Big Data Analytics: Navigating the Complex Landscape with OpenLM


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What is Big Data?

Big Data is a term to describe data sets that contain huge volumes of complicated data and information. With the rise of digital technologies, businesses, governments, as well as individual users are generating large chunks of data every minute – from activities such as online purchases, generating content on social media, sending emails, adapting to IoT (Internet of Things) devices such as home-security systems and fitness-tracking smartwatches, and more.

The development of Big Data Technologies such as Hadoop Spark and NoSQL databases has enabled organizations to efficiently store, process, and analyze large volumes of data, largely by using machine learning and AI. It helps organizations to track patterns in user behavior and environmental conditions and make data-driven decisions.

Explosion of data volume in recent years: What are the major reasons?

In the last decade, Big Data has grown exponentially to cover different aspects of our lives as well as business. Here’s a look at the key aspects of this boom.

  • Massive growth of user-generated data: Includes social media content, IoT device data, online purchase activities, and more
  • Technological advancements: Include powerful and affordable computer hardware allowed for storing and processing huge datasets, software advancements like Hadoop for distributed computing, and more
  • Increased preferability of cloud computing: Cloud platforms provide cost-effective solutions for storing and processing Big Data. As a result, businesses can access vast computing power on demand, making Big Data analytics accessible to even small companies.
  • Shift to data-driven decisions: Businesses increasingly recognize the value of data-driven insights. Big Data analytics allowed them to extract valuable information from vast datasets, leading to better decision-making in areas like marketing, product development, and customer service.
  • Regulatory requirements: Regulatory requirements, such as data privacy laws (e.g., GDPR, CCPA) and industry-specific regulations, have increased the importance of collecting, managing, and protecting data effectively. Compliance with these regulations often requires organizations to implement robust data management and governance practices, driving the need for Big Data solutions.

Challenges in Big Data: What are the major hurdles to cross?

While handling Big Data insights, challenges are inevitable. And if organizations don’t act on them on time, they may lead to unfavorable results. Here’s a look at the major challenges in Big Data.

  • Poor quality of data
  • Scaling issues
  • Error in integration
  • Inefficient processing of real-time data
  • Lack of expertise
  • Security and privacy issues
  • Organizational resistance
  • Data verification
  • Extensive costs
  • Variety of wide technologies

BI to rescue: How business intelligence helps you stand out

When organizations face any of the challenges related to Big Data, a prudent way forward is leveraging a Business Intelligence (BI) solution. Traditional BI solutions, such as Microsoft Power BI tool, usually have the following features:

  • Data integration: BI Platforms excel at integrating data from different sources, whether it’s databases, cloud applications, spreadsheets, or IoT devices.
  • Data analysis and reporting: BI enables users to analyze data and generate insightful reports quickly and efficiently.
  • Performance monitoring: BI platforms often include performance monitoring features that allow organizations to track various aspects of their businesses. Usual components of performance monitoring include key performance indicators (KPIs), dashboards, reports, alerts and notifications, data integration, and more. Use cases of performance monitoring include financial performance monitoring, employee appraisal, customer service tracking, improving operational efficiency, and more.
  • Ad-hoc querying: BI tools empower users to ask ad-hoc questions about their data without using traditional querying methods (crafting configurations, writing long SQL analytics functions, and more).
  • Predictive analysis: BI increasingly incorporates predictive analysis capabilities, allowing organizations to forecast future trends and outcomes based on historical data and statistical algorithms. This will help organizations foresee things like customer demand for a product and plan accordingly.
  • Scalability and flexibility: BI platforms are designed to scale with organizations’ growing data needs, whether they’re increasing data volume, expanding user bases, or adding new data sources.
OpenLM Power BI Usage Report
OpenLM Power BI Usage Report
OpenLM Power BI License Report
OpenLM Power BI License Report
OpenLM Power BI Sessions Report
OpenLM Power BI Sessions Report

Big Data analytics: What are the four pillars of it?

The four pillars of Big Data analytics include predictive analysis, descriptive analysis, diagnostic analysis and prescriptive analysis. In the last paragraph, we have already covered predictive analysis. Here’s a look at how the other pillars work.

  • Descriptive analytics: It involves summarizing historical data to understand past events and trends. Use cases include visualizing website traffic over time, creating a dashboard to monitor inventory levels, and more.
  • Diagnostic analytics: It dives deeper into data to identify the root cause of past outcomes and anomalies. It aims to diagnose things like the reasons behind the sudden increase in customer numbers or analyze factors contributing to the decrease in product sales.
  • Prescriptive analytics: Prescriptive analytics goes beyond predicting future outcomes by recommending actions to optimize decisions and outcomes. Use cases include recommending personalized marketing strategies for individual customers, optimizing supply chain logistics to minimize cost, and more.

Future of Big Data and BI: What’s in store?

The combination of Big Data and BI offers a future that will be ruled by the following factors.

  • Data governance: Today, organizations increasingly rely on Big Data for decision-making, ensuring data quality and integrity. Factors such as security and privacy have also become paramount. Under such circumstances, data governance frameworks will continue to evolve to establish policies, processes, and controls for managing data assets effectively.
  • Artificial intelligence: AI will become increasingly integrated into BI and Big Data analytics solutions, enhancing their capabilities for Big Data processing and decision-making.
  • Data automation: It will streamline and accelerate various aspects of data management and analytics, from data ingestion and pre-processing to analysis of the reporting.
  • Data-driven culture: Cultivating a data-driven culture will be essential for organizations to harness the full potential of Big Data and BI. This involves fostering a mindset where data is viewed as a strategic asset and decisions are based on empirical evidence rather than intuition or gut feeling.

OpenLM and Big Data: Ready for tomorrow!

We are launching OpenLM NewGen in 2024. It will be a future-ready SLM solution with a tech stack optimized for Big Data, leveraging Apache, Spark, and Kafka for real-time processing and seamless tool integration. OpenLM NewGen ensures high-speed analytics and actionable insights. Our scalable architecture and robust data governance make OpenLM NewGen a strategic asset for enterprises navigating the complexities of the digital age.

Are you wondering what else is in OpenLM NewGen 2024? Get in touch for details regarding our new Agent, microservices, real-time reporting, and more.

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