Cloud Analytics
Cloud Analytics:
Exploration and analysis of data generated by a company’s online activities such as website traffic, social media usage, and online sales. Cloud analytics allows businesses to track customer behavior and better understand how to engage with target markets. Data analysis processes are stored in the cloud. Cloud analytics can be used to track and predict customer behavior, trends, market changes and more.
However, cloud analytics is generally a more efficient alternative to on-premises analytics, which requires organizations to purchase, house, and maintain expensive data centers. On-premises analytics solutions allow organizations to manage privacy and security in-house, but are difficult and expensive to scale. Cloud analytics, on the other hand, benefits from the scalability, service model, and cost savings of cloud computing.
Companies generate large amount of data in their daily operations. Today, most of this data comes from websites, social media, IT devices, financial software, and more. We are in the cloud. Cloud analytics tools and software are particularly efficient at processing these large amounts of data, generating insights in easily digestible formats, and creating insights from data available in the cloud on demand. Cloud analytics tools and software are particularly efficient at processing these massive datasets, delivering on-demand insights in an easily digestible format for a better and streamlined user experience.
Cloud Analytics Overview
Relation between Cloud Computing and Cloud Analytics
Cloud analytics is the process of analyzing data stored in the cloud. Cloud computing provides computing services such as servers, storage, databases, networks, software, analytics, and intelligence over the Internet (the “cloud”). This enables faster innovation, flexibility, and more, unlike cloud computing.
Resources, economies of scale. A type of computing in which information is stored on remote servers and accessed over the Internet. This type of data processing allows companies to save hardware and IT costs and grow their business in a scalable and flexible way.
To see how cloud analytics works, we need to start with the cloud computing model. Cloud computing is the provision of computer services over the Internet. The “cloud” is a metaphor for the many computer clusters that make up the Internet infrastructure. The cloud computing model allows companies to rent the IT infrastructure and services they need from cloud service providers when they need them, rather than buying and operating their own data centers. These services include everything from critical infrastructure such as networks, servers, storage, databases and software to advanced tools such as artificial intelligence (AI) and machine learning systems.
Cloud computing enables businesses to reduce costs and increase productivity by reducing the need to purchase and maintain their own devices. And you can easily scale your resources to meet your changing business needs. And because these services are decentralized, they can be accessed from any web-enabled device.
Cloud analytics Work Flow
1. Data Sources:
These are various sources of business data. Common examples include web usage and social media data, and data from CRM and ERP systems.
2. Data Model:
A data model structure takes data and standardizes how data points relate to each other for analysis. Models can be simple (such as using data from a single column of a table) or complex with multiple triggers and parameters across multiple dimensions.
3. Application processing:
Cloud analytics uses specialized applications to process the large amounts of information stored in data warehouses and speed time to insight.
4. Computing power:
Cloud analytics requires sufficient computing power to ingest, cleanse, structure, and analyze large amounts of data.
5. Analytical Models:
These are mathematical models that can be used to analyze complex data sets and predict outcomes.
6. Data sharing and storage:
Cloud analytics solutions provide a data warehouse as a service so your business can scale quickly and easily.
Cloud Analytics Architecture
Infrastructure Layer
This layer is responsible for storing and managing data. This layer consists of structured and unstructured data collected from various data sources. Machine / sensor data, social data, video streaming data, and data from third-party providers are available on a pay-as-you-go basis for easy integration with company data. All infrastructure management activities such as dynamic provisioning, monitoring, and automatic provisioning are handled by this layer. Peak demand and rapid database growth are handled by the horizontal and vertical scalability of the cloud infrastructure. It is also the foundation of a cloud analytics architecture that enables enterprises to take advantage of fast, low-risk infrastructure deployments.
Data Management Layer
This is the data storage repository layer, also known as the “data lake”. Large amounts of raw data in structured, unstructured, and semi-structured formats are stored as flat files for analytical purposes. Schema and data format requirements are not defined until the query is triggered. Each data element in this layer is provided with a unique identifier and is identified by a metadata tag. This layer is managed by Hadoop-oriented object storage to which queries are applied to retrieve information. Unlike Enterprise Data Warehouse (EDW), it uses schema- on-read processing, is agile and reusable, and has low storage costs. Node failure and data movement between nodes is a routine process of data management using cloud setup. This is done to maintain the fault-tolerant capabilities of cloud analytics by using automatic data replication between the nodes in this tier of cloud clusters.
