Data quality has perhaps never been more important. And a year from now, then a year beyond that, it will likely be even more important than it is now.
The reason: AI, and in particular, generative AI.
Given its potential benefits, including exponentially increased efficiency and more widespread use of data to inform decisions, enterprise interest in generative AI is exploding. But for enterprises to benefit from generative AI, the data used to inform models and applications needs to be high-quality. The data must be accurate for the generative AI outputs to be accurate.
Meanwhile, generative AI models and applications require massive amounts of data to understand how to respond to a user’s query. Their outputs aren’t based on individual data points, but instead on aggregations of data. So, even if the data used to train a model or application is high-quality, if there’s not enough of it, the model or application will be prone to deliver an incorrect output called an AI hallucination.
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With so much data needed to reduce the likelihood of hallucinations, data pipelines need to be automated. Therefore, with data pipelines automated and humans unable to monitor every data point or data set at every step of the pipeline, it’s imperative that the data be high-quality from the start and there be checks on outputs at the end, according to David Menninger, an analyst at ISG’s Ventana Research.
Otherwise, not only inaccuracies, but also biased and potentially offensive outputs could result.
As we’re deploying more and more generative AI, if you’re not paying attention to data quality, you run the risks of toxicity, of bias. You’ve got to curate your data before training the models, and you have to do some postprocessing to ensure the quality of the results. David MenningerAnalyst, ISG’s Ventana Research
“Data quality affects all types of analytics, but now, as we’re deploying more and more generative AI, if you’re not paying attention to data quality, you run the risks of toxicity, of bias,” Menninger said. “You’ve got to curate your data before training the models, and you have to do some postprocessing to ensure the quality of the results.”
“We’re actually seeing it more than expected,” he said.
Likewise, Madhukar Kumar, chief marketing officer at data platform provider SingleStore, said he is seeing increased emphasis on data quality. And it goes beyond just accuracy, he noted. Security is an important aspect of data quality. So is the ability to explain decisions and outcomes.
“The reason you need clean data is because GenAI has become so common that it’s everywhere,” Kumar said. “That is why it has become supremely important.”
However, ensuring data quality to get the benefits of AI isn’t simple. Nor are the consequences of bad data quality.
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The rise of GenAI
The reason interest in generative AI is exploding — the “why” behind generative AI being everywhere and requiring that data quality become a priority — is that it has transformative potential in the enterprise.
Data-driven decisions have proven to be more effective than those not informed by data. As a result, organizations have long wanted to get data in the hands of more employees to enable them to get in on the decision-making process.
But despite the desire to broaden analytics use, only about a quarter of employees within most organizations use data and analytics as part of their workflow. And that has been the case for years, perhaps dating back to the start of the 21st century.
The culprit is complexity. Analytics and data management platforms are intricate. They largely require coding to prepare and query data, and data literacy training to analyze and interpret it.
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Vendors have attempted to simplify the use of their tools with low-code/no-code capabilities and natural language processing features, but to little avail. Low-code/no-code capabilities don’t enable deep exploration, and the NLP capabilities developed by data management and analytics vendors have limited vocabularies and still require data literacy training to use.
Generative AI lowers the barriers that have held back wider analytics use. Large language models have vocabularies as large as any dictionary and therefore enable true natural language interactions that reduce the need for coding skills. In addition, LLMs can infer intent, further enabling NLP.
When generative AI is combined with an enterprise’s proprietary data, suddenly any employee with a smartphone and proper clearance can work with data and use analytics to inform decisions.
“With generative AI, for the first time, we have the opportunity to use natural language processing broadly in various software applications,” Menninger said. “That … makes technology available to a larger portion of the enterprise. Not everybody knows how to use a piece of software. You don’t have to know how to use the software; you just have to know how to ask a question.”
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Generative AI chatbots — tools that enable users to ask questions using natural language and get responses in natural language — are not foolproof, Menninger added.
“But they’re a huge improvement,” he said. “Software becomes easier to use. More people use it. You get more value from it.”
Meanwhile, data management and analytics processes — integrating and preparing data to make it consumable; developing data pipelines; building reports, dashboards and models — require tedious, time-consuming work by data experts. Even more tedious is documenting all that work.
Generative AI changes that as well. NLP reduces coding requirements by enabling developers to write commands in natural language that generative AI can translate to code. In addition, generative AI can be trained to carry out certain repetitive tasks on its own, such as writing code, creating data pipelines and documenting work.
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“There are a lot of tasks humans do,” Abhyankar said. “People are overworked, and if you ask them what they are able to do versus what they’d like to be able to do, most will say they want to do five or 10 times more. One benefit of good data with AI on top of it is that it becomes a lever and a tool to help the human being be potentially multiple times more efficient than they are.”
