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Successful AI Requires the Right Data Architecture – Here’s How

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Successful AI Requires the Right Data Architecture - Here’s How


For companies that can master it, Artificial Intelligence (AI) promises to deliver cost savings, a competitive edge, and a foothold in the future of business. But while the rate of AI adoption continues to rise, the level of investment is often out of kilter with monetary returns. To be successful with AI you’ll want the right data architecture. This article tells you how.

Currently, only 26% of AI initiatives are being put into widespread production with an organization. Unfortunately, this means many companies spend a lot of time on AI deployments without seeing tangible ROI.

All Companies Must Perform Like a Tech Company

Meanwhile, in a world where every company must perform like a tech company to stay ahead, there’s increasing pressure on technical teams and Engineering and IT leaders to harness data for commercial growth. Especially as spending on cloud storage increases, businesses are keen to improve efficiency and maximize ROI from data that are costly to store. But unfortunately, they don’t have the luxury of time.

To meet this demand for rapid results, mapping data architecture can no longer stretch on for months with no defined goal. At the same time, focusing on standard data cleaning or Business Intelligence (BI) reporting is regressive.

Tech leaders must build data architecture with AI at the forefront of their objectives.

To do otherwise — they’ll find themselves retrofitting it later. In today’s businesses, data architecture should drive toward a defined outcome—and that outcome should include AI applications with clear benefits for end-users. This is key to setting your business up for future success, even if you’re not (yet) ready for AI.

Starting From Scratch? Begin With Best Practices for Data

Data Architecture requires knowledge. There are a lot of tools out there, and how you stitch them together is governed by your business and what you need to achieve. The starting point is always a literature review to understand what has worked for similar enterprises, as well as a deep dive into the tools you’re considering and their use cases.

Microsoft has a good repository for data models, plus a lot of literature on best data practices. There are also some great books out there that can help you develop a more strategic, business-minded approach to data architecture.

Prediction Machines by Ajay Agarwal, Joshua Gans, and Avi Goldfarb is ideal for understanding AI at a more foundational level, with functional insights into how to use AI and data to run efficiently. Finally, for more seasoned engineers and technical experts, I recommend Designing Data-Intensive Applications by Martin Kleppmann. This book will give you the very latest thinking in the field, with actionable guidance on how to build data applications, architecture, and strategy.

Three Fundamentals for a Successful Data Architecture

Several core principles will help you design a data architecture capable of powering AI applications that deliver ROI. Think of the following as compass points to check yourself against whenever you’re building, formatting, and organizing data:

  • Building Toward an Objective:

    Always have your eye on the business outcome you’re working toward as you build and develop your data architecture is the cardinal rule. In particular, I recommend looking at your company’s near-term goals and aligning your data strategy accordingly.

    For example, if your business strategy is to achieve $30M in revenues by year-end, figure out how you can use data to drive this. It doesn’t have to be daunting: break the more important goal down into smaller objectives, and work toward those.

  • Designing for Rapid Value Creation:

    While setting a clear objective is key, the end solution must always be agile enough to adapt to changing business needs. For example, small-scale projects might grow to become multi-channel, and you need to build with that in mind. Fixed modeling and fixed rules will only create more work down the line.

    Any architecture you design should be capable of accommodating more data as it becomes available and leveraging that data toward your company’s latest goals. I also recommend automating as much as you can. This will help you make a valuable business impact with your data strategy quickly and repeatedly over time.

    For example, automate this process from the get-go if you know you need to deliver monthly reporting. That way, you’ll only spend time on it during the first month. From there, the impact will be consistently efficient and positive.

  • Knowing How to Test for Success:

    To keep yourself on the right track, it’s essential to know if your data architecture is performing effectively. Data architecture works when it can (1) support AI and (2) deliver usable, relevant data to every employee in the business. Keeping close to these guardrails will help ensure your data strategy is fit for purpose and fit for the future.

The Future of Data Architecture: Innovations to Know About

While these key principles are a great starting place for technical leaders and teams, it’s also important not to get stuck in one way of doing things. Otherwise, businesses risk missing opportunities that could deliver even greater value in the long term. Instead, tech leaders must constantly be plugged into the new technologies coming to market that can enhance their work and deliver better outcomes for their business:

  • Cheaper Processing:

    We’re already seeing innovations making processing more cost-efficient. This is critical because many of the advanced technologies being developed require such high levels of computer power they only exist in theory. Neural networks are a prime example. But as the required level of computer power becomes more feasible, we’ll have access to more sophisticated ways of solving problems.

