Christian Buckner, SVP Data Analytics and IoT at Altair<\/strong><\/h3>\nThe rise of generative AI startups<\/strong><\/em><\/p>\n\u201cGenerative artificial intelligence exploded in 2022. In this next year, we will see text processing and visual art using generative AI continue to improve. Entrepreneurs will look to get in on the action (and the $$) and many startups will emerge that create simple experiences for non-technical people based on generative AI. This could range from advertising copy, SQL queries, documentation copy, blog title ideas, code comments, instructional material, and even deepfake video content. Increasingly the creative output from these models is indistinguishable from \u2013 and in many cases superior to \u2013 human output.”<\/i><\/p>\n<\/div>\n
Rosemary Francis, Chief Scientist at Altair<\/strong><\/h3>\nDeep learning is here<\/strong><\/em><\/p>\n\u201cThe next step for artificial intelligence in 2023 is deep learning. While AI so far has mostly been a mix of supervised machine learning and data analytics, the rise of deep learning will usher in a new era where computers are able to learn without supervision. Advancements in deep learning will lead to innovations in robotics, generative AI, natural language processing and speech recognition, and scientific breakthroughs in health, sustainability, and more. Of course, as with any AI models, the key for organizations to ensure the results are accurate and comply with new regulations emerging is to make sure there is still a human element for routine monitoring and trusted accuracy of the ML models.”<\/i><\/p>\n<\/div>\n
Mamdouh Refaat, Data Scientist at Altair<\/strong><\/h3>\nAI and machine learning models should be explainable<\/strong><\/em><\/p>\n\u201cBuilding AI models is very complex. For AI to really take off, data science and business professionals need to better understand the technology and have access to tools that are easy to use and efficient in building models and gathering data.”<\/i><\/p>\n<\/div>\n
Ryan Welsh, Founder and CEO at Kyndi<\/strong><\/h3>\nThe world reached the era of “peak data scientist”<\/strong><\/em><\/p>\n\u201cThe shortfall of data scientists and machine learning engineers has always been a bottleneck in companies realizing value from AI. Two things have happened as result: (1) more people have pursued data science degrees and accreditation, increasing the number of data scientists; and (2) vendors have come up with novel ways to minimize the involvement of data scientists in the AI production roll out.<\/em><\/p>\nThe coincident interference of these two waves yields \u201cpeak data scientist”, because with the advent of foundational models, companies can build their own applications on top of these models rather than requiring every company to train their own models from scratch. Less bespoke model training requires fewer data scientists and MLEs at the same time that more are graduating. In 2023, expect the market to react accordingly resulting in data science oversaturation.”<\/em><\/p>\nThe AI industry will offer more tools that can be operated directly by business users<\/strong><\/em><\/p>\n“Companies have been hiring more and more data scientists and MLEs but net AI adoption in production has not increased at the same rate. While a lot of research and trials are being executed, companies are not benefiting from production AI solutions that can be scaled and managed easily as the business climate evolves.\u00a0<\/em><\/p>\nIn the coming year, AI will start to become more democratized such that less technical people can directly leverage tools that abstract\u00a0all the machine learning complexity. Knowledge workers and citizen \u201cdata scientists\u201d\u00a0without formal training in advanced statistics and\/or mathematics will be extracting high-value insights from data using these self-service tools allowing them to perform advanced analytics and solve specific business problems at the speed of the business.”<\/em><\/p>\n<\/div>\n
Ravi Mayuram, CTO at Couchbase<\/strong><\/h3>\nDevOps will adopt blockchain and newer programming languages<\/strong><\/em><\/p>\n\u201cWith the emergence of Web 3.0, working with blockchain (specifically Ethereum) will be in every DevOps pro\u2019s playbook\u2014mirroring the boom of adopting machine learning. Newer programming languages, such as Solidity, that are designed for developing smart contracts will enter the toolkits of developers.”<\/i><\/p>\n
In an increasingly data-centric world, edge computing will fuel the evolution of reliable AI<\/strong><\/em><\/p>\n“AI is ubiquitous in our everyday lives. It suggests what to buy and the news we read. It could determine the emails we receive and augment the cars we drive. Moving forward, AI will be even more embedded in our world. It\u2019ll go through a maturation phase that enables us to rely on it more. Predictability and explainability of AI will improve dramatically as we move forward. Moreover, AI will evolve from being algorithm driven to being more data driven. In order for this to be effective, more and more computation will happen at the edge for AI to be reliable, responsive and cost-effective. This trend of more data influencing the algorithms will determine how AI will evolve to be a tool that is relied upon heavily in this data centric future.”<\/em><\/p>\n<\/div>\n
