In the past several years, we have been involved in or advised on many transformations. As a result, we grew an appreciation for the complexity and difficulty of true transformation and what it takes to be successful. 

Most leaders underestimate how much coordination and cooperation are needed to succeed. Any one of the key interrelated domains – technology, data, process, or organisation change capability – can sabotage an otherwise well-conceived transformation. From creating a compelling vision and communicating it to creating a plan and adjusting it as the project unfolds, people are the key to all of it.

Transformation requires talent more than anything else. Creating a team of technology, data, and process experts who can work together – under the direction of a strong leader who can bring about change – may be the single most important step a company can take when considering a transformation. Talent alone doesn’t ensure success, of course. It is almost impossible to succeed without it.

Here’s a look at the talent needed for each of the key domains.

Whether it’s blockchain, data lakes or artificial intelligence, emerging technologies have incredible potential. It is extremely challenging to understand how any particular technology contributes to a company’s transformation, adapt it to a specific business need, and integrate it with its existing systems, even if some are becoming easier to use. Furthermore, most companies are struggling to change their legacy technologies – which are embedded in the organisation’s infrastructure. Solving these issues requires people with technological depth and breadth, as well as the ability to work hand-in-hand with the business.

Data-driven best practices

The biggest challenge, however, is that many business people have lost faith in their IT department’s ability to drive major change. Many IT functions are primarily focused on “keeping the lights on,” leaving institutional IT out of the equation, so rebuilding trust is critical. 

In other words, technologists must always demonstrate a link between their technology innovations and business outcomes. In order to be successful in technology leadership, leaders must be adept communicators and possess the strategic sense to decide what to do about technical debt while trying to balance innovation.

The Data Itself

Today, many companies have substandard data quality and analytics services, so undergoing digital transformation will require significant improvements. A transition almost certainly involves uncovering new types of unstructured data, leveraging external data, integrating proprietary data, and shedding huge amounts of data that has never been used (and likely will never be). Many companies are aware of the importance of data and understand its poor quality, yet fail to organise roles and responsibilities properly. IT functions are often blamed for all of these failures.

To be successful in the data industry, you need to have strong analytical abilities. In addition, it is critical to encourage people working at the front lines of organisations to transition to roles as data customers and data creators. This means communicating what they need now and what they will need after the transformation. Also, it requires supporting front-line workers to improve their own work processes and tasks in order to generate data correctly.

Transformation requires both an end-to-end approach and the ability to manage across silos moving forward. It also demands a rethinking of ways to meet customer needs and ensuring seamless communication among workgroups. It is natural to align these needs with process-oriented thinking. However, traditional hierarchical thinking makes process management – horizontally, cross-silos, and centred on customers – difficult to implement. This powerful concept has stagnated as a result. If it is absent, transformation is merely a string of incremental improvements, which are useful, but not transformative.

When recruiting talent in this domain, look for companies that can work together to align silos in the direction of their customers to improve existing processes and design new ones, as well as have a sense of strategic direction to know when incremental improvements are needed and when radical changes are needed.

Our domain of change management includes leadership, teamwork, courage, emotional intelligence, and other aspects. Since much has been written about this area for years, we won’t rehash it here, but we will note that anyone responsible for transformation needs to be familiar with the impact of organisational change.

Regardless of the evidence to support it, it appears that those who gravitate toward technology, data, and processes are somewhat less likely to embrace change on the human level. It is of course vital to seek individuals with excellent people skills in the recommendations we provided above. In the event that they are not readily available, you can additionally try to recruit individuals who are able to work on both sides of the transformation.

We have been discussing the concepts of technology, data, processes, and organisational change capabilities as if they existed as separate things, which of course they do not. Instead, they are a part of a bigger picture. Data is the fuel of transformation, technology is the engine, the process is the navigation system, and organisational change capability is the landing gear. All of them have to work together well.

Think of the “our systems don’t talk” problem, which plagues most companies and hinders transformation. How should it be classified? In addition to being a technical issue, it also leads to huge inefficiencies in processes. But it is rooted in a lack of reliable data architecture, and it may involve organisational structures and political processes that are difficult to change. One could argue that any domain should take the lead. However, the best solution involves all domains working together.

Many businesses fail to realise the full potential of transformation because they lack a deep understanding of each domain. There is, however, not one individual who possesses all the knowledge and abilities necessary. We have therefore called for talent to be assembled within each area.

The sequence in which technology, data, and the process is worked on is also critical. Process improvement and re-engineering in many cases must come first before automating a process. Alternatively, some transformations will make heavy use of artificial intelligence. Despite the fact that poor data impedes the development of good AI models, in these cases, data analysis should take priority over AI modelling. Determine your end goals first, then develop a sequence of actions that can help you achieve them.

Transformation efforts should be guided by the most important needs of the company. These priorities will also influence the talent needed. In addition to having the requisite domain expertise, the talent must also have previous success in creating and implementing transformation programs.

About the Author

Jason Novobranec is Implementary’s Chief Operating Officer.

With over 20 years of Consulting, Program Management & Senior Leadership experience, Jason has delivered initiatives for large multi-national / multi-regional organisations as well as SME’s and is an expert in shaping solutions to fit a customer’s project needs.