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Gartner Quashes 5 Biggest Data Myths

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With so much hype about big data, it’s hard for IT leaders to know how to exploit its potential, even as Gartner, Inc. dispels five myths to help IT leaders evolve their information infrastructure strategies.

“Big data offers big opportunities, but poses even bigger challenges. Its sheer volume doesn’t solve the problems inherent in all data,” said Alexander Linden, research director at Gartner. “IT leaders need to cut through the hype and confusion, and base their actions on known facts and business-driven outcomes.”

Myth No. 1: Everyone Is Ahead of Us in Adopting Big Data

Interest in big data technologies and services is at a record high, with 73 percent of the organizations Gartner surveyed in 2014 investing or planning to invest in them.

But most organizations are still in the very early stages of adoption — only 13 percent of those we surveyed had actually deployed these solutions.

The biggest challenges that organizations face are to determine how to obtain value from big data, and how to decide where to start.

Many organizations get stuck at the pilot stage because they don’t tie the technology to business processes or concrete use cases.

Myth No. 2: We Have So Much Data, We Don’t Need to Worry About Every Little Data Flaw

IT leaders believe that the huge volume of data that organizations now manage makes individual data quality flaws insignificant due to the “law of large numbers.”

Their view is that individual data quality flaws don’t influence the overall outcome when the data is analyzed because each flaw is only a tiny part of the mass of data in their organization.

“In reality, although each individual flaw has a much smaller impact on the whole dataset than it did when there was less data, there are more flaws than before because there is more data,” said Ted Friedman, vice president and distinguished analyst at Gartner. “Therefore, the overall impact of poor-quality data on the whole dataset remains the same. In addition, much of the data that organizations use in a big data context comes from outside, or is of unknown structure and origin. This means that the likelihood of data quality issues is even higher than before. So data quality is actually more important in the world of big data.”

Myth No. 3: Big Data Technology Will Eliminate the Need for Data Integration

The general view is that big data technology — specifically the potential to process information via a “schema on read” approach — will enable organizations to read the same sources using multiple data models. Many people believe this flexibility will enable end users to determine how to interpret any data asset on demand. It will also, they believe, provide data access tailored to individual users.

In reality, most information users rely significantly on “schema on write” scenarios in which data is described, content is prescribed, and there is agreement about the integrity of data and how it relates to the scenarios.

Myth No. 4: It’s Pointless Using a Data Warehouse for Advanced Analytics

Many information management (IM) leaders consider building a data warehouse to be a time-consuming and pointless exercise when advanced analytics use new types of data beyond the data warehouse.

The reality is that many advanced analytics projects use a data warehouse during the analysis.

In other cases, IM leaders must refine new data types that are part of big data to make them suitable for analysis.

They have to decide which data is relevant, how to aggregate it, and the level of data quality necessary — and this data refinement can happen in places other than the data warehouse.

Myth No. 5: Data Lakes Will Replace the Data Warehouse

Vendors market data lakes as enterprisewide data management platforms for analyzing disparate sources of data in their native formats.

In reality, it’s misleading for vendors to position data lakes as replacements for data warehouses or as critical elements of customers’ analytical infrastructure.

A data lake’s foundational technologies lack the maturity and breadth of the features found in established data warehouse technologies. “Data warehouses already have the capabilities to support a broad variety of users throughout an organization. IM leaders don’t have to wait for data lakes to catch up,” said Nick Heudecker, research director at Gartner.


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Schmidt, Ex Google Chief Says AI Risky in Terrorist Hands

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Eric Schmidt, former Google CEO
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Eric Schmidt, former Google CEO has expressed concerns about the extreme risks posed by artificial intelligence (AI) falling into the hands of terrorists or rogue states.

Schmidt, Ex Google Chief Says AI Risky in Terrorist Hands

Eric Schmidt, former Google CEO

He warned that nations such as North Korea, Iran, and Russia could adopt AI technologies to develop weapons capable of causing significant harm, including biological weapons.

Schmidt urged governments to oversee private tech companies, emphasising, “The real fears I have are not the ones most people discuss about AI, I talk about extreme risk.”

“I’m always worried about an ‘Osama Bin Laden’ scenario, where truly evil individuals take control of some aspect of modern life to harm innocent people,” he added.

