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Five steps to successful digital transformation

The near-overnight change to working practices in the last few months showed the indisputable need for safe, secure and reliable remote data access. Its presence or absence defined how organisations weathered the storm or did not.

With so many organisations’ data infrastructures exposed as vulnerable, unreliable, impractical, obstructive or simply out of date and expensive, digital transformation – that cumbersome, confusing, catch-all term – has been given a new urgency.

But if building a digital transformation is a daunting and confusing undertaking at the best of times, when it becomes a “do or die” necessity for saving costs and retaining customers, then starting the process correctly is essential.

The problem is most will not. Most will start with their business objectives. It seems so strategic and so prudent. And yet it is exactly the same mistake that has plagued so many digital transformation projects over the years. So if the obvious route is not the correct one, where should we start?

Calligo, an end-to-end managed data services provider, recently launched new research into the digital transformation approaches of more than 500 businesses across North America and Europe, and how it impacts their productivity, profitability, customer satisfaction, data security and more.

From Calligo’s Reinventing Digital Transformation report, it is possible to distil the five essential steps to emulating the highest performers, beginning with their starting point: data.

Calligo infographic

1. Make understanding data the starting point

Most people don’t know the difference between a technology strategy and a data strategy. If you take a technology-first approach to digital transformation, it relies on identifying business problems and deploying the most appropriate technology to fix them.

“It might seem strategic because it starts with business needs, but it’s actually narrow minded,” says Adam Ryan, Calligo’s chief services officer. “It presupposes the chosen business needs are the right areas to focus on to improve organisational performance.”

In contrast, a data strategy starts by examining how data moves through the business, identifying areas of inefficiency, data governance weakness, overspend, security gaps and so on.

“The places where improvement is needed most are unlikely to be either found or solved if you focus solely on fixing preconceived business needs,” Ryan argues. Put another way: a data strategy enables issues to be resolved from the “data up”, by finding and setting its own objectives along the way, rather than prescribing objectives and deploying “tech down” to repair them.

“It’s a classic example of ‘you don’t know what you don’t know’,” says Ryan. “It is a far more fundamental approach to digital transformation, improving businesses from their very foundations and bringing more value as a result.”

2. Adopt a privacy mindset

For most businesses, a data-centric approach is alien. The easiest way to shift perspective, and to secure quick wins soonest, is to look at the data environment’s ability to preserve data privacy.

This is because the only way to understand whether a business respects its data privacy obligations is to achieve the most granular visibility of every way in which data enters the business, is interacted with and treated.

“By starting your analysis with data privacy, you achieve two key benefits,” says Ryan. “Obviously you will reveal your privacy liabilities, which cannot be underestimated in terms of both regulatory compliance, but also consumer trust. But the granularity of the process is often unprecedented for the business. Suddenly every data workflow’s security weaknesses, inefficiencies and cost sinkholes appear.”

3. What would your customers do?

A core part of the data strategy investigation is your customers’ journey through the business. Most organisations see understanding their buyers’ needs as mainly the responsibility of sales and marketing, but it has a far more fundamental impact than that.

“Customers are in essence data,” says Ryan. “Easing customers’ routes through your business and directing your innovation at how to best make their experience with you efficient, intuitive, safe and even pre-emptive is where the real benefits lie. Understanding your workflows from the perspective of a customer means you can start to make it easier for your customers to interact with you, trust you and enjoy working with you.”

The places where improvement is needed most are unlikely to be either found or solved if you focus solely on fixing preconceived business needs

4. Add the right technology in the right places

Once you reach this step, now and only now is the time to start thinking about the technology that applies to the problems your data strategy has discovered. “It will almost certainly be an entirely different set of technologies to the ones you would have chosen under the ‘old way’,” says Ryan. “Further, a proportion of the problems you have identified will often be solved by better processes, not by technology; just another example of how data strategies are more cost effective.”

Much of the digital transformation conversation often inevitably points towards machine learning and automation, and rightly so. It certainly has a role to play. But finding the correct role for such technology is vital.

“The general level of understanding of machine learning and automation is so low there is a tendency to deploy it without proper understanding of where the real problems lie and even whether they are truly best fixed by these tools,” says Ryan. “And, more worryingly, this actually increases a business’s inefficiency, spend and risk profile, which they probably won’t realise until much further down the line.”

5. Keep iterating and evolving your data strategy

If you have a robust data strategy in place, you can be more productive, cost efficient, agile and drive more and better innovation. Your data architecture goes from being a blocker to becoming something you use to drive competitiveness and protect against any threats, ranging from cyberattacks to socio-economic unpredictability.  Keep iterating and evolving your data strategy

But once started, data-first digital transformation never stops. Your environment and the strategy that maintains it has to be kept under constant review.

“The types of data you gather, how you interact with it, the ways you might use it, the laws that may govern it; everything changes constantly,” says Ryan. “Data is the most fluid asset you own and therefore the most difficult to manage, the trickiest to keep efficient and arguably the most dangerous. No data strategy can stand still, because your data never stands still.”

To download your copy of Calligo’s research into data-first Digital Transformation, visit