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As new approaches to managing data chaos continue to emerge, it can be easy to jump on the latest ‘next big thing’ and think it will solve all your data problems. But, organisations need to separate the hype from the overhyped - sometimes new isn’t always better. Let’s look at what data hypes of 2022 we can learn from and possibly leave behind in 2023.
Data mesh needs more than ‘hype’ to succeed
Data mesh is all the rage. It’s a hot topic in the data industry right now. But as we enter 2023, we believe that organisations will ignore the hype and focus on the value a data mesh approach can provide, especially when paired with data mastering. For data mesh to work, organisations need to break down data silos and publish clean, curated, comprehensive and continuously updated data.
The only method of achieving this at scale is through ML-driven data mastering - it’s a critical component of the modern data ecosystem. Data mastering can serve as both a complement and an augmentation to 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.
No-code AI isn’t a ‘quick fix’ to business problems
Many data companies today tout their no-code AI tools. But buyer beware - not all no-code AI tools are created equal! It’s not that no-code AI tools don’t have their place in the data ecosystem. When evaluating these solutions, you must look for ones from reputable vendors who understand your business problem. So, where do you start? Below are five principles that we believe the best no-code AI MDM solutions embrace:
1.Integrates easily: No-code AI should use platforms and modules that are simple to integrate so that they are easy to tailor to a company’s particular needs. That way, business users may utilise domain-specific knowledge and quickly develop AI solutions thanks to no-code solutions.
2.Accelerates processes: Cleaning data, classifying, organising data, training, and debugging the model are all necessary steps in creating unique AI solutions. AI should be able to automate repetitive operations, allowing businesses to perform tasks faster.
3.Costs less than custom AI: No-code AI should be low-cost and easy to implement for organisations wanting to implement AI with less stress and without the need to hire staff with AI expertise.
4.Empowers business intelligence at scale: End users should be able to utilise no-code AI to build new solutions without knowing how to code, which improves business efficiency, productivity, ROI, and customer retention.
5.Enables you to realise quick time to value: No-code platforms should allow you to experiment with your concept on a budget and in a limited amount of time.
It’s important to remember that AI alone is not enough. It’s critical that you also keep humans in the loop, as they are the ones who can provide critical feedback and context to ensure the accuracy of the model results.
Data must be ingrained across company culture
It’s a well-known fact that data-driven organisations do things differently. They mandate that data is the responsibility of everyone in the organisation – not just the CDO and the data team. They treat data as their most valuable asset by managing it as a product. And they always ask “is data in the room” when making business decisions.
As we head into 2023, data cultures will continue to change. But mindsets must change too, both in focus and perception. We’ll see existing roles converge and expand, new roles emerge, and the focus of data leaders shift. Take for example the roles of Chief Data Officer (CDO) and Chief Analytics Officer (CAO). For many years, the CDO and Chief Analytics Officer (CAO) have been evolving as relatively separate roles, each emerging with its own responsibilities and remit. But as data continues to evolve and change, these roles must converge, empowering CDOs to embrace a more holistic view of data consumption across their organisation.
Similarly, CAOs are realising that without clean, curated, continuously-updated data, they cannot deliver on their remit of democratised analytics. In fact, most CAOs now believe that clean, curated, high-quality data is the bottleneck to their analytic initiatives.
As a result of this convergence, today, there are more Chief Data and Analytic Officers (CDAO) than in the past. And many more CDAOs will emerge in the years ahead.
As we head into 2023, I’ll leave you with two final thoughts: anything that feels like a panacea is probably wrong and your biggest barrier to becoming data-driven isn’t technology. It’s people. We’ve reached the point where technology is no longer the issue. It’s a person’s ability to consume, organise, and understand the data available to them is the most significant barrier we face.