Zohra Ladha, Senior Director of Data Science at Tredence<\/strong><\/h3>\nAugmented intelligence<\/strong><\/em><\/p>\n\u201cTo date, AI and ML have chiefly been used in predicting outcomes and as standalone applications. In the next year, machine learning and natural language processing will both be used to better automate processes and process data while also deriving insights from them as a task that would otherwise be handled by humans, thus increasing workflow efficiencies. Augmented intelligence can transform data analytics with intelligent automation and actionable insights.”<\/i><\/p>\n
Technology proliferation and personalization through quantum ML<\/strong><\/em><\/p>\n“Experimentation of quantum computing to build more powerful ML models will grow in 2023. With big players like Microsoft and Amazon enabling quantum computing resources via the cloud, this may soon become a reality.”<\/em><\/p>\nTechnology proliferation and personalization through consolidation of MLOps<\/strong><\/em><\/p>\n“In 2022, enterprise adoption of MLOPs\u2014which deliver scale, speed, and production diagnostics to improve existing models\u2014took shape in a big way. In the coming year, companies are expected to increase their ML budgets fourfold, with a substantial part of it dedicated to MLOps to support enhanced real-time collaboration between teams. While downstream integrations will continue to be a challenge, additional processes and frameworks will be put in place at the initial stage of development to address this issue.”<\/em><\/p>\nFinding the right talent (crunch)<\/strong><\/em><\/p>\n“The gap between supply and demand regarding data science talent will continue to widen in 2023. Companies must spend a wealth of time, money, and resources to find the best available data scientists. They should focus on organizing Hackathons, bootcamps, and meetups to target new-age skillsets in AI and data science. Identifying niche skillsets through conventional hiring channels could be challenging. For example, full-stack data science skillsets will now include business domain, machine learning, software engineering, ML engineering, and infrastructure engineering to build end-to-end assets.”<\/em><\/p>\nCitizen data scientists<\/strong><\/em><\/p>\n“The one-two punch of the data scientist talent crunch and the increase in no-code\/low-code machine learning platforms will strengthen and grow the citizen data scientist community to deliver self-service ML as a business user. Citizen data scientists can bolster organizational value, solve a host of business-specific issues, and deliver meaningful prescriptive analytics.”<\/em><\/p>\n<\/div>\n
Scott Francis, Technology Evangelist at PFU America<\/strong><\/h3>\nAI gets smarter in 2023<\/strong><\/em><\/p>\n\u201cAI will not only grow smarter in 2023, it will become more affordable, simple, and accessible. Companies will automate many processes done manually before, such as invoicing, transcriptions, medical charting, and resume processing. As companies digitize their document archives and historic data, they will use Natural Language Processing (NLP) to make it searchable in real-time. This will increase employee productivity and accelerate the availability of information to business units, delivering richer insights and stronger ROI.”<\/i><\/p>\n
Companies will leverage AI to shed some light on dark data<\/strong><\/em><\/p>\n“The lack of insight caused by the \u201cblack hole\u201d of dark data will continue to plague companies in 2023, including the loss of \u201ctribal\u201d knowledge every time an employee leaves the company. This knowledge gap will lead organizations to leverage AI to classify employee knowledge, making sure everything is captured and searchable, thus enabling team members and new employees to quickly ramp up rather than starting from ground zero.”<\/p>\n
<\/div>\n
Vamsi Paladugu, Senior Director Lyve Cloud Analytics at Seagate Technology<\/strong><\/h3>\nData analytics are increasingly being asked to drive greater data freedom in the multicloud<\/strong><\/em><\/p>\n\u201cData freedom is data\u2019s ability to be accessed and to move, and therefore the freedom to deliver insights and the business value that comes from these insights. It means a frictionless flow\u2014unobstructed by structural, economic, and other barriers. One way in which analytics can drive data freedom is via open data lake architectures that leverage open-source frameworks. “<\/i><\/p>\n
Data democratization will be the number one competitive advantage in 2023<\/strong><\/em><\/p>\n“As companies prepare for economic softening, leaders are leaning on their (often downsized) teams now more than ever. When companies provide teams with unrestricted access to data, employees are empowered to unlock the rich insights deep customer data analysis offers. The companies that can ground individuals throughout the organization in data that helps them to better solve for customer pain points will achieve higher levels of customer satisfaction and retention.”<\/em><\/p>\n<\/div>\n
