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Weekly News Digest

November 15, 2022 — In addition to this week's NewsBreaks article and the monthly NewsLink Spotlight, Information Today, Inc. (ITI) offers Weekly News Digests that feature recent product news and company announcements. Watch for additional coverage to appear in the next print issue of Information Today. CLICK HERE to view more Weekly News Digest items.

Five Data Management Predictions for 2023

1. As recession looms, companies will look to optimize infrastructure cost.

Whether North America is in a recession or not, companies are actively cutting costs and reducing IT infrastructure, which has always been an easy choice for CEOs. While compute and storage costs continue to be reduced through the usage of cloud solutions, it still can lead to huge bills for organizations given their heavy investments in data and analytics infrastructure. Thanks in part to the breadth of choices of storage and applications, companies often take a rip-and-replace strategy to modernize their data and analytics efforts. That approach is not only costly, but it can often lead to disruption in IT operations. In 2023, more companies will see IT focusing on modern, non-disruptive ways to update their IT infrastructure, whether their data resides entirely in one cloud, multiple clouds, or a hybrid environment, including on premises.

2. While multi-cloud gets real, FinOps in cloud becomes necessary.

For many companies, strategic data assets are spread across multiple clouds and geographical locations, whether that is because various business units or locations have their preferred cloud service provider (CSP) or because mergers and acquisitions have led these assets to reside in different cloud providers’ boundaries. As more data continues to move to the cloud, and different geographies see prominence of certain cloud providers versus the others, there is accelerated adoption of multi-cloud architecture for multinational corporations. Currently, there is no easy way to manage and integrate data and services across these different CSPs. Failure to address this problem always results in data silos and a fragmented approach to data management, leading to data access and data governance complications.

Also, and contrary to popular belief, cloud costs are increasingly becoming a material expense due to the sheer volume of data and related egress charges, to name a few. For many organizations, cloud investments do not deliver the economic and business benefits as intended. As a result, they are leveraging FinOps to provide a framework for controlling cloud costs and usage, identify cost versus value, and understand ways to optimally manage it across modern hybrid and multi-cloud environments. In the coming year, expect FinOps to gain momentum as a critical initiative to help companies better manage their hybrid-cloud and multi-cloud spend.

3. There will be accelerated adoption of data fabric and data mesh.

Over the past 2 decades, data management has gone through cycles of centralization versus decentralization, including databases, data warehouses, cloud data stores, and data lakes. 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. 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 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 are important to every data stakeholder—from data scientists all the way to executives. After all, it is critical for dashboarding and reporting, advanced analytics, machine learning (ML), and artificial intelligence (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 a rapid increase in adoption of both architectural approaches within mid-to-large size enterprises.

4. Ethical AI becomes paramount as commercial adoption of AI-based decision making increases.

Companies across industries are accelerating the usage of AI for their data-based decision making, whether it’s about social media platforms suppressing posts, connecting healthcare professionals with patients, or large wealth-management banks granting credits to their end consumers. However, when AI 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 European Union 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 these applications accordingly.

In 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.

5. There will be augmentation of data quality, data preparation, metadata management, and analytics.

While the end result of many data management efforts is to feed advanced analytics and support AI and ML efforts, proper data management itself is pivotal to an organization’s success. Data is often being called the new oil, because data- and analytics-based insights are constantly propelling business innovation. As organizations accelerate their usage of data, it’s critical for companies to keep a close eye on data governance, data quality, and metadata management. Yet, while the growing amount of volume, variety, and velocity of data continues, these various aspects of data management have become too complex to manage at scale. Consider the amount of time data scientists and data engineers spend finding and preparing the data before they can start utilizing it. That is why augmented data management has recently been embraced by various data management vendors where, with the application of AI, organizations are able to automate many data management tasks.

According to some of the top analyst firms, each layer of a data fabric—namely data ingestion, data processing, data orchestration, data governance, etc.—should have AI/ML baked into it to automate each stage of the data management process. In 2023, augmented data management will find strong market traction, helping data management professionals focus on delivering data-driven insights rather than being held back with routine administrative tasks.

While these are the five most important trends in my mind, there are other areas of data and analytics practice that will shape how digital business will not only survive but thrive in 2023 and beyond. The last 2–3 years have definitely taught us that digital business is not really a fallback option when the world cannot meet in person, but that is where the future lies. Hopefully, your organization can gain some insights from this article as you lay out your digital business plan.

Five Artificial Intelligence and Data Predictions for 2023

1. The world reaches the era of “peak data scientist.”

The 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 rollout.

The coincident interference of these two waves yields “peak 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 (machine learning engineers) at the same time that more are graduating. In 2023, expect the market to react accordingly, resulting in data science oversaturation.

2. The AI industry will offer more tools that can be operated directly by business users.

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. 