Analytics Layer
This is the core layer of cloud analysis because it contains business intelligence applications. It consists of a pre-analysis or filter tool and an analysis tool. Pre- analysis tools are used to cleanse, select, and organize the data retrieved from the data management layer, and analysis tools are used to identify patterns and extract actionable insights from the data. This layer uses a variety of data and text mining algorithms such as clustering algorithms, association rules, general pattern matching algorithms, and genetic algorithms to capture information. Supervised machine learning techniques such as classification and regression are used in existing datasets to perform predictive analytics. The various analytical tools and software used at this layer include R, SAS, Mathematica, Matlab, MapReduce, Pig, Hive, and ETL tools.
Visualization Layer
This layer provides a user interface for accepting user inquiries and displaying analysis results. Quick and effective response is the most important and desirable feature of this layer for quick and deeper insights. This layer helps users customize their data analysis visualizations. This layer allows subject matter experts (SMEs) to investigate data without the assistance of IT professionals. It also provides the flexibility to investigate business queries using various parameters and aspects that help you make accurate decisions. Eliminating support for extracting insights improves the quality of information retrieval by allowing SMEs to work directly with the data without intervention, thereby eliminating requirement specification degradation.
How do you choose the Best Cloud Analytics Platform?
There is no easy answer when it comes to choosing the best cloud analytics platform. However, some factors to consider include the specific needs of your business, the amount of data you have, and your budget. Additionally, it is important to consider the features and capabilities of each platform to ensure that it can meet your specific needs. Not all cloud analytics platforms are created equal, so taking the time to understand your organization’s unique needs before making a purchasing decision pays off in the long run.
What are the Benefits of Cloud Analytics?
Data Integration
Big data, generated from many disparate sources across the enterprise, makes it nearly impossible to get a unified view. Cloud analytics brings together all your enterprise data sources for a more complete picture. All stakeholders, regardless of their physical location (or data location), can easily access this data in one place to gain deeper insights and better real-time business decisions can be made.
Sharing and Collaborating
The large amount of data stored in individual departments such as Finance and Human Resources affects the entire organization. Cloud analytics solutions can better integrate data from different parts of an organization according to configurable role-based access controls, resulting in improved communication and decision-making.
Scalability
As workloads and data volumes grow rapidly, administrators running on-premises platforms must purchase and install new hardware to meet the increased demand in the future. Cloud analytics services allow businesses to respond to spikes in demand by bringing more instances online (or reducing instances when demand drops) and pay only for what they use.
Cost Savings
In addition to the cost of various hardware requirements, on-premises platforms require frequent upgrades and migrations, inevitably causing system downtime that impacts business continuity. On-premise analytics also requires specialized skills that some companies don’t have or can afford in-house. With cloud analytics, organizations no longer need to purchase and support additional hardware and can leverage the in-house expertise of service providers.
Security
Security Monitoring is typically just one of many areas of responsibility for an organization’s IT staff, but it is a full-time focus for cloud hosts. Cloud analytics providers also use robust encryption to protect data in transit over their networks
Examples of Cloud Analytics
You can perform different types of cloud analytics depending on your organization’s specific needs.
Some common examples are: -
1. Analyze customer behavior data to improve customer retention
2. Analyze social media data to understand customer sentiment
3. Analyze web traffic data to identify trends and improve web Optimize your site design
4. Analyze financial data to predict market trends and optimize investment strategies
5. Analyze sales data to identify opportunities and optimize marketing campaigns
6. Text analysis or image recognition.
Future of Cloud Analytics
Cloud analytics is still in its infancy and has potential for growth. The cloud computing market is expected to grow at a compound annual growth rate (CAGR) of 18.5%, from $67.84 billion in 2015 to $162.74 billion in 2020.
As the cloud computing market grows, so will the demand for cloud analytics. Cloud analytics providers are constantly innovating and introducing new features to their products. In the future, we expect cloud analytics to become more user-friendly with an easy-to-use interface and more advanced features.
Conclusion
Company generates large amounts of data every day. Cloud analytics offers a compelling way to integrate and transform this data into actionable information while reducing acquisition and maintenance costs. It’s important to determine your business needs upfront so that you can get the most out of your chosen cloud analytics platform. Get the insights you need to gain a competitive advantage and grow your business.
Cloud Analytics makes them available. It is used to collect, analyze and report data from various sources such as social media, sensors, web applications and mobile devices. Cloud analytics can be used to track and analyze customer behavior, marketing campaigns, website traffic, and other data. Cloud analytics is a relatively new field and there are many vendors offering cloud-based analytics solutions.
Thank you for Reading!
Blog by Abhishek Kumbhar, Pranav Biyani, Samruddhi Deshmukh, Shubhayu Gaikwad.