    For example, a data scientist must train every machine learning model. But in the future, there’s potential to build models that can train other models. Of course, this is still just a theory, but we’ll definitely see innovation like this accelerate as processing power becomes more accessible.

  • Bundled Tools:

    Additionally, when it comes to apps or software that can decrease time to value for AI, we’re in a phase now where most technology available can only do one thing well. The tools needed to productionize AI — like storage, machine learning providers, API deployment, and quality control — are unbundled.

    Currently, businesses risk wasting precious time simply figuring out which tools they need and how to integrate them. But technology is gradually emerging that can help solve for multiple data architecture use cases, as well as databases that are specialized for powering AI applications.

    These more bundled offerings will help businesses put AI into production faster. It’s similar to what we’ve seen in the fintech space. Companies initially focused on being the best in one core competency before eventually merging to create bundled solutions.

  • Data Marts vs. Data Warehouses:

    Looking further into the future, it seems safe to predict that data lakes will become the most important AI and data stack investment for all organizations. Data lakes will help organizations understand predictions and how best to execute those insights. I see data marts becoming increasingly valuable for the future.

    Marts deliver the same data to every team in a business in a format they can understand. For example, Marketing and Finance teams see the same data represented in metrics that are familiar and – most importantly – a format they can use. The new generation of data marts will have more than dimensions, facts, and hierarchy. They won’t just be slicing and dicing information — but will support decision-making within specific departments.

Conclusion

As the technology continues to develop, it’s critical that businesses stay up to speed, or they’ll get left behind. That means tech leaders staying connected to their teams, and allowing them to bring new innovations to the table.

Even as a company’s data architecture and AI applications grow more robust, it’s essential to make time to experiment, learn and (ultimately) innovate.

Image Credit: by Polina Zimmerman; Pexels; Thank you!

Atul Sharma

Atul founded Decision Intelligence company Peak in 2015 with Richard Potter and David Leitch. He has played a pivotal role in shaping Peak’s Decision Intelligence platform, which emerged as an early leader in a category that is expected to be the biggest technology movement for a generation. Peak’s platform is used by leading brands including Nike, Pepsico, KFC and Sika.
On a mission to change the way the world works, the tech scaleup has grown quickly over the last seven years and now numbers over 250 people globally. Regularly named a top place to work in the UK, this year Peak received the Best Companies 3-star accreditation, which recognizes extraordinary levels of employee engagement.
Prior to Peak, Atul spent over 20 years working in data architecture and data engineering. He has worked on designing and implementing data integration and data warehouse engagements for global companies such as Morrisons Plc, The Economist, HBOS, Admin Re (Part of Swiss Re) and Shell.

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Fintech Kennek raises $12.5M seed round to digitize lending

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Google eyed for $2 billion Anthropic deal after major Amazon play


London-based fintech startup Kennek has raised $12.5 million in seed funding to expand its lending operating system.

According to an Oct. 10 tech.eu report, the round was led by HV Capital and included participation from Dutch Founders Fund, AlbionVC, FFVC, Plug & Play Ventures, and Syndicate One. Kennek offers software-as-a-service tools to help non-bank lenders streamline their operations using open banking, open finance, and payments.

The platform aims to automate time-consuming manual tasks and consolidate fragmented data to simplify lending. Xavier De Pauw, founder of Kennek said:

“Until kennek, lenders had to devote countless hours to menial operational tasks and deal with jumbled and hard-coded data – which makes every other part of lending a headache. As former lenders ourselves, we lived and breathed these frustrations, and built kennek to make them a thing of the past.”

The company said the latest funding round was oversubscribed and closed quickly despite the challenging fundraising environment. The new capital will be used to expand Kennek’s engineering team and strengthen its market position in the UK while exploring expansion into other European markets. Barbod Namini, Partner at lead investor HV Capital, commented on the investment:

“Kennek has developed an ambitious and genuinely unique proposition which we think can be the foundation of the entire alternative lending space. […] It is a complicated market and a solution that brings together all information and stakeholders onto a single platform is highly compelling for both lenders & the ecosystem as a whole.”

The fintech lending space has grown rapidly in recent years, but many lenders still rely on legacy systems and manual processes that limit efficiency and scalability. Kennek aims to leverage open banking and data integration to provide lenders with a more streamlined, automated lending experience.

The seed funding will allow the London-based startup to continue developing its platform and expanding its team to meet demand from non-bank lenders looking to digitize operations. Kennek’s focus on the UK and Europe also comes amid rising adoption of open banking and open finance in the regions.

Featured Image Credit: Photo from Kennek.io; Thank you!

Radek Zielinski

Radek Zielinski is an experienced technology and financial journalist with a passion for cybersecurity and futurology.

