When we think about the trends for the year ahead, we often predict new influences. However, change is not only defined by what is coming but also by what we leave behind.

And arguably, this is more valuable to the Chief Data Officer (CDO). By looking back at what trends haven’t lived up to their hype, data leaders can constructively analyse what alternative practices and technologies might enable them to seize the promised benefits.

So, what can we learn from the hype of 2022? And – arguably more importantly – what learnings shouldn’t we bring into the new year?

Big (data) isn’t always better

Big data has been a big topic for many years. But what many CDOs are beginning to understand now is that there’s no point in having lots of data if you can’t derive value from it. In 2023, we should shift our focus away from big data and, instead, focus on the need for clean, curated, continuously updated, accurate data. After all, no matter how much data you hold, if it is dirty, it’s impossible to realise value from it.

Hype alone won’t make data mesh a success

In 2022, the pursuit of data mesh was driven by massive hype. But I don’t believe that will continue as we enter 2023. Instead, organisations will take a step back and focus on the value a data mesh approach can provide when paired with data mastering.

For data mesh to work, organisations need to cleanse and standardise their enterprise data. That’s why data mastering is so critical. It can serve as both a complement and an augmentation to their distributed data initiatives. Data mastering provides standardised keys for data that can be understood across systems and domains and creates useful mappings between data identifiers across the organisation, both of which are often critical bottlenecks in a data mesh strategy.

Few no-code AI tools will solve actual business problems

Many data companies today tout their no-code AI tools. But while many organisations will get caught up in the hype, few will find they solve an actual business problem.

As André Vargas, Chief Data Officer at the Creative Artists Agency, argues “...it’s overhyped if an AI vendor says, ‘just use my AI and your problems are solved’ without actually understanding my business problem.”

It’s not that no-code AI tools don’t have their place in the data ecosystem. But I believe that anything that feels like a panacea is probably wrong. So, organisations looking for a quick fix should proceed with caution.

Data lakes are dead and dying

Data lakes held great promise but turned into data swamps for the most part. Because while HDFS was a fine file system, it was built for a specific purpose and irrationally applied more generally. This led to disappointment and many organisations are now aggressively moving away from them.

Having clean, curated data is valuable. That’s a fact. But dirty data swamps are not.

Strip back the C-suite

For many years, the CDO and Chief Analytics Officer (CAO) have been evolving as relatively separate roles, each emerging with their own responsibilities and remit.

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But as data continues to evolve and change, these roles must converge. CDOs need to embrace a more holistic view of data consumption across their organisation. And with CAOs now realising that clean, curated, continuously-updated data is critical to the delivery of their remit of democratised analytics, the pursuit of this ultimate goal – from data collection and mastering to analysis and insights – must fall under the purview of one individual.

Dr Richard Wang, Director, Chief Data Officer & Info Quality Program at MIT and Professor at the University of Arkansas, Little Rockk, articulated the challenge of having two separate roles and the need for change in 2023.

“Some people say, ‘this is data analytics’ and others say ‘no, this is data management.’ These roles need to converge – and be empowered with budget, authority, and resources.” 

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