Daniel Fallman, CEO at Mindbreeze<\/strong><\/h3>\nTechnology innovation and investment will not slow down; insight engines will be key<\/strong><\/em><\/p>\n\u201cEven though companies are scaling back given the looming recession, teams are still going to be expected to produce at the same – or even accelerated – pace. Companies need to be prepared to produce the same output, or potentially more, with less human capital. Insight engines will be key to aggregate knowledge and increase efficiency.”<\/i><\/p>\n
AI-driven question answering will come to the forefront<\/strong><\/em><\/p>\n“It’s what everyone has been waiting for \u2013 really making a proper intelligent model to answer questions from inside the organization, with direct extraction of the answer from the source content. Like having a personal assistant, the technology will allow anyone within the organization to ask a question and find the answer from the knowledge base.”<\/i><\/p>\n
Customization & AI democratization will open doors<\/strong><\/em><\/p>\n“2023 will see a massive boost in roll out of highly specific applications for information insights and apps in low-to-no code fashion to open the door to more use of product among less technically trained individuals. Companies like Mindbreeze are making AI simpler to use so that companies can more easily leverage technology\u2019s capabilities with ease and see more ROI.”<\/i><\/p>\n<\/div>\n
Yali Sassoon, Co-Founder at Snowplow<\/strong><\/h3>\nData creation as an approach<\/strong><\/em><\/p>\n\u201cOne of the most exciting developments in 2022 was the introduction of Data Creation as an approach. This is the process of intentionally creating data to power AI and advanced data applications, as opposed to data exhaust, which is the byproduct of data emitted from existing systems. Data Contracts have been a much-discussed topic in the data community this year. In the context of the Modern Data Stack, this sensible idea has been controversial primarily because practitioners are stuck in the \u2018data is oil\u2019 paradigm, in which they assume that data is extracted and not intentionally created. We expect the \u2018data product manager\u2019 role and \u2018data product management\u2019 skill set to continue to gain traction in 2023. They are instrumental for organizations to successfully treat data as a product, design and implement the right data contracts (as part of data products) and enable them to build out a \u2018data mesh.”<\/em><\/p>\nChris Lubasch, Chief Data Officer & RVP DACH at Snowplow<\/strong><\/h3>\nArtificial intelligence<\/strong><\/em><\/p>\n\u201cAs much as we love AI, a lot of companies have burned their fingers with huge investments into silo use cases, without seeing their huge, anticipated returns. We will see a shift from \u2018AI will solve all my problems; let\u2019s just hire enough data scientists\u2019 to a more thorough approach. AI will still be extremely valuable, but major issues are grounded on the foundations not being ready. Data quality is an issue more often than not. With data cleaning\/transforming can take up 80 percent of the data scientist\u2019s time, there is frequently little time left to the real work around AI. More companies will realize that investing in AI isn\u2019t a shortcut to bypass 10 data maturity steps at once.\u201d<\/i><\/p>\n
Data ethics<\/strong><\/em><\/p>\n\u201cData Ethics is a topic that\u2019s not yet mainstream, but it should be. With more and more technical capabilities on the rise, in particular, in the field of AI, we need to talk more about how to use data, our algorithms, and our findings in an ethically bearable manner. There\u2019s more than one story of machine-learning trained models that discriminate certain groups of people (e.g. because the training data was already reflecting a certain amount of bias), algorithms denying credits based on questionable correlations, or companies sending out sensitive messages to customers, entering a very delicate field of intimacy and privacy.\u201d<\/i><\/p>\n
Modern data stack<\/strong><\/em><\/p>\n\u201cIt was a year of fast-moving discussions around the modern data stack. Lots of new vendors popped up, and major ones like Snowflake and Databricks continue their journey to take over many technical components, despite the challenging economic situation. But at the same time, voices emerged who questioned the modern data stack as such, whose decoupled approach often leads to many tools and high costs, let alone the complexity of getting it all together. The discussions around the \u2018postmodern data stack\u2019 (as just one out of many terms) were started, and we\u2019re all eager to see where this will lead us in the coming years.\u201d<\/i><\/p>\n
Tech-driven investments<\/strong><\/em><\/p>\n\u201cFor years, we\u2019ve seen a lot of siloed and very tech-driven investments in data. Yet, there\u2019s regularly no coherent data strategy in place, that ties all data efforts together. More importantly, it\u2019s absolutely crucial to connect the data strategy properly to the 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.\u201d<\/i><\/p>\n<\/div>\n
