2022 was a momentous year for the data analytics and management space. We saw data quantities explode and disperse — a trend that will only continue as data volumes are expected to almost double in size between 2022 and 2026. We supported IT teams as they realised that many data management approaches that may have worked a year ago – such as monolithic data architectures – no longer suffice in today’s data-driven environment, leading to missed business opportunities. With that, in a business landscape where data is power, organisations have begun to adopt new data management strategies, architectures and tools to better leverage this influx of data.
So, what’s in store for the year ahead? From AI/ML, data mesh, and metadata management, to sustainability goals and cloud financial governance, our expert team at Exasol has identified five topics data leaders should keep in mind for data analytics and management success in 2023:
A new era in Loading...data science has dawned but the struggle to deploy AI/ML at scale persists
We have entered a new era of data science, and companies must embrace this as a paradigm shift in the way data is analysed. Loading...Advanced analytics converges traditional data warehousing with modern data science techniques through Loading...artificial intelligence (AI) — and in particular, through Loading...machine learning (ML) — which has fueled the journey from descriptive to predictive to prescriptive analytics. There’s no doubt that AI/ML is key in driving business value from Loading...advanced analytics, yet organisations struggle to deploy these capabilities at scale.
In 2023, we expect extracting value from AI to be an ongoing challenge — but of utmost priority as investments in AI soar. Organisations will have much to gain from harnessing AI/ML considering that AI investments are projected to grow exponentially (from $122 billion in 2022 to more than $300 billion in 2026). What’s more, Infosys also estimates that companies can generate over $460 billion in incremental profit if they are able to optimise AI and data practices.
Businesses should prepare for heightened scrutiny on delivering ROI from their AI investments in 2023. As teams are increasingly tasked with providing real, tangible business value from AI investment, tools and technologies for scaling and operationalising AI will become vital.
At the same time, successful AI/ML deployment will depend on the relationship between data scientists and data engineers. Harmonious collaboration is critical to ensure that ML scoring models created by data scientists, for example, are properly integrated into production systems and processes by data engineers.
Data mesh initiatives gain momentum but misinformation threatens to slow adoption
Companies that rely on data management tools to make decisions are 58% more likely to beat their revenue goals than non-data-driven companies, according to a study from Collibra and Forrester Consulting. And data-driven organisations are 162% more likely to significantly surpass revenue goals than those that are not data-driven. The enterprise has reached a turning point in the evolution of data management. We are increasingly seeing companies pivot away from traditional, monolithic approaches, and instead embrace new strategies and solutions in an effort to become more data-driven. One growing trend is the adoption of a data mesh architecture, which many organisations view as an answer to their challenges around data ownership, quality, governance and access. A data mesh offers a distributed model where domain experts have an ownership role over their data products.
In 2023, we anticipate even greater pressure on organisations to move faster and build resilient, agile data architectures that will push data teams towards data mesh implementations. However, despite the growing enthusiasm around data mesh, we do expect roadblocks due to misinformation. In order to move forward, misinformation needs to be eradicated so that data mesh can be successfully adopted at scale. For example, despite being marketed as such, you cannot buy a data mesh — it is not a technology. There is also still much discussion and confusion about how to prevent data meshes from exacerbating data silos, and whether or not data mesh and data fabric are actually the same thing. To overcome these challenges and move beyond any debates or uncertainties, companies must take responsibility for educating themselves to strengthen their understanding of what a data mesh is and how it can optimise their data management strategy.
Moving to a metadata mindset: metadata-based data management emerges from the shadows
Metadata is emerging as a vital component of data management as organisations look to accelerate the time to value of their data, optimise costs, and comply with the ever-evolving landscape of industry and governmental regulations. In 2023, the role of metadata in the data ecosystem will continue to grow, spurred by more organisations shifting to the cloud, and a growing interest in data discovery, governance, virtualisation and catalogs, as well as the need to speed up data delivery through the automation of data pipelines and warehouse automation.
However, metadata is still often overlooked and understated in data analytics and data management. Businesses should take heed, as this alone could sabotage your data management strategy. When it comes to good quality data, metadata is the foundation. Simply put, metadata is data that provides information about other data so that it can be more easily understood and used by the organisation. It answers the who, what, when, where, why, and how questions for data users. Metadata management also plays a large part in supporting Loading...data governance programs.
With the recent influx of new regulatory compliance laws, businesses are increasingly investing in Loading...data governance programs to help securely manage their data assets. Metadata and metadata management can support these efforts by providing the foundation for identifying, defining, and classifying data. And with data quality standards established, metadata management can ensure that the necessary regulatory controls are applied to the corresponding data. As we look ahead, we encourage modern enterprise teams to embrace a metadata mindset, and we expect a new wave of metadata management tools and best practices to be a focal point in the market.
A call to action: Leveraging data analytics to meet sustainability goals
On the heels of the COP27 climate summit, the European Parliament adopted the Corporate Sustainability Reporting Directive (CSRD), expanding the existing legislation to increase transparency and ensure businesses are publicly accountable for their impact on the environment. Under the new rules, all large companies in the EU will need to report on environmental, social and governance (ESG) factors, disclosing data on the impact of their activities on people and the planet and any sustainability risks they are exposed to. With government agencies progressing regulations, pressure is mounting from diverse stakeholders — including investors, customers, regulators, the supply chain and the public — for organisations to prove their commitments are being met. In fact, Gartner says CEOs are now emphasising environmental sustainability as a top business priority next year.
Data and analytics can help businesses pinpoint the barriers to meeting their sustainability goals, but collecting and analysing ESG data is a complex task, especially for legacy technology systems as manual preparation is inadequate. In 2023, businesses will lead the way in reporting ESG performance by analysing changes in their organisations’ performance over time, comparing it against set goals — and against their competitors. Focusing on data quality and transparency is key, and a siloed and piecemeal approach only reduces the value and accuracy of reporting, unnecessarily slowing progress toward achieving sustainability goals. Businesses should look to adopt a robust data strategy to align business operations and technical capabilities with sustainability goals and requirements.
Financial governance top of mind: CFOs must become more cloud-savvy amidst recession fears
As we face a potential economic recession heading into 2023, organisations must learn how to do more with less when it comes to their IT budgets. Cloud services will remain a necessity for most businesses during this time, however, organisations will need to cut back and optimise their cloud programs to only the essentials and leverage more economical services with fewer hidden costs. As a result, CFOs — who have the ultimate say on cloud spending — will need to become more cloud-savvy to ensure their organisations are making the right deployment and financial decisions when it comes to the cloud at a time where every penny counts.
With this, we’ll also see organisations focused more on financial governance when it comes to applications running in the cloud. Normal approaches to managing infrastructure spend do not apply to the public cloud due to unexpected egress fees and ever-changing cloud capabilities. Therefore, organisations that do not have a handle on their cloud financial management practices are often faced with unexpectedly large bills. In 2023, we expect organisations to enter into a new era in which cloud financial management is a top priority and a core element of business strategies that will need to be focused on maximising the ROI of every project.
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There’s no guarantee for business success in 2023, but keeping a finger on the pulse of these trends will be vital to establishing the most effective data-driven strategies. Now is the time to invest in the right data management and analytics tools in order to tap into the full value of your data and deliver tangible business results. In 2023 one thing will remain certain – the organisations that do this, will be the ones that come out on top in their markets.
Helena Schwenk, VP Chief Data & Analytics Office, Exasol
Jens Graupmann, SVP Product & Innovation, Exasol