From cloud to AI and onto new technologies and methodologies such as DataOps, another year means more solutions to choose from. Here, executives of leading companies provide predictions for what's ahead in 2023 for big data. Plans for 5G deployments, new AI technologies like the metaverse or digital twins, and the rise of data fabric and data mesh are just some of the latest trends experts see coming to fruition in 2023.
- Plan for digital transformation: We often see resistance to change at the individual and organizational level. Organizations that do not devote the proper amount of time to create a change management plan for digital transformation will be at risk of falling behind competitors. To remove barriers, increase trust and deliver successful change, a well-planned change strategy is necessary.—Lela McKenna, practice director of change management and training at Apps Associates
- Data lineage gains steam: Organizations will weather economic turbulence in 2023 and will rely on data lineage to enable cost savings and competitive intelligence. Data lineage improves visibility and transparency for data architecture, reducing the time needed when analyzing, planning and implementing changes, chasing incidents, or running bigger modernization projects. It provides significant cost savings by powering data fabric and enabling higher levels of automation around change management, incident management and data pipeline development. During a time when it’s more important than ever for organizations to have trust in their data, offering users a clear view on data’s origin and journey, especially when combined with data quality and privacy, will be critical in powering the next wave of digital transformation projects.—Jan Ulrych, VP of research and education at MANTA
- SQL workloads will explode as more NLP (Natural Language Processing) and other Machine Learning (ML) applications generate SQL: While data analysts and scientists continue to uncover insights using SQL, increasingly we’ll see apps that “speak SQL” drive a large portion of the analytical compute. Natural Language Processing (NLP) applications are enabling citizen data analysts and demand more compute on data platforms. Similarly, ML applications can dig into datasets in new ways which will blow through today's level of demand for analytic compute. SQL is not only the ‘lingua franca’ of data analysis, SQL is the ‘lingua franca’ of ML and NLP too. —Steven Mih, co-founder and CEO of Ahana
- Demand for simplified data access and data sharing is on the rise: Data has become increasingly distributed as the amount of data grows. In 2023, 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.—Haoyuan Li, founder and CEO of Alluxio
- Innovations in High Availability: New predictive application monitoring tools, simulation tools and modeling techniques, based on the wealth of logs, data, and interconnected devices will combine with robust HA solutions capable of identifying threats to availability, predicting and mitigating impending outages, and providing IT administrators with greater diagnostics for unexpected incidents. These innovations will drive reductions in downtime and faster (spell out RCAs) by combining HA solution expertise with the power of data, learned behavior, and self learning technology.—Cassius Rhue, VP of customer experience at SIOS
- Data Center Buildouts Will Ramp Up Significantly as Demand for Digitization Increases Alongside 5G Deployment: In 2023, the ubiquity of 5G will drive a tremendous amount of traffic into the core center and cloud. Data center construction will ramp up significantly, and power demand will spike in response to ongoing digitization priorities. U.S.-based original equipment manufacturers (OEMs) and data center designers are considering lowering reliance on other countries for materials and labor, as national security and economic concerns remain at the forefront for US enterprises.—ABB Power Conversion’s data center segment leader Vito Savino
- Digital and synthetic twins take center stage: The next generation of the analytics life cycle will see a focus on simulating complex systems to help prepare for any possible scenario or disruptive event with digital and synthetic twins. Introducing rare events into our modelling and simulation will be key to understanding the highest probabilities of outcomes when the past is not a predictor of the future. From there, businesses can make rapid and resilient decisions to minimize risk and maximize profits.—Bryan Harris, executive vice president and chief technology officer, SAS
- DevOps backlash: After years of DevOps fever, criticism towards DevOps is going to grow, for two different reasons. First, many businesses fail to reap the benefits because they have just implemented tools without changing their working practices. Second, many corporations have, and will continue to, reduce IT operations personnel assuming that Ops would somehow happen by itself in DevOps. Nevertheless, DevOps will continue to deliver success and gain popularity among those that implement it right and, despite temporary hiccups, the crowd of successful DevOps adopters keeps growing.—Esko Hannula, VP of product management at Copado
- The Return of Data Modeling: In 2023, industry veterans who spent nearly a decade calling for thoughtfulness in building fundamental data infrastructure instead of rushing to build buzzworthy products, will get their “I told you so” moment. Data modeling is making a comeback, alongside the realization that without the infrastructure to deliver high-quality data, businesses will not get very far towards the promise of predictive analytics, machine learning/AI, or even making truly data-driven decisions.—Satish Jayanthi, CTO and co-founder of Coalesce
- Accelerated adoption of data fabric and data mesh: Over the past two decades, data management has gone through cycles of centralization vs. decentralization, including databases, data warehouses, cloud data stores, data lakes, etc. While the debate over which approach is best has its own proponents and opponents, the last few years have proven that data is more distributed than centralized for most of the organizations. While there are numerous options for deploying enterprise data architecture, 2022 saw accelerated adoption of two data architectural approaches – data fabric and data mesh, to better manage and access the distributed data. While there is an inherent difference between the two, data fabric is a composable stack of data management technologies and data mesh is a process orientation for a distributed groups of teams to manage enterprise data as they see fit. Both are critical to enterprises that want to manage their data better. Easy access to data and ensuring it’s governed and secure, is important to every data stakeholders—from data scientists all the way to executives. After-all, it is critical for dashboarding and reporting, advanced analytics, machine learning, and AI projects. Both data fabric and data mesh can play critical roles in enterprise wide data access, integration, management and delivery, when constructed properly with the right data infrastructure in place. So in 2023, expect toa rapid increase in adoption of both architectural approaches within mid-to-large size enterprises.—Angel Viña, CEO and founder of Denodo