Chris Gladwin, Co-Founder and CEO at Ocient<\/strong><\/h3>\nData analysis will be continuous<\/strong><\/em><\/p>\n“Data warehouses are becoming \u201calways on\u201d analytics environments. In the years ahead, the flow of data into and out of data warehouses will be not only faster, but continuous.<\/em><\/p>\nTechnology strategists have long sought to utilize real-time data for business decision-making, but architectural and system limitations have made that a challenge, if not impossible. Also, consumption-based pricing could make continuous data cost prohibitive.\u00a0<\/em><\/p>\nIncreasingly, however, data warehouses and other infrastructure are offering new ways to stream data for real-time applications and use cases.<\/em><\/p>\nPopular examples of real-time data in action include stock-ticker feeds, ATM transactions, and interactive games. Now, emerging use cases such as IoT sensor networks, robotic automation, and self-driving vehicles are generating ever more real-time data, which needs to be monitored, analyzed, and utilized.”<\/em><\/p>\n<\/div>\n
Dhruba Borthakur, Co-Founder and CTO at Rockset<\/strong><\/h3>\n2023 will be the year of the data app<\/strong><\/em><\/p>\n“In the past ten years we\u2019ve seen the rise of the web app and the phone app, but 2023 is the year of the data app. Reliable, high performing data applications will prove to be a critical tool for success as businesses seek new solutions to improve customer facing applications and internal business operations. With on-demand data apps like Uber, Lyft and Doordash available at our fingertips, there\u2019s nothing worse for a customer than to be stuck with the spinning wheel of doom and a request not going through. Powered by a foundation of real-time analytics, we will see increased pressure on data applications to not only be real-time, but to be fail safe.”<\/em><\/p>\nReal-time data goes mainstream<\/strong><\/em><\/p>\n“In 2022, we saw strong growth of real-time data analytics as more enterprises realized just how valuable it is, which was just a foretaste of its true potential. 2023 is the year that real-time analytics goes mainstream. We are seeing more and more instances of organizations realizing tremendous benefits of real-time spanning across industries. For example, sporting event organizers are using ticket booking data in real-time to adjust prices and local governments are using real-time traffic patterns to dynamically tune traffic signals. With the current bearish market economy, it\u2019s the efficiency and performance of these data systems that will prove to be the key to success. “<\/em><\/p>\nCloud Efficiency Benchmark Wars<\/strong><\/em><\/p>\n“Real-time data analytics has become a necessity in 2023, and the efficiency and performance of these cloud data systems is the key to adoption and success. With the current bearish market economy, every business is feeling the need to reassess the cost of these real-time data analytics systems to better understand price-performance. We are seeing more benchmarks competition from data vendors like Snowflake and Databricks to prove its value to customers, and the data systems that can do more with less are the clear winners. In 2023, we will see benchmark wars between cloud data vendors showing one system being more efficient compared to the other.”<\/em><\/p>\nRise of Real-time Machine Learning<\/strong><\/em><\/p>\n“With all the real-time data being collected, stored, and constantly changing, the demand for real-time ML will be on the rise in 2023. The shortcomings of batch predictions are apparent in the user experience and engagement metrics for recommendation engines, but become more pronounced in the case of online systems that do fraud detection, since catching fraud 3 hours later introduces very high risk for the business. In addition real-time ML is proving to be more efficient both in terms of cost and complexity of ML operations. While some companies are still debating whether there\u2019s value in online inference, those who have already embraced it are seeing the return on their investment and surging ahead of their competitors.”<\/em><\/p>\n<\/div>\n
Haoyuan Li, CEO at Alluxio<\/strong><\/h3>\nMore large-scale analytics and AI workloads will be containerized, but the talent pool is a bottleneck<\/strong><\/em><\/p>\n“In the cloud-native era, Kubernetes has become the de facto standard, with a variety of commercial platforms available on the market. Organizations are increasingly deploying large-scale analytics and AI workloads in containerized environments. While containers provide many benefits, the transition to containers is very complex. As a result, in 2023 the main bottleneck to container adoption will be the shortage of talent with the necessary skill set for tools like Kubernetes.”<\/em><\/p>\nBig models for AI are driving innovations in specialized infrastructure and solutions<\/strong><\/em><\/p>\n“Over the past few years, AI and deep learning have become mainstream and reached the same maturity level as data analytics. Big models, from OpenAI\u2019s DALL-E 2 image generation model to Google\u2019s LaMDA conversation agent, are expected to dominate the landscape in 2023. Billions of files will be used to train big models for longer periods of time, requiring more specialized infrastructure and solutions. Next-generation AI infrastructure will be developed to handle the scale.”<\/em><\/p>\nDemand for simplified data access and data sharing is one on the rise<\/strong><\/em><\/p>\n“Data has become increasingly distributed as the amount of data grows. In 2023,\u00a0<\/span>organizations will have an ever-increasing need to manage their scattered data wherever it exists. Furthermore, data sharing across organizations and platforms will become more critical. It will be necessary for organizations to develop and implement a data strategy for managing and sharing distributed data across regions, organizations, clouds and platforms.”<\/span><\/em><\/p>\n<\/div>\n
