In today’s data-driven world, effective data analytics is no longer a competitive advantage, but a business imperative. Extracting valuable insights from vast troves of data enables companies to make informed decisions, predict market trends, and foster a data-driven culture to drive significant business value.

“A traditional EA approach that focuses on technical and solution architecture is no longer enough to meet the needs of today’s enterprises. Today’s enterprise architects are responsible for designing intelligence into the business and operating models, identifying ways to help their organization use data, analytics, and artificial intelligence to plan, track, and manage digital business investments.”

– Saul Brand, Senior Director Analyst, Gartner

A McKinsey report estimates that data-driven insights can generate up to trillions in value annually across the global economy, with generative AI alone potentially generating $2.6 trillion to $4.4 trillion across industries. The firm also found that organizations that made data-driven decisions are 23 times more likely to acquire customers.

Enterprise architects play a crucial role in unlocking this potential by providing a visual blueprint of the organization and showing key relationships between data, processes, applications, and strategies. An architect’s ability to optimize fragmented processes, information, and technology into an integration environment facilitates better decision-maker and change implementation – which are both crucial to a business landscape that is evolving at unprecedented levels.

Here are seven ways enterprise architecture (EA) can act as a bridge between raw data and strategic decision-making to empower data analytics:

1. Visualizing Key Relationships

One of the core strengths of EA lies in its ability to visualize the organization’s data landscape. Through techniques like data flow diagrams and entity relationship models (ERs), EA provides a clear picture of how data flows throughout the organization, highlighting key relationships between entities and data points.

This visual representation allows data analysts to:

• Identify data lineage: Tracing the origin and movement of data points becomes straightforward, allowing analysts to understand the context and reliability of data used in analytics.
• Uncover hidden connections: ER models reveal previously unseen relationships between data sets, potentially leading to the discovery of new insights and opportunities.

Case study: Maintaining Security While Enabling Growth

Organization: Cybersecurity services company – Sophos

Objective: As Sophos grew rapidly – doubling in size in just five years – they needed to address the company’s growth while ensuring cybersecurity best practices, in addition to finding  better ways to visualize their architecture as data.

How EA empowered data analytics: Sophos’ EA team built an application catalog to determine the security risk of each application. They did this by creating a matrix of potential risks based on the probability and impact of attacks. This algorithm was continually updated to reflect improvements in their application and reduce risk. This visualization also helped higher-level officials see the value of each application to reduce cybersecurity risks – which has major financial and reputational implications.

2. Identifying Data Gaps

A robust EA strategy helps identify data gaps, which is critical as missing information hinders comprehensive analysis. These gaps might be technical such as inconsistent data formats or strategic such as lack of customer sentiment data.

Either way, pinpointing these gaps is how EA can empower data teams to:

• Prioritize data collection efforts: Allocate resources towards capturing missing data points crucial for specific analytics initiatives.
• Develop data acquisition strategies: Implement solutions like data integration tools or customer surveys to bridge identified data gaps.

3. Aligning Data Initiatives with Business Strategies

Beyond simply highlighting gaps, modeling processes by enterprise architects will also indicate where additional data capture or integration could provide valuable insights from a business perspective, thus ensuring proper correlation across data sources. This in turn leads to better aligning of data initiatives with business strategies. After all, data analytics thrives on a unified vision.

EA facilitates alignment between business strategies and data initiatives by:

• Mapping data assets to business goals: Clearly define which data sets are critical for achieving specific business objectives.
• Standardizing data governance policies: Ensure data quality and consistency across departments, fostering trust in analytical results.

For example, an EA-driven approach can ensure that a company’s customer churn reduction strategy aligns with the collection and analysis of customer satisfaction data.

The guiding framework for assigning business metrics to strategic data goals and initiatives provided by enterprise architects allows for more effective evaluation of data analytics tools, plans, and solutions.

4. Supporting Data Governance

Data governance plays a critical role in data analytics and enterprise architects help determine the dependencies of data governance programs on architecture components to ensure new data analytics initiatives properly consider privacy, metadata management, data quality, master data management, and other governance concerns.

EA supports data governance by defining:

• Data ownership and access controls: Clearly define who owns specific data sets and who has permission to access them.
• Data security protocols: Implement security measures to ensure data integrity and prevent unauthorized access.

Robust data governance fosters trust in the data, encouraging collaboration between business stakeholders, IT teams, and data analysts.

Case study: Empowering data users while ensuring governance

Organization: Pet health and wellness MNC – Petco

Objective: The company wanted to empower data users with self-service capabilities while ensuring appropriate data governance, security, and consistency.

How EA Empowered Data Analytics: The company established a data governance framework that maintained data use guardrails that still allowed self-service. The framework also fostered a culture of data literacy among data users by promoting the development and definition of business metrics. This led to data users becoming data champions who felt ownership and responsibility over the data quality.

5. Providing Context for Analytics

Data analytics alone paints a limited picture. EA provides context by integrating business processes, organizational structure, and external factors into the data ecosystem. Broad metrics such as key performance indicators (KPIs) and conversions are related to architectural components, therefore providing critical context for evaluating insights and identifying drivers of performance, risks, costs, and more.

This enriched context empowers analysts to:

• Understand the “why” behind the data: Relate data points to specific business activities, providing a deeper understanding of trends and patterns.
• Develop more nuanced insights: Analyze data within the framework of relevant business context, leading to more informed recommendations.

For example, a retail chain might use EA to contextualize sales data with customer demographics and promotional campaigns. This contextual analysis could reveal which demographics are most responsive to specific promotions, enabling targeted marketing strategies.

6. Modeling Future Changes

EA empowers data teams to go beyond analysis and venture into the realm of prediction. By creating data models that mimic real-world processes, EA facilitates the simulation of “what-if” scenarios and analysis of potential impacts, evaluation of options, and stress-testing decisions.

 This allows businesses to:

• Test the impact of potential changes: Experiment with different business strategies in a simulated environment, minimizing risk when implementing real-world changes.
• Optimize resource allocation: Simulate the impact of resource allocation decisions to identify the most efficient strategies.

For example, an airline might use EA data models to simulate the impact of fuel price fluctuations on its flight schedules and pricing strategies.

Case Study: Forecasting Future Roadmaps from Ongoing Changes

Organization: Higher education institute – University of Edinburgh

Objective: The Research Excellence Framework (REF) grading challenge that is compulsory for all universities in the country is particularly complex given its unique way of grading research and research impact. This was evolving while the university’s own IT estate was changing. The university needed to balance its current REF return with future changes to the framework and its IT estate.

How EA empowered Data Analytics: The EA team mapped all processes involved with key stakeholders, applications, and datasets before creating a series of potential architectures to inform them of possible changes to help identify areas of focus and a relevant timeline. This helped the team track and input changes to their IT estate and the national REF and create a timeline for future critical time points.

7. Standardizing Enterprise-wide Data and Analytics

Data siloed across departmental applications creates a significant roadblock for comprehensive analytics. Enterprise architects, on the other hand, promote integration, interoperability, modularity, and reuse. This strengthens the overall data analytics environment and minimizes redundant or inconsistent data sources.

EA promotes standardization through:

• Data modeling standards: Define consistent data definitions, formats, and naming conventions across departments.
• Common analytics tools and methodologies: Ensure all data teams utilize similar tools and techniques, facilitating collaboration and knowledge sharing.

Standardization ensures data interoperability, allowing seamless integration and analysis of data from diverse sources.

By implementing a well-defined EA strategy that considers analytics capabilities, organizations can transform their data into a powerful asset that drives informed decision-making, fosters innovation, and ultimately propels business success.