Eventually, generative AI could wind up being as transformational for knowledge workers as the industrial revolution was for manual laborers, he said. Just as an excavator is multiple times more efficient at digging a hole than a construction worker with a shovel, AI-powered tools have the potential to make knowledge workers multiple times more efficient.
Donald Farmer, founder and principal of TreeHive Strategy, likewise noted that one of the main potential benefits of effective AI is efficiency.
“It enables enterprises to scale their processes with greater confidence,” he said.
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However, the data used to train the AI applications that enable almost anyone within an organization to ask questions of their data and use the responses to inform decisions had better be right. Similarly, the data used to train the applications that take on time-consuming, repetitive tasks that dominate data experts’ time had better be right.
The need for data quality
Data quality has always been important. It didn’t just become important in November 2022 when OpenAI’s launch of ChatGPT — which represented a significant improvement in LLM capabilities — initiated an explosion of interest in developing AI models and applications.
Bad data has long led to misinformed decisions, while good data has always led to informed decisions.
But the scale and speed of decision-making were different before generative AI. So were the checks and balances. As a result, both the benefits of good data quality and consequences of bad data quality were different.
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Until the onset of self-service analytics spurred by vendors such as Tableau and Qlik some 15 years ago, data management and analytics were isolated to teams of IT professionals working in concert with data analysts. Consumers — the analysts — usually had to submit a request to data stewards, who would then take the request and develop a report or dashboard that could be analyzed to inform a decision.
The process could often take months and at least took days. And even when the report or dashboard was developed, it often had to be redone multiple times as the end user realized the question they asked wasn’t quite right or the resulting data product led to follow-up questions.
During the development process, IT teams worked closely with the data used to inform the reports and dashboards they built. They were hands-on, and they had time to make sure the data was accurate.
Self-service analytics altered the paradigm, removing some of the control from centralized IT departments and enabling end users with the proper skills and training to work with data on their own. In response, enterprises developed data governance frameworks to both set limits on what self-service users could do with data — to protect against self-service users going too far — and also give the business users freedom to explore within certain parameters.
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The speed and scale of data management and analytics-based decision-making increased, but it was still limited to a group of trained users who, with their expertise, were usually able to recognize when something seemed off in the data and not hastily take actions.
Now, just as generative AI changes who within an organization can work with data and what experts can do with it, it changes the speed and scale of data-informed decisions and actions. To feed that speed and scale with good data, automated processes — overseen by humans who can intervene when necessary — are required, according to Farmer.
“It puts an emphasis on processes that can be automated, identifying data-cleaning processes that require less expertise than before,” Farmer said. “That’s where it’s changing. We’re trying to do things at much greater scale, and you just can’t have a human in the loop at that scale. Whether the process can be audited is very important.”
Abhyankar compared the past and present to the difference between a small, Michelin-starred gourmet restaurant and a fast-food chain.
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The chef at the small restaurant, each day, can shop for the ingredients of every dish and then oversee the kitchen as each dish gets made. At a chain, the scale of what needs to be bought and the speed with which the food needs to be made make it impossible for a chef to oversee every detail. Instead, a process ensures no bad meat or produce makes it into meals served to consumers.
“[Data quality] is really important in a world where you’re going from hand-created dashboards and reports to a world where you want AI to do [analysis] at scale,” Abhyankar said. “But you can’t scale unless you have a system in place so [the AI application] can be precise and personalized to serve many more people with many more insights on the fly. To do that, the data quality simply has to be there.”
Benefits and consequences
The whole reason enterprise interest is rising in developing AI models and applications and using AI to inform decisions and automate processes — all of which need high-quality data as a foundation — is the potential benefits.
The construction worker who now has an excavator to dig a hole rather than a shovel can be multiple times more efficient. And in concert with a few others at the controls of excavators, they can dig the foundation for a new building perhaps a hundred times faster than they could by hand.
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A construction worker with a cement mixer can follow up and pour the foundation multiple times faster than if they had to mix the cement and pour it by hand. Next, the girders can be moved into place by cranes rather than carried by humans, and so on.
It adds up to an exponentially more efficient construction process.
The same is true of AI in the enterprise. Just as construction teams can rely on the engines and controls in excavators, cement mixers, cranes and other vehicles that scale the construction process, if the data fueling AI models and applications is trustworthy, organizations can confidently scale business processes with AI, according to Farmer.
And scale in the business world — being able to do exponentially more without having to expand staff — means growth.
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“Data quality enables enterprises to scale their processes with greater confidence,” he said. “It enables them to build fine-grained processes like hyperpersonalization with greater confidence. Next-best offers, recommendation engines, things that can be highly optimized for an individual — that sort of thing becomes very possible.”
