Understanding the Value of Big Data

The primary reason for Big Data's rapid growth in recent years is that it provides long-term corporate value. Organisations realise this value through increased revenue, cost reductions, and improved profit margins. As a result, Big Data enables organisations to acquire a competitive advantage by leveraging their corporate data. Because of its wide range of applications, Big Data is being accepted by all organisations, from healthcare, banking, and insurance to academic and non-profit sectors.

Big Data skills and technology may help businesses capture value in a variety of ways. These companies use data analysis tools and tactics to improve corporate performance and drive growth. The ambition of most firms to become "data-driven" is tied to a "digital transformation" programme and is supported by senior management. Most businesses are driven by the prospect of gaining a competitive advantage via the use of data and technology. However, most firms have realised that digital transformation is not an easy task and that it involves fundamental changes in the way organisations are formed, organised, and managed.

Big Data's potential is always expanding, driven by innovation and decreased costs of data storage and processing capacity in enterprises. In general, most businesses utilise their data to generate value in one of the five ways stated below:

1 – Creating Transparency

Making Big Data more accessible and timely throughout the organisation provides huge benefit. Most businesses lack convenient access to information across several functional areas, resulting in fragmented insights and decisions. Organisations may improve performance, reduce the amount of repeated labour done across departments, and uncover inefficiencies by increasing openness and sharing Big Data across departments. Many corporate enterprises may reap major operational gains as information becomes more widely available. Integrating data from R&D, engineering, and manufacturing divisions, for example, across many organisations may enable concurrent engineering processes that greatly reduce waste caused by the need to rework designs, hence reducing time to market.

2 – Data Driven Discovery

Sensors in an increasing number of devices now collect data about corporate operations, consumer behaviours, and the use of products and services via Internet-of-Things (IoT) technology. Companies that examine the data produced by these sensors may modify their decision-making processes and service offerings. Companies will be able to develop Big Data-driven improvement programmes since they will know exactly how their items will travel through their supply chain. Furthermore, Big Data supplies organisations with previously undisclosed information about how customers utilise products and services. Big Data, for example, may aid in assessing successful items and refining ways of calculating insurance premiums based on previously reported customer claims in the insurance industry.

3 – Customer Segmentation and Customised Marketing

Customer segmentation and targeted marketing are strategies widely used by firms that provide goods or services to customers. Big Data takes consumer segmentation and targeted marketing to new heights, giving for greater flexibility in tailoring product-market offerings to specific client groups. Data on user or customer behaviour enables the establishment of numerous consumer profiles that may be targeted appropriately. Social media data enhances these abilities, allowing firms to provide products and services to highly targeted client profiles. Location-based data, which may be obtained via Bluetooth or GPS, adds a whole new dimension to targeted marketing and emphasises Big Data methodologies.

4 – Support Decisions with Automated Algorithms

Data-driven decision making is one of the key drivers of Big Data. Analytics and algorithms may significantly improve decision-making. Big Data techniques may help organisations discover patterns, spot anomalies, and decrease risk. Big Data algorithms may be used by businesses to automate operations that result in more accurate decisions. Big Data algorithms, for example, may help bank employees reduce risk while providing financial solutions to their customers. Similarly, Big Data approaches might be utilised in the accounting profession to detect abnormalities in audits or to identify situations that need further examination. Algorithms can help people make more informed decisions based on company data.

5 – Product Development and Innovation

Big Data may show trends that signal the need for new products or services, or it may be used to improve the design of existing products or services. Product development and innovation are intrinsically tied with Data Driven discovery, which may help firms find new business opportunities. Organisations may discover demand for previously unknown things by analysing search queries, product use statistics, and user-experience indicators. Universities and colleges, for example, may analyse their internet traffic and search volumes to estimate class enrollment and distribute teaching resources accordingly.

 

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