Critical factors to succeed with your data-driven transformation

After many years of successful or attempted digital transformation, businesses are accelerating the next data-driven transformational wave in response to the pandemic. While placing data at the center of their transformation process should result in an autonomous, highly agile and scalable program, the sheer magnitude of available data can often cause significant challenges. In order to make their data-driven transformation successful, businesses must approach a number of critical components thoroughly and holistically. Below, we have outlined five essential areas organizations can consider when transforming their business.

1. A data-centric operating model

Business leaders need to understand the challenges and opportunities currently present in their industries and companies caused by the massive creation and dissemination of data. In addition, it is imperative to know how this data can help to improve customer experience, revenue, operational and financial performance, or to manage risk and compliance. Companies must also analyze how the impact of machine learning (ML), AI, and automation is rapidly changing the industry landscape. Once they’ve established their current situation, enterprises need a simple, concise Core Diagram (see MIT CISR) for the entire company to align around. This will offer all employees the same understanding of the present state, future vision, and their individual role in the transition and future success.

2. Establish enterprise level objectives and priorities

A directional roadmap is needed to fulfil the digital transformation vision; knowing where to start is critical for unlocking value through a business transformation. Imperatives like critical dependencies, cost, and incremental benefits need to be addressed at a granular level with transparency across the entire enterprise. This helps in the clarity of the plan and to install confidence in the team.

3. A coherent business transformation and change management framework

The adoption of new processes or technologies by different lines of business (LOBs) has always been complex and requires a well-defined change management plan. In 2018, the Gartner study ‘Organizational change success, a prerequisite for a successful data-driven transformation’ concluded that 50% of organizational transformations result in apparent failure and only 34% in apparent success. To solve this issue, and to ensure a smooth transition across all LOBs, businesses should look to implement an enterprise-level change management framework.

4. Understanding business processes and interdependencies

The ‘If you build it, they will come’ idiom may have value from a symbolic perspective, but it isn’t best practice for a large-scale data transformation. Starting with a significant IT initiative and hoping the user will adopt it because it has obvious practical advantages is a recipe for failure. A detailed understanding of each business process with its value streams and the corresponding internal and external actors, of current perceived limitation and tolerance for risk, and of implicit and explicit governance frameworks, is essential to articulate the transformation in a way that facilitates adoption and minimizes potential negative impacts.

5. A well-oiled development and operations engine

The transformation process will not work without a robust development and operations engine. Such an engine can support discovery and pilots, while scaling to massive loads and complex processing in an agile, obvious and easy to manage manner. This needs to be achieved through a well-defined, unified data platform, alongside software development and IT operations like DevOps, DataOps and MLOps to shorten the development life cycle and provide continuous delivery with high software quality. While such platforms are offered by all major cloud providers, software vendors, consulting companies and open source foundations, a clear understanding of how the platform can be implemented and operated incrementally to deliver value while minimizing risks continuously are usually missing.

Data-driven transformation is a process that increases viability and efficacy if executed correctly. Hence, most enterprises will look to accelerate their programs and transform as quickly as possible. However, very few will address the critical elements adequately – and their initiatives will fail as a result. Ultimately, anything short of a complete vision will gradually move the business to a disadvantageous position, potentially resulting in long-term market loss.