Big Data Challenges and Solutions

Critical Data Challenges

Managing Big Data Eco Framework requires dexterity in the midst of interruptions

Big Data poses profound difficulties for data integration best practices

Real-time big data analytics conveys change to data administration

Big data architectures confront huge obstacles with technology consolidation

Data Analytics Models are still inadequate

How Data Challenges Affects Business

  • Big Data makes data preparation steps more confounded to explore. One size fits all approach may not work in data preparation
  • Companies need to ensure that the data they collect and analyze meets a specific level of quality and reliability for it to be trustworthy. Data capturing is an area that needs more focus
  • Building big data architectures can be tedious and trying initially
  • Deployment of big data systems stalls due to their complexity. Lack of big data skills when deploying a Hadoop environment affects usability and acts as a hindrance while leveraging the passive data sets
  • Ingesting the data is the most challenging part of big data applications. Being able to pull growing amounts of data into the vendor’s big data architecture without any missteps is crucial for the success of a big data project
  • Once data ingestion is complete, master data sorting to define different governance policies is another challenge

Big Data Solutions

Mastech InfoTrellis’ Approach to Big Data Solutions

Big Data Analytics Hub

  • Governed, managed and self-sufficient data lake
  • Seamless interaction with varied big data sources
  • Modernized self-serve analytical platforms
  • Built on robust big data technologies
  • Plug and play with BI and Analytics Systems

AllSight Customer Intelligent Management (CIM) System

  • Pre-built, with modern technology to deliver an intelligent Customer 360-degree view
  • Ingests raw data, at the level of the individual, be it structured or unstructured, synthesizes it into larger customer records by stitching the tiny fragments of data together, reasons on that data by drawing inferences, enriches the data, makes predictions of future events, recalls customer information on demand and learns continuously to evolve and improve on data
  • Delivers actionable customer intelligence to all marketing users. Marketers, Customer Service and, Sales get access to complete and relevant customer data in real time
  • Understands and synthesizes all fragments of customer data in data lakes, creates a clear customer 360-degree view, adds analytical enrichments to customer 360 which can then be used by the customer-facing systems in the organization
  • Manage and understand any form of customer data, assign a confidence score to every piece of customer data. It uses Genetics Algorithm to understand expected user behavior and improves its performance progressively
  • Omni-channel Personalized Care — AllSight can understand the entire customer journey, present that to your customer care users, and even predict the next steps in the customer journey
  • Complete understanding of the customer to an organization’s sales team, linking all data sources into a cohesive likeness of customer accounts and contacts

Conclusion

--

--

--

Developer and Designer

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Binomial Random Variables

A Prognostic Model for Multiple Sclerosis Progression

Data Analysis and Visualisations using R

HOT vs. COLDTREATMENT https://t.co/kflz9WzjSj

Making predictions using a very small dataset

Model Architecture and different Approaches

Uber’s Machine Learning Let Me Down

Game of Life 生命遊戲 (II)

Boost Your Time Series Forecasts Combining Gradient Boosting Models with Prophet Features

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Abhikhya Ashi

Abhikhya Ashi

Developer and Designer

More from Medium

Data Engineering for Beginners: Data Lake and Data Warehousing

What is Data Mining? And Techniques In 2022

How Freda, a data quality platform can make your job easier?

Is Parquet Faster than CSV for sentiment analysis?