A mid- to large-sized Professional Services firm offers a broad range of accounting and consulting services to clients in both B2B and B2C markets. When they decided to create a centralized team to execute their vision for data governance, data management, data infrastructure management and data analytics development, they discovered that they had an exciting task at hand!

The roadmap included strategic layout of data storage technologies, specialist technologies for a newly hired group of data scientists and eventually a larger roadmap of technology and talent acquisition for the broadening variety of data being curated. The organization’s vision scaled in a span of two  years as they went from only providing real-time analytics for strategic decision making to feeding data aggregates back into the system to enable efficiencies in identifying the right client/project profile to justify the cost of delivery.

The analytics derived from the many different data domains helped them make strategic decisions within their M&A operations for new market penetrations – either by offering existing services in newer geos or acquiring businesses that offered services that tagged on their existing ones as a value-add. The analytics and data aggregates drove bottom-line efficiency improvement initiatives, cost cutting measures and resource allocation workflows for their time and billing deliveries. A true success story.  

All businesses like the one we’ve just described want intelligence to drive their business growth in the present-day world. They want to provide a personalized customer experience through hyper-personalization and reduce operational costs and time. In a nutshell, businesses want to create value from their data and thus are scaling their ability to capture and store large amounts of data. Existing data management techniques, however, are still playing catch-up. After all, a traditional monolithic approach to data architecture – where data is ingested into a central data store from the many different data sources to centralizing the data transformation so it is cleansed, enriched and serving the many different consumers within the organization – just may not be scalable as the volume, variety and velocity of data continues to grow.   

Imagine the simple scenario of delivering customer information to the various stakeholders at the Professional Services firm for day-to-day operations within the context of each operational domain: 

The firm faced several challenges as they kicked off their journey: 

1. Centralizing the ingestion and storing of the data meant the architecture needed to keep up on the response time to the constantly increasing number of data sources  
2. Varied areas of operations where this data is being consumed, as shown above, and the endless list of use cases from each, leading to multiple issues: 
3. Data team’s unmanageable and ever-growing backlog for analytics and aggregates constantly hit with priorities from each business group for their independent requests. 
4. Data team’s turnaround time for the above requests with missed priorities that could have been critical for a business group’s strategic decision or time to market.  
5. The centralized Data team being removed from the day-to-day business operation and business context of the related data. 
6. Talent attrition in the Data team due to team members being stretched thin and not realizing personal growth within a specific domain, leading to loss of information for the organization. 

As the firm charted out a path towards recourse, they made several changes to address these challenges and progress towards their Data Strategy maturity: 

1. Grouped master data domains within business context to enlist data owners and stakeholders from varied business groups 
2. Identified data assets and refined the list to action-enabling data within the data catalog as well as broadened the data sets to non-transactional data 
3. Identified gaps in definitions, labels, storage and movement of the data assets to include all use case scenarios, thus focusing on people, processes and systems overlapping and interweaving in the organization’s data ecosystem 
4. Identified gaps in IT oversight to protect the data assets through IT policies and standards 
5. Implemented a global governance body to standardize data discovery, data integrity, data definitions and data security 
6. Developed an ongoing education plan for continuous management and improvement of data assets 

Taking the next steps towards maturing from here, organizations like this will realize that data-centric architecture is going to be the main driver of a technology system/platform to deliver business and strategic value from their data assets. One of the newer architectural patterns is called data mesh, which identifies data as a product. It reinforces that data, as a product, should be discoverable, trustworthy and secure – and should scale over time. It also defines data as being domain bound. When data domain is established within the business context and data ownership is rooted within this premise, it promotes data integrity through its lineage and its lifecycle.  The present-day Data Governance & Analytics bodies promote the idea of data stewardship and ownership within the bounds of business context, thus giving rise to long term ownership commitments and talent growth, which become fundamental to datacentric organizations. 

There is an increasing need for governance and oversight across the various data domains in businesses. Data mesh architecture brings forth the idea of global federated governance with a strong central governing body, which has an impact on the organization as a whole. Identifying all data products, ensuring they are cataloged, establishing data quality metrics, documenting identity management techniques, documenting encryption needs for data in rest and motion, establishing education plan for responsible data sharing within the data classification scheme needs and maintaining up-to-date data retention policies are all areas where governance needs to be established so businesses can start realizing significant returns on investments from their data.    Fundamental to your data strategy is owning the right technology platform that can help you establish your foundation today with a plan to scale. Microsoft’s Cloud Adoption Framework details out the many different architectural approaches for planning your technology roadmap and data strategy. Please visit our website to learn more about the Microsoft applications that can help you get started.