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In the last year, both companies and individuals have had to be resilient to adapt to the new normal. Technology and, in particular, data analysis have become great allies to face the business, social and cultural challenges that the pandemic has posed.
So much so that, according to IDC’s Global Big Data and Analytics Spending Guide, European spending on Big Data solutions and business analytics (BDA) will reach 50 billion dollars this year, 7% more than 2020. In addition, it is estimated that in 2025 there will be 25,000 million connected devices that will share information thanks to artificial intelligence, which will mean a massive increase in the volume of data available for analysis.
At the gates of 2022, SDG Group presents its report for the fourth consecutive year on the prominent trends that will sweep the market and focus on advances in innovation and the development of new technologies in the field of Data and Analytics.
SDG Group announces the new Data Analytics trends for 2022 by breaking down the ranking in Given Trends, the trends that are already taking over; Trends on the Rise, that is, those emerging trends that will have a significant impact in the medium term and, finally, Slow-Shift Trend, the group of movements that is looming on the horizon and will gradually enter the game.
Companies are embracing increasingly deep, scalable, and transformational data architectures, modelling, and structures with the cloud. The cloud opens the door to the new generation of Data Warehouse by enabling Data Mesh, Data Vault 2.0, and Data Fabric, technologies and practices thought and created natively in the cloud.
An example of this is Data Mesh, a holistic approach to data management. An architecture controlled by distributed domains converges data treatment as a product, self-service data infrastructures’ design, and ecosystems’ governance. The “data mesh” allows data products to be linked between domains, allowing information exchange without depending on storage.
And the Data Fabric, an architecture that enables data access and exchange in a distributed environment, regardless of whether they are in the private, public, on-premises, or multi-cloud cloud. This layer of data and scalable connection processes automate ingestion, selection, and integration bypassing data silos. In this way, the “data fabric” continually identifies and connects data from disparate applications, discovering unique and business-relevant relationships. This information enables more effective decision-making, providing value through rapid access and understanding relative to traditional data management practices.
We also find the Data Vault 2.0, the evolution to the next level of the Data Vault that originates thanks to the cloud. DataOps is an agile framework for the configuration and collaborative management of technologies, processes, and data. All these current trends have a common denominator: they are based on the cloud’s immense potential to provide quality responses to the continuous demands for innovation and flexibility of organizations.
DataOps is the technology framework inspired by the DevOps movement. Its goal is to create predictable deliverables and change management for data, models, and related artefacts. How do you get it? Leveraging technology to automate data delivery with ideal security, quality, and metadata level to improve data use and value in a dynamic environment. DataOps activates the levers that data-driven businesses demand: governance, flexibility, scalability, efficiency, and automation.
This trend – which we already mentioned last year – is now evolving to the next level thanks to artificial intelligence and Machine Learning creating hyper-automation environments. Now organizations quickly identify, examine and automate data management processes, which directly influences Data Quality – allowing companies to be faster when it comes to profiling and polishing their data – in Data Observability – providing greater agility to, for example, monitor data pipelines-, in the Data Catalogue -facilitating an increasingly strategic vision in functionalities such as lineage and inventory- and in DevOps Since intelligent automation reduces manual operations and increases cross-team collaboration.
Furthermore, within DataOps and its “vertical” Data Governance by Design, the Metadata LakeHouse is gaining importance. This metadata platform makes it possible for the metadata to become the manager and brain of a company’s entire data management environment.
To respond to customer needs, we do not have to put them at the centre of the shopping experience. In this area, the multichannel strategy has been superseded by omnichannel, enabled mainly by hyperconnectivity (cloud, 5G, IoT). In other words, the barriers between digital and physical channels no longer exist and campaigns conceived by platform. Now the focus is no longer on the product and the different “windows” where it can be sold but instead focuses on the customer to provide a unique and homogeneous shopping experience wherever they are.
The relevant information is extracted from the different channels about the entire consumer journey by collecting data from the other channels. With this information, it is possible to analyze the impact of each point of contact while optimizing processes and improving the service or product offered according to the feedback received. Companies provide hyper-personalized products and services thanks to data analytics and intelligent process automation. This approach facilitates the feeding of artificial intelligence models activated in real-time – or even predictively – thus reducing latency times, time-to-market, and associated costs.
The data has no value per se but is postulated as a business axis to the extent that it becomes a monetizable and differentiating asset. DATA is to be understood here as the data set, algorithms, practices, and information available to a company. Organizations that take advantage of the information that data provides and extract value from it differentiate themselves from their competitors.
