Tue. Sep 21st, 2021
Challenges-of-Big-Data-Management

Challenges of Big Data Management- Customers today have many options to reach and interact with your brand, as well as your competitors. The speed and efficiency with which you respond, connect, and build a relationship with them will depend on the insights that drive this conversation with the customer.

A research report stated that Indians consumed an average of 308 petabytes (PB) of data daily at the
beginning of the pandemic, giving rise to structured and unstructured data. Many businesses are still faced with the challenge of capturing and analyzing this reservoir of information to drive meaningful insights as their data resides in silos across the organization. According to a survey conducted by Harvard Business Review, while 69% of those surveyed recognize the importance of a comprehensive data strategy, only 35% believe that their current data management and analytics capabilities are sufficient.

If enterprises are unable to process data in real time, their outcome prediction may not be useful as it relies on snapshots of the past. Businesses must therefore tackle the inherent issues that lie with data management before they initiate the process of collection and analysis of data. Some of those issues include:

1. Not letting business needs define the data and cloud strategy
When dealing with Big Data, enterprises often do not follow the right order while managing it. For example, some businesses consider “cloud first” as a solution to their data management needs — such as storage, data capture, data processing and infrastructure in the current business environment — without determining their business needs first. To effectively manage data, enterprises need to clearly define the business outcomes for their data strategy prior to finalising the cloud strategy.

2. Lack of agility in catering to customer requirements
An organization’s data pool is a gold mine of insights about their customers – from what they purchase, what they seek, to what they respond well to. Business must be agile enough to extract this data in real time and acknowledge customer needs as quickly as possible. Organizations with an agile data operation can reduce time-to-market, attract new customers and open new channels for customer interaction.

3. Failing to leverage the Internet of Things (IoT)
The digital world has transformed itself manifold, with the introduction of connected devices in India’s ecosystem. According to Zinnov estimates, the number of IoT devices in India will grow tenfold to touch 2 Billion devices by 2021, compared to 2019, which witnessed 200-250 Million connected devices. While personal IoT devices such as smartphones, smart watches, and smart air conditioners are a common sight today, this “smart” revolution is also increasing the sources and entry points of data for businesses. As a result, enterprises are tasked with collecting large volumes of data, ensuring governance and security, and enforcing data regulation norms, as necessary. Data management is further complicated if data has travelled through different paths and has been altered on the way by different users. The overall process of collecting, managing and governing data often presents an obstacle for many businesses in leveraging this abundant information to the fullest.

4. Lacking flexible infrastructure to support big data management
While companies are familiar with the four big Vs of data – volume, variety, velocity and veracity — they do not fully understand the infrastructure needed to manage them. To do so, enterprises need an agile and flexible infrastructure that can scale depending on changing business needs. Cloud is often used by organizations to address this need but that comes with its own set of challenges.

Cost prediction, avoiding vendor lock-in, and reducing unutilized resources are some of the issues here that most businesses try to avoid.

Read more at EC

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