Beyond retail, another common example is fraud detection, according to Menninger. Detecting fraud amid millions of transactions can be nearly impossible. AI models can check all those transactions, while not even teams of humans have the capacity to look at them all, much less find patterns and relationships between them.
“If accurate data is being fed into the models to detect fraud, and you can improve the detection even just slightly, that ends up having a large impact,” Menninger said.
But just as the potential benefits of good-quality data at the core of AI are greater than good data without AI, the consequences of bad data at the core of AI are greater than the consequences of bad data without AI. The speed and scale that AI models and applications enable result in the broader and faster spread of fallout from poor decisions and actions.
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Back when IT teams controlled their organizations’ data and when a limited number of self-service users contributed to decisions, the main risk of bad data was lack of trust in data-informed decisions and the resulting loss of efficiencies, according to MicroStrategy’s Abhyankar. In rare cases, it could lead to something more severe, but there was usually time for someone to step in and stop something from happening before it spread.
Now, the potential exists to not only scale previous problems, but also create new ones.
If AI models and applications are running processes and making decisions without someone checking them before actions are taken, it could lead to significant ethical problems such as baselessly denying an applicant a credit card or mortgage. Similarly, if a human uses AI outputs to make decisions, but the output is misinformed, it could result in serious ethical issues.
“You scale the previous problems,” Abhyankar said. “But it’s actually worse than that. In scenarios where the AI is making decisions, you’re making bad decisions at scale. If you run into ethical problems, it’s catastrophically bad for an organization. But even when AI is just delivering information to a human being, you’re scaling the problems.”
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Farmer noted that AI doesn’t deliver outputs based on single data points. AI models and applications are statistical, looking at broad swaths of data to inform their actions. As long as most of the data used to train a model or application is correct, the model or application will be useful.
“If a data set is poor quality, you’ll get poor results,” Farmer said. “But if one piece of data is wrong, it’s not going to make much difference to the AI because it’s looking at statistics as a whole.”
That is, unless it’s that fine-grained decision about an individual such as whether to approve a mortgage application. In that case, if the data is wrong, it can lead to serious ethical consequences. Even more catastrophically, in a healthcare scenario, bad data could lead to the difference between life and death.
“If we’re using AI to make decisions about individuals — are we going to give someone a mortgage — then having high-quality individual data becomes extremely important, because then we have given this system over,” Farmer said. “If we’re talking about AI making fine-grained decisions, then the data has to be very high-quality.”
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Ensuring data quality
With data quality so critical to the success of AI, as well as reaping the benefits of broader use of technologies and exponentially increased efficiency, the obvious question is how enterprises can ensure good data goes into models and applications so that good outputs result.
There is, unfortunately, no simple solution — no fail-safe.
Data quality is difficult. Enterprises have always struggled to ensure only good-quality data is used to inform decisions. In the era of AI, including generative AI, that’s no different.
“The problem is still hard,” Abhyankar said.
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But there are steps that organizations can take to lessen the likelihood of bad data slipping through the cracks and affecting the accuracy of models and applications. There are technologies they can use and processes they can implement.
Ironically, many of the technologies that can detect bad data use AI to do so.
Vendors such as Informatica and Oracle offer tools designed specifically to monitor data quality. These tools can look at data characteristics such as metadata and data lineage, sometimes have master data management capabilities, and in general are built to detect problematic data. Other vendors such as Alation and Collibra provide data catalogs that help enterprises organize and govern data, including descriptions of data, to provide users with information before they operationalize any data.
Still other vendors including Acceldata and Monte Carlo offer data observability platforms that use AI to monitor data as it moves through data pipelines, detecting irregularities as they occur and automatically alerting customers to potential problems. But unlike data quality tools and data catalogs that address data quality while data is at rest before being used to train AI models and applications, observability tools monitor data while it is in motion on its way to a model or application.
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“Increasingly, AI is actually in a sense running its own data quality,” Farmer said. “Many of those tools work on inferences, work on discovering patterns of the data. It turns out that AI is very good at that and doing it at scale.”
More important than any tooling, however, is that humans always remain involved and check any output before it is used to take action.
Just as a hybrid approach emerged as ideal for cloud computing — including on-premises, private cloud and public cloud — a hybrid approach that uses technology to augment humans is emerging as the ideal approach to working with the data used to train AI, according to SingleStore’s Kumar.
“First and foremost is to allow humans to have control,” he said.
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Humans simply know more about their organization’s data than machines and can better spot when something seems off. Humans have been working with their organization’s data from their organization’s founding, which in some cases means there are decades’ worth of code used to develop and inform dashboards and reports that humans can perfectly replicate, but a machine might not know.
Humans, in a simple example, know whether their company’s fiscal year starts on Jan. 1 or some other date, while a model might assume it starts on Jan. 1.
