Digital transformation is a long process that requires thorough planning. Implementation of changes always raises challenges and thinking ahead allows preparing for them in advance.
Challenges for Data Science Team to Address
What makes digital transformation difficult is that, unlike common misconception, it does not mean an armed race of adopting the newest technology. It is, in fact, a mindset. According to McKinsey and Company’s survey, less than 33% of the digital transformation projects go smoothly because companies don’t understand what the concept truly entails.
This leads to them being oblivious of what challenges they will be facing ahead, therefore incapable of tackling them head-on. In this article we will outline five challenges your data science team is likely to run into.
The most common organisational structure focuses on optimisation of individual department’s objectives that are periodically reported to stakeholders. In other words, this means departments don’t really have visibility of each other as they don’t exchange data across teams. Such data blindness causes inflexible workflows with limited efficiency and therefore stunt the digital transformation process.
Worst of all, most companies are opposed to change, which hinders digital transformation only further. Think about it: onboarding and learning to navigate new tools is a challenge of its own. When met with resistance, it only gets worse.
This is why it’s important to follow the process of embedding a data science team into your company. It also puts emphasis on how important the role of a data translator is: this is the person to manage a data-exchange driven conversation across the business. Seeing and understanding data collected by different departments allows tailoring a strategy that unites individual efforts and objectives and directs them towards overall business goals.
Common misconception that stops digital transformation is that the business model is an absolute foundation that cannot be changed under any circumstances. The reality is that digitalisation is very likely to require changing the business model. Clinging onto it, in the long run, is likely to damage or even destroy your business. Remember: it’s mostly psychological and not structural factors that hinder digital transformation.
To give you an example, let’s talk about Netflix and Blockbuster. Back in the day, Netflix embraced digital transformation by shifting from renting out DVDs to streaming. In other words, the business model changed from rental to subscription-based. This enabled collecting data easily and acting on it, such as creating an AI system that gives users recommendations based on the previous shows they’ve watched. Blockbuster, on the other hand, chose to stick to its legacy in-store model, stripping themselves of the opportunity to collect actionable data about their clientele. Conclusion? Netflix is the biggest streaming service and Blockbuster stopped existing altogether.
Of course, digital transformation doesn’t necessarily mean your business model needs to change. We suggest you fill out our questionnaire designed specifically for determining how ready you are to embrace digital transformation.
This point is closely tied to the one above. In short, it’s not just refusal to change the business model that may get in the way but also unwillingness to change business processes. Digital transformation requires data being a top priority – and existing processes should be assessed from this perspective. If they do not provide valuable data – they are not efficient.
And if they’re not efficient, why keep them? At the end of the day, they won’t be reflecting the growth you’re striving to achieve. It may sound insane to abandon legacy processes that have been laid out on day one, but the reality is that if they don’t contribute towards digital transformation – they’re preventing it. The key is focusing on long-term goals over short-term target deliveries.
Data collection, analysis and implementation are the engine that moves digital transformation. For that, you need top-notch analytics and 53% of companies have reported theirs not living up to their requirements. Older analytics are simply incapable of measuring enormous amounts of data we collect daily that leads to business disasters.
Inability to collect and process relevant data affects not just digital transformation but growth of your whole business. Problem is, volumes of data collected only grow and analytics systems struggle to keep up with it. The solution would be implementation of AI technologies that allow crunching data. Better analytics directly influence better decision making.
The way people interact with businesses changes everyday. It can be influenced by virtually anything, from technological advancements to social climate. For example, take the hospitality sector during the COVID19 pandemic. In a day all the data collected through digital booking systems became irrelevant and stopped coming altogether because people could no longer visit restaurants. Customer behaviour then shifted towards home delivery instead.
In other words, what data is collected and where it should be applied can shift depending on changes in customer behaviour. This is one of the reasons why a data scientist must possess business acumen – they must be able to identify problems your business can solve based on changes in customer behaviour and prioritise accordingly. Restaurants that quickly shifted on improving their food delivery value ended up a whole lot more successful than those who delayed it in hopes the pandemic won’t last long.
Digital transformation will bring up many challenges across the entirety of the business. Being aware and realistic about what you seek to achieve allows you foresee and therefore overcome them.
Your data science team is the driving force behind the process. Addressing challenges they may run into should be your key priority.
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