Businesses today rely majorly on data, irrespective of the industry they serve. Data is the most crucial decision-making factor for any industry, say finance, healthcare, or operating multifaceted omnichannel retail operations, unveiling new opportunities and understanding the inefficiencies that stop your businesses from delivering profitable data engineering services.
Is data alone enough to drive businesses toward success? Building a technology stack that connects with data sources is essential to achieve business goals and derive valuable insights for business growth. Data analytics tools help businesses achieve such insights, and here in this blog, we shall discuss the available tools, how to choose the right data analytics tool, and the best practices for data analysis.
Types of Data Analytic Tools and Key Features
The realm of tools used for Big Data analytics encompasses a wide array, though they generally fall into a few key categories:
In general, the classification of data analytics domain tools includes a few categories, and here is the list:
Business Intelligence (BI) Tools:
Business intelligence tools in this modern era facilitate comprehensive data analysis and encompass three popular categories; online analytical processing (OLAP), information delivery, and business intelligence integration, which follow sequential patterns to provide final analytical reports. OLAP helps analyze complex data sets and draw quality insights, and information delivery translates the insights into visualizations and reports, which further needs Bi integrations. This bi-integration addresses metadata management to provide a development environment. BI tools help organizations embrace data-driven procedures like data mining, predictive modeling, and natural language processing for better business outcomes.
Customer Analytic Tools:
Customer analytics tools help in managing the analytics process, starting from the preparation to the insight generation. These platforms with pre-built data models assist data analyst practices in forecasting, statistical analysis, and understanding customer engagement patterns. Customer analytics tools are more advanced than marketing and sales analytics tools. Pre-built models help non-technical users to work with the tools, and you need advanced data science skills to customize the models.
Customer Data Platforms (CDPs)
A customer data platform (CDP) is similar to a customer relationship management (CRM) platform that captures customer information to improve business processes, services, and product sales. CRM will accumulate data from intentional interactions, but the CDPs provide additional options to collect information from anonymous website visitors to manage diverse data from multiple sources. The code activities of CDPs include providing a comprehensive customer view to the business stakeholders by connecting multiple data sources and unifying customer data across global systems.
Digital Experience Platforms (DXPs):
Digital experience platforms provide options to optimize the customer experience with the application across multiple touchpoints. DXPs help streamline processes, provide personalized recommended content to users, and facilitate many branding opportunities to improve digital presence. These platforms focus on personalizing and coordinating the content across available digital channels using the Internet of Things, digital assistants, and Augmented reality(AR) / virtual reality (VR) experiences.
Points to consider while choosing the right Data Analytics Platform
An expert survey of using Business intelligence reveals that the leading companies have been tailoring their solutions and investing in specific tools that align with business objectives to satisfy desired capabilities. Many organizations will need clarification on whether to buy or make the necessary tools. Still, it depends on each layer of the technology stack business need and the data processing, AI, machine learning, storage, integrations, and predictive models you need for your business process. Data analytics tools have contributed significantly to a wide range of use cases, applications, and industries with complex datasets, and choosing the right tool can be challenging.
Maintenance and Support
Saas products by third-party providers will prioritize customer retention and provide hands-on support, reducing the burden on the organization and saving time and money spent on customer retention post-sales. At the same time, self-hosted applications need more resources to work for the same amount of features they provide to the users.
Scalability and Customization
SaaS and self-hosting applications will cater to different needs, of which scalability and customization are crucial for most businesses. The cost of resources like skilled professionals, licensing, and data storage solutions will be higher when self-hosted than that of Saas applications by vendors. However, it works the other way for large and long-run applications and services.
Data reporting
Drawing insights will contribute valuable inputs for the business's success. Preparing reports and presenting insights to maximize the value of the information available is crucial. These report generation should be accessible and provide simple user-understandable UI, which allows even non-technical users to work without confusion.
Data Volumes and integrations
When working with vast amounts of data, businesses need more sophisticated tools to analyze the data and seek support from ai and machine learning to improve IT capabilities as the best data analytics practices. This big data processing involves data integration platforms to tear down data from various data points, like IoT endpoints, applications, and types, into silos. Saas application offerings are convenient in this scenario, but the self-hosting provides customizing options.
Selecting the right data analytics tools and platforms takes a few crucial considerations, like understanding the business objectives, aligning the goals with the ongoing trends, target audiences like tool users and executives, and core expectations from the data. The best data engineering services evaluate various criteria and key features to build a tailored analytics tool stack that propels organizations towards data-driven success.
