Six Attributes of Successful Data-Driven Organizations

The ability to make data-driven decisions is an essential component of an agile work environment. It empowers teams to make fact-based decisions quickly and efficiently, and can accelerate effective responses to ever-changing customer needs.

The shift to a data-driven approach to decision-making often requires both cultural and operational changes. Some companies require help properly collecting and analyzing data, implementing processes and making decisions. Others need employees to become data literate, organize around business or functional domains, and leverage data for decision-making support.

Successful data-driven companies will make data securely available across the organization, integrate that data into their applications, establish proper governance controls and use their data to make critical decisions. Businesses should analyze data from product, sales, marketing, customer and market perspectives, then use the findings from those analyses to support research and forecasting efforts.

Keep reading to learn more about the six building blocks that I believe underpin the operations of data-driven organizations.

Six attributes of successful data-driven organizations:

1. They build and use smart, data-aware applications

What this enables:

• Decision support capabilities are built into applications to power innovation
• You can augment predictions and interactions with AI and machine learning

In a data-driven environment, all applications are designed and optimized to allow users to get the most from data using the latest technologies, including AI models. And we’re talking about all applications within the organization, from those that support internal processes to those that are customer facing. A smart application uses advanced algorithms to learn and adapt to user behavior, automate tasks and provide personalized recommendations. Users today can curate data, build machine learning models, and use the data and AI models to create smart applications.

The next step after creating data products is to create smart applications and this is being facilitated by a new wave of self-service analytics capabilities, which allow citizen users to quickly build analytic applications using no-code / low-code advanced analytics solutions.

The buildout of smart applications is also a significant step in the process of creating new data products. These products form the foundation for a new crop of self-service analytics capabilities where citizen users are quickly able to build analytic applications using no-code / low-code analytics solutions.

Real-world customer scenario:

A global technology leader integrated machine learning models into its water industry management systems to enable predictive maintenance and reduce costs for their customers.

How can you get there:

• Upskill employees to make them self-sufficient through data literacy programs
• Build tooling that allows you to create applications quickly with no-code/low-code capabilities
• Harness the power of AI to transform your products, consumer applications, workflows and business processes

2. Their business is connected

What this enables:

• Connected streams of data can improve business models and drive new revenue streams
• Increased collaboration of data products through federated and/or event-driven architectures

To avoid silos, data-driven organizations must connect every business unit and integrate as much data as possible. Each business unit can manage its own data, but it should also be integrated into domain-driven data models. The connected data supply chain acts as the neurons of the organization.

Linking different groups and data sets together creates cohesiveness and unity. Think of it as being a bit like how the human central nervous systems help us breathe, think, speak and do everything we do. By connecting all their data, organizations can gain insights, share knowledge and make more informed decisions.

While technology helps provide the connective tissue, it's equally important to emphasize the significance of organizational roles and responsibilities that shape how the data comes together. This includes roles associated with quality controls, shared-state data dictionaries, and ensuring the discoverability of the data.

Real-world customer scenario:

A global furniture retailer achieved economies of scale and increased collaboration and efficient business reporting by consolidating its wide array of source systems within each individual country and creating a global federated model that allowed them to roll out international operations with ease. 

How to get there:

• Define a data strategy that allows you to employ a connected architecture pattern designed to fit your organization’s size and scale
• Upskill your existing talent and bring in selective new talent that can utilize these architectural patterns and build a connected enterprise
• Invest in a technology and governance road map that is aligned with your workloads and team needs

3. They are adaptive to change

What this enables:

• Quicker pace of innovation with the agility to meet rapidly shifting trends in technology
• Re-useable patterns to accelerate building data products

As data becomes embedded in business processes, it must remain resilient both upstream and downstream. This requires open cultures, agile architectures and the ability to adjust strategies in response to change.

Reusable patterns can accelerate the process of building data products and enabling composable analytics and applications. It's essential to replace outdated data lakes and Hadoop systems with newer architecture patterns. Creating a flexible data operating model and architecture is a critical aspect of preparing for technological shifts — and for staying ahead.

As business processes continue to evolve, automation reduces complexity. It's not just architectures that need to be adaptable; productivity gains can also be achieved through the adoption of low-code/no-code tools, allowing for greater agility and easier accommodation of change with lower tech debt.

