Enterprise Digital Platform

Managed Data Analytics Services and Solutions | XenonStack

Navdeep Singh Gill | 23 December 2022


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Overview of Managed Data Analytics

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. From Customer 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.
Analytics At work Davenport, Harris and Morison

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. Analytic processes maturity model (APMM) by Robert L.Grossman
  • 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. From the Article, Enterprise Search Service Offerings

What are the skills required for Managed Analytics?

 Data analytics requires knowledge of more than just analytics. To collect the required data Data Engineering and Operations skills also matter, which is usually termed data ingestion. Data Ingestion is not a one-time process; it needs regular data updates and monitoring for any changes in the data structure.

Data Ingestion

Data is not always readily available. If it is available, required data to be distributed in more than one space. Fetching data is one first and foremost task here to work on it. There are different ways to fetch data to storage, where basic analytics will happen. API and regular Update jobs are two of them.

Data Engineering

Different Programming skills required for the is mold or derived for requirements.

Different type of Analytics

Analytics has different purposes, which are defined based on the business requirements. The analytic reports always point to the business value of the data. Different types of analytics are applied to various phases of projects and hold particular importance.

  • Descriptive analytics
  • Diagnostics
  • Predictive analytics
  • Prescriptive analytics
Big data makes the insurance industry a perfect sphere for data analytics to construct basic patterns. Click to explore about our, Data Analytics in Insurance Industry

Benefits of enabling Managed Analytics?Five Stages of Analytics Maturity Davenport and Harris

Effective Business Insights

Managed Analytics services extend practical business insights and support businesses to Identify KPIs within data and further facilitate better Business Decisions.

Leading Analytics Tools

Leverage and make the most beneficial use of the best tools and technologies to simplify operational procedures and analytical processes.

Reduced Total Cost of Ownership

With Managed Analytics solutions Businesses can decrease Total Cost of Ownership (TCO) and enable Quick, responsive access to data and facilitate real-time operations

Customer 360 Degree View

Building a 360-degree customer view helps in Customer alignment, Drives customer Intelligence and Customer Loyalty.

Big data analytics has always been a fundamental approach for companies to become a competing edge and accomplish their aims. Click to explore about our, Latest Trends in Big Data Analytics

What are the reporting tools used for Analytics?

The reporting tools are built to ease the visualization and better presentation of your data. The Analysts can spend more time on business requirements and logic than creating visual presentations.

Most reporting tools have drag-and-drop functionalities, which makes it easier to start with. Data modeling and integration features make these tools more favorable for analysts.


Excel is an easy tool to work for analysis. It is simple and has excellent functionalities. Formulas and Macros can take it to another level. At the same time, the abilities are limited. Built-in Pivot table and other visualization features make the analytics reports easy to understand.

Power BI

Power BI is designed to make reporting more accessible, with hundreds of built-in features. It has various data connection abilities to connect with more than 100 different databases. Easy steps and drag and drop make it the first reporting analyst choice. Data modeling and DAX usage can be tricky to start. Also, setting up gateways and Optimisations need more knowledge of the tools.


Tableau is another tool to visualize the data. It includes Tableau Desktop, Tableau Server, Tableau Online, Tableau Vizable, Tableau Public, and Tableau Reader. WhereTableau has better and easier features than PowerBI, and at the same time, it is expensive.


SSRS is an SQL Server Reporting Services product by Microsoft. It has limitations and is cheaper than both PowerBI and Tableau.

Artificial Intelligence and Cognitive Automation solutions for empowering enterprises to improve business outcomes and facilitate Enterprise Data Strategy. From the Article, Artificial Intelligence Services and Solutions

Managed Analytics Service Offerings

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
Java vs Kotlin
A process that describes task description, time requirements, Deliverables, and pitfalls. Download to explore the potential of Data Warehouse

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.
  • Data Quality and Governance
  • Master Data Management
  • DataOps Solutions
  • Data Visibility

Business Intelligence and Data Visualization

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

Building Real Time Integration Platform with Data Governance and Data Quality Best Practices. From the Article, Data Catalog Platform for Data Driven Enterprise

Managed Analytics with Xenonstack

Xenonstack helps businesses with the best possible solutions using all the latest technologies. The teams here are well-versed in all data management and analytics techniques requiring data-driven decisions.


Transform your
Enterprise With XS

  • Adapt to new evolving tech stack solutions to ensure informed business decisions.

  • Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions.

  • Leverage the True potential of AI-driven implementation to streamline the development of applications.