Big data analytics services

To make the most of their data sets (that grow in volume, complexity, and velocity), businesses need to focus on two key things: making their big data systems bigger and smarter. That’s where scaling analytics and bringing in artificial intelligence (AI) and machine learning (ML) come into play. Many companies embrace big data services and turn to big data analytics service providers to navigate these challenges using their expertise and specialized tools.

Scaling analytics capabilities: Growing your big data muscle

Scalability is about making sure your systems can handle more data and more users as your business grows. It’s like designing an office space that can easily accommodate additional workstations and employees, rather than being locked into a fixed layout that becomes overcrowded as the company expands.

Scalability is crucial for businesses of all sizes, but especially for those experiencing rapid growth or dealing with large amounts of data. Think e-commerce platforms handling millions of transactions, social media networks processing billions of posts, or healthcare systems managing patient data across multiple hospitals.

Here’s how big data analytics service providers help enterprises tackle the scalability challenge:

1. Embracing the cloud: Using cloud platforms like AWS, Azure, and Google Cloud that act as giant, flexible data centers and grow with your business needs. With cloud platforms, you can easily increase or decrease computing power, storage, and other resources on demand without investing in physical hardware. For example, during peak periods, you can quickly scale up processing power, then scale it down when demand lessens.
2. Implementing scalable NoSQL databases: Guiding companies in choosing and setting up databases like MongoDB or Cassandra that are designed to handle large amounts of diverse data across many devices.
3. Deploying smart data processing tools: Implementing technologies like Apache Spark and Apache Flink. These frameworks are key for organizations dealing with data at scale, allowing for both batch and real-time data processing.
4. Architecting flexible systems: Designing microservices and event-driven architectures, creating interconnected services that can scale independently as needed.

For example, Yalantis helped a large US shipping company set up a scalable system to convert raw data from multiple sources (customer logs, supply chain systems, RFID tags, and IoT devices) into valuable information to support decision-making. We used:

• Amazon S3 to provide unlimited and low-cost scalability for the client’s serverless data lake
• Amazon QuickSight to provide a scalable and serverless business intelligence (BI) service for data visualization

Integrating AI and machine learning: Making your data smarter

Once a robust big data system is in place to handle vast amounts of information and grow easily, the next step could be using AI and machine learning to extract meaningful insights. Big data analytics service providers play a crucial role in this integration. Let’s explore how, using Yalantis’ work with a major US logistics company as an example. Our client struggled with effectively analyzing large amounts of unstructured data.

For this client, Yalantis integrated several key big data analytics components using Amazon Web Services (AWS):

• A data lake for centralized data storage (AWS Lake Formation)
• Real-time data extraction for up-to-the-minute information (AWS Data Exchange, Kinesis Data Firehose)
• Data visualization for an intuitive understanding of supply chain operations, customer behavior, and operational efficiency (AWS QuickSight)

The cherry on top was embedding functionality to forecast business trends. We used Amazon SageMaker for machine learning and predictive analytics on large-scale data.

Now, let’s see some real-life examples of how businesses put AI and ML to work:

• Automated insight generation: Machine learning models can quickly identify patterns and anomalies and analyze data in complex datasets. For example, Netflix uses ML algorithms to analyze viewing history and provide personalized content recommendations, increasing customer retention and generating $1 billion in value annually.
• Predictive analytics: By combining historical data with ML models, businesses can forecast trends and make data-driven decisions. Scotiabank, a Canadian digital bank, uses big data analytics to generate customer offers in hours instead of weeks, allowing them to respond quickly to changing customer needs.
• Real-time decision-making: The combination of streaming data processing and machine learning enables instant analytics and decision-making. This is crucial in areas like fraud detection, recommendation systems, and IoT applications. For instance, Command Alkon, a construction logistics company, implemented a real-time data analytics platform to manage material deliveries efficiently, providing users with critical insights in seconds.
• Natural language processing (NLP): NLP techniques analyze unstructured text data from sources like customer feedback and social media, opening up new avenues for insights.
• AutoML and model management: As ML use grows, tools for automating model development and managing models in production are becoming essential for scaling AI-driven analytics across organizations.

Note, however, that successful AI/ML integration requires not just technical implementation but also organizational changes to foster a data-driven culture and ensure ethical use of AI.

Challenges in scaling big data analytics and integrating AI/ML

While scaling analytics capabilities and integrating AI/ML offer great benefits, they come with their own set of challenges:

• Data quality and governance: As analytics capabilities scale, ensuring data quality and maintaining robust governance becomes complex but also increasingly critical. Poor data quality can lead to inaccurate business insights and decisions, while weak data governance can result in data breaches or non-compliance with regulations.
• Skills gaps: There’s a growing demand for professionals who understand both domain-specific analytics and the latest innovations in AI/ML technologies. This shortage can slow down implementation of advanced analytics solutions and limit an organization’s ability to fully use its data.
• Ethical considerations: As AI becomes more prevalent in analytics, organizations must address issues of bias, fairness, and transparency in their models and decisions. Failing to do so can lead to discriminatory outcomes, reputational damage, and loss of customer trust.
• Integration with existing systems: Many organizations struggle with integrating big data technologies and new, scalable analytics capabilities with legacy systems and processes. This can result in data silos, inefficiencies, and difficulty achieving a unified view of the business.

These challenges can be daunting, especially for organizations that lack in-house expertise in big data analytics and AI/ML. This is where the concept of big data analytics as a service comes into play, offering a solution that addresses many of these hurdles.

What is big data analytics as a service?

Big data analytics as a service (BDAaaS) is a cloud-based service model that provides end-to-end big data analytics solutions. This includes collecting and processing large datasets, performing advanced analytics, and generating actionable insights from both structured and unstructured data.

BDAaaS offers businesses access to sophisticated analytics tools, scalable computing infrastructure, and data science expertise without the complexities and costs of building and maintaining on-premises analytics systems.

Yalantis has experience working with leading BDAaaS providers powered by Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) to deliver comprehensive big data analytics solutions.

For example, in a recent project, we used AWS BDAaaS offerings to help a retail company analyze vast amounts of data from various sources, including point-of-sale systems, online transactions, and customer behavior data. Our data scientists and analysts used the following AWS services:

• Amazon EMR for large-scale data processing
• Amazon SageMaker for machine learning and predictive modeling
• Amazon QuickSight for data visualization and business intelligence

As a result, the client received a custom big data analytics platform that provided deep insights into customer behavior and sales trends.

Here’s a breakdown of services and benefits we offer when partnering with BDAaaS providers for big data analytics projects:

• Comprehensive analytics solutions: We use BDAaaS providers to deliver end-to-end analytics services, from data ingestion and processing to advanced analytics and visualization.
• Scalability and performance: BDAaaS solutions can handle massive datasets and complex analytics tasks, scaling seamlessly as your data and analytics needs grow.
• Cost-effectiveness: By using cloud-based services, BDAaaS eliminates the need for large upfront investments in analytics infrastructure and software.
• Advanced analytics capabilities: We integrate cutting-edge machine learning solutions and AI technologies provided by BDAaaS platforms to extract deeper insights from your data.
• Expertise and support: Our data scientists and analysts work with you to implement the most relevant BDAaaS tools and techniques, ensuring you maximize the value of your big data analytics initiatives.