Business Analytics with Big Data - its significance, importance, and strategies for business implementation
Big data analytics is revolutionizing the business landscape, offering companies a powerful tool to understand their customers, find trends, and monitor competition. This transformative technology is a four-stage process: data collection, data processing, cleaning of data, and data analysis.
At the heart of big data analytics lies structured data, organized with defined dimensions according to set parameters. However, the world of data isn't always so neat and orderly. Big data can also be divided into three primary categories: structured data, unstructured data, and semi-structured data. The latter, a mix of both structured and unstructured data types, presents a unique challenge due to its unfamiliar model or structure, making it difficult to process and analyze.
To tackle this challenge, big data integration is used to prepare data for analysis stages like data discovery, cleansing, and validation. Efficient data governance is needed to ensure that the collection of data is consistent and complies with data standards and privacy regulations.
Big data tools can be harnessed to collate trends from social media and traditional media sets, customer behavioral patterns, and inform businesses of where they should focus their energy on a proactive level both in terms of targeted advertisements and customer retention.
Companies such as Anthropic, Scale AI, Hugging Face, DXC Technology (with startups Acumino, CAMB.AI, GreenMatterAI), and Unisys are at the forefront of this revolution, driving innovation across various sectors including automotive, manufacturing, cloud services, and security.
Big data analytics is not just about making sense of massive amounts of data; it's about promoting agility and enabling quick, data-centric decisions. Common data analytics methods include data mining, predictive analytics, and deep learning. Big data analytics applications are used for a variety of purposes, including reporting, advanced analytics like finding anomalies, and machine learning.
However, businesses should approach big data with caution. While it offers immense potential, it also presents challenges such as managing data, maintaining data quality, and keeping data secure. Starting small, such as identifying the products customers are viewing and leaving the page from, can be a less expensive and faster way to get started.
In the realm of big data, every step of integration incorporates data quality management practices to improve accuracy and compliance. A recent success story involves a data analytics platform created for a leading telecom company in the US, resulting in 100% processing of customer consumption data and an 85% increase in data quality and accessibility.
Appinventiv's data analysis team can support businesses in identifying areas where big data services can be most beneficial and in its incorporation. The goal should always be to solve problems, not create them with big data. After all, in the digital age, understanding and harnessing big data is the key to unlocking a world of opportunities.