With private companies driving AI advancements, he stressed the need for careful government monitoring and regulation. “It’s really important that governments understand what we’re doing and keep their eye on us,” he said.

His remarks followed a two-day AI summit in Paris, where the UK and the U.S. declined to sign a communiqué outlining the future direction of AI. The declaration on “inclusive and sustainable artificial intelligence for people and the planet” was endorsed by 57 countries, including India, China, the Vatican, the EU, and the African Union Commission.

The UK justified its decision, stating that the agreement lacked “practical clarity” on global AI governance and national security concerns.

Schmidt supports U.S. export controls restricting the sale of advanced AI microchips to certain countries, aiming to slow adversaries’ progress in AI research.

He also highlights the importance of international collaboration on AI safety, suggesting that cooperation with nations like China is essential to addressing global AI challenges.

 

 


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OpenAI CEO Rejects $97.4Bn Takeover Bid from Elon Musk

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Sam Altman, chief executive of ChatGPT-owner OpenAI
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Sam Altman, chief executive of ChatGPT-owner OpenAI, has firmly declared the company “not for sale” following a $97.4bn (£78.4bn) takeover bid from a consortium led by Elon Musk.

OpenAI CEO Rejects $97.4Bn Takeover Bid from Elon Musk

Elon Musk

Speaking at the AI Action Summit in Paris, Altman emphasised OpenAI’s mission to develop AGI (artificial general intelligence) for the benefit of humanity.

Marc Toberoff, attorney for Elon Musk, confirmed the bid submission on Monday.

In response, Altman humorously offered to buy Twitter for $9.74 billion on Musk’s platform.

Unlike many tech giants, OpenAI is not publicly traded but operates through a complex partnership between non-profit and for-profit entities.

Musk aims to return OpenAI to its non-profit roots, despite owning a rival firm, xAI.

Christie Pitts, a tech investor, expressed scepticism about Musk’s intentions, noting his competitive interests.

Altman echoed this sentiment, suggesting Musk’s move disregards OpenAI’s mission.

Altman, who holds no stock in OpenAI, advocates transforming the organisation into a fully for-profit company to raise more funds for AI research.

Although the board has the final say, the $97.4bn offer falls short of OpenAI’s previous $157bn valuation and rumoured $300bn in future funding talks.

Toberoff stated the consortium might increase their bid. Meanwhile, OpenAI is collaborating with Oracle, a Japanese investment firm, and an Emirati sovereign wealth fund on “The Stargate Project,” a $500 billion AI infrastructure initiative announced by President Donald Trump.

 

 

 

 


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Adobe Launches AI Video Tool to Compete with OpenAI

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Adobe yesterday released the first public version of an artificial intelligence tool that can generate video clips and revealed how much it will charge, but said it will not set pricing for major users such as studios until later this year.

The Firefly Video Model, as Adobe is calling the service, will compete against Sora, a model developed by ChatGPT creator OpenAI, and startup Runway, both of which currently offer video-generation services. Facebook owner Meta Platforms has also developed a video-generation AI model but has not given a timeline for when it will be released.

Adobe’s model differs from its rivals because it is geared toward generating clips that will fit into how film and television studios use Premiere Pro, its flagship video editing software.

To that end, many of the features that Adobe is emphasizing revolve around feeding existing shots into the video model and asking it to generate clips that fix or expand on shots that were taken on a real production set but that did not come out quite right.

Adobe said the service will generate five-second clips at 1080p resolution. While that is shorter than the clips of up to 20 seconds generated by OpenAI’s service, Adobe executives said the majority of individual clips in most productions are only three seconds.

Adobe said a user can generate 20 clips per month for $9.99 and 70 clips for $29.99. That compares with 50 videos for $20 per month with OpenAI’s plan at lower resolution and a $200 OpenAI plan that can handle longer, higher resolution videos.

Adobe is also working on a “Premium” pricing plan for studios and other high-volume video users and will release those pricing details later this year. Alexandru Costin, Adobe’s vice president of generative AI, said the company is working to generate 4K video and will remain focused on quality rather than longer clips.

“We actually think that great motion, great structure, great definition scheme, making the actual clip look like it was film, is more important than making a longer clip that’s unusable,” Costin told Reuters.

 


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