AI-powered CRM Solutions are Driving Business Growth

AI in tandem with Optical Character Recognition (OCR) systems is streamlining and accelerating data extraction and insight generation processes.

As an experiential provider of artificial intelligence development services, Oodles AI highlights some effective AI-powered OCR applications for dynamic business operations.AI-powered OCR applications are also commonly known as “Intelligent Data Processing” (IDP) or “Intelligent Data Capture” (IDC).

Under AI, natural language, computer vision services, and machine learning technologies are combined to automate data and insight extraction from documents. 

CRM software for lead management in companies of all sizes helps nurture leads into a sale by converting cold leads into warm ones.

This further manages the customer lifecycle value for the company, proving to be an effective key in generating leads.For more - https://bit.ly/2UwMncR

According to Market Research Future, the market for the global AI-powered storage has been segmented on the basis of type, application, industry, and region.

The enterprises are registering a need for a refreshed AI-based storage architecture.

Factors such as the growing adoption of cloud-based services, a rise in the number of hyperscale data centers, and the deployment of data analytics tools across small and medium enterprises have positively impacted the demand for AI-powered storage.

However, the network-attached storage systems segment is expected to register the highest CAGR during the forecast period from 2019 to 2025 owing to the multiple client access feature that it provides.Based on storage architecture, the market has been segmented into file storage, object storage, and block storage.

The solid-state drive is further bifurcated into all-flash array and hybrid flash array.

Hybrid flash array is witnessing high demand as they are cost-effective and provide high performance.Based on end use, the market has been segmented into BFSI, healthcare, media & entertainment, retail, manufacturing, telecommunication, and others.

The report "AI-Powered Storage Market by Offering (Hardware, Software), Storage System (DAS, NAS, SAN), Storage Architecture (File & Object-Based Storage), Storage Medium (SSD, HDD), & End User (Enterprises, CSP, Government, Telecom) - Global Forecast to 2024", is expected to grow from USD 10.4 billion in 2019 to USD 34.5 billion by 2024; growing at a CAGR of 27.1%.

Major factors driving the AI-powered storage market growth are the massive growth in data volumes, the need for global enterprise infrastructure to refresh their storage architecture, increasing adoption of cloud-based services, and growing demand for AI in HPC data centers.

Storage medium include solid-state drive (SSD) and hard disk drive (HDD).

Other hardware include processors including CPU and GPU.

File storage manages the layout, structure of files, and directories— cataloging structure, which contains references to other computer files on physical storage.

Within file- and object-based storage, object storage is gaining major market share as it helps handle huge unstructured data, unlike file storage, which is mainly used to store structured data.

CRM or Customer Relation Management system can streamline business processes and operations.

However, for effective CRM development, there are different technologies which are available.

Most of the CRM development services are using these technologies for their CRM solutions.

Let’s have a look at the best technologies that should be used for CRM development.

AI and machine learning workloads require vast amounts of storage to extract translate and load input data, do exploratory data analysis to see what data is relevant, create test data, build and train the models, retrain the models to reflect changing data patterns, inferences, and discoveries and keep them running.

Data does not lose value, so data must be retained for as long as there is perceived value, and regulatory requirements like GDPR may require retention of data in a form that explains the decisions made by AI.

Scalability includes the ability to minimize the footprint and cost of storage through intelligent use of deduplication, compression, virtualization, tiering, archiving, indexing cataloging, and shredding.The efficacies of AI infrastructure generally focus on computing hardware like the general-purpose CPUs, GPUs, FPGAs, and tensor processing units.

It is astute to keep a potent compute engine well-utilized, which needs feeding it with vast amounts of information as fast as possible.

It is no longer enough to deploy capacity and connect faster drives or buy an all-flash array and assume that flash resolves all consumer problems.

There are numerous phases with different processing requirements, from model deployment, data ingestion, data discovery and visualization, data engineering, model development and training, and retention, all with various performance, tiering, and protocol requirements.