Estimated reading time: 5 minutes.
Audience: Management

Digital Transformation is, no doubt, one of the hottest current topics. I was invited the other day to a most interesting round-table discussion on why the customer should be at the heart of a digital supply chain strategy. It was a brilliant exchange of views from across the industry – and my thanks go to @TobyWright and to @JohnMcNiff for organising the round-table, at The Telegraph.

In preparation for the round-table I put some thoughts together on the subject of Digital Transformation. I’d like to share them with you and incite some comments. Here we go.

Take your employees along on the digital transformation journey

Customer-centricity is absolute key to a successful digital transformation. My approach to a digital transformation is to start with understanding the customer, then design the business digital strategy around it, then look at the Data, People, Processes and Technology required to support it.

Considering that the customer’s needs and behaviours change and evolve, it is critical to build agility in the organisation’s structures and processes, so that the organisation can evolve as well. This is where engagement with employees becomes critical. I involve staff as early as possible in the design of the cross-functional teams, to build the agility required by the company. This follows training of employees and a continuous improvement approach, where processes and technology are further optimised.

The digital transformation mantra of “customer centricity” needs to be extended to internal changes as well. When changes are introduced to internal processes and technology, staff need to be treated as customers in relation to the respective tools. When they are not, it creates gaps that are reflected in poor technology adoption and ineffective new processes.

Make the most out of new tech

The greatest opportunities come from the way one can combine these new technologies, as building blocks for an engaging and coherent experience to the customer. We need to re-think e-commerce interaction, reaching customers in a familiar environment through deep marketing, and offering them a meaningful and guided experience. Machine learning and AI, coupled with solid data are core ingredients – from smart Customer Relationship Management, to friction-less Enterprise Resource Planning, to intelligent CPQ (configure, price, quote), etc.

Get data right. Seriously.

I’ll keep this short as talking about data is a guaranteed way to put people to sleep 🙂
Data is the cornerstone for a digital transformation. By data I mean both the data structures (fields, relationships etc), and the actual information that populates these structures. Data structures and models need to be mapped and defined. Data quality needs to be managed and should give you a good understanding of how complete, unique, timely, accurate and consistent it is.

Getting someone who can understand data and can drive the effort of keeping your data clean is essential. Artificial intelligence, machine learning, all those advanced technologies that you want to take advantage of, won’t work without good clean data. The principle of garbage in – garbage out still applies.

Overcome barriers while turning your data into insights

Skills, Repository, Tools, Security, Integration, Costs, Buy-in – these are all barriers that need to be overcome when turning your data into insights. Regardless of whether the data is in the cloud or not, the most important factors relate to your analytics team. My favourite recipe is to embed a data scientists (someone who is genuinely curious about data) with a business team (who have the business expertise) and with a good storyteller. The data scientist finds what looks to be of potential interest, the business team validates its relevance, and the storyteller describes this in a way others in the company can make sense of it.

After getting the analytics team sorted, the next question is how you democratise access to insights – which is key if you want to have a data-driven organisation. This is where data architecture plays a heavy role – defining the best repository for the type and size of your data. Is it structured, is it big-data, is it going to grow? Then comes the visualisation element – how are you going to empower your staff to access analytics? The interfaces play a significant role here. Depending on audience and the type of insight, you might need self-service dashboards for common reports, and advanced tools for data exploration and deep insight.

The architecture and its interfaces need to be reviewed by cyber security and legal (think GDPR), and then you need to have a cost estimate. Be aware of the data gravitation – the more data you have in one location, the more other data and applications it’s likely to attract, and for cloud models, this is going to impact future costs.

Last but not least, implement an awareness program to ensure buy-in from the right audiences. Insight can go against gut-feel, and there is a need for introducing analytics in the right way to ensure staff embrace it and see the value added to their job.

Get the right people with the right skills

My technology operating models are of the type “IT Light” and “cloud-first”, and this drives the type of skills I am after. This meant that I have to buy, steal, borrow and develop people with skills in agile development, automated testing, dev-ops, infrastructure-as-code, etc. Data analytics skills is an area that is more complex, not only because of the complexities of the field, but because an organisation most benefits when the analytical skills are complemented with business ones. Depending on the situation, there is a judgement call to be had. Is it more effective to get a data specialist trained to understand the business, or a business person to understand data analytics? On the latter one can take advantage of government apprenticeships schemes, to train junior staff into becoming the next data analysts embedded with the business. The time it takes to get these skills on-board varies, and the shortest path might be just to combine data analysts and business people in a multi-disciplinary team – which brings me back to the agile approach… 🙂