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

Despite the emergence of evidence-based innovation, data-driven culture remains elusive among enterprises.

Accenture research suggests 48 percent of employees – from C-suite to entry-level – continue to follow their gut instinct instead of data-driven insights.

The access to available data and advances in analytical technologies now create tremendous opportunities for innovation. But unfortunately, enterprises sometimes ignore the fact that today’s complex problems to be solved by innovation are modelled using data.

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

At a high level, data strategy is a roadmap to a data-driven organization; at a low level, it comprises an organizational framework, change management, and architecture considerations to build a data supply chain. 64% of senior decision-makers (SDMs) report that a mature data strategy results in stronger levels of resiliency.

But, how do you know what constitutes a winning data strategy to connect business plans and priorities to data and analytics requirements?

Here is the data strategy checklist that every enterprise must take care of to become data-driven:

Improve the Level of Data Literacy

Since data consumers in an organization are not homogeneous, data generated or collected must address the specific business needs of different user segments to help reach strategic goals and generate real value. In an organization, these requirements must be identified and defined across all stakeholders to understand what the business is trying to accomplish. But, stakeholders must improve their level of data literacy to research and analyze available data to trigger new ideas and create business values.

A robust data strategy should be framed to outline steps to improve data handling, monitor progress, and create a vision that guides the activities. In addition, the strategy must focus on driving transparency, making data more visible and easily accessible to users.

Based on the data evidence and insights, enterprises can better decide which areas to focus on, postpone or drop to get expected business values.

Handle Data Holistically

Since data can be made functional and usable from the moment it is produced, data production sources must take data-related needs and specific business requirements of data consumers into account. In contrast, data consumers need to comprehend the limitations of data producers.

To comprehensively support data management across an enterprise, leaders must address the five core components of a data strategy: identify, store, provision, process, and govern. The strength of these components stems from the enhanced visibility they offer at every level of a data strategy – from defining goals and infrastructure to building skills and implementing data governance. These components also focus on dissolving cross-organizational and project boundaries.

By creating solutions that lower the bar for technical competency required for data utilisation, organizations can solve many of the resource challenges that thwart the accomplishment of their data-driven vision and their ability to identify digital transformation opportunities.

Implement Advanced Architecture and Technology

A flexible and scalable data architecture is one of the most critical constructs for unlocking the power of data. While new and more ubiquitous technologies are paving the path for faster and more powerful insights, enterprises must not get caught up in the hype of recent technologies without considering their business case. There are many approaches and options to deploy the right technology architecture for supporting analytical, data warehousing, integration, and reporting needs. A modern architecture spans all stages of the data lifecycle, from data production to consumption and analytics.

Considerations must go beyond local data consumption to build smart processes with an increased focus on high-quality data production. Existing IT architectures may need transformation to enable the integration of siloed information and seamless management of unstructured data. Data management, governance, and information security should be automated using user-friendly and intuitive tools and interfaces that help frontline managers with their jobs. Most importantly, the architecture must weather the evolving changes in a digital world to keep pace with the functional and data management needs.

Herein, enterprise architecture and technology innovation contribute to a data-driven organization, offering an enterprise view of data that needs to be mapped against strategic business priorities.

Make the Necessary Culture Change

There are a variety of data strategies that enterprises adopt to embark on their data-driven roadmap. However, all these strategies are doomed to fail if they skip culture transformation to modernize their data architecture. To become data-driven, enterprises need a culture change, cultivating innovation that positions data at the core of every strategy. It wouldn’t be entirely wrong to say that culture continues to eat strategy for breakfast. 92.2% of the leading companies believe that culture – people, process, organization, and change management – is the biggest obstacle to becoming data-driven. Yet, this aspect is highly critical for long-term success.

The first step to fostering this mindset will be creating data-driven capabilities like governance, and accountability, to maintain adherence to the data-driven culture. These capabilities help establish clear responsibilities for data across the business and improve data literacy with targeted staff development and training. In addition, strategic orientation and active support of senior executives and a cross-functional team of mid-level directors and managers are needed to foster and thrive in this transformed culture.

Implement. Test. Measure. Scale

A data strategy initiative addressing the long-term goals is not a once-and-done effort. This means that the data maturity model requires the constant reassessment to improve and scale the data strategy.

As the technology and capability landscape continues to change rapidly, elements of a data strategy will also need tweaks based on these changes. For instance, if a product launches a new feature that brings in more customer data, it will require a change in the data strategy – shifting it to a distributed strategy from a centralized one. On the other hand, if there are no changes, the data strategy still needs to be revised and updated with time to ensure it stays relevant.

A review or reassessment is mandatory every six months to bring everyone up to speed with their respective progress and changes. By taking stock of where an enterprise currently stands, leaders can measure, improve, and scale their strategies to meet the specific needs of their business.

The power of a data strategy lies in its ability to deliver the best possible solution, keeping pace with the growing needs of an enterprise. As and when new requirements and gaps become visible, the framework must be flexible enough to accommodate these changes across the data management capability and technology areas.

From Data-Informed to Data-Driven

In conclusion, the data strategy considerations can be the springboard to help organizations become data-driven. These aspects working together mark the culmination of the process to arrive at the targeted future state and the vision to create strategic advantage through data.

Data-led insights and soaring data literacy help redefine the data-driven enterprise. However, it is not a tactical bandage that provides a quick fix to existing issues and shortcuts to the data-driven enterprise. Instead, enterprises must establish a disciplined, robust and composable approach to managing their data as an asset across all operational pieces and gain new learning. And then apply that learning to the next new idea to achieve the data-driven vision.

About the Author

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

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