Marketing Analytics Using Azure Synapse - Part 2

In part one of this blog series, we walked through many of the common issues seen today with traditional data platforms that automate Marketing Analytics, including siloed data, separate setups for performance reporting and data science workloads, and inflexible use cases that prevent easily deriving new insights.

We discussed some of the characteristics of a more efficient Marketing Solution, including having a Data Lake with clean curated raw data to feed downstream data stores and maintaining an environment that allows Data Scientists and Analysts to experiment on their own without having to rely on IT for everything. Enabling Analysts to spend their time focusing on deriving insights rather than dealing with data issues and fighting to meet report SLAs should be a top priority of any Marketing Analytics solution.

Lastly, we introduced Azure Synapse as an end-to-end Marketing Analytics solution that enables its users to ingest curate and explore data, create an enterprise data model, and execute workloads to answer business questions on demand. Now we are going to dive into more Azure Synapse capabilities to see how they can solve some of the challenges seen in implementing traditional Marketing Analytics solutions.

Believe it or not, Data Ingestion is the single most common problem that creates data silos, limiting media analysts in their ability to answer even simple questions such as “What is my Total Spend across all campaigns?”. Synapse addresses this issue by delivering 85+ out of the box data connectors that will allow you to connect with typical media data connectors and collect data quickly. This includes Media Sources, CRM Sources, Traditional Data Sources and others allowing for easy cross platform data migration.

Not able to find connectors in Synapse? Contact us and we can help you build custom connectors for Synapse that can help you meet your business objectives

Data Pipelines: When it comes to setting up an enterprise media data store to standardize metrics across the organization, we need both power and flexibility to crunch data. Azure Synapse data pipelines provide both ease and power to develop big data processing capabilities particularly to overcome the challenges involved in media processing data. Data Quality is a big issue when it comes to processing media data as it involves dealing with data coming from 100’s of publishers with varying levels of complexity. Thanks to tools like Data Flow Wrangling and Data Mapping, which provides WYSIWYG interfaces to review Data Quality issues and automate the fixes, data ingestion has never been easier. Synapse also provides advanced workflows to execute Spark scripts to process big data with complex data transformation logic.

One of the biggest strengths of synapse is that Azure Synapse lies at the center of bringing all data professionals together so that they can achieve more and accelerate the time to insight. It can do so with Synapse Studio, a hub which brings frictionless collaboration for Data Analysts using Power BI, Data Engineers using Azure Synapse, and Data Scientists with Azure Machine learning. It provides web-native experience that ties everything together for data engineers, allowing them to do every task needed to build a complete solution, all in one location.

Traditional solutions would have required 2 separate implementations of the data platform. One to build enterprise data models to meet client SLAs and another data lake analytics based, solution to process data science workloads. But Azure Synapse is different, it brings together solutions that address both approaches of Data Lakes and Data Warehouses in a service that provides limitless analytics.

Enterprise Data Store: Enterprise models consolidate and standardize media metrics that can be used across various downstream systems including but not limited to Media Performance Reporting, Ad Operations Reporting, Data Science Workloads and Ad Hoc Workloads. This also helps setup a centralized Single Source of Truth for the entire organization. Additionally, they enable:

• Consistency: Standardize business processes such as Taxonomy Management and Cost Corrections, as well as standardize media metrics, etc. that can be built once and shared as an organization standard.
• Efficiency: If models already exist in a central data store, there is no need to repeat the work of design, preparing and loading data, or securing the model.

Advanced Analytics: Advanced Analytics and Machine Learning is the #1 investment for Business Leaders. However, most organizations struggle to maximize ROI because, according to a recent study published by Harvard Business Review, 80% of organizations struggle to attain the data maturity necessary to implement Machine Leaning enabled solutions. With Azure Synapse, Data Scientists can now import models developed externally and execute them within Synapse using native SQL or Spark scripts. The power of having a single platform that will allow data scientists to explore data, run ML models, and visualize the results with integrated Power BI dashboards, is that it truly accelerates access to deeper insights about the business.

Pricing: Synapse’s pricing is based on usage of services across Storage, Transformation, Reporting, and extended capabilities, such as Machine Learning. This provides organizations the flexibility to scale operations by Storage, Compute and Delivery independently depending on their workloads, allowing for optimization of operational costs for implementing and managing Data Platforms. According to a study from GigaOm, which recently benchmarked all the top cloud data warehouse providers, found that for every dollar spent on cloud infrastructure for data analytics solutions with Amazon Redshift and Google BigQuery, customers using Azure pay $0.75 and $0.06, respectively. This means that solutions implemented using Azure Synapse can be 25% less expensive than Redshift and 94% less expensive than Big Query when implemented the correctly.

Synapse offers multiple levels of security to secure data in the warehouse and Data Lake. With a comprehensive security plan, all at no additional cost, it is easier to implement security compliance standards such as GDPR, CCPA, etc.

• With threat protection, get notified of potential threats with auditing tools and anomalous activity alerting
• Leverage network security to ensure that all the data is within the secure environment
• Centrally manage and control identity and user access with Azure Active Directory
• Granular access control to data in both data lake and warehouse
• Data is protected at rest, in transit and in use.

With tight integration of Synapse analytics with PowerBI, Data Analysts can now standardize and deliver powerful reporting without having to depend on IT to create every single metric that is delivered to client. Synapse and Power BI enable Tech-smart Analysts to explore data with agility and meet the customer demands with ease. Enabling interactive data exploration, self-service analytics, predictive analytics and machine learning, and prescriptive analytics.

Conclusion: All in all, Azure Synapse Analytics will allow organizations to implement an efficient Marketing Analytics platform by bringing Data Engineers, Data Analysts and Data Scientists together improving collaboration and productivity. With enterprise data warehousing and big data analytics capabilities, Azure Synapse provides media organizations the foundation to implement Advanced Analytics and Predictive Analytics solutions.

Contact us for a Demo on how to implement Media Analytics Platforms using Azure Synapse Analytics.