Key digital transformation pitfalls to avoid for your enterprise during transition

These are peculiar times wherein the organisations are swaying between uncertain growth and failures. Digital transformation, as we all know, is at the helm of building fortunes overnight. While it continues to be an important subject of discussion every year, 2022 is still special, says Yash Mehta, an IoT and big data science specialist.

It is special for the markets that are implementing their post-COVID recovery plans. It is also special for increasing end-user adoption of digital technologies and new technologies finding their place. However, the fact that 70% of digital transformation projects fail, is alarming news for organisations set to launch their digital transformation projects.

If you are planning new digital transformation initiatives, here are the top pitfalls you must avoid:-

Ignoring web 3.0 

The inability to detach from legacy systems is the greatest pitfall on the road to transformation. It’s a fact well proven when we entered web 2.0 a decade ago. Not changing something because it’s working fine is like attracting competition to outrun you. You may have heard about companies embracing blockchains for decentralised apps (dApps) building. 

The 3rd generation of the web goes beyond being interactive. The network of decentralised control of information & transactions for individual, institutional and enterprise processes will require an in-the-moment and immersive customer experience (CX). As a result, your centre of excellence should include expertise in blockchain, AI, IoT and other technologies.

This further implies that digital transformation without focussing on user-inclined decision making in real-time is a huge pitfall. If your users are unable to extract the above values then most likely your efforts to transform into an end-to-end digital service are insufficient.

How to start? 

Start small! Start by understanding the current process & systems in place and identify which technologies can be adopted. Then replace manual & repetitive processes with AI modules. Even if an organisation is struggling with chatbots, they are still on the right path. AI improves itself and produces value for earlier entrants. Steadily, as your AI expertise improves, focus on building mathematical models of cross-functional processes. 

Next, move selective processes over the blockchain. Understand the impact of decentralisation and then further expand it to more processes. While we are at it, the scope of web 3.0 is huge. At the current pace, the Web3 market could value at USD 87761.35 M by 2030. Imagine the ocean of opportunity you are ignoring! 

 Not upskilling your business analysis team

So far from the discussion, it is clear that the impact of advanced tech is inevitable and organisations should embrace it first. In pursuing the same, it is also important to identify where exactly in the org hierarchy that change should begin. Business analysis (BA), as we all know, is the iterative study of business outcomes in the past, learning from the insights and improving plans for the future. 

However, organisations tend to stick to traditional BA, don’t upskill their teams and ultimately limit their growth prospects ahead. They should move on from a requirement-centric approach to a more agile responsive strategic outlook. 

Project-specific BA is a thing of the past. In the post-COVID era, companies need to prepare a BA team that addresses the problem statement with modern tools. Needless to say, they require thorough training in all the technologies discussed earlier. They should also produce business analysts who understand the impact of peer-to-peer tech through self-governed products and further learn other roles. 

How to start? 

Partner with a professional business analysis training provider. Besides having corporate experience, the training partner should be able to upskill the workforce in the latest technologies. For example, Adaptive US has been providing business analysis solutions training to professionals in large corporations. Their comprehensive curriculum emphasizes additional areas such as process improvement, product management, agile product development and others. The training partner’s BA course is curated around IIBA’s standards which include multiple certifications such as CBAP and others. In addition to it, you must also focus on leadership training. This would help in producing subject matter advisors and thought-leaders for your enterprise.

 Ignoring big data management

Data is the new gold. An accomplished enterprise vision requires data-informed decisions. However, most enterprises still haven’t woken up to the reality that a strong foundation in data management is essential to keep your digital transformation goals moving. Despite being aware of the importance and impact of big data, most organisations have not been able to deploy a management system to put it to analytical use. What’s the point of collecting and holding large volumes of data when you can’t use it effectively? Such an inability is a major deviation from digital transformation. 

Where’s the gap? Is it because your employees can’t handle complex processes? No. No matter how skilled the workforce, manual work limits the scope to scale. Shockingly, even tech-savvy companies coordinate on excel, manual email updates, unplanned meetings and others. 

While we are at it, corporations have their lapses when it comes to data management. With no concrete data fabric model in place, they end up creating a mess. 

Understand this. Digital transformation requires continuous learning which further requires correct, valuable and actionable data sets. With a fabric model, businesses can elevate their data analytics competency; extract insights on demand and thus take key decisions. 

How to start? 

First things first, sign-up for a test data management solution and start with structured Extract, Transform and Load (ETL) projects. Once you see a positive change in the value of extracted insights, move to more critical unstructured data. Use Extract, Load, and Transform (ELT) methodology to harness the potential. A good data fabric would help you perform data collection from a multitude of sources, filtration, orchestration and integration with finesse and accuracy. 

Yash Mehta

It is imperative to optimise business processes that are inefficient and becoming a liability. With a standard data management model, you can prioritise things as per customer behaviour. Subsequently, that leads to a chain reaction of better product designs, optimised services and thus more meaningful business outcomes.

Conclusion 

For organisations, the impact of digital transformation is inevitable. They should embrace advanced tech and stay, if not ahead, in line with the trends. What digital transformation blueprint do you have? Were you aware of the above pitfalls? Share in the comments.

The author is Yash Mehta, an IoT and big data science specialist.

Comment on this article below or via Twitter: @IoTNow_OR @jcIoTnow

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