How To Turn Data Into Value To Become a Data-Driven Enterprise

Despite the rise of evidence-based innovation, a data-driven culture in organizations remains elusive.

Research from Accenture suggests that 48 percent of employees — from C-suite to entry-level — continue to follow gut instinct over data-driven insights.

Access to available data and advances in analytical technologies are now creating tremendous opportunities for innovation. Unfortunately, companies sometimes ignore the fact that today’s complex problems that innovation must solve are modeled using data.

To change this narrative, organizations need a realistic and practical data strategy to unlock the potential of what they already have, rather than looking for new data sources and infrastructure.

At a high level, data strategy is a roadmap for a data-driven organization. At a low level, it includes an organizational framework, change management, and architectural considerations for building a data supply chain. 64% of senior decision makers (SDMs) say a mature data strategy leads to greater resiliency.

But how do you know what makes a successful data strategy to connect business plans and priorities to data and analytics needs?

Here’s the data strategy checklist every business needs to follow to become data-driven:

Improve the level of data literacy

Since data consumers are not homogeneous in an organization, generated or collected data must consider the specific business needs of different user segments in order to achieve strategic goals and create real value. In an organization, these requirements need to be identified and defined for all stakeholders to understand what the business is trying to achieve. But stakeholders need to improve their data literacy to research and analyze available data to spark new ideas and create business value.

A solid data strategy should be devised to outline steps to improve data processing, monitor progress, and create a vision that guides activities. In addition, the strategy must focus on promoting transparency, making data more visible and easily accessible for users.

Based on the data evidence and insights, organizations can better decide which areas to focus, move, or drop in order to deliver the expected business value.

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treat data holistically

Since data can be made functional and usable from that moment on, data production sources must consider data-related needs and specific business requirements of data consumers. In contrast, data consumers need to understand the limitations of data producers.

To fully support data management in an organization, leaders must address the five core components of a data strategy: identify, store, deliver, process and govern. The power of these components comes from the enhanced visibility they provide at every level of a data strategy – from defining goals and infrastructure to building capabilities and implementing data governance. These components also aim to dissolve cross-organizational and cross-project boundaries.

By creating solutions that lower the bar on the technical expertise required to leverage data, organizations can solve many of the resource challenges that are frustrating the realization of their data-driven vision and their ability to recognize digital transformation opportunities.

Implement advanced architecture and technology

A flexible and scalable data architecture is one of the most important constructs to unleash the power of data. As new and more ubiquitous technologies pave the way for faster and more meaningful insights, organizations must not get caught up in the hype about the latest technologies without considering their business value. There are many approaches and options for providing the right technology architecture to support analytics, data warehousing, integration, and reporting needs. A modern architecture encompasses all phases of the data life cycle, from data production to consumption and analysis.

Considerations must go beyond local data consumption to build intelligent processes with an increased focus on high-quality data production. Existing IT architectures may need to be transformed to enable integration of siled information and seamless management of unstructured data. Data management, governance and information security should be automated using easy-to-use and intuitive tools and interfaces that support frontline managers in their work. Most importantly, the architecture must survive the evolving changes in a digital world to keep pace with functional and data management requirements.

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Here, enterprise architecture and technological innovation contribute to a data-driven organization by providing an enterprise view of data that needs to be aligned with strategic business priorities.

Make the necessary culture change

There are a variety of data strategies companies are employing to tackle their data-driven roadmap. However, all of these strategies are doomed to fail if they skip culture shift to modernize their data architecture. To become data-driven, companies need a culture shift by cultivating innovation that puts data at the heart of every strategy. It wouldn’t be entirely wrong to say that culture continues to eat strategy for breakfast. 92.2% of leading companies believe that culture – people, process, organization and change management – is the biggest obstacle to data-driven development. However, this aspect is crucial for long-term success.

The first step in fostering this mindset is to create data-driven functions like governance and accountability to maintain adherence to the data-driven culture. These capabilities help establish clear accountability for data across the organization and improve data literacy through targeted employee development and training. In addition, strategic direction and active support from senior leadership and a cross-functional team of directors and mid-level managers are required to nurture and thrive this shifting culture.

To implement. Check. Measure. scale

A data strategy initiative that addresses the long-term goals is not a one-off effort. This means that the data maturity model requires constant re-evaluation to improve and scale the data strategy.

As the technology and performance landscape continues to change rapidly, elements of a data strategy must also adapt based on these changes. For example, when a product introduces a new feature that brings in more customer data, it requires a change in data strategy—from a centralized to a distributed strategy. On the other hand, if there are no changes, the data strategy still needs to be revised and updated over time to ensure it stays relevant.

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A review or re-evaluation is mandatory every six months to keep everyone updated on their respective progress and changes. By taking stock of a company’s current state, leaders can measure, improve, and scale their strategies to meet the specific needs of their organization.

The power of a data strategy lies in its ability to deliver the best possible solution and keep up with the evolving needs of an organization. As new requirements and gaps become apparent, the framework must be flexible enough to accommodate these changes in data management capabilities and technology areas.

From data-informed to data-driven

In summary, data strategy considerations can be the stepping stone to helping organizations become data-driven. The convergence of these aspects marks the culmination of the process towards achieving the desired future state and the vision of creating a strategic advantage through data.

Data-driven insights and rising data literacy are helping to redefine the data-driven enterprise. However, it is not a tactical association that offers a quick fix to existing problems and links to the data-driven enterprise. Instead, organizations need to establish a disciplined, robust, and collapsible approach to managing their data as an asset across all areas of operations and gain new insights. And then apply what you’ve learned to the next new idea to make that data-driven vision a reality.


About the author

Dietmar Rietsch is CEO of Pimcore. A serial entrepreneur with a keen sense for innovation, technology and digital transformation. He is a passionate entrepreneur who has been designing and implementing exciting digital projects for more than 20 years.

Featured image: Adobe Stock


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