A new year – a new you – a new blog!
We’d like to introduce to you a new blog series, the PoolParty Lifesaver.
The aim of the PoolParty Lifesaver series is to give you technical news and information in quick-read digestible chunks, saving you time from skimming the big reports to give you exactly what you need – we’re keeping it simple.
We don’t claim to have a crystal ball, but what we do have is years of experience in the field and a gut feeling for things that pop up on our radar. Speaking of radar, the following blog will centre on the Emerging Tech Impact Radar: Data and Analytics provided by Gartner (November 2023). In this report, Gartner has used their leading expertise to make the following “strategic planning assumptions” in their report:
- By 2024, use of synthetic data created with generative AI will half the volume of real data needed for machine learning.
- By 2024, more than 25% of data management vendors will provide a complete framework for data fabric support through a combination of their own products and partners — up from below 5% today.
- By 2025, at least 35% of organizations will utilize generative AI as part of their identity fabric functions. These organizations will substantially improve user experience and efficiency of their identity and access management (IAM) controls.
- By 2025, graph technologies will be used in 80% of data and analytics innovations — up from 10% in 2021 — facilitating rapid decision making across the enterprise.
Gartner, 2023
Based on 29 data and analytics (D&A) related technologies, Gartner has identified the following four overarching themes:
Weaving the Data Fabric with new technologies to make it easier to consume and process structured and unstructured data, minimizing the need for data integration. Composable D&A architecture enables fast and efficient building and assembly of business capabilities, allowing for differentiation and innovation. Generative Decisions and Actions combines analytics, business intelligence, data science, AI, decision management suites, OpenAI, and generative AI which become key for decision-centric user interface and action enablement. Accelerating D&A focuses on products and services that improve data and analytics processes and provide a modern user experience.
Situating the above overarching themes into quadrants, adding the expected time we will see the tech as mainstream, and the importance it will play in the tech world (from low to very high), we have the below Emerging Tech Impact Radar: Data and Analytics figure. Go ahead, take some time and take it in, some very high paid individuals spent a lot of time creating this to make all those data points easily digestible.
Source: Emerging Tech Impact Radar: Data and Analytics, Gartner 2023
We’re not saying any one of the listed technologies is any more important than the other – but we are saying there are a few that stood out more than others to us. With that, we are throwing the magic bones on the above Emerging Tech Impact Radar: Data and Analytics, and identifying the following 5 Technologies of increasing interest to us – and we think they should be to you as well.
Data Fabric (6 to 8 Years)
What is it?
This isn’t something new to us, we’ve been talking about data fabric, data mesh, or semantic layer for a number of years. Data fabric is a data management design for better semantic enrichment, integration, and organization of data through metadata. It is not a technology, but an architectural pattern that utilizes various data management technologies. It pulls data from company systems and storage centers and processes them in one collective “space,” making the data more usable in a front-end application.
The design evolves over time as metadata is captured and used to provide better recommendations for improving data management designs. As the fabric design matures, participating systems actively adapt to the recommendations generated by the fabric. This continuous cycle of metadata capture and activation enables the design to improve itself over time.
Why is it important?
A data fabric’s importance lies in its ability to integrate structured and unstructured data across multiple sources and locations creating a single point of truth – a one stop shop for the user.
It is important to keep in mind that data, and more importantly, metadata have no standards of definition, storage, and interchange. That is why a push to change this is needed to bring metadata to the forefront, making it as important as the data it describes. This is a long-term investment for most organizations but the benefits will feed directly into the data fabric architecture to provide a seamless solution of constant improvement.
Interestingly enough, customers are moving the conversation from the potential of a data fabric to asking how to implement it. With this shift, Gartner predicts that data fabric will reach an early majority in six to eight years, as depicted on their radar.
Where to learn more?
Semantic Business Layer (3 to 6 Years)
What is it?
The semantic business layer is a layer that sits between the data repositories and the user applications, allowing business users to interact with organizational data. Using a knowledge graph as a focal point, it includes business entities and their relatedness to each other. This gives way for semantic capabilities such as:
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- Active Metadata that allows for consistent tagging and a more proactive approach to data quality and governance
- Taxonomy Management helps you organize your content and make information easy to find, use and analyze
- Auto Tagging saves you time by removing the need for manual tagging by automatically tagging content by concept
- Semantic Search focuses on the meaning of a user query for more relevant results in less time
- Recommender Systems for sophisticated matches between objects using the context with a knowledge graph
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Emerging trends in this space aim to support modern data architectures by using data virtualization, analytic query accelerator, and knowledge graph. Though the concept of this layer is not new, it has an exciting journey of development ahead.
