What this blog covers:
- Data analytics as a business imperative
- Evolution and types of data analytics
- Significance of data analytics platform
- Benefits and features of data analytics platform
- Why Kyvos, a high-speed data analytics platform is a strategic choice for businesses seeking scalability, robust data governance, and real-time insights for sustainable growth.
In today’s data-driven world, where data is the soul of every business worldwide, making the right business decisions is the only path to success. Every piece of information or transaction in our digital world creates a trail of raw data. What’s the use of collecting an immense amount of this raw data if it can’t be used to improve performance metrics across various departments, predict future outcomes and anomalies before they occur and optimize costs?
Modern businesses have recognized the impact of harnessing this data to optimize processes that can drive success. As a result, the domain of data analytics is booming across different industries.
What is Data Analytics?
Data analytics is the process of uncovering massive datasets, extracting valuable trends and patterns from raw data and transforming them to gain insights. Being a vast concept, it involves various activities like creating data models based on statistical analysis, data mining, machine learning and predictive modeling that help businesses predict future sales, shape customer experiences, find the root causes of problems and do much more.
Evolution and Types of Data Analytics
The evolution of data analytics from traditional methodologies to the current era of AI-powered insights has undergone a remarkable journey. In the early days, analytics was primarily confined to on-premises systems, spreadsheets and manual processes, requiring significant time and resources for data processing and analysis. Apart from these processes, businesses used to rely on their gut instincts to make critical business decisions.
Now, the waves are moving another way. With the advent of technology, analytics have shifted from spreadsheets and gut instincts to advanced tools that make data accessible and accelerate decision-making to unleash real-time opportunities for the organization. However, do organizations really know how to use their data to draw valuable insights from it?
The key to unlocking the true potential of data lies in understanding diverse data analysis techniques and leveraging them effectively.
Types of Data Analytics
There are four types of data analytics techniques that include:
Descriptive analytics facilitates business users with insights linked to what happened in the past. It focuses on outlining and analyzing historical data to gain an understanding of patterns and KPIs that must have led to success or failure in the past. The technique is generally used for management-related reporting, such as sales reports, data summarization and visualization. Descriptive analysis answers questions like:
- Which paid ads generated the most leads?
- Which product category has generated high revenue in the last six months?
Diagnostic analytics empowers businesses to comprehend why something happened in the past. It aims to understand the root causes behind any event by diving deep into historical data and drilling down to the granular details to find any dependency and correlation to uncover relationships within data. Diagnostic analytics answers questions like:
- Why did sales drop by 20% in any specific region last quarter?
- What is the reason behind the increased customer churn rate in the previous month?
Predictive analytics enables businesses to predict what’s likely to happen using historical data and statistical algorithms and forecast future outcomes based on the patterns found. It determines the possible outcome due to an event using regression analysis, machine learning and data mining techniques. Predictive analytics answers questions like –
- What will be the approximate sales figures for the next three months?
- Which marketing strategy will work to improve the conversion rate?
The insights gained from prescriptive analytics are based on the results gathered by predictive analytics. It provides information about when and why an event will occur and offers potential actions to control those outcomes based on the forecast. It goes beyond foreseeing what will happen next. The insights are based on mathematical science, business rules and machine learning.
Prescriptive analytics answers questions like –
- What could be done to improve the customer retention rate?
- How do we optimize operational processes cost-effectively without impacting product quality?
At its core, data analytics is a guiding light for organizations across industries, enabling them to understand the potential of their data. However, with the data buried across the complex landscape of different departments, multiple data sources and databases, the path of transforming data into insights is filled with complexities.
Enter the era of comprehensive data analytics platforms.
What is a Data Analytics Platform?
Data analytics platforms are a set of indigenous tools built to offer various functionalities to enable the handling, manipulation and mining of actionable intelligence from massive datasets. These platforms can enable businesses to transform their data into actionable insights, essentially converting data into gold. They open up many possibilities to extract value from diverse data resources for sustainability and development.
What are the Benefits of a Data Analytics Platform?
The capabilities of a data analytics platform extend far beyond uncovering hidden trends and patterns. Let’s see how businesses can benefit from a data platform:
- Informed decision-making: Data analytics platforms can help businesses derive accurate insights from vast datasets without any latency.
