Whether you’re solving a business problem or tackling a technical challenge, success is in the details. I was reminded of this fundamental principle by two recent blog posts by Sean Bryson, Vice President of Microsoft Technology at Hitachi Consulting, digging into Workplace Analytics (WpA) and the idea of a WpA-powered Center of Excellence (CoE). One of Sean’s points, which I want to expand on here, is that advanced analytics can identify wellsprings of real value and drive organizational change—if you sweat the right details.

Advanced Data Analytics as the Driver

The purpose of a WpA-powered CoE is to bring business and people data together and apply advanced analytics techniques such as machine learning (ML) and data science to drive evidence-based, quantifiable decision-making. Setting up a CoE entails gathering and orchestrating all kinds of internal and external data, enabled by an up-to-date data and analytics reference architecture. 

Here is an example use case for WpA-powered CoE: extracting customer-facing insights for a service provider. To obtain those insights, the analytics will require a broad cross-section of customer information, housed in a data lake. The information includes:

  • CRM data: Customer-level detail on wins and lost sales opportunities, including specifics of the offers being pitched
  • Discover Org data: External data about sales prospects, which can be overlaid onto the customer profile
  • Financial information on customers pulled from Dun & Bradstreet. Machine learning can be used to detect patterns that predict future spend and financials and applied to other customer data
  • Legal data: Details on master service agreements (MSAs) in place (if any), their age and expiration dates, among other information
  • Customer Surveys Data: TDirect customer feedback and perspectives
  • ERP data: Details of deliveries and billing, project staffing, other information
  • Social Media and Marketing Data: Additional details of social media activity and other targeted activity, which data scientists can use for correlation modeling
  • Internal Data: Details of the number of people in the organization who interface with a customer; the network depth of those working on a customer account and the time spent collaborating with the customer team through email, meetings, instant messages and other means; the time sellers allocate to each account, set against the customer’s financials to gauge whether sellers are focusing too much or too little on specific accounts, based on their lifetime value

Data Analytics Platform Choices

As that catalog of data types suggests, any effective data platform in today’s digital environment must be capable of storing and analyzing data that may stretch the traditional limits on the volume, variety and velocity of big data (the famous “3 ‘V’s,”). It must also be able to rapidly deliver a fourth “V”: value. Of all the data analytics platforms on the market today, be they on-premise, cloud or hybrid, Azure is one of the best that I have encountered. It swings a powerful punch across all 4 V’s.

Last year, when Microsoft and Databricks announced their ground-breaking partnership, they brought the de facto winner of the big data movement (Spark!) into Azure as a platform-as-a-service (PaaS) offering. Azure Databricks adds levels of integration, ease of use and considerable power. Better yet, it combines with tools like Azure Data Factory and Azure SQL DW to create a singularly compelling platform for the cloud-scale analytics that can make a WpA-powered CoE a reality. It integrates seamlessly with PowerBI and offers self-serve, highly intuitive data analysis and visualization.

Why Azure Databricks

Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. Databricks is integrated with Azure to provide one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.

As illustrated in the image above, Azure Databricks integrates effortlessly with a wide variety of data stores and services such as Azure SQL Data Warehouse, Azure Cosmos DB, Azure Data Lake Store, Azure Blob storage, and Azure Event Hub. Power BI instantly adds and integrates artificial intelligence capabilities to derive insights.

Maybe the most important lesson I’ve learned in my 20-plus years of consulting is that top performance flows from the right data and insights. A scalable and modern platform like Azure Databricks is key to gaining the insights that work through people and culture to improve an organization. And that, in the end, is what digital transformation is all about.