The calculation of the value of the data and what it encompasses -from the algorithms, how to work with them, the practices, etc.- directly affect the price and the attractiveness of companies. Now or never: the market rules are already changing -see the examples of companies committed to R&D or the start-ups themselves- so it is time to focus on the competitive advantage that data implies, understanding and harnessing all its transformational power.
Companies adopt Zero Trust cybersecurity strategies that protect the traditional perimeter. It is a proactive approach to cybersecurity based on identity. It uses data collection and analysis ( Cybersecurity Analytics ) for faster detection of threats and the automation of manual security tasks.
The cybersecurity environment is also supported by Blockchain technology, a great ally of cybersecurity since it guarantees the storage of data through its decentralization and encrypted information. This technology provides excellent value, especially in identity protection, infrastructure protection, and data flow traceability.
In this context, Privacy-Enhancing Computation (PEC) also appears on the scene, a set of technologies that protect data while processed, shared, transferred, and analyzed. PEC adoption is increasing, particularly for fraud prevention. According to Gartner, “by 2025, 50% of large organizations will adopt technology to increase the privacy of data processing in untrusted environments or use cases of analysis of multiple data sources.”
Companies are betting on Self-Service 2.0 and the Auto Machine Learning model to increase their capabilities for extracting insights. This occurs because these technologies accelerate the adoption of solutions by giving direct access to end-users, democratizing access to data, and focusing on generating insights.
On the one hand, Self Service 2.0 is integrating and leveraging the analytical power of AI-powered models. On the other hand, Auto ML uses the visual and reporting part to present its advanced algorithms. These evolutions show how these technologies are trying to facilitate 360º in each area, covering the users’ analytical aspects.
Another example of this “shock” is that we live a moment of enthusiasm in which movements and acquisitions are detected by companies that include Self-Service and Auto ML in their portfolio. Companies are trying to increase their capabilities, closing the gap between advanced analytics and BI by making it easier for non-data scientist users to gain predictive capabilities.
The disruption of Quantum Computing and AI leads us to have a great responsibility around the ethical management of data. After the success of privacy (driven by the RGPD legislation), now is the time to regulate its use, guaranteeing its ethical and responsible development when it impacts citizens. Companies and institutions must define their “IA for Good” strategy to minimize technical debt and commit to making good engineering processes transparent and fair algorithms.
Along these lines, the new concept of Private IA arises. In public administrations or entities where data sharing is complex, AI strategies are being created that allow obtaining insights using encryption and thus exposing the data as little as possible.
More and more companies are investing in Quantum AI because they expect it to become the next revolution. At present, we are experiencing an important parallel in how quantum computing is developing and its convergence with advanced analytical techniques; we must make conscious and consistent use of this new paradigm’s benefits.
Quantum AI will take advantage of the processing superiority of Quantum Computing to obtain results unattainable with classical computing technologies. It will allow the processing of large data series, the more agile resolution of complex problems, and an improvement of the business models and vision. There are many benefits that these techniques provide after the leap from science to business. Few companies today do not take advantage of the benefits of encapsulating previously only human-actionable knowledge within the framework of intelligent and agile decision-making. We are on the verge of a technological trend that will reshape markets and industries for decades to come.
Metaverse is not just a buzzword in the tech industry. The Metaverse is an ecosystem that will facilitate the so-called EX, extended reality. Under the umbrella of the EX, we find all the immersive technologies that merge the real world with the virtual one: augmented, virtual and mixed reality.
The set of products and services built around the Metaverse encourages innovation in the devices and hardware – such as glasses and contact lenses – that facilitate extended reality, which will become increasingly accessible for companies and end-users. The rise of the Metaverse will directly influence the innovation and maturity of EX devices: they will cost less and accelerate the entire technology cycle. The forecast is that the Metaverse ecosystem will move around $ 800 billion in 2024 and $ 2.5 trillion by 2030 (Bloomberg Intelligence). Extended reality is a set of technological resources that will allow the user to immerse themselves in interactive experiences from the combination of the virtual and physical dimensions.
Artificial intelligence is often used to train algorithms based on conclusions, but do we know if it can create content and innovate on its own? The answer is yes, and it lies in Generative AI, one of the most promising advancements in the AI environment for years to come. Generative AI enables computers to automatically recognize underlying patterns related to input information and then generate new original content.
In other words, Generative AI is a form of AI that learns a digital representation of existing content, such as transaction data, texts, audio files, or images. It uses it to generate new, original and realistic artefacts that are similar to the training data. This enables generative AI to be a rapid innovation engine for companies in software creation, pharmaceutical new product production, weather analysis, and fraud detection.
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