Managed Analytics as a Service empowers enterprises to automate the process of turning data into insights further facilitating your business achieve goals such as customer retention success and predict customer behaviour using predictive modelling, and 360 Degree Customer View for understanding customers.
Enterprise Data Silos across the departments and Leveraging the all Data assets for Compliance, Audit and Security is a time-consuming task.
Delivering Change Request ( BI And Data Warehouse Applications) and mapping Business Objectives need Collaboration across the IT and Business units within Defined SLA.
Organisational Effectiveness and Efficiency Depends on a few critical People for Delivery of Critical Projects for a competitive edge.
Digital transformation and Cloud Migrations and Upgradations are time intensive and Delay in Reporting Analytics.
Customer Analytics Services empower Enterprises to target customers using personalised product service recommendations, predict customer behavior using predictive modeling. FromCustomer Analytics Solutions and Services
XenonStack Supports organizational design and develops an analytics function that brings operational efficiency and Value for the business. With Managed Analytics as a Services (MaaS) Enterprises can -
Building Analytics Capabilities.
Interpret your data in more profound ways.
Enable fact-based insights into your decisions.
Enhance customer experience with AI-based forecasts.
Drive customer Intelligence and Customer Loyalty.
Analytic Processes Maturity Model (APMM) by Robert L.Grossman
The APMM described here is based upon a framework for analytics that divides analytic processes into six areas:
Building analytic models - The process of building an analytic model takes as the input: i) data and ii) the appropriate business requirements and produces an analytic model as the output.
Deploying analytic models - These sets of processes take an analytic model that has been developed and integrates it into an organization’s products, services, and operations in such a way as to deliver the desired business value.
Managing analytic infrastructure - Managing the IT infrastructure required to build and deploy analytic models (as mentioned above we call this analytic infrastructure following (Grossman, 2009)) has historically been challenging for many organizations, but is becoming even more so as the volume, velocity and variety of data grows. Analytic infrastructure includes both the IT infrastructure for building and deploying models.
Operating an analytic governance structure - Operating a governance structure to support analytics is critical since those selecting analytic opportunities, building analytic models, deploying analytic models and managing data required for building and deploying analytic models are usually in different organizations. Without analytic governance, it is difficult for most organizations to successfully build and deploy analytic models, much less do this with a repeatable process. Analytic processes required for building and deploying analytic models.
Providing security and compliance for analytic assets - A growing priority of IT organizations has been IT security. Analytics and big data present some additional challenges, such as: i) protecting data privacy when side channel attacks on data are growing increasingly easy; ii) managing analytic infrastructure for big data, which can be so large that manual processes for infrastructure provisioning are no longer adequate; iii) and following appropriate security and privacy procedures when working with third party data. Setting up appropriate security and compliance processes to reduce risk, protect analytic assets, and to satisfy any required regulations is an important component of a mature analytic organization.
Developing an analytic strategy - Developing an analytic strategy and using the analytic strategy to select appropriate analytic opportunities. Almost all organizations have more analytic opportunities than the resources required by the opportunities and the first set of processes involve selecting which analytic opportunities to pursue based upon the short and long terms requirements and opportunities of the organization.
Cloud-based Search Solutions for Enterprises to build Next Generation Search-based Analytics Applications and Services.
XenonStack offers managed services in several models, i.e. Consulting, Managed Solution and Subscription-based including Data as a Service, and MetaData Platform as a Service.
Search Based Analytics
Cloud-Enabled Search Analytics Solutions to help Enterprises to build powerful search applications with enterprise search engine characteristics and implementation services
Enterprise Search Applications Assessment
Search-based analytics applications Development
Search relevancy improvement
Cloud Based Search
Artificial Intelligence Driven Analytics
Artificial Intelligence driven analytics services for Empowering Enterprises to leverage analytics for gaining Real-Time insights and developing scalable and Integrated Machine Learning Applications
Model Designing and Development
Managed AI Workloads
Machine Learning Governance
Managed Cognitive Analytics
Data Discovery and MetaData Platform
With Enterprise Data Discovery and MetaData Platform enterprises can Empower self-service capabilities, discovery for important data. Metadata solutions for presenting a comprehensive view of data associations which further helps to enhance quality, confidence in the data.
XenonStack offers Business Intelligence and Data Visualization Services for Business Intelligence Dashboard Development, BI Implementation, and BI Architecture Designing. Enterprise Data Visualization Services offerings for -
Business Intelligence Consulting
Business Intelligence Dashboard Implementation
Data Visualization with Web Assembly
Real-Time Visualization for Actionable Insights
XenonStack provides Advanced Analytics Services with five levels of an analytics maturity model to drive enterprise performance and operations analytics.