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Fortune 500’s race for generative AI breakthroughs

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Deanna Ritchie


As excitement around generative AI grows, Fortune 500 companies, including Goldman Sachs, are carefully examining the possible applications of this technology. A recent survey of U.S. executives indicated that 60% believe generative AI will substantially impact their businesses in the long term. However, they anticipate a one to two-year timeframe before implementing their initial solutions. This optimism stems from the potential of generative AI to revolutionize various aspects of businesses, from enhancing customer experiences to optimizing internal processes. In the short term, companies will likely focus on pilot projects and experimentation, gradually integrating generative AI into their operations as they witness its positive influence on efficiency and profitability.

Goldman Sachs’ Cautious Approach to Implementing Generative AI

In a recent interview, Goldman Sachs CIO Marco Argenti revealed that the firm has not yet implemented any generative AI use cases. Instead, the company focuses on experimentation and setting high standards before adopting the technology. Argenti recognized the desire for outcomes in areas like developer and operational efficiency but emphasized ensuring precision before putting experimental AI use cases into production.

According to Argenti, striking the right balance between driving innovation and maintaining accuracy is crucial for successfully integrating generative AI within the firm. Goldman Sachs intends to continue exploring this emerging technology’s potential benefits and applications while diligently assessing risks to ensure it meets the company’s stringent quality standards.

One possible application for Goldman Sachs is in software development, where the company has observed a 20-40% productivity increase during its trials. The goal is for 1,000 developers to utilize generative AI tools by year’s end. However, Argenti emphasized that a well-defined expectation of return on investment is necessary before fully integrating generative AI into production.

To achieve this, the company plans to implement a systematic and strategic approach to adopting generative AI, ensuring that it complements and enhances the skills of its developers. Additionally, Goldman Sachs intends to evaluate the long-term impact of generative AI on their software development processes and the overall quality of the applications being developed.

Goldman Sachs’ approach to AI implementation goes beyond merely executing models. The firm has created a platform encompassing technical, legal, and compliance assessments to filter out improper content and keep track of all interactions. This comprehensive system ensures seamless integration of artificial intelligence in operations while adhering to regulatory standards and maintaining client confidentiality. Moreover, the platform continuously improves and adapts its algorithms, allowing Goldman Sachs to stay at the forefront of technology and offer its clients the most efficient and secure services.

Featured Image Credit: Photo by Google DeepMind; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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UK seizes web3 opportunity simplifying crypto regulations

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Deanna Ritchie


As Web3 companies increasingly consider leaving the United States due to regulatory ambiguity, the United Kingdom must simplify its cryptocurrency regulations to attract these businesses. The conservative think tank Policy Exchange recently released a report detailing ten suggestions for improving Web3 regulation in the country. Among the recommendations are reducing liability for token holders in decentralized autonomous organizations (DAOs) and encouraging the Financial Conduct Authority (FCA) to adopt alternative Know Your Customer (KYC) methodologies, such as digital identities and blockchain analytics tools. These suggestions aim to position the UK as a hub for Web3 innovation and attract blockchain-based businesses looking for a more conducive regulatory environment.

Streamlining Cryptocurrency Regulations for Innovation

To make it easier for emerging Web3 companies to navigate existing legal frameworks and contribute to the UK’s digital economy growth, the government must streamline cryptocurrency regulations and adopt forward-looking approaches. By making the regulatory landscape clear and straightforward, the UK can create an environment that fosters innovation, growth, and competitiveness in the global fintech industry.

The Policy Exchange report also recommends not weakening self-hosted wallets or treating proof-of-stake (PoS) services as financial services. This approach aims to protect the fundamental principles of decentralization and user autonomy while strongly emphasizing security and regulatory compliance. By doing so, the UK can nurture an environment that encourages innovation and the continued growth of blockchain technology.

Despite recent strict measures by UK authorities, such as His Majesty’s Treasury and the FCA, toward the digital assets sector, the proposed changes in the Policy Exchange report strive to make the UK a more attractive location for Web3 enterprises. By adopting these suggestions, the UK can demonstrate its commitment to fostering innovation in the rapidly evolving blockchain and cryptocurrency industries while ensuring a robust and transparent regulatory environment.

The ongoing uncertainty surrounding cryptocurrency regulations in various countries has prompted Web3 companies to explore alternative jurisdictions with more precise legal frameworks. As the United States grapples with regulatory ambiguity, the United Kingdom can position itself as a hub for Web3 innovation by simplifying and streamlining its cryptocurrency regulations.

Featured Image Credit: Photo by Jonathan Borba; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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