Every organisation has a unique pool of data at their disposal. These pools often face challenges from within organisations, such as the inability to manage, integrate and analyze colossal amounts of information, resistance to change and the lack of proper technologies to help drive it. In the long term, failure to address these shortcomings and form a successful digital transformation strategy can have a detrimental impact.

Polaroid is a prime example of a company that failed to adapt to changing market needs, and as a consequence, went from being valued as a $3 billion company in 1991, to filing for bankruptcy in 2001, eventually selling its entire brand and assets. Its decision to prioritise its primary business of selling instant film, and half-hearted effort at digital transformation, prevented it from cashing in on the success of digital images.

So, what are the key considerations for an organisation in forming an effective digital transformation strategy?

Insights From Data Direct Business Change

Digital transformation thrives on insights derived from data that provide a new perspective into business activity, and allows creation of new business models. Insights are distilled from data in context. Insights back up a hypothesis. Data is the raw material.

Digital transformation is powered by data-driven insight, which helps inform new perspectives on business activity, and which subsequently aids the development of new business models. So, are you systematically seeking and creating new forms of insight?

In order to promote digital transformation, the insights dimension requires more data, more tools for analytics, quicker cycle times for analysis, and further automation of data preparation and engineering tasks. All this combines to form the raw materials of insights.

Richer Data Models Unveil New Business Opportunities

Applying data insights into a larger segment of the enterprise allows firms to form a better perspective of the world, in addition to an awareness of new business challenges and opportunities. These insights can form the foundation for more complex and richer data models, allowing firms to identify how events are linked and the choices that we make.

What types of awareness are essential in your digital transformation program? To generate awareness, organisations need better tools for modelling and exploring data, in addition to the interactive and automated analysis of it. Digital customer data is the raw material that creates human awareness, and also creates the foundation for autonomous systems.

Optimization Creates Business Value

Digital transformation programs are iterative. Insights drive awareness, and awareness drives new business models. These should be implemented in stages, ideally with a small project to assess the product/market fit of the new digital model. Optimisation is integrating this process into a larger system, until the central business model driving the digital transformation expands to all relevant situations.

Digital transformation often demands products to constantly evolve. The challenge is to move quickly and in the right direction. What optimization will be needed as your digital business model expands? Organisations need a strong data foundation to help them analyze and monitor the usage of a product, in addition to strong product management to facilitate collaborative thinking on how to evolve the product.

Automation Enables Scalability

While optimising processes could help a business grow, automation ensures scalability in the longer term. Regardless of industry, a prerequisite of the digital age is that businesses are required to represent themselves digitally. Companies often find that full automation is difficult as it requires a complete remodelling of business processes into a brand-new architecture.

Automation used to be only about APIs and coding, which is still true. However, increasingly, automation is powered by machine learning algorithms that can identify patterns which humans can’t. So, what is your strategy for creating an automation architecture for your digital business model? The functional landscape for automation needs to include as much coverage of the product by APIs as possible. This allows scripts, more advanced programs 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 enable learning and problem-solving.

Scalable Architecture Grows

The scalability of digital operations is a core component of digital transformation. It allows enterprises to continue building on iterative successes achieved and makes optimisation possible. Scalable architecture can be built in stages, too. We often think of scalability as one that can expand, and even contract, when needed. For example, Amazon Web Services offers auto-scaling, which adjusts when you need more performance and winds down when you need less.

It is important to consider whether your existing architecture is scalable for the current stage. Are there signs it is wearing out? Although it is not mandatory to have the architecture for scalability at the start of an initiative, it is important to always plan for growth.

Ultimately, the hallmark of a successful digital business is the ability to remain agile even as the business grows; being able to renew itself, adapt and change simultaneously with its environment. As organisations today operate in a constantly changing technology environment, agility is the key to operating successfully; leveraging new data and insights to facilitate growth and stability.

Machine Learning Can Jump-Start Your Strategy

Creating a new digital strategy is not easy, and creating the wrong digital strategy can be costly. One fundamental key to making sure you’re on the right track is to continuously look for and create new insights across your organisation, data, documents, and systems.

To capture these insights requires more data and more analytics for data preparation. Manual data preparation alone can take months, and lead to wasted time and resources. And given that most automation requires rules and cleanly labelled datasets, this can become problematic.

However, machine learning techniques like clustering and classification that rely on algorithms to group similar pieces of data together can help reduce the manual burden of preparing the data needed to plan, execute, and measure your digital transformation.

Transform To Survive

Developing a winning digital transformation strategy can be challenging. Having crucial insights is imperative, and having the right tools to find those insights are fundamental to moving your business forward. And, make no mistake, there are many companies today that are considering if they’re able to harness the people and data needed to transform to the next big thing, or whether they’ll be left with diminishing revenues, uncompetitive and ultimately failing.

The original version of this article can be found at Digitalization World.