Azure Advanced Analytics Enablement

Uncover hidden insights from disparate data, improve decision-making, optimize processes, enhance customer experiences, and drive innovation with advanced analytics on Azure.

Traditional descriptive analytics can provide valuable insights into past events; however, they have limitations in terms of predicting future outcomes, explaining causality, providing a comprehensive understanding of complex systems, and providing actionable recommendations. To overcome these limitations, organizations may need to leverage more advanced analytics techniques, such as predictive or prescriptive analytics.

Making the shift to a predictive approach using advanced analytics can have a significant impact on various aspects of your business, such as enhancing customer experience, optimizing supply chain operations, developing new business models, and much more. It’s essential to have a comprehensive understanding of the potential of advanced analytics and how to apply it to your unique business challenges. With the right approach, advanced analytics can provide you with the right insights to stay ahead of the curve and drive business success.

Why Embrace Azure Advanced Analytics?

Azure advanced analytics enablement can help organizations make better decisions, increase efficiency, gain a competitive advantage, improve customer experiences, and mitigate risks.

Improved Decision-Making

With advanced analytics, organizations can gain valuable insights from their data to enhance data-driven decisions at all organizational levels and improve their overall business performance.

Increased Efficiency

Advanced analytics can help organizations automate processes and identify inefficiencies in their operations. This can help them streamline their workflows and reduce costs.

Competitive Advantage

Advanced analytics can give organizations a competitive advantage by providing them with insights that their competitors may not have. This can help them make better strategic decisions and stay ahead of the curve.

Better Customer Experiences

Advanced analytics can help organizations better understand their customers’ needs and preferences. This can help them tailor their products and services to better meet their customers’ expectations and improve their overall customer experience.

Better Risk Management

Advanced analytics can help organizations identify potential risks and mitigate them before they become major issues. This can help them avoid costly mistakes and ensure the long-term success of their business.

Higher Innovation Potential

Advanced analytics make it easier for organizations to embrace innovation, meet compute demands, embrace scalable big data processing tools, and leverage out-of-the-box data streaming capabilities.

Featured Success Stories

Azure Advanced Analytics Use Case

Adastra supports your organization through all phases and transfers the required knowledge to support your Azure analytics solution. As a Gold Microsoft Partner, you can tap into our vast expertise and let us help you plan, build, and maintain your move to Azure advanced analytics. In this Azure advanced analytics case Adastra recommends using Azure Synapse Analytics and follows the below Azure cloud data zones:

  • Data stored in a source schema structure with no transformation 
  • Varying formats for data storage (Parquet, SQL, csv, orc, dat, json, XML, …) 
  • Low effort to implement 
  • Accessed by IT Professionals 

  • Data stored in a source schema structure with limited transformation
  • Common format for data storage, i.e., Parquet, SQL
  • Data stored in data lake/delta lake.
  • Low effort to implement
  • Accessed by IT professionals/data analysts/data scientists

  • Data stored in an enterprise model schema with significant integration/transformation
  • Common format for data storage, i.e., Parquet, SQL
  • Data stored in a delta lake/SQL table
  • High effort to implement
  • Accessed by IT professionals/data analysts/data scientists

  • Data Stored in a BI model schema aligned to self–service analytics, by subject area 
  • Common format for data storage, i.e., SQL (physical), view (logical), tabular (options) 
  • Data stored in a BI model 
  • Medium effort to implement 
  • Accessed by business analysts 

Approach to Azure Advanced Analytics 

Adastra’s approach to Azure advanced analytics involves defining the business problem, gathering and preparing data, selecting an analytics tool, and building, validating, testing and deploying analytics models. Following these steps can help organizations leverage advanced analytics to gain valuable insights and improve their business performance.

01

Define the Business Problem

Identify the specific business problem or use case you are trying to solve with advanced analytics. This could be anything from improving customer retention to reducing supply chain costs. It is important to prioritize business requirements and processes to build a high-level solution design.

02

Gather and Prepare Data

Gather the data you will need to analyze, and ensure it is clean and properly formatted. This includes data from various sources such as databases, spreadsheets, and sensors. It is essential to ensure that the data you use is accurate, reliable, and complete.

03

Establish Technology and Integrations

Choose an appropriate analytics tool to perform the analysis and validate technology choices. Azure offers various analytics tools such as Azure Machine Learning, Azure Databricks, and Azure Synapse Analytics. Depending on the specific business problem, you may need to choose different tools or a combination of tools.

04

Build Analytics Models

Use the analytics tool to build models that can analyze the data and provide insights. This may involve selecting an appropriate algorithm, setting parameters, and tuning the model to get the best results. Perform platform design reviews. Develop solutions, implement user stories and prepare training materials.

05

Validate and Test Models

Validate the models by testing them against historical data, comparing them to other models, and conducting experiments to ensure they are accurate and reliable. It’s essential to ensure that the models you build are valid and reliable before moving to the next step.

06

Deploy and Monitor

Deploy the models in a production environment and monitor their performance. This includes monitoring data inputs and outputs and analyzing the results of the models. It is important to continue monitoring the models and adjust as necessary to ensure their ongoing accuracy and effectiveness.

Put Advanced Analytics to Work on Azure

Let’s identify the forecasting, optimization and customer use cases in your data and plan how to deliver them on Azure.

Azure Advanced Analytics FAQ

What is advanced analytics?

Advanced analytics goes beyond the historical reporting and data aggregation of traditional business intelligence. It uses mathematical, probabilistic and statistical modeling to enable predictive processing and automated decision-making. Instead of only describing what happened, predictive and prescriptive analytics help you forecast outcomes, explain causes and get actionable recommendations for your business.

Advanced analytics helps you make better decisions at every level, automate processes and remove inefficiencies, understand customer needs and tailor products and services, and identify risks before they become major issues. It also makes innovation easier, with scalable big data processing and out-of-the-box data streaming capabilities that meet growing compute demands.

Apache Spark in Azure Synapse Analytics is Microsoft’s cloud implementation of Spark, a parallel processing framework with in-memory processing for big data applications. It offers speed and efficiency, ease of creation and use, preloaded libraries for machine learning, data analysis and visualization, and scalability. Azure Machine Learning and Azure Databricks are also options.

You bring structured, semi-structured and unstructured data together in Azure Data Lake Storage, then use Apache Spark pools to clean and transform it. The results can be queried in Power BI or moved to Azure Cosmos DB, so web and mobile apps can use the insights. This keeps one consistent flow from raw data to business use.

For data engineering, Spark prepares and processes large data volumes in C#, Scala, PySpark, Spark SQL or R. For machine learning, you can use MLlib and the Anaconda distribution directly from a Spark pool. For streaming, Spark structured streaming runs continuously, so Adastra sets up monitoring and restarts for errors and maintenance.

Adastra recommends Azure Synapse Analytics with four data zones. The raw zone keeps source data untransformed, the standardized zone applies limited transformation in a common format, the curated zone integrates data into an enterprise model and the provisioned zone holds BI models for self-service analytics. Each zone serves IT professionals, data scientists or business analysts.

Adastra follows six steps: define the business problem, gather and prepare data, establish technology and integrations, build analytics models, validate and test them against historical data, and deploy and monitor them in production. As a Gold Microsoft Partner, we support every phase and transfer the knowledge your team needs to run the solution.