“Hybrid means human plus AI,” Kumar said. “There are things AI is really good at, like repetition and automation, but when it comes to quality, there’s still the fact that humans are a lot better because they have a lot more context about their data.”
If there’s a human at the end of the process to check outputs, organizations can better ensure actions taken will have their intended results, and some potentially damaging actions can be avoided.
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If there’s a person to make sure a mortgage application should be rejected or approved, it will benefit their organization’s bottom line. The approved mortgage will result in profits, as well as avoid the serious consequences of mistakenly declining someone’s application based on biased data, while the declined mortgage will avoid potential losses related to a default.
If there’s a healthcare worker to check whether a patient is allergic to a recommended medication or that medication might interact badly with another medication the patient is taking, it could save a life.
The AI models and applications, fueled by data, can be left to do their work. They can automate repetitive processes, generate code to develop applications, write summaries and documentation, respond to user questions in natural language and so on. They’re good at those tasks, when informed by good-quality data.
But they’re not perfect, even when the data used to train them is as good as possible.
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“There always has to be human intervention,” Menninger said.
Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.
For a good chunk of my young-adult life, I was obsessed with the idea of creating my masterpiece. It’s not even that I wanted to create a great work of art with something to say; I felt I had to. My fear of death led me to believe that I needed to find a way to leave a lasting legacy behind, like the filmmakers and playwrights I revered at the time. While that feeling dissipated in later years, it reformed as a constant imposter syndrome that I still grapple with from time to time. There are moments where I feel that my writing or music isn’t good enough. At other times, I become bitter when a work I’m proud of doesn’t get the attention I wished it deserved. It’s a vicious ouroboros that I struggle to break out of.
This may sound like a strangely dramatic way to introduce Starstruck: Hands of Time. If you look at the new PC game’s Steam page, you’ll find what looks like a goofy adventure that takes notes from Earthbound, Guitar Hero, and Katamari Damacy. While that’s all true, the avant-garde adventure is hiding something much more grotesque below its bubbly surface. It’s a slow-bubbling anxiety attack, one that makes for one of 2024’s most unexpectedly vital games.
Starstruck: Hands of Time begins in a playful fashion. An astronaut travels back to the past after the Earth of the future is overtaken by a mysterious mold. With the help of their cheerful robo companion, they head back to the past to find the source of this sludge. That takes them to an unassumingly small town inhabited by a happy-go-lucky kid named Edwin. It’s a normal, and very misleading, start to a wild four-hour odyssey that doesn’t go anywhere you’re expecting.
In those early moments, Starstruck sets the stage for a charming suburban adventure about Edwin, a young guitarist, trying to rise to stardom within his town. His first mission is to head to a local venue and play a gig with his pals. It’s a sweet start that immediately calls Earthbound to mind, a game that’s become an important touchstone for indie developers in recent years. It makes sense; Nintendo’s classic RPG is one of the few games that really feels like it understands young people and the personal struggles they face in everyday life. In its most direct reference, Starstruck’s characters are displayed as handmade clay models that call back to the physical figures used in Earthbound’s original marketing materials.
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Createdelic, LLC
The more Starstruck sets up its story, the more light-hearted it becomes. When I get to the venue for my show, I’m introduced to an entire Guitar Hero-type rhythm game where I play along to songs (Starstruck is even compatible with some guitar controllers). It’s a messy minigame due to some hard-to-parse guitar riffs and sloppy controller integration, but it’s another callback that puts me into a time and place. I’m once again in the mindset of a young adult wondering when my life is going to begin in between Freebird solos.
Even then, Starstruck still hasn’t played all its gameplay cards. When Edwin has trouble getting into the venue, the astronaut observing them steps in to help by sending their hand down to Earth. In a minigame reminiscent of Katamari Damacy, I need to smash as much stuff as I can around town until I can summon a hammer to knock an opening into the fence surrounding the venue. It’s a bizarre visual, but another filled with a familiar youthful energy.
Things get much weirder from there.
Only near its halfway mark, after going through those minigames a few times and meeting a few friends, does Starstruck show its hands. Edwin and his friends begin to let their different anxieties slip. It turns out that the gang is suffering from different identity issues. One charter struggles with imposter syndrome over her music; another is desperate to be the center of attention and have his work celebrated. The more those feelings come out, the more the game itself corrupts.
Createdelic, LLC
There’s no way to easily describe what unfolds in Starstruck’s back half; you’ll really have to see it for yourself to fully soak in its overwhelming panic attack. A cute adventure veers into eldritch horror territory as each character succumbs to their anxieties. The cheery visuals give way to avant-garde eeriness, in a turn that calls Neon Genesis Evangelion’s striking midseason direction shift to mind. The deeper these characters get into their minds, wishing they could be anywhere else than where they are in life, the farther they spin away from Earth. There’s nothing up there but darkness. It slowly swallows the entire adventure like a snake eating its own tail.
