Value of Big Data

The primary reason why Big Data has developed rapidly over the last years is because it provides long-term enterprise value. This value is captured by organizations through revenue expansion, cost reductions and increased profit margins. As a result, Big Data provides companies with the opportunity to use their enterprise data as a competitive advantage. Due to its wide range of applications, Big Data is embraced by all types of industries, ranging from healthcare, finance and insurance, to the academic and non-profit sectors.

There are various ways in which Big Data skills and technology can help enterprises to capture value. These organizations use data analysis tools and techniques to increase corporate performance and facilitate growth. For most enterprises, the quest to become ‘data-driven’ is coupled to a ‘digital transformation’ program and is supported by senior leadership. The realization that data and technology can help to obtain a competitive advantage, is a key business driver for most organizations. However, most organizations have found that this is not an easy task, and that digital transformation requires profound changes to the way the organization need to be designed, organized and managed.

The possibilities of Big Data continue to evolve rapidly, driven by innovation and the reduced cost of data storage and processing capabilities in organizations. In general terms, most organizations leverage their data to create value in any of the following 5 ways[i]:

1. Creating Transparency

Making Big Data more easily accessible across the enterprise in a timely way creates tremendous value. In most organizations, information is not easily accessible across different functional departments, resulting in siloed insights and decision-making. Through increasing transparency and sharing Big Data across departments, organizations can improve performance, reduce the amount of work that is done repetitiously across multiple departments and identify inefficiencies. In many enterprise organizations, major operational improvements can be made when information is more easily available. For example, integrating data from R&D, engineering, and manufacturing units, potentially across several enterprises, can enable concurrent engineering techniques that can greatly reduce the waste caused from having to rework designs, and thus accelerate time to market.

2. Data Driven Discovery

Through Internet-of-Things (IoT) technologies, more and more products contain sensors, which capture data about enterprise processes, customer behaviors and the use of products and services. By analyzing the data that is generated by these sensors, companies can alter their decision-making processes, and adjust their service offerings. Through Big Data, companies will know exactly how their products will flow through their supply chain, making it possible to plan improvement projects accordingly. Additionally, Big Data offers new insights in the way customers are using products and services, offering companies insights that might not have been identified previously. In the insurance industry, for example, Big Data can help to determine profitable products and provide improved ways to calculate insurance premiums based on previously submitted claims of customers.

3. Customer Segmentation and Customized Marketing

Customer segmentation and targeted marketing are concepts which have been widely adopted by enterprises who sell products or services to consumers. Big Data brings customer segmentation and customized marketing to entirely new levels, providing improved opportunities to customize product-market offerings to specific segments of customers. Data about user or customer behavior makes it possible to build different customer profiles that can be targeted accordingly. Data that is captured on social media enhances these capabilities, enabling companies to target products and services to highly targeted customer profiles. Location based data, which can be captured through Bluetooth or GPS, adds a completely new dimension to targeted marketing and will provide a further focus on Big Data practices.

4. Support Decisions with Automated Algorithms

Data-driven decision making is one of the key drivers for Big Data. Through analytics and algorithms, decision-making can be significantly improved. Organizations that utilize Big Data techniques are able to discover patterns, detect anomalies and minimize risk. Through Big Data algorithms (which we will further discuss in Chapter 5), organizations can automate processes that will lead to more accurate decisions. In the banking industry, for example, Big Data algorithms can help bank employees to minimize risk when offering financial products to their customers. Similarly, the accounting industry can use Big Data algorithms to detect anomalies in audits or highlight cases that need further inspection. With the assistance of algorithms, people are able to make more accurate decisions supported by their enterprise data.

5. Product Development and Innovation

Big Data can unearth patterns that identify the need of new products or increase the design of current products or services. Product development and Innovation is tightly coupled with Data Driven discovery and can help companies discover new business opportunities. Through the analysis of search queries, product usage data and user-experience metrics, organizations can identify demand for products that the organization was previously unaware of. Universities or colleges, for example, might study their website traffic and search volumes to forecast class enrollment and allocate teaching resources accordingly.

[i] Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Byers, A.H., 2011. Big data: The next frontier for innovation, competition, and productivity.