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How to form a data-driven digital transformation strategy

(Image credit: Image Credit: Chombosan / Shutterstock)

Digital transformation is about finding new ways to use data to drive efficiency, business insights and improve revenue.  Digital transformation has virtually become a requirement for modern business, and is estimated to be worth $18 trillion in additional business value, according to IDC.

Organisations typically possess unique pools of data at their disposal. However, business leaders are often up against various challenges, such as the inability to effectively manage, integrate and analyse these heaps of data, resistance to change and a lack of the right smart technology. Over time, this inability to address these challenges will inevitably lead to a decline in business performance.

So, what are the key points to consider for organisations looking to build an effective digital transformation strategy? 

Data-driven insights empowers business change

Digital transformation is fuelled by data-driven insight, which helps inform new perspectives on business activity, and which aids the development of new business models. Insights are distilled from data in-context and back up a hypothesis. Data is the raw material.

So, are you identifying and generating new forms of insight? To promote digital transformation, the insights dimension needs more data, more tools for analytics, shorter cycle times for analysis and further automation of data preparation and engineering tasks. These elements combine to form the raw materials of insights.

Awareness brings business opportunities

Integrating data insights into a larger segment of the enterprise allow organisations to form a better view of the world, in addition to bringing awareness to new business challenges and opportunities. These insights help build a foundation for more complex and richer data models, allowing businesses to identify connections between events, and the impact their choices have on business outcomes.

So, what types of awareness are critical to your digital transformation programme? To build awareness, organisations must equip themselves with tools that are capable of modelling and exploring data, in addition to providing an interactive and automated analysis of it. Consider digital customer data as the raw material that creates human awareness, and a foundation for autonomous systems.

Optimisation provides business value

Digital transformation programmes are iterative. Insights generate awareness, and awareness generates advanced business models. This process should be integrated into stages, using a small project to gauge the potential market fit of the new digital model. Optimisation means integrating this process across a larger system that creates more value.  This process  continues until the central business model that drives digital transformation expands and adapts to all relevant situations.

So, how does your digital business model need to be optimised as it expands? Digital transformation requires products to be constantly evolving.  The real challenge, however, is to move both quickly and in the right direction.  A strong data foundation to help organisations analyse and track the usage of a product, and a strong product management team can promote collaborative thinking on how the product can be evolved further.

Enabling scalability with automation

While optimising processes can assist business growth, automation guarantees long-term business scalability.  A prerequisite of the digital age is that all businesses must be represented digitally. Companies often face challenges with full automation, as it requires them to remodel business processes into a new architecture.

So, how do you develop an automation architecture for your digital business model? Automation was once strictly about APIs and coding; however, automation is increasingly powered by machine learning algorithms that can identify patterns which humans can’t. The functional landscape for automation requires as much coverage of the product by APIs as possible. This allows scripts, more advanced programmes and machine learning systems to manage the behaviour of the product or support system. Instrumentation should be integrated at all levels, to support feedback loops that facilitate learning and problem-solving.

Growing with scalable architecture

A core component of digital transformation is also the scalability of your digital operations. Scaling allows enterprises to further build on iterative successes achieved and makes optimisation possible. 

Scalable architecture can also be implemented in stages. We generally consider scalability as something that can expand, and even contract, when needed. For example, Amazon Web Services offers auto-scaling, which can adjust when you need more performance, and wind down as less is required.

So, consider whether your existing architecture is scalable in your current stage? Are there any signs of it wearing out?  While it is not essential to have the architecture for scalability at the very beginning, it is crucial to plan for long-term growth. Ultimately, the ability to remain agile, while simultaneously expanding, is the hallmark of a successful digital business. Digital businesses today need to be able to constantly renew themselves and adapt to the environment.  As the technology environment today is rapidly changing, agility is the key to operating successfully; using new data and insights to promote growth and stability.

Jump-start your strategy with machine-learning

Building a new digital strategy is by no means simple, and getting it wrong can be detrimental.  Be sure to constantly seek and create new insights across your organisation, data, documents and systems, in order to stay on track.

Capturing these insights requires more data and analytics for data preparation. Manual data preparation can cost a significant amount of resources, and given that automation typically requires rules and cleanly labelled datasets, this can become problematic. However, machine learning techniques like clustering and classification, which use algorithms to segment similar pieces of data can greatly reduce the burden of manual data preparation required for planning, executing and measuring digital transformation.


Building a winning digital transformation strategy is not an easy feat by any means.   Having crucial insights is key, as is having the correct tools to identify how these insights are fundamental to business growth.  Companies today are faced with a clear strategic choice. They can either learn to fully leverage the power of the people and data within the organisation, evolve to the next big thing, stay agile and ahead of the pack, or if not, the organisation risks being beaten by competitors, left with diminishing revenues and ultimately failing.

Frazer LaChance, Client Development Manager, Europe, Lucidworks (opens in new tab)

Frazer LaChance is Client Development Manager, Europe, Lucidworks, a leader in AI-powered search, and he helps the world’s largest companies solve their biggest data problems.