If this all sounds like a baffling mess, it is at times. Starstruck takes some wild swings that don’t always feel like they cleanly connect. Its personal story takes several detours to showcase the history of art theft, delve into the history of the Roman empire, revisit the moon landing, and more. Its gameplay can similarly feel unfocused as it hops between ideas at a rapid-fire pace. It’s confounding, but effective too. Starstruck feels like a mental breakdown in motion; it’s a throbbing brain that can’t keep its focus as it spirals deeper and deeper into philosophical despair.
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Despite how out there it is, Starstruck tells a down-to-earth story that’s still sticking with me days after rolling credits. I can see myself in its insecure heroes, so desperate to be the center of the universe that they’re left alone in the cold vacuum of space. Maybe we take how miraculous it is to be a face in a crowd here on this planet for granted.
In terms of cybersecurity, 2024 has been especially unfortunate for AT&T. Agencies like the SEC and the carrier itself confirmed some data breach incidents that affected millions of customers’ data. Now, the FCC says that AT&T could have prevented one of the customer data leaks related to the hack of its cloud vendor, but it didn’t.
AT&T got a $13 million fine for a 2023 data breach related to a cloud vendor
In April of this year, AT&T found that a team of hackers breached the security of one of its cloud vendors and disclosed it publicly. The hackers were able to download millions of the carrier’s customers’ call and text records. The mobile carrier now faces a $13 million fine for its failure to protect the data. Furthermore, the government agency revealed more details regarding the incident
The name of the cloud vendor whose security was breached is not known, as the FCC’s public report refers to it as “Vendor X.” According to the report, AT&T gave “Vendor X” access to customer data from 2015 to 2017 to create personalized videos related to billing and marketing. A clause in the deal stated that the data must be “securely destroyed or deleted” by 2018. However, neither AT&T nor the cloud vendor guaranteed the destruction of the data.
The data breach originated in early 2023, several years after the 2018 deadline. So, basically, the hackers had access to information that was supposed to be destroyed years ago. The FCC revealed that the hacking team managed to download data from about 8.9 million AT&T wireless customers.
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It was forced to establish new procedures for handling customer data
AT&T’s failure to take appropriate action represented a violation of data protection laws that all carriers must follow. As a result, the company was fined $13 million and forced to establish new methods for managing customer information. The monetary fine is “symbolic” considering the company’s billion-dollar profits. Investing in new security systems and procedures will likely cost more.
Fortunately, the hackers did not access extremely sensitive data such as social security or credit card numbers. However, it is surprising that AT&T left the security of millions of customers’ data in the air. This year, AT&T confirmed a separate data breach involving Snowflake, another cloud provider. This hack was especially severe, affecting call and SMS records from May to October 2022 from “nearly all” AT&T customers.
Couchbase on Tuesday made Capella Columnar generally available on AWS in a move aimed at helping customers streamline application development by centralizing real-time data analysis and operational workloads together in a single location.
In addition, the vendor launched Couchbase Mobile with vector search so that users can conduct hybrid and similarity searches in mobile applications at the edge rather than just their traditional database environment.
Based in Santa Clara, Calif., Couchbase is a NoSQL database vendor that competes with other database specialists such as Redis and MongoDB, as well as tech giants including AWS, Google, Microsoft and Oracle that offer database platforms.
Despite a crowded database market, Couchbase has been able to differentiate itself with forward-thinking product development such as its launch of Capella Columnar, according to Stephen Catanzano, an analyst at TechTarget’s Enterprise Strategy Group.
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“Couchbase is seen as an innovative player,” he said. “Compared to its peers, Couchbase stands out for its ability to handle both transactional and analytical workloads in a unified platform. Columnar adds to this.”
Couchbase is seen as an innovative player. Compared to its peers, Couchbase stands out for its ability to handle both transactional and analytical workloads in a unified platform. Stephen CatanzanoAnalyst, Enterprise Strategy Group
Doug Henschen, an analyst at Constellation Research, likewise noted that Couchbase stands out despite strong competition, saying the vendor provides a leading NoSQL database.
Neither columnar capabilities nor vector search are new, he continued. For example, Couchbase first unveiled vector search in February. Meanwhile, MongoDB offered columnar capabilities as part of its Atlas Data Lake launch in 2022.
However, vector search for mobile is unique.
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“The move makes sense, given the rise of edge applications and mobility demands,” Henschen said.
First known as Membase before a 2011 merger with CouchOne, Couchbase now provides Capella, a database-as-a-service platform geared toward cloud-based customers, which was first launched in 2021. In addition, the vendor offers Couchbase Enterprise for on-premises users.
