics benefits businesses by conveying the risk level of implementing the recommended action. There are circumstances when there is a high cost for acting on false-positive or false-negative predictions. In such cases, companies can adjust the parameters of the predictive analytics model to choose not to act on low confidence predictions or avoid high-cost false prediction.

One of our recent case studies highlighted how advanced analytics helped a consumer products manufacturer to develop an aggressive growth-centric data strategy. Read the full story here.

Final Thoughts

Advanced analytics can help organizations to deal with confusions and challenges by delivering future-oriented and fact-based insights. Furthermore, it strategically extends traditional analytics solutions from being descriptive to predictive, and ideally prescriptive.

We understand the challenges companies face in managing large volumes of data and set up analytical frameworks to derive real-time insights that facilitate more informed decisions. Our advanced analytics solutions provide best-in-class frameworks for multi-dimensional data aggregation and use visualization-based data discovery tools for insight generation. Request a free demo below for more insights.

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Top 3 Ways Advanced Analytics Can Transform Media and Entertainment Industry

Art and science are often seen as two different entities, especially with art exploring what gives it meaning and science focused on explaining how the world works. But today, the trend in the media and entertainment industry to link the two and get better returns by using technologies like advanced analytics, the Internet of Things (IoT), artificial intelligence (AI), and machine learning is growing. In this competitive era, companies in the media and entertainment industry are also facing the heat of increasingly competitive and uncertain markets. The growing focus on data-driven marketing and digitization has induced a revolution in the media and entertainment industry on how it uses advanced analytics. The future of the media and entertainment industry is dependent on the fusion of both digital and analytical solutions. Now, enterprises are eager to transform media platforms in order to provide more personalized content to its audiences. In this article, we have offered a detailed insight into how advanced analytics can revolutionize the media and entertainment industry and how it can be a palpable game-changer in the current circumstances and dynamics of the quickly changing world of media.

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Advanced Analytics Techniques for Insurance Industry – An Advanced Analytics Case Study

Insurance Industry Trends

As technology innovation, disruptive newcomers, and higher customer expectations redefine the marketplace, insurance industry players remain focused on improving bottom-line profitability, top-line sales, addressing challenges, and contending in this dynamic industry. Additionally, insurance industry players have a lot on their plates. Regulatory and political commotions across the globe are changing some of the ground rules about how organizations may operate. Also, a fast-tracking evolution in the way business is conducted is being driven by higher customer expectations and innovation, while troublemaking newcomers are looking to grab the market share from incumbent insurance industry players.

Advanced Analytics Techniques 

Advanced analytics represents a collection of techniques and methods used to model internal and external data to produce valuable insights that can drive business-refining actions. Advanced analytics also refers to quantitative methods such as algorithms, statistics, and stochastic processes. Advanced analytics techniques can be categorized into three major functions:

  • Predictive Analysis

The predictive analysis evaluates the hidden associations between factors and conclusions and then forecasts or estimates an unfamiliar value.

  • Descriptive Analysis

The descriptive analysis aims to comprehend a fundamental process or phenomenon. The analysis will answer queries like, “What are the typical features of customers who tend to churn?”, and, “Which products do consumers usually buy together?”.

  • Optimization and Simulation

Optimization ranks the decision options based on key performance indexes. Simulation emulates the characteristics and operations of a process and summarizes the outcome.

Continue reading to know about the scope of this advanced analytics engagement or request a free proposal

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