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This year has been one of rapid change across the globe, with geopolitical unrest, a challenging economic situation and the COVID-19 pandemic still impacting our day-to-day lives. Despite these challenges, one area that consistently-growing area is data — it is expected to hit more than 180 zettabytes by 2025, according to Statistica Research.
As technology rapidly advances and data rises, it’s no wonder why McKinsey is predicting 2025 to be the year of “the data-driven enterprise.” They predict that in just two years, data will be embedded in every decision, interaction and process as enterprises increasingly rely on data for insights and driving value. Gartner further predicts that in 2023, “optimizing IT systems for greater reliability, improving data-driven decision making and maintaining the value integrity of production AI systems” are key to remaining strategic.
For business leaders, data is at the heart of strategic decision-making and will continue to remain vital. As we we look to 2023 and beyond, here are my top predictions for all things data, including artificial intelligence (AI), C-Suite leadership, and privacy.
The foundational work behind AI
As much as we love AI, a lot of companies have burned their fingers with huge investments in siloed use cases, without seeing large, anticipated returns. Thus, in 2023, we will see a further shift from “AI will solve all my problems; let’s just hire enough data scientists” to a more thorough approach.
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AI will still be extremely valuable, but major issues are grounded on the foundations not being ready, with data quality being the underlying issue more often than not. According to research, data cleaning and transforming can take up 80% of a data scientist’s time — leaving little to do the real work around AI. More companies will realize that investing in AI isn’t a shortcut to bypass 10 data maturity steps at once.
Only after data quality has been prioritized and invested in can organizations leverage behavioral data and unlock the value of data being created. With behavioral data, companies are enabled to create data that is meaningful to their specialized situation — for instance, with data product accelerators.
Data creation and data operations
Companies are drowning in data these days. Gone are the times when everybody primarily wanted to generate “more data.” Making better use of data is the mantra going forward. We will see more and more organizations adopting a deliberate approach in data creation, which creates or collects the data you really need, in a defined, aligned, and agreed quality that is production ready for business intelligence (BI) and AI use cases. This is a theme Snowplow refers to as “Data Creation.” Of course, this means that teams must be very specific and explicit about which data is created, and why and when.
Data contracts are a big topic to watch out for — a technical definition of how data needs to look like to be correctly validated (that is, accepted) by the data pipelines/stack, that’s both human-and machine-readable and typically comes with API functionality. Contracts are agreements between those who “need” the data (the marketing team) and the data/IT teams. Data that’s contracted and validated is exactly represented in the way you want and need it to, saving tons of time further downstream, in particular in the data warehouse or data science scenarios.
On a broader level, DataOps is on the rise, aimed at reducing the time-to-value in our data lifecycle. Many battle-proven processes, best practices and technologies from IT and development teams will be applied to the data world, including the enforcement of interfaces, or contracts, between systems or APIs.
From observability to data lineage, agile development methods and more, there’s a lot to learn and adapt from technical teams. In targeting the delivery of insights and actionable recommendations to the business, there is a significant human component. Collaboration and governance requires unique approaches rather than simply copying learnings from IT.
Data ethics and AI
This also leads to the importance of data ethics of AI, which will likely gain more traction in the coming years. While data ethics is not yet mainstream, it should be. With more and more technical capabilities on the rise, particularly in the field of AI, we need to talk more about how to use data, our algorithms and findings in an ethically bearable manner.
There’s more than one story of machine learning (ML)-trained models that discriminate against certain groups of people. For example, because the training data was already reflecting a certain amount of bias, algorithms denying credits based on questionable correlations, or companies sending out “you are very likely pregnant” messages to customers, entering a very delicate field of intimacy and privacy.
The bottom line is that conversations about data ethics and AI are essential to have. Globally, this issue is drawing more attention with more standards and frameworks being created. For example, The Council on the Responsible Use of AI was formed, a consortium in Singapore was created to drive the ethical use of AI and data analytics in the financial sector, and some of the biggest technology firms established The Partnership on AI.
The role of the Chief Data Officer
For years, we’ve seen a lot of siloed and tech-driven investments in data. Yet, there’s regularly no coherent data strategy in place that ties all data efforts together. More importantly, it’s crucial to connect data strategy properly to business strategy and desired outcomes. Many companies will upgrade their existing strategic and operational efforts to clearly show how data helps to create business value and contribute to concrete goals.
Research points to the benefits of companies that have a dedicated data chief. Two-thirds of businesses that have a Chief Data Officer (CDO) say they are outperforming rivals in market share and data-driven innovation. In 2021, Gartner estimated that less than 50% of large companies have a CDO role in place. However, with digitalization continuously disrupting business models and technology landscapes — let alone continuous investments in AI — many companies will likely follow suit.
Whether we look at Amazon, Netflix, Meta, Apple or non-digital natives like Walmart, all are known for their serious investments and the great benefits of deeply integrating data analytics and AI into their business operations and decision-making. We expect more and more companies to create space in their C-Suite, understanding that data is so much more than their weekly PDF reporting.
It’s fundamental to digital business, in a similar manner as electricity is in our modern world. Data-driven winners embed data in all their decisions, their meetings, R&D and of course, all customer-facing functions. To guide this transformational change, a proper stake is required at the executive table.
Data privacy and compliance
One of the hot topics in Europe and beyond will continue to be data privacy and compliance. In a survey from KPMG, 86% of respondents cited data privacy as a growing concern. Whether it’s because customers are increasingly aware of how brands use their data, or regulatory bodies are significantly increasing scrutiny and de-facto banning Google Analytics in some countries, it’s never been more important for organizations to consider how data compliance and ongoing data management form a critical part of their business and data strategy.
Companies must realize that this is our new reality. Privacy regulations are here to stay, no matter how they look in detail. Instead of continuing to exploit datasets to the maximum, often without proper knowledge, consent or understanding of their customers, organizations need to embrace this unique opportunity before their competition.
It’s a chance and necessity to enter a new relationship with users and customers, one that is guided by getting something back in return for sharing private data. It will continue to play an essential role in learning what works and what doesn’t, or data-empowering decisions made across the board.
The days to exhaust all data points possible are finally over. Less is more. Deliberately creating and using what you need will become the new status quo.
As we look back on 2022, it was a year of much innovation for organizations globally, despite the ongoing geopolitical and economic struggles. For 2023, I predict that there will be much change and innovation in the realm of data, whether that be in AI, the CDO leadership position or data privacy.
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