- Enhanced efficiency: Users can automate complex tasks and processes such as data processing, analysis and visualization, saving time and resources. The platforms also help identify glitches in products, optimize operations, reduce computing costs and improve productivity for optimized operational models.
- Competitive advantage: By using analytics platforms, organizations can identify market trends, customer needs and opportunities to understand their customers and market better.
- Personalized customer experiences: Data analytics platforms can help businesses analyze customer behavior and their purchasing patterns. These insights can help create personalized experiences for loyal customers, improving customer retention.
- Risk mitigation: Data analytics platforms assist in identifying and mitigating risks by analyzing historical data patterns, predicting potential risks and enabling proactive risk management strategies.
- Revenue growth: Businesses can harness massive volumes of data using analytics platforms and gain insights that can help them optimize business strategies and drive revenue growth.
What Is a Cloud-Based Data Analytics Platform
A cloud-based data analytics platform is a solution that operates on cloud infrastructure, providing tools and services for collecting, processing, analyzing and visualizing data. These platforms leverage cloud computing resources and technologies to offer scalable, flexible and cost-effective solutions for handling data analytics tasks.
Finding the right data analytics platform lays the foundation for meaningful insights and effective decision-making. Businesses should precisely be aware of what competencies they are looking for. Let’s look into some of the key features that can help choose the right one:
- Multidimensional and high-speed analytics: A cloud-based data analytics platform enables self-serve multidimensional analytics by building data models on top of the cloud platform. It processes calculations on massive data, delivering instant responses to all queries, standard or ad-hoc.
- Visualization: The data analytics platform paints a vivid picture of whatâ€™s going on in the organization. It aids businesses in creating customized charts, graphs and dashboards. These visual portrayals are windows for valuable data communication and meaningful insights and help users acknowledge their data and make intuitive decisions. A cloud-based data analytics platform enables users to create highly responsive dashboards and reports with built-in enterprise-wide collaboration. Users can instantly drill up and down on cloud-scale data using visual filters and intuitive workflows.
- Scalability: A data analytics platform should have the capability to scale with ease to incorporate the continuously swelling data volumes without affecting performance. Today, cloud resources donâ€™t come cheap. If not planned properly, it can cost more than expected. The cloud-based data analytics platform enables scheduled scaling, making it possible to scale resources at specific preset times or intervals. It allows pre-provisioning of adequate capacity for anticipated demand. Users can also configure schedules to reduce capacity when the demand decreases.
- Data security and governance: Data analytics platforms should be armed with three key capabilitiesâ€“ integrity, security and governance to protect digital data from unauthorized access. They should be equipped to offer role-based access control with row and column-level security at the group and user-level to ensure that only authorized users have access to the specific data. Along with multi-level security, a data analytics platform must safeguard data accuracy and compliance with regulatory standards, thus upholding data assets.
- AI/ML powered insights: There are many advanced platforms that combine AI/ML capabilities with analytics. They use advanced algorithms to analyze complex data sets, associate correlations and facilitate augmented analytics with predictive modeling. The platforms offer deeper insights by learning from historical data patterns, detecting trends and making predictions without explicit programming.
- Cost-Effective analytics: Cloud-based analytics platforms offer a pay-as-you-go model where organizations only pay for the resources they use. It is more cost-effective than investing in and maintaining on-premise infrastructure because these platforms process all the queries in advance and feed them into data models to reduce runtime compute costs. Additionally, these platforms reduce resource consumption automatically during lean periods to save costs.
- Mobile analytics: Some cloud-based data analytics platforms provide dedicated apps that enable stakeholders, managers, analysts and business executives to easily access key metrics and analytical reports on the go. Stakeholders can use mobile analytics to stay informed with day-to-day performance metrics and latest trends critical for making informed decisions. Mobile analytics facilitates users to set up custom alerts based on pre-defined changes in metrics, ensuring awareness of critical issues or development.
These are some of the key features to look for while choosing a data analytics platform.
Architecture of Data Analytics Platform
Data analytics platform architecture includes essential layers that act as a foundation for robust and effective end-to-end processing.
- Data integration and processing: Data integration is concerned with collecting and importing data from various sources into the analytics platform, staging a unified view for businesses. The platform then transforms raw data into an operational format by cleaning, filtering, aggregating and organizing it to make it more accessible for enterprise-wide users.