Why is it important?
It allows orgranizations to continue using their proven apps and keep their data where it is. You can think of it as the mortar between the bricks of a data fabric. Many organizations have implemented knowledge graphs as a first step toward a data fabric, the next step is a semantic business layer – without it, and organization will not be able to achieve the desired data fabricity.
Where to learn more?
Generative Data and Analytics (1 to 3 Years)
What is it?
Generative Data & Analytics (D&A) uses AI capabilities and automation to enhance the analytics development process and consumer experience through natural language querying, visualizations, and insights narratives. It enables vendors to support data pipeline generation, workflow generation, and decision support through prompt-driven code and conversational interfaces. The term generative refers to the ability to create or extend analytics-informed solutions without permission or restriction. It is becoming available in analytics tools and process-flow tools via AI assistants with natural language instructions.
Why is it important?
How much time do you have? Over the past year we have seen the exponential growth of generative AI, both good and bad. Generative D&A will soon follow suit with (fingers crossed) the added element of control – read more about this in our blog “LLMs and Knowledge Graphs: A Technological Waltz”.
Where we have seen some generative AI go off the rails by being completely open source and free range, generative D&A will be shaped thanks to knowledge graphs to aid business users in “intelligent decision automation.” This will lead to quicker, deeper, and more efficient analysis of data for the business user in the enterprise.
Where to learn more?
Graph and Vector Technologies (1 to 3 Years)
What is it?
Graph and vector technologies include graph and vector database management systems (DBMSs), data science solutions, and front-end tools for manipulating and analyzing data relationships. They focus on understanding the meaning and purpose of connections between data elements, which outstrip the capabilities of traditional relational database management systems (RDBMSs). Vector search is a key use case for unstructured data search, allowing for the building of LLMs, AI assistants, and other GenAI applications.
Why is it important?
Vector technology is ideal for semantic search and generative inference, driving the need for optimized vector solutions for hybrid models. Vector functionality is necessary for low latency, processing a lot of data with little delay, and high concurrency, multiple users at the same time, for generative AI use cases.
Where to learn more?
Data Literacy (6 to 8 Years)
What is it?
Data literacy is the ability to understand and act on data, which is foundational to the digital economy and society. It helps stakeholders unlock their business acumen, identify and manage datasets, and describe advanced analytics techniques. Data literacy is not a product but can be acquired through training, coaching, and education services. Low data literacy is a critical obstacle for software and service D&A providers.
Data literacy is the “skill” aspect of D&A.
Gartner, 2023
Why is it important?
As with any new technology, the key is understanding. Because of this, the progress of data literacy is not reflected in the technology itself, but the users of that technology. This is why data literacy can be seen as a spectrum of understanding – each user or user group having different levels or areas of knowledge with the technology.
Thus, the importance of data literacy is the more literate your users are, the more effectively they can utilize the technology. It is important for organizations to understand that data literacy will affect even the most basic of users and they must take a “data-literacy-active” or “data-literacy-passive” approach – advanced solutions for mature organizations vs. empowering business users of smaller or new organizations.
Though this concept lies at the heart of all emerging technologies, the inevitable full penetration into organizations will be slow. Gartner modestly predicts this will occur between six and eight years.
Where to learn more?
We hope you’ve enjoyed this first blog of the PoolParty Lifesaver series emphasizing the importance of data fabric, semantic business layer, generative data and analytics, graph and vector technologies, and data literacy in the evolving tech landscape.
At Semantic Web Company, we’re especially happy to see predicted demand for data fabric and the semantic business layer as well as growth in the AI space. Adding semantic technologies to the mix is precisely what PoolParty can be used for – so that organizations can benefit from enriched metadata, better search, and more control of their data.
Keep your eyes peeled for these and other emerging technologies in the media, in your inbox, and around the office water cooler – you never know when they will come handy.
If you liked this blog, and want to keep up to date, click the join mailing list button below and you’ll get a notification right in your inbox when a new installment of the PoolParty Lifesaver is available.
Sources:
Emerging Tech Impact Radar: Data and Analystics, Gartner 2023
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Making Knowledge Management Clickable: Knowledge Management Systems Strategy, Design, and Implementation, Hilger & Wahl 2022
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