28/01/2020

How to take advantage of trends in big data?

As businesses increasingly seek to leverage big data, understanding the emerging trends and selecting the right tools and strategies are key to staying competitive. Below are insights into the future developments in big data and analytics and factors to consider when choosing a solution that best fits your needs.

Key Developments in Big Data and Analytics

Robotic Process Automation (RPA) and AI Integration:
1. RPA and AI (RPA 2.0): RPA mimics human behavior to automate repetitive tasks, increasing efficiency and consistency. The integration of AI will further enhance RPA’s capabilities, allowing for smarter decision-making and reducing administrative overhead. For example, a chatbot handling a complex customer request across multiple departments could automate five steps, significantly reducing manual intervention.
The Internet of Things (IoT) and Edge Computing:
1. IoT devices are becoming more common in both consumer and industrial environments, providing vast amounts of data. Combining IoT with edge computing (processing data closer to its source) will greatly impact big data projects by improving real-time decision-making and reducing latency. As smart devices continue to grow in popularity, businesses are eager to invest in technologies that support the management and analysis of data from these devices.
Artificial Intelligence and Machine Learning (AI/ML):
1. Business Intelligence Enhancement: AI and machine learning are set to revolutionize Business Intelligence (BI) by enabling deeper insights through tasks such as data segmentation, categorization, and predictive analytics. These technologies allow businesses to forecast trends, understand customer behavior, and improve decision-making by extracting actionable insights from data.
DevOps in Data Analytics:
1. The adoption of DevOps in the data analytics lifecycle will drive flexibility and speed, streamlining the deployment of data-driven applications. DevOps will enable more frequent updates and quicker iterations of data systems, improving the overall agility of data operations within businesses.
Hybrid Cloud Models:
1. Hybrid cloud solutions, which combine the flexibility of public cloud with the security and control of private cloud, are gaining popularity. Many businesses initially moved to public clouds for scalability and cost efficiency. However, as their data sets grew in complexity, public cloud costs became prohibitive. A hybrid cloud model offers the best of both worlds, providing scalability while controlling costs.

How to Choose the Best Big Data Solution

When selecting a big data solution, businesses need to evaluate the following aspects:

Technology Maturity:
1. The big data landscape is constantly evolving, with some technologies being well-established while others are still emerging. Choosing proven technologies can minimize risks, especially in industries like banking where system reliability is critical.
Understanding the 3 Vs of Big Data:
1. Volume: The amount of data generated is continuously growing. It’s essential to understand the volume and expected growth of your data to choose a solution that scales accordingly.
2. Variety: Data comes in various forms, such as social media posts, sensor data, and videos. Legacy databases may struggle to manage this variety, making newer solutions necessary.
3. Velocity: Speed refers to both the rate at which data is generated and how quickly it can be processed and analyzed. Real-time data access is crucial for making timely decisions, so your solution should be able to handle high-velocity data flows.
Assess Your Company’s Risk Appetite:
1. Understanding your company’s approach to risk is essential when selecting a technology. If your business needs high reliability and minimal downtime (e.g., in financial sectors), mature and stable technologies may be a better fit. For more experimental or flexible business models, newer technologies might offer greater innovation opportunities.

Combining Deep Knowledge with the Right Tools

The best way to approach big data solutions is by combining in-depth data knowledge with modern technologies. Companies should aim for platforms that are scalable, easy to manage, and can integrate advanced algorithms and data science to create actionable insights. Working with a partner experienced in big data architecture can help navigate the complexities and deliver tailored, business-ready solutions.

Partnering for Success

Given the complexity of managing big data, many businesses opt to partner with specialists who have real-world experience in big data architecture. A reliable partner can assist in transforming data analytics capabilities into practical business solutions, ensuring that your company can keep pace with evolving data trends and technologies. Working with big data is a long-term endeavor, and having the right expertise and infrastructure in place will help you achieve sustainable growth and innovation.