- Catalog and storage layer: This layer is responsible for cataloging and storage, characteristically administering metadata management, data organization and numerous storage-related functions, safeguarding a streamlined approach to well-organized data. Additionally, it stores processed data and oversees critical tasks such as partitioning, replication, backup and recovery. The specific technology utilized within this layer varies depending on the platform’s architecture.
- The BI and analytics layer: Using this layer, users can craft comprehensible data visualization to represent information in the form of charts, graphs, heatmaps and much more. Some data analytics platforms integrate AI/ML within this layer to provide advanced analytics with predictive modeling capabilities.
- Semantic layer: Not every data analytics platform provides a semantic layer within the architecture. A semantic layer is a layer of abstraction that sits between enterprise data and business users and acts as a centralized repository for all the metric definitions and calculations. The layer hides the complexity of underlying data and maintains a single source of truth to deliver a consistent view of the dimensions, measures, and hierarchies.
- Data protection and compliance layer: Security and governance are vital in business operations due to the necessity of adhering to industry regulations governing data usage. The layer encompasses data encryption, access controls, user authentication, and authorization mechanisms. It also prevents data from unauthorized access and data breaches to mitigate losses and enforces rules for data quality management, lineage tracking, policy implementation, and metadata management to uphold robust data governance practices. These elements ensure data integrity, reliability, and adherence to established data policies.
Traditional Data Analytics Platform vs. Cloud Based Data Analytics Platform
|Often rely on on-premise hardware, requiring organizations to invest in servers, storage and networking equipment.
|Utilize cloud infrastructure provided by a third party, eliminating the need for on-premise hardware.
|Organizations need to bear the cost of hardware, software licenses, maintenance and scaling resources.
|Operating on a pay-as-you-go model, it offers flexibility in resource utilization and pricing.
|Requires physical expansion, which can be time-consuming and may not be able to handle sudden spikes in data volumes.
|Offers seamless scalability enabling organizations to scale resources up and down as per need.
|Limited remote access and collaboration.
|Offers remote accessibility and facilitates collaboration.
|Security is managed by the organizationâ€™s IT team, which entails setting up and maintaining security protocols and systems.
|Enables organizations to implement robust security measures, including encryption, compliance certifications and regular security updates for data protection.
Use Cases of Data Analytics Platform
Data analytics platforms have become indispensable for businesses. They empower businesses across domains to make informed decisions. Here are the use cases that exemplify the pivotal role of data analytics platform.
- Business Intelligence (BI): The data analytics platforms provide the necessary infrastructure tools and capabilities to process, analyze and visualize raw data effectively and convert it into operational data. It helps in analyzing operational data for creating intuitive dashboards, reports and visualizations to monitor KPIs, sales trends and market insights. These platforms offer easy-to-scale architecture that efficiently manages increasing workloads while ensuring high concurrency, supercharged performance and cost-effective analytics.
- Customer analytics: Get a unified view of a customerâ€™s journey and unlock hidden insights to understand customer behavior, preferences and sentiments. This helps businesses to personalize marketing strategies, enhance customer experiences and foster brand loyalty. Additionally, it can help in consolidating data influx from diverse digital channels to capture multiple facets of their experience and relevant offers to the right customers at the right time to improve upsell and cross-sell.
- Predictive maintenance: Data analytics platforms use historical and real time data from machinery, sensors and other sources and monitor their temperature, vibration, fluid levels and other relevant aspects to analyze data patterns, detect anomalies and predict equipment failures. Using these platforms for predictive analytics can help in optimizing maintenance schedules and minimizing downtime in manufacturing and asset-heavy industries.
- Healthcare analytics: Healthcare industry can leverage these platforms to analyze patient records, diagnostic reports, lab results, medications and treatment histories that can help in treating patients more effectively. These platforms can also be used for clinical research analytics to enhance the patient admission process and retention rates, expediting research timelines. These platforms can improve healthcare delivery, optimize resource allocation and identify patterns for disease prevention.
- Financial analysis: A data analytics platform can make financial planning and analytics more agile, accurate, scalable and cost-effective. It can help in modeling operations using advanced visualizations and self-service analytical capabilities to perform ad hoc analysis to forecast future scenarios, conduct risk assessment, detect frauds and manage portfolios for making informed investment decisions and minimizing financial risks. The platform can provide granular insights, easy-to-understand dashboards and faster processing.
