- October 7, 2020
According to a 2018 report published in Forbes, we produce nearly 2.5 quintillion bytes of data every day. This volume may have snowballed by many folds! Hence, it is safe to say that 2020 is officially the decade of data, more specifically, big data and big data analytics.
Big data management has emerged as the latest buzzword ruling all industries. The usability of structured data through its searchable and organized format, and the potential offered by unstructured data – all point at the opportunities that big data is unlocking.
In this post, we explore everything that one needs to know about big data management and analytics, along with some practical use cases!
Big Data Analytics in Numbers
Big data and big data analytics are unlocking opportunities across various industries. When it comes to data, it is an immutable fact that numbers don’t lie. Hence, here is a breakdown of big data in figures:
- A forecast from International Data Corporation (IDC) estimates that with 41.6 billion connected IoT devices, the world will be generating an excess of 79.4 zettabytes (ZB) of data by 2025!
- At this rate, businesses will have to analyze 150ZB of data to keep up with the demands of data in 2025.
- As per a report published by Dresner Advisory Services, the adoption rate of big data analytics was notched at 53% in 2017, a notable increase from 17% in 2015.
- According to Statista, the global Big Data market revenues are projected to rise from $42B to $103B, with a Compound Annual Growth Rate (CAGR) of 10.48% for the forecast period 2018-2027.
How Does Big Data Matter to Businesses?
You may have already heard quips about how data is powering businesses or has emerged as the oil that is fueling digital transformation. However, as cliche as it sounds, these claims are true indeed. Here is how big data is offering a competitive advantage to businesses:
1. Creating a 360-Degree View of the Customer
The 360-degree view refers to the aggregation of data from different touchpoints that offer relevant insights and can be used to create new value and improve customer experience.
2. Reducing Costs
Despite the high upfront capital investment, big data management can be quite pocket friendly in the long run. Integrating tools like Hadoop with big data analytics can improve your business’ profitability and overall ROI.
3. Cutting Down Operational Costs
By depending on data-driven strategies, businesses can obtain insights faster, enabling business executives to make quicker yet well-thought-out business decisions.
4. Understanding Market Conditions
Big data analytics sheds light on the prevailing market conditions. For instance, businesses can leverage indicators such as customer purchase patterns to estimate the demand for the product or service.
5. Managing Online Reputation
From detailed surveys to regular feedback to detecting negative comments – big data can monitor and leverage these data bits to put your best foot forward!
6. Boosting Customer Acquisition and Retention
Big data analytics can help increase customer acquisition and retention by boosting customer loyalty.
7. Promoting Marketing and Advertising Efforts
Big data breaks down customer complexities into bite-sized data, allowing you to match customer expectations, develop and launch new products, and run successful marketing and advertising campaigns.
8. Driving Innovation and Product Development
The ability to hear from the client allows businesses to improve on their current line and plan out a new line of products. The continuous development of your offering will boost customer loyalty further and keep you ahead of the competition.
Use-cases for Big Data Analytics
Both public and private companies are making use of big data to achieve their goals. Here are a few classic examples of big data analytics in action:
Major retailers, such as Macy’s and Sterling Jewelers, have used big data to boost store sales by 10 percent and increase holiday sales by 49 percent.
In retail, big data analytics allows you to:
- Recommend products based on the account holder’s purchase history
- Personalize the shopping experience
- Forecast peak demand and slow periods
- Map out the customer journey
- Speed up the entire shopping process
The telecom sector is using big data to:
- Build and develop smarter networks depending on network traffic
- Customizing plans and packages for clients
- Optimizing network performance
- Preventing fraudulent activities by detecting clone SIM cards
Healthcare is one of the latest entrants in the field of big data analytics. However, it is also one of the most rapidly transforming areas with the market for big data analytics in healthcare slated to grow at a CAGR of 22% and a valuation of $22.7B by 2023.
It also grants some of the most life-saving benefits, such as:
- Improved staffing depending on patient requirements
- Management of digitized or electronic health records
- Real-time health monitoring and alerts
- Managing assets, staff, and inventory for effective resource allocation
- Research and development of medicines
- Telemedicine for last-mile healthcare reach
- Development of a single-point repository for medical knowledge base
With the next generation embracing data in all its myriad ways, incorporating big data analytics in the field of education is a wise move. Here is how big data is disrupting education:
- A quick and cost-effective way to maintain student records
- Offers insights into shifting trends, such as high drop-outs, etc.
- Improves student engagement and retention
- Standardization of grades and results
- Offering career guidance to students
- Introducing new learning methodologies and plans
The banking and financial services industry will greatly benefit from big data, primarily in the area of reducing risks and frauds. According to Mordor Intelligence, the BFSI sector occupies 36% of the market share.
