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Data analytics has come a long way from simple operational reporting to becoming the de-facto tool for business efficiency.
With advances in data science technologies, patterns among historical and current data become clear. This has enabled businesses to personalize customer experiences on a 1:1 basis, while enabling better forecasting.
Our expertise in predictive and cognitive analytics is broad and deep. We closely work with business stakeholders to understand their information needs for decision making and provide solutions accordingly.
We have worked with clients to deliver insights from complex structured and unstructured datasets, including images, videos, audio, etc. Our team leverages machine learning (ML), artificial intelligence (AI) and neural network programming to innovate.
Business impact versus engagement maturity
There’s no denying that there’s power in data. But data mining in the traditional sense can only help to a certain degree. Value is truly realized when intelligence is applied to provide insight into the future. The ability of machines to self-learn and make decisions with minimal human intervention is predictive and cognitive analytics in a nutshell. We can bring much-needed intelligence to data.
Advanced analytics discern patterns from disparate systems to draw meaning. AI/ML models use self-learning and deep learning to provide feedback to traditional systems in a way that makes them smarter/cognitive. Use cases include:
This type of analytics uses tools such as facial recognition, video content analysis, image mining, character recognition, speech recognition, and more for image classification, object identification, or even speech analytics.
This can be used across domains for experiences, such as know your customer (KYC) onboarding, invoice processing, employee productivity, gesture recognition, footfall analysis, and so on.
Speech recognition involves converting speech to free text. It diarizes speech, allowing for individual voice identification for better data synthesis. For example, unstructured data (e.g., contact center calls) can be converted into structured data for insights or metrics.
This paves the way for many use cases, such as sentiment analysis, translation, summarization, entity recognition, fraud/compliance detection, employee and customer satisfaction, and topic modeling.
Geographic analytics is gaining popularity in business scenarios that depend on certain geographies for their sustenance and growth. Geo-analytics adds a spatial dimension to traditional pattern-based analytics.
Use cases include weather mapping, cropping patterns, demographic analysis, land usage, cartographic mapping, and marketing data mining to examine how digital and physical touchpoints in various locations react to and interact with products. This is vital for offering more reliable services.
Our framework is aligned with cross-industry standard practices for data mining. This allows us to replicate projects faster and make analytics pervasive across organizations. We build solutions iteratively, with iterations focused on continuous improvement. Here’s how:
Our data strategy team includes individuals with cross-functional expertise in the areas of digital strategy, data architecture, marketing, domain expertise, design, and more.
We work closely with executives to outline scope, define multi-year roadmaps, identify operating models, and recommend technologies.
Speak to our experts on how to get started with AI/ML pilot projects.
HGS Digital provides end-to-end data and analytics services.
HGS brings extensive data and analytics expertise, along with in-depth domain proficiency, to help clients transform their businesses. We provide strategic CX and technology consulting, along with resources to implement end-to-end data and analytics solutions.
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