New capabilities
Couchbase first unveiled Capella Columnar in preview during AWS re:Invent 2023. The service, which is only available on AWS at this point, aims to bring together operational database workloads with real-time analytics in a columnar format that analytics tools can understand.
Many developers, including Couchbase customers, use JSON — a data interchange format used to move data between web clients and web servers — when building enterprise applications. JSON, however, can be difficult to use with analytics systems that use different, more rigid formats for storage and analysis, the vendor noted.
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As a result, unstructured JSON data often goes unused and lies dormant in a database. Meanwhile, with enterprises now developing generative AI applications that require huge amounts of proprietary data to understand the enterprise’s business and respond accurately to business-specific queries, unstructured data is becoming critical.
Unstructured data such as text, images, videos and audio files is estimated to make up more than 80% of all data, with the structured data traditionally used to inform analytics just a small part of an enterprise’s overall cache of information. Without accessing unstructured data, enterprises don’t get a complete view of their business, and AI applications trained on their data are more prone to deliver incorrect outputs.
Capella Columnar transforms JSON data so that it can be recognized by analytics tools, making previously inaccessible data accessible for informing decisions and training AI models and applications. The feature reduces the cumbersome extract, transform and load (ETL) process by supporting real-time data ingestion, using Capella iQ to automatically write SQL to calculate an analytical metric and writing back the metric to the operational side of Capella, where it can be used in an application.
Because Capella Columnar enables operational processing and real-time analytics in one database, its release is an important development for Couchbase users, according to Catanzano.
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“The launch of Couchbase Columnar is significant,” he said. “It addresses a longstanding challenge of making JSON data useful for analytics, which has traditionally been difficult due to its unstructured nature.”
An added benefit could be cost reduction, Catanzano continued, noting that it adds expenses to do operational processing and real-time analytics on separate platforms.
Matt McDonough, Couchbase’s senior vice president of product and partners, said that while many enterprises are attempting to build more AI applications, including generative AI tools, such applications remain more an idea than a reality. Tools such as Capella Columnar aim to make it easier to develop AI-powered applications that can be used widely across organizations rather than by just data science teams.
“AI-powered apps have been a relatively abstract concept,” McDonough said. “With the availability of these new features in Capella, developers can bring AI apps to life because they’re no longer bogged down with rigid systems or complex ETL processes.”
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Like Capella Columnar, Couchbase Mobile with vector search aims to speed and simplify application development.
Vector search has become a key component of retrieval-augmented generation (RAG) pipelines commonly used to train generative AI models and applications. Vector embeddings are a way to give structure to unstructured data by assigning it a numerical value so that it can be searched and used in training. In addition, vectors enable similarity search that makes data discovery easier than the more limiting keyword search, helping users find enough data to properly inform AI tools.
Following its initial introduction of vector search capabilities in February, Couchbase is now extending those capabilities beyond its traditional database environment to edge devices in a move that stands to benefit customers, according to Henschen.
With Couchbase Lite, a document database that can be embedded into edge devices to enable real-time decisions, developers can build applications using mobile devices that can subsequently be consumed on mobile devices.
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“The availability of vector information supports similarity search and improves search accuracy, so it’s nice to see in the mobile database as well as the core product,” Henschen said.
The impetus for developing both the new mobile feature and Capella Columnar came from Couchbase’s recognition that enterprises are struggling to build AI applications, according to McDonough.
Many organizations have complex data systems that include numerous different platforms that don’t natively integrate with one another. As a result, the pieces don’t always work smoothly together, leading to data quality issues. In addition, if different departments within organizations use different tools, data often gets isolated.
As Couchbase develops new features, one of its primary goals is to consolidate capabilities in a single database platform.
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“For developers to evolve in the age of AI, they have to clean up complex architectures, which means consolidating platforms, eliminating data silos [and] making sure they’re working with trustworthy data,” McDonough said. “To do this, they need the right resources.”
Beyond Capella Columnar and Couchbase Mobile with vector search, Couchbase unveiled a new free tier that will be available starting Sept. 9.
Plans
Toward Couchbase’s goal of making it faster and easier to build AI applications, the vendor’s roadmap includes improving the developer experience through partnerships and integrations that create an ecosystem and provide key capabilities, according to McDonough.
Catanzano, meanwhile, said Couchbase’s focus on enabling users to develop AI tools is appropriate.
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In particular, the vendor would be wise to concentrate on helping customers ensure trusted, high-quality data is used to inform models and applications, he said. Given the decision-making speed and scale generative AI enables, it is increasingly critical that the data used to inform generative AI tools is accurate.