- Supply chain optimization: Data analytics platforms can help in building customer-focused supply chains, reducing costs and driving strategic decisions. The insights can help in predicting product demand in a particular region or store to improve sales and manage inventory. By analyzing historical data, users can gain insights to reduce days of supply, solve inventory related issues and improve planning. Supply chain data analytics can also help streamline logistics, forecast demand and enhance supplier relationships.
Know Kyvos as a Data Analytics Platform
Kyvos is a high-speed data analytics platform designed to handle massive datasets with ease. It empowers business users to analyze billions of rows of data instantly. The platform’s universal semantic layer empowers every user in the enterprise to access data, fostering self-service analytics. Using its AI-powered modern technology, it streamlines advanced analytics, significantly cutting down the time and expenses required to derive insights. Additionally, Kyvos seamlessly integrates with leading analytics tools such as Tableau, MicroStrategy, Excel, Looker, Power BI, SSRS, and data science tools like R and Python, becoming a pivotal asset for organizations seeking scalability, robust data governance and a unified view of enterprise-wide data.
Kyvos: Achieve High-Speed Analytics at Any Scale of Data
Kyvos leverages its AI-powered smart aggregation technology and distributed architecture to ensure exceptional scalability and lightning-fast query responses even when dealing with colossal datasets. Here are the four pillars of Kyvos-
- Analytical data warehouse: Kyvosâ€™ AI/ML-driven analytical data warehouse is a modern, unified and cloud-native storage system to swiftly extract actionable insights from enterprise-wide data, instantly and efficiently. It is a centralized repository for data analytics that reduces querying costs and ensures high performance at any scale using any analytics tool of choice. Analyze aggregated and raw data with zero latency and gain instant insights with granular details without waiting for data to refresh. Empower users of all coding skills, from data scientists to analysts, with intuitive analytics workflows with Kyvos’ well-integrated interface.
- Universal semantic layer: Simplify analytics by hiding data complexity and standardizing business logic across the enterprise to get consistent answers from any data source on any analytics tool. Kyvosâ€™ universal semantic layer enables accelerated consumption of unified data to ensure a standardized and trusted view. The layer helps create a data-driven culture within an organization by allowing democratized, secure and self-service access to data. It simplifies both – the metadata existing within the data layers and business logic-related concepts outside this layer.
- Data mesh: Kyvos combines accessibility, performance and robust governance while enabling self-service analytics for all users. With its ability to create data models, it can combine domain-level data products into a universal semantic layer, ensuring a single representation of enterprise-wide data and delivering consistently high-speed querying across a wide data space.
- BI & reporting: Kyvosâ€™ built-in data science and machine learning capabilities allow business users to analyze historical and real-time streaming data with customizable and interactive 360Â° dashboards without any time lag to make informed decisions. The platform enables modern, data-driven enterprises to go beyond the typical BI and experience a digital transformation like never before. With Kyvos, users can optimize business processes, enhance customer experience and discover new opportunities every day.
Businesses should opt for Kyvos for its cutting-edge analytics capabilities, exceptional scalability and high-performance analytics. They can effortlessly handle massive volumes of data, instantaneously derive actionable insights and simplify complex analytics processes. The ability to provide real-time insights and streamline analytics operations makes Kyvos a strategic choice for businesses aiming to harness the full potential of their data for informed decision-making and sustainable growth.
ML provides strong capabilities in pattern recognition, anomaly detection and sentiment extraction. It also helps with tasks like text summarization on data analytics platforms. It automates repetitive tasks within the platform, and speeds up data processing, cleansing and engineering.
Data analytics platforms are indeed very different from one another. You can see these differences in their features, functions, target users, deployment models (on-premises vs. SaaS), scalability performance and integrations and how much they charge or whether they have fixed pricing at all, as well as the scope of use cases which each serves best.
Indeed, a strong data analytics platform should be able to combine with different sources. Users can then collect and analyze data from disparate origins in one place.
Yes, modern data analytics platforms can handle high volumes of data. These platforms have strong architecture and elastic infrastructure to process large data sets.
To meet business needs, data analytics platforms typically provide some customization. The degree of customization may vary, but virtually all modern platforms provide a certain amount of flexibility to suit unique needs.