Across Industries, Big Data is the Engine of Digital Innovation
Across Industries, Big Data is the Engine of Digital Innovation
See how companies and developers handle compliance issues when steeped within digital transformation, particularly when incorporating Big Data and IoT.
Join the DZone community and get the full member experience.Join For Free
Hortonworks Sandbox for HDP and HDF is your chance to get started on learning, developing, testing and trying out new features. Each download comes preconfigured with interactive tutorials, sample data and developments from the Apache community.
Enterprises across the globe have the need to create a digital strategy. While the term itself may seem intimidating to some, it essentially represents an Agile culture built on customer centricity and responsiveness. The only way to attain digital success is to understand your customers at a micro level while making strategic decisions on your offerings to the market. Big Data has become the catalyst in this massive disruption as it can help businesses in any vertical solve their need to understand their customers better. It aids this by providing a foundational platform for amazing products.
We have seen how exploding data generation across the global has become a clear and present business and IT phenomenon. Data volumes are rapidly expanding across industries. However, while the production of data itself that has increased but it is also driving the need for organizations to derive business value from it. This calls for the collection and curation of data from dynamic, and highly distributed sources such as consumer transactions, B2B interactions, machines such as ATMs and geo-location devices, click streams, social media feeds, server and application log files, and multimedia content such as videos, etc. It needs to be noted that data volumes here consist of multi-varied formats, differing schemas, transport protocols, and velocities.
The Internet of Things (IoT) has become an entire phenomenon to itself. It is truly a horizontal-vertical (no pun intended) as the proliferation of applications of sensors is causing a rapid change in system and application architectures. The system of IoT is burgeoning from the initial sensors, digital devices, mechanical automatons to cars, process monitoring systems, browsers, television, traffic cameras, etc.
Big data is thus crossing the innovation chasm. A vast majority of early adopter projects are finding business success with a strong gain in ROI (Return On Investment). The skills gap is beginning to slowly decrease with the Hadoop ecosystem becoming a skill that every modern application developer needs to have. Increasingly, customers are leading the way by deploying big data in new and previously uncharted areas like cyber security, leading to massive cross vertical interest.
The five elements in Digital Transformation, irrespective of the business vertical you operate in, are:
- Customer centricity
- Real-time multichannel analytics
- Operational improvements (risk, fraud, and compliance)
- Ability of the business to visualize data
- Marketing and campaign optimization
The first element in Digital is the customer centricity.
Big data drives this in myriad ways:
- Obtaining a real-time single view of an entity (typically a customer across multiple channels, product silos, and geographies).
- Customer segmentation by helping businesses understand their customers down to the individual level as well as at a segment level.
- Customer sentiment analysis by combining internal organizational data, clickstream data, and sentiment analysis with structured sales history to provide a clear view into consumer behavior.
- Product recommendation engines which provide compelling personal product recommendations by mining real-time consumer sentiment and product affinity information with historical data.
- Market basket analysis observing consumer purchase history and enriching this data with social media, web activity, and community sentiment regarding past purchase and future buying trends.
Real-time multichannel analytics is the second piece of a digital strategy.
Mobile applications first began forcing the need for enterprise to begin supporting multiple channels of interaction with their consumers. For example, banking now requires an ability to engage consumers in a seamless experience across an average of four to five channels: mobile, eBanking, call center, kiosk, etc. Healthcare is a close second where caregivers expect patient, medication & disease data at their fingertips with a few finger swipes on an iPad app. The healthcare industry stores patient data across multiple silos – ADT (Admit Discharge Transfer) systems, medication systems, CRM systems, etc., but all of this must be exposed across different mediums of access.
Data Lakes provide an ability to visualize all of the patients' data in one place, thus improving outcomes. Every customer-facing application needs to be both multi-channel as well as be one that supports a unified 360-degree customer view across all these engagement points. Applications developed in 2016 and beyond must take a 360-degree based approach to ensuring a continuous client experience across the spectrum of endpoints and the platforms that span them from a data visualization standpoint. Every serious business needs to provide a unified view of a customer across tens of product lines and geographies. Big data not only provides the core foundational elements for a real-time view of the moving parts of the business but also enables businesses to listen to their customers.
A strategic approach to improving risk, fraud, and compliance analytics can add massive value and competitive differentiation in three distinct categories as shown below.
- Exponentially improve existing business processes, i.e., risk data aggregation and measurement, HIPAA/SOX/manufacturing compliance, and fraud detection.
- Help create new business models and go to market strategies by monetizing multiple data sources–both internal and external.
- Vastly improve regulatory compliance by generating fresher and more accurate insights across silos of proprietary data.
The onset of digital architectures in enterprise businesses implies the ability to drive continuous online interactions with global consumers, customers, clients, or patients. The goal is not just provide engaging visualization but also to personalize services that clients care about across multiple modes of interaction.
The ability of outbound marketing campaigns to reach engaged customers in a proactive manner using the right channel has been a big gap in their effectiveness. The old school strategy of blasting out direct mailers and emails does not work anymore both from a cost as well as a customer engagement standpoint. Nowadays, campaigns for exciting new products and promotions need to be built on the rich customer intelligence assets that Big Data enables you to build. Examples of these capabilities are replete in sectors like retail where offering a positive purchase experience in terms of personalized offers, price comparisons, social network based sharing of experiences, etc. drive higher customer engagement and loyalty.
The Final Word
My goal for this post was to communicate a business revelation that I have had in past year. While the semantics of business processes, the usecases and the data sources, elements, formats may vary from industry to industry (i.e., Banking to Healthcare to Manufacturing to Telecom), the approaches as well as the benefits from leveraging a data- and analytics-driven business model essentially remain the same. These capabilities are beginning to separate the winners from the rest of the pack.
Published at DZone with permission of Vamsi Chemitiganti , DZone MVB. See the original article here.
Opinions expressed by DZone contributors are their own.