Angel Vi\u00f1a, CEO and Founder at Denodo<\/strong><\/h3>\nEthical AI becomes paramount as commercial adoption of AI-based decision-making increases<\/strong><\/p>\n“Companies across industries are accelerating the usage of AI for their data-based decision making. Whether it\u2019s about social media platforms suppressing posts, connecting healthcare professionals with patients, or large wealth management banks granting credits to their end consumers; However, when artificial intelligence decides the end result, currently there is no way to suppress the inherent bias in the algorithm. That is why emerging regulations such as the proposed EU Artificial Intelligence Act, and Canada’s Bill C-27 (which may become the Artificial Intelligence and Data Act if enacted) are starting to put a regulatory framework around the use of AI in commercial organizations. These new regulations classify the risk of AI applications as unacceptable, high, medium, or low risk and prohibit or manage the use of\u00a0 these applications accordingly.<\/em><\/p>\nIn 2023, Organizations will need to be able to comply with these proposed regulations, including ensuring privacy and data governance, algorithmic transparency, fairness and non-discrimination, accountability, and auditability. With this in mind, organizations have to implement their own frameworks to support ethical AI e.g. guidelines for trustworthy AI, peer review frameworks, and AI Ethics committees. As more and more companies put AI to work, ethical AI is bound to become more important than ever in the coming year.”<\/em><\/p>\n<\/div>\n
Luke Han, CEO at Kyligence<\/strong><\/h3>\nData & analytics ROI is key metric<\/strong><\/p>\n“Data and analytics continue to be the focus of IT transformation for many organizations, but as the market evolves\u2013and in an environment of economic uncertainty\u2013most will demand that their investments in analytics show a path to a clear return on investment in the near-term.<\/em><\/p>\nLessons learned during the pandemic and from the experiences of organizations that are already a good distance down the analytics road, show that producing faster, more accurate business intelligence is an attainable goal. That puts an emphasis on production rather than proof-of-concept, and ROI is a key metric for enterprises to consider before adopting any analytics platform or product.”<\/em><\/p>\nAnalytics as a Service & BYOD<\/strong><\/em><\/p>\n“As the power of analytics becomes more obvious, SaaS analytics applications will emerge as a powerful option to get in on the action, especially for organizations without the human capital or data infrastructure to build and manage their own data operations. Analytics as a Service tools will allow organizations to operationalize their own data on sophisticated tools in the cloud, avoiding costly and time-consuming DevOps cycles.<\/em><\/p>\nAs a result, Analytics as a Service will further democratize the power of data by delivering a faster time-to-insight while allowing organizations, departments, and even individuals to bring their own data to the application and focus on extracting its value rather than fussing with technology.”<\/em><\/p>\nRe-emergence of KPIs<\/strong><\/em><\/p>\n“As more enterprises focus on IT operations and data efficiency, refined management has emerged as an important element of enterprise management. In turn, that has placed an emphasis on measuring and managing performance based on quantitative results. Because of this, key performance indicators (KPIs) will overtake objectives and key results (OKR) as a means of evaluating performance, and for driving decisions at the operations level.<\/em><\/p>\nThat means goals must be translated and progress tracked using measurable data, including leading metrics that take lag into account so that adjustments can be made in near real-time as performance tracks to established KPIs.”<\/em><\/p>\nDashboards are dead<\/strong><\/em><\/p>\n“Experienced business intelligence and data engineers have long been aware that dashboards\u2013often counted as a business asset\u2013tend to generate a great deal of technical debt that accrues over time, hampering performance. That\u2019s because, for all the ease-of-use glitz and democratization acclaim, dashboards are just dirty tools for connecting data silos.<\/em><\/p>\nWith enterprises rushing to adopt public cloud applications and services, using business intelligence dashboards for managing processes and reviewing results will create more problems than they solve. But while BI dashboards die off, the metrics store concept will displace them as the preferred method for efficiently\u00a0 managing data on one platform across the entire\u00a0 enterprise.”<\/em><\/p>\nAI-augmented analytics will enable democratization<\/strong><\/em><\/p>\n
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