Here are some other areas of application of big data analytics in this sector:
- Reactive and preventive fraud detection
- Higher regulatory compliance
- Managing customer data
- Risk modeling and management
- Personalized marketing of tailored goods
- Customer lifetime value (CLV) predictions
- Trade analytics
- Customer segmentation
- Improved customer support
About 90% of the 3PL partners believe that big data analytics will enrich their networks. However, only 35% of shippers can support big data, which opens up several ripe opportunities in this field.
As logistics push for last-mile connectivity and rush deliveries, big data analytics can help them in the following ways:
- Making shipping and deliveries faster
- Improved operational transparency
- Effective route or fleet optimization
- Improved shipping and handling, especially of high-value or fragile items
- Prioritizing delivery of perishable goods
- Better warehouse management
- Making tracking accessible
With the rise of digital technologies, there is a growing risk of data security and privacy concerns. Big data analytics is alleviating these concerns by:
- Real-time security monitoring
- Analyzing user behavior
- Capturing network traffic insights
- Detecting data exfiltration
- Detecting internal threats
- Investigating incidents
- Actively hunting for threats
Big Data Analytics and Management: Technology Stack
Typically, a basic big data architecture constitutes the following elements:
- Data Layer for the storage of raw data
- Integration and Ingestion Layer for cleaning, prepping, and organizing the raw data into usable forms
- Process Layer for processing and manipulating data
- Analytics and BI Layer for the final analysis, deriving insights, and visualizing them
Tools commonly used to implement these layers include:
- Data Layer – Hadoop HDFS, Amazon S3, MongoDB, IBM GPFS, etc.
- Integration and Ingestion Layer – Apache Kafka, Apache Flume, Stitch, Blendo, etc.
- Process Layer – Apache Spark, Apache Storm, PostgreSQL, Amazon Redshift, Hadoop MapReduce, etc.
- Analytics and BI Layer – Tableau, Looker, Chartio, etc.
Big data is technology agnostic, which means that businesses can use the tools that meet their means and is compatible with their existing technologies.
Data Personified and People Digitized
As with every mode of technology, success depends on the people driving this change. Businesses must invest in the right sort of talent to make big data management and analytics more fulfilling and rewarding. Having a hands-on workforce that follows a data-driven mindset would be more adept at utilizing the information extracted from powerful big data systems.
The unstoppable combination of people and technology will propel your business further and keep you ahead of your competition at all times!
With an AI-first approach and strong expertise in AI frameworks, RecoSense is a one-stop partner for end-to-end Data Intelligence Solutions. Our industry-unique cognitive computing platform based on Natural Language Processing and Machine Learning frameworks offers Intelligent contextual interpretation of the Content & Users.
- Analytics (12)
- Artificial Intelligence (25)
- Aviation (3)
- CDP (20)
- clickstream data analysis (1)
- content intelligence (8)
- contextual insights (2)
- cross-selling (5)
- Data Engineering (10)
- Finance (29)
- Healthcare (1)
- IDP (3)
- Legal (1)
- Manufacturing (2)
- Marketing (21)
- Media (32)
- Natural Language Processing (5)
- NLP (10)
- Retail (60)
- Shopify (24)
- Sports (1)
- Technology (2)
- Travel (5)
- Uncategorized (3)
- Upselling (4)
AI for Fraud Detection
Mar 17, 2023AI and ML Use Cases in Insurance Industry
Jan 10, 2023AI in Equity Research
Nov 21, 2022
TagsAI AI personalization Analytics artificial intelligence boost sales Buyer Persona content discovery content personalization content recommendation cross-sell cross-selling customer data platform customer engagement customer experience customer journey customer journey mapping customer loyalty customer retention Customer Segmentation Data Analytics digital experience ecommerce ecommerce personalization Hyper Personalization machine learning magento extensions magento plugin marketing media omnichannel personalization ott ott trends personalisation in small eCommerce Personalization predictive analysis predictive analytics product discovery product recommendation retention sentiment analysis shopping upsell upselling user engagement user experience
Get regular updates on data science, artificial intelligence, machine[contact-form-7 id="2755" title="Comming soon"]
You might also likeCDP   contextual insights
What Is Data Retention? How to Create a Policy That Protects Privacy?Artificial Intelligence   CDP   Data Engineering   Media   Retail
Humanizing Big Data: Key Approaches That Give You the Best of Both Worlds
What is User Journey Mapping & Why is it Important for Media Websites?
Your A-Z Guide to Customer Lifetime Value and How to Make the Most From It