Real-world customer scenario:

A global financial analytics firm was able to innovate and accelerate value for its end customers by processing hundreds of millions of data points across hundreds of thousands of attributes every day recomputing the day’s scores for firms worldwide.

How to get there:

• Leverage a metadata driven approach to creating repeatable patterns for data pipelines for common data transformations 
• Reduce technical debt related to specific third-party products or black boxes
• Keep the data easily accessible from a wide variety of platforms and the transformations void of proprietary lock-in to adapt to evolving trends and requirements

4. They prioritize governance

What this enables:

• Consistent access to trusted, self-describing datasets on an internal data marketplace
• The ability to address compliance, security and auditing needs based on a zero-trust framework

Building trust into everything you do with data is the key to creating a strong foundational capability for data management. It’s important to ensure that data is both secure and compliant. Data is a valuable asset, and you should prioritize its protection from external and internal threats. Implementing zero trust keeps you from working under assumptions of perimeter security.

Another reason to prioritize data governance is to prevent failures. When your business makes decisions based on incomplete or inaccurate data, you risk misalignment, wasted resources and potential business losses. The data-driven organization prioritizes compliant data management to help prevent these issues and ensure data accuracy and security.

Governance also involves the federation of access controls and the creation of trust domains outside of specific groups, business units and even the organization as a whole. Technological advancements, such as data clean rooms, facilitate secure and compliant data sharing with customers, partners and suppliers. This controlled access to data enables innovation and opportunities that were not previously available.

Real-world customer scenario:

A science and engineering non-profit built a target operating model and governance framework, which enabled it to expand its data analytics program from fewer than ten analysts in 2020 to more than 100 by 2022.

How can you get there:

• Build verified datasets that guarantee the quality and trustworthiness of data
• Establish governance controls based on a zero-trust framework that provide the required security while allowing businesses to effectively utilize the data
• Enable data loss prevention, tokenization and data encryption at various layers of data access
• Leverage AI to identify and handle compliance and privacy requirements

5. They ensure scalability

What this enables:

• Improve TCO & ROI of your data estate
• Democratize your data through sustainable architectures and platforms

With the constant proliferation of data, it's crucial to build scalable data platforms that can handle storage, processing and querying while also managing costs effectively. A business seeking to become data-driven needs to promote scalability to ensure its data processing requirements can effectively adapt to future needs and demands.

As data quantities and use cases grow, companies must scale seamlessly to prevent business interruptions. Scalability and resiliency should be key architecture considerations of every data solution. Since demand spikes can be unpredictable, it's crucial to build systems that can scale up in seconds to meet demand and scale back down during non-peak usage.

Real-world customer scenario:

A major telco and utility billing customer modernized from a legacy database to a modern, open source database and application portfolio, allowing it to leverage the scale benefits of the cloud and open up its data for use in advanced analytics workloads. 

How can you get there:

• Design data platforms that leverage serverless architectures for data processing and querying
• Put sustainable architectures at the core of building scalable systems designed to control costs, reduce waste and meet peak demand needs

6. They strive for operational excellence

What this enables:

• Workflow automation and increased observability that can drive efficiency
• Self-healing systems that are resilient against system failures, evolving schemas and data integrity issues

Finally, the data-driven organization must be resilient against systems failures, operational failures and human errors. A platform is only useful if you can operate it at scale and without interruption. Otherwise, you spend most of your time operating the platform and less time running your business and innovating with new use cases.

Enabling visibility, tracking metrics for data quality and confirming data integrity can secure your access to accurate, up-to-date data, and enable you to quickly generate valuable insights.

Real-world customer scenario:

A marketing technology customer operationalized its customer data platform by monitoring and optimizing their data pipelines that process tens of terabytes of data every day. Implementation of observability and data pipeline resilience allows it to operate at scale across thousands of pipelines every day while providing uninterrupted service for its clients.

How can you get there:

• Set up automation, observability of the platform and data attributes, scoreboard of key KPIs, and institute data lifecycle management
• Implement a DataOps practice to handle change management, process automation and implement agile development practices
• Plan for continuous improvement — there is always room for efficiency gains

It's a tall order to become a data-driven organization. But the tools have never been more accessible, and the payoff has never been greater. I hope you’ll consider these six attributes as you pursue your data initiatives.

Join the Conversation: Find Solve on Twitter and LinkedIn, or follow along via RSS.

Stay on top of what's next in technology

Learn about tech trends, innovations and how technologists are working today.