“[Couchbase should] continue to innovate around bringing highly trusted enterprise data into GenAI models in a secure way, using RAG and vector capabilities to help create new and innovative solutions,” Catanzano said.
Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.
The original PlayStation console, otherwise called the PS1, came out in Japan in late 1994. So we are quickly coming up on the console’s 30th birthday. To commemorate the occasion, Sony just revealed nostalgia-tinged redesigns of both the PS5 and the forthcoming PS5 Pro. They look like the original PlayStation, with that classic gray colorway and the old-school logo. Gamers of a certain age will have a hard time resisting these things. Sony did something similar in 2014 with the PS4 for the console line’s 20th anniversary.
This isn’t a quick and dirty redesign. There was legitimate thought put into this. The updated DualSense controller doesn’t quite match the original design, but does mesh with the overall aesthetic. Sony’s throwing in a retro-looking cable connector housing, PlayStation-shaped cable ties and a themed vertical stand. The box even looks like it came from a Toys “R” Us in the 1990s.
There are two bundles to choose from. The PS5 bundle ships with the digital version of the console (so no disc drive,) a standard DualSense controller, the aforementioned accessories and additional goodies like a sticker, a poster and, uh, a PlayStation paperclip.
The PS5 Pro bundle includes everything mentioned above, but includes both a standard controller and the DualSense Edge. It also includes a retro cover for the optional disc drive and the charging stand. It’s easy to dunk on that costly PS5 Pro when it looks basically the same as a regular PS5. It’s much harder to do when it looks like it stepped out of a 1995 fever dream.
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Sony
Even the bizarre pseudo-portable PlayStation Portal is getting a themed refresh, which features the iconic gray exterior. Sony fans can even pick up redesigned controllers without springing for an entire console.
Preorders start on September 26 at participating retailers and via the company itself. These items will be released on November 21. That’s just a couple of weeks after the PS5 Pro launches. To that end, Sony’s only making 12,300 of the PS5 Pro retro consoles, so we recommend getting that preorder in early. The company hasn’t released pricing information, unfortunately, and it’s likely that the PS5 Pro bundle will absolutely obliterate bank accounts. We reached out to ask about pricing and will update this post when we hear back.
While we wait for the pre-orders to start, Senior reporter Jessica Conditt got a brief glimpse of the 30th anniversary edition PS5 Pro and DualSense controllers, which you can see below:
Photo by Jessica Conditt / Engadget
Photo by Jessica Conditt / Engadget
Photo by Jessica Conditt / Engadget
Update, September 20 2024, 2:00PM ET: This story has been updated with photos of the 30th-anniversary PlayStation 5 Pro console and its controller.
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EcoFlow’s Alternator Charger is a device you install in your pickup truck, van, or RV to charge the giant power station you carry to keep all your gear running.
While your vehicle’s on, the Alternator Charger produces up to 800W. That’s about eight times more power than you can typically extract from a 12V cigarette lighter jack, and it’s enough to charge EcoFlow’s new 1kWh Delta 3 from zero to full in a little over one hour of driving. It takes five hours if you’re traveling with EcoFlow’s larger 4kWh Delta Pro 3.
It’s also clever enough to reverse the flow of electrons, using the power station to maintain your starter battery with a trickle charge or jump-start it back to life. When you return home from the job site or vacation, those big-ass portable batteries can be connected to EcoFlow’s $200 balcony solar kit to help offset your energy bill and provide emergency power during a blackout.
The vehicle’s alternator sends up to 800W through EcoFlow’s Alternator Charger to an EcoFlow power station.GIF: EcoFlow
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EcoFlow’s Alternator Charger is far from an industry first, and it uses proprietary connectors that only work with Ecoflow’s own batteries. But the company brings simplicity, elegance, and a superior user experience to a product usually designed for electricians and mechanics.
After 3,700 miles (6,000km) of testing, I can say that the $599 Alternator Charger could be a game-changer for many. It allowed my wife and I to live and work carefree from a Sprinter van this summer, comforted by all the modern conveniences afforded by so much on-demand power.
It’s fairly common for RV builders to install aftermarket DC-to-DC chargers on a vehicle’s alternator. They’re incredibly adept at keeping stacks of leisure batteries charged to power off-grid luxuries like e-bikes, projectors, 3-in-1 refrigerator-freezers with ice makers, coffee makers, and air conditioners. Some basic chargers cost less and others are more powerful than EcoFlow’s, especially when built around a secondary alternator — but those offer fewer features and require professional installation.
To avoid overloading the vehicle’s alternator, EcoFlow’s charger regulates itself so that only surplus power, which can be less than 800W, is sent to the power station. (The Alternator Charger can pull a maximum of 76 amps.) In my case, the Sprinter’s beefy alternator has enough capacity to easily deliver a near-continuous 800W even with the A/C running and the wipers and lights on.