In the coming year, AI will start to become more democratized such that less technical people can directly leverage tools that abstract all of the machine learning complexity. Knowledge workers and citizen “data scientists” without 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.

3. Chatbots will chat less and answer questions more.

Humans don’t want to spend more time interacting with machines as if they were talking to people; they really just want their questions answered quickly and efficiently from the start without lengthy wait times or having to choose from myriad options. Although many chatbots accurately execute the specific tasks they were designed to do, they fall far short of end-user expectations because they rarely answer their actual questions. 

In 2023, organizations will finally be able to complement chatbots with Natural Language Search capabilities. Because Natural Language Search understands human language and can process unstructured text-based data (documents, etc.), individuals can phrase questions using their own words—as if they were speaking to a person—and receive all of the relevant answers back instantly.

4. Line of business leaders will take matters into their own hands.

Twenty years ago, companies had two choices in the CRM (customer relationship management) space: They could pay millions for a Siebel Systems CRM, or they could pay a fraction of that amount monthly on a per user basis … which ushered in the cloud era. The same thing is happening now for business users when it comes to AI.

In 2023, if the use case provides exceptional value, business users will decide whether it makes sense to hire expensive and difficult-to-recruit data scientists and MLEs, label thousands of datapoints, train and re-train models over months, and repeat this process as the underlying data changes. Alternatively, if the value of this AI project does not justify the significant upfront and ongoing cost, then the organization will find a vendor that can remove all of the complexity for business users.

5. Businesses will finally benefit from their unstructured data.

IDC estimates that by 2025, 80% of all data will be unstructured, or free-form, making it difficult to assess and derive insights. Organizations struggle to extract relevant insights when they search for answers in text data, mainly because the search tools they are using are not capable of effectively and efficiently processing unstructured data.

Recognizing the immense value that is being left on the table, organizations in 2023 will apply practical methods to dramatically improve efficiency and unlock the value that has been elusive for so long. Remote and hybrid work has exacerbated the pain of unsatisfying search outcomes because so many employees work from their own locations and access information at different hours, making information sharing within an organization a major challenge. You can’t simply reach out to your colleague sitting next to you for answers whenever you think necessary. In the coming year, expect to see employees turning to Natural Language Search tools to find relevant information across all structured and unstructured sources.

The New York Times Tests AI on Recipe Writing

[People] can’t read every mashed potato recipe on the internet before coming up with their own version. They can’t analyze thousands of techniques in search of the best way to make a pie crust.

Machines can. Computer systems driven by artificial intelligence can compose tweets and blog posts, create art, even generate computer code. And now they’re starting to write recipes.

These recipes have all the components of their handmade forebears: lists of ingredients, precise measurements, step-by-step instructions and introductory notes with (fabricated) personal touches. Their advantage, in theory, is that they draw on a vast trove of online information about food and cooking.

But are they any good? And can they improve on millenniums’ worth of lived culinary experience?

For more information, read the article.

The Trouble With TikTok

TikTok appears to be Congress’s next Big Tech target. The Big Tech antitrust bills that once seemed sure to pass this year are likely dead. It’s uncertain if and how they’ll be revived in the next Congress. There’s also the fact that some of those Big Tech companies aren’t quite so big anymore, which makes it harder to make the argument that they’re hugely powerful and dominant companies that can only be curbed through targeted legislation. But the TikTok threat is something both sides might be able to agree on.

That scrutiny isn’t limited to the legislative branch. The Biden administration hasn’t gone as far as its predecessor, but this past September, it issued an executive order that seems very much aimed at the company. Meanwhile, Republican Federal Communications Commissioner Brendan Carr can’t stop talking about the dangers he believes TikTok poses, calling for Google and Apple to ban it from their app stores and saying he thinks the government should ban TikTok … Certain parts of the government—including branches of the military—have already banned workers from having TikTok on their phones at all. …

What did TikTok do to incur the wrath of DC? It all comes down to data and China.

For more information, read the article.

The Fully OA Blog Allows for Publisher Collaboration and Idea-Sharing

A brand new initiative, the Fully OA blog, was launched this week by the Fully OA Group. Born out of the OASPA Interest Group of Fully OA journal organizations, the group provides a forum for the exchange of ideas and, where appropriate, collaboration amongst publishers that only publish Open Access. The aim of the group—and of the Fully OA blog—is to provide unity, not by creating a single voice, but by bringing together a range of different voices and perspectives that share one commitment: full Open Access. …

The Fully OA blog will shine a light on projects, people, and organizations that are dedicated to Open Access and work towards removing barriers to a fully OA future. The blog welcomes proposals for contributions from anyone fully committed to OA. …

Current and founding members of the Fully OA Group include JMIR Publications, Frontiers, PeerJ, MDPI, Open Library of Humanities, Copernicus Publications, PLOS and Ubiquity Press.

For more information, read the press release.