We live in the big data era where tumultuous shifts are underway in BI, analytics, and data management, prompting enterprises to take a new perspective on creating a big data ecosystem. Also, owing to the ongoing developments, big data ecosystems are no longer the outmoded, insular systems contained within corporate walls. The ability of businesses to interpret data and act on insights can be augmented using a connected big data ecosystem that includes a complex network of applications, infrastructure, and advanced big data analytics tools that help capture and analyze data. As a result, enterprises are not just looking at revamping their big data ecosystem but are learning to integrate expertise and insight to drive growth and innovation.
Though the dynamism in the market and ever-evolving trends impact the big data ecosystem and its ability to generate comprehensive insights, our big data analytics experts have identified four crucial steps that form the basis of a connected data ecosystem.
Enterprises are looking to gain better insights by tapping into third-party data sets that not just offer new opportunities but pose several challenges. Request for a free proposal to learn how to tackle those using big data analytics!
Steps to Build a Modern Big Data Ecosystem
Step 1: Data Discovery & Repository Creation
The first step in building a big data ecosystem revolves around collecting data and analyzing their sources while creating a unified source of truth. In an urge to devise a connected big data ecosystem, businesses tend to gather information from disparate sources across the organization and then integrate domain-specific data pre-processing techniques & build big data and machine learning models to address critical issues. By adopting such an approach, businesses end up creating their version of a single source of truth and continue benchmarking results unaware of its impact on the business process. To avoid such issues, it’s essential to create a single source of truth that unifies domains and promotes collaboration to build and deliver a 360-degree view of the business goals.
Step 2: Centralized, Connected Big Data Ecosystem Design
Quantzig’s approach to devising a connected big data ecosystem depends on the level of analytical maturity within an organization. Before designing the centralized data ecosystem, our big data analytics experts suggest businesses must conduct an analytical maturity assessment that can offer insights on the right tools, the approach, and technology based on the business goals. Having worked with business leaders from different verticals, we do see a growing trend toward the deployment of a robust, organized big data ecosystem that can power the next wave of data-driven decision making at scale. By analyzing the analytics maturity of our clients and assessing the ideal characteristics of a data repository, we help prospects design a centralized big data ecosystem that aligns with their goals and helps fuel growth and profitability.
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Step 3: Data Collation & Analysis
Mastering data collation and analysis is how businesses today avoid flying blind. Increasingly, this requires tapping into insights from external data sources and a growing number of businesses are doing so in pursuit of a competitive edge. However, the actual data analysis begins after designing the big data ecosystem. A robust data repository plays a vital role in helping businesses collect and analyze data from disparate sources. With the unrelenting pressures to innovate and grow, companies must consider enhancing their ability to collate, segment, and analyze data using a connected, evolving big data ecosystem. Through our advanced big data analytics solutions, our team of data scientists, evangelists, and analysts help clients unravel new insights from the data they possess.
Step 4: Insight Generation Using Big Data Analytics
The final step in creating a connected big data ecosystem revolves around insight generation and all the processes that help analyze data. During this phase, our analytics experts collaborate with representatives from various teams within your organization to communicate the finding and share personalized recommendations to meet your business goals and KPI’s. Once a strong foundation for insight generation is set, the processes can be duplicated to ensure your teams are abreast of the latest findings and can leverage new insights to inform decision making.
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With the ongoing developments and technological advancements across industries, the big data ecosystem of businesses will continue to lag, leading to challenges in data analysis and insight generation. These challenges are poised to present major opportunities for those who can leverage big data analytics to meet the evolving market needs.