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I also travel with 420W of solar panels installed on the roof for an extra boost, resulting in just over 1,100W of simultaneous real-world charge when driving on sunny days. This combo also works while the van is parked and idling if I ever need the Sprinter to act like an emergency diesel generator.
Installation
EcoFlow’s installation qualifies as a DIY project for many Verge readers, though in my case I turned to an expert for help: Fabian van Doeselaar, who was already outfitting my stock cargo van with his Solo interiors and previously helped out with my review of the EcoFlow Power Kit.
Installing the Alternator Charger requires wiring it back to the starter battery, not the alternator itself. The specific steps for each vehicle will vary, but in the case of my Sprinter, we ran the thick 16-foot (five-meter) cable up to the busbar in the auxiliary battery fuse box, which meant removing the driver’s seat. The cable was long enough to reach the Alternator Charger box mounted inside a cabinet in the back where I manage my electricity.
My Sprinter van is designed from the ground up to be powered by any portable solar generator, which is just a large power station that includes an MPPT charge controller for solar panels. For this review, we connected my van’s circuitry to EcoFlow’s original Delta Pro which in turn was connected to the Alternator Charger using a proprietary EcoFlow cable and adapter.
Testing EcoFlow’s giant Delta Pro power station connected to the Alternator Charger.
The Alternator Charger mounted inside a wheel well cabinet where I manage my van’s electrical connections.
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The five meter cable that runs to the starter battery is more than long enough for 6-meter L2 Sprinter vans.
It’s better than it looks. Here we were staging the installation, testing that big Alternator Charger cable connected directly to the starter battery (to the left of the cordless screwdriver), and on the busbar located beneath the driver’s seat.
The Delta Pro keeps my laptops, phones, drones, and headphones charged, in addition to powering my Starlink internet, lights, fridge, water pump, induction cooktop, and rooftop ventilation, as well as EcoFlow’s Wave 2 air conditioner and heater combo I just reviewed. So having a way to reliably charge it was critical this summer since I wanted to live and work as remotely as possible.
Performance
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After a straightforward installation, it was time to configure the Alternator Charger in the excellent EcoFlow app, which makes monitoring performance both fun and addictive.
The Alternator Charger only sends power to the power station after two conditions are met. First, the charger has to be turned on with a button on the unit itself or from a “start working” toggle in the EcoFlow app. Then, the voltage measured at the starter battery has to surpass the “start voltage” threshold you set in the EcoFlow app. If left on, it should automatically charge the attached power station when driving — but that didn’t quite work for my setup.
With the “start voltage” set to 13V, you can see the Alternator Charger charging at 800W while driving, but then drop off as the voltage produced by the alternator dropped to 13.0V and below. Setting it to start at 12.5V produced a near constant 800W but also started draining my starter battery when parked. Sigh.
I initially went with the app’s default 13.0V start voltage. Starting the van causes the starter battery’s voltage to jump from about 12.6V – 12.8V to beyond 14V, thus triggering the 800W charging session. But my van’s fitted with a smart alternator which causes the voltage to fluctuate over time, occasionally dipping below that 13.0V threshold. This causes the Alternator Charger to shut off and on repeatedly, thus reducing the speed at which the Delta Pro is charged.
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To “fix” this, I lowered the charger’s start voltage to 12.5V (it’s limited to 0.5V adjustments) in the app with a predictable side effect — when I arrived and shut off the motor, the Alternator Charger began depleting my van’s battery and would have continued doing so until it reached the 12.5V threshold and stopped.
That’s not the end of the world, but it is below the 12.6V resting threshold considered healthy for a lead-acid starter battery. EcoFlow does make it easy to manually move that stored energy from the Delta Pro’s battery back to the Sprinter’s by switching the Alternator Charger into Reverse Charge or 100W Battery Maintenance modes — but this is far from ideal.
Ideally, all this would work automatically, so that every time I drive I know that 800W is being fed back into my power station, and I don’t have to worry about the health of my starter battery after I park. Lacking those assurances, I decided to play it safe, and leave the start voltage at 12.5V but toggle the “start working” switch in the app manually every time I started and stopped driving.
Still, after testing EcoFlow’s Alternator Charger, I can tell you $599 is a small price to pay for the peace of mind of having all that power available any time I needed it for two months this summer — rain or shine, even in the middle of nowhere. Shame that it has to be turned on and off manually in my case, and only works with EcoFlow’s own batteries.
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EcoFlow’s products can often be found on sale throughout the year with reductions also found in bundles. An $848 bundle that includes the Alternator Charger and new $649 Delta 3 Plus looks pretty compelling for a 1kWh solar generator that can grow with your needs.
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