#data silos

[hanasatoblogs]

Banking with AI: Building a Unified Data Foundation for Success

The banking industry is undergoing a major transformation as technological advancements, such as artificial intelligence (AI), redefine traditional operations. AI offers significant opportunities for financial institutions to streamline processes, improve customer experiences, and make data-driven decisions. However, without a unified data foundation, many of these advancements are difficult to implement effectively. Banks face significant challenges due to fragmented data systems and data silos that hinder efficiency and innovation.

This article explores the potential of AI in banking, the importance of a unified data foundation, and how financial institutions can leverage these tools to unlock their full potential.

The Challenge of Data Silos in Banking

For decades, financial institutions have relied on legacy systems that collect and store data in isolated environments, leading to data silos. These silos prevent the seamless sharing of information across departments, limit collaboration, and create inefficiencies that hinder AI deployment.

Data silos in banking can exist in various departments, such as retail banking, corporate banking, risk management, and customer service. Each of these silos may hold valuable customer data, but without a unified system, institutions miss the opportunity to leverage this information for strategic insights. For example, siloed data makes it difficult to gain a 360-degree view of customer interactions, preventing personalized services and limiting a bank’s ability to offer timely, relevant solutions.

Real-World Example:

A major global bank experienced significant delays in fraud detection due to fragmented data systems across different regions. Without a unified data foundation, fraud patterns were not detected in real time, resulting in higher operational risks. By adopting a unified data platform and AI, the bank was able to connect its systems and drastically improve fraud detection accuracy, reducing fraud-related losses by 30%.

The Power of a Unified Data Foundation

A unified data foundation eliminates the fragmentation caused by data silos by consolidating all sources of information into a single, integrated platform. This integration allows financial institutions to gather insights from various touchpoints, including customer interactions, transactional data, and operational processes. When AI is layered on top of this unified foundation, banks can unlock immense potential for efficiency, innovation, and customer satisfaction.

Key Benefits of a Unified Data Foundation:

Holistic Customer View: With consolidated data, banks can build comprehensive profiles of their customers, enabling personalized services and improving overall customer engagement.

Operational Efficiency: Unified data reduces duplication, streamlines processes, and enhances collaboration across departments, leading to faster decision-making and reduced costs.

Enhanced Risk Management: By breaking down silos, banks can detect risks earlier, whether related to fraud, credit, or compliance, and take proactive measures.

Seamless AI Integration: A unified data platform is crucial for effectively deploying AI, enabling financial institutions to build predictive models, optimize operations, and enhance decision-making.

AI in Banking: Transforming Operations

As financial institutions adopt AI to revolutionize their processes, a unified data foundation becomes even more critical. AI’s success in banking depends heavily on the quality and accessibility of data. From fraud detection to personalized customer service, AI can optimize banking in numerous ways.

1. Fraud Detection and Prevention

AI is transforming fraud detection in banking by analyzing vast amounts of transactional data in real time. AI algorithms can quickly detect unusual patterns or anomalies, flagging potential fraud before it causes significant damage. However, AI's effectiveness in this area relies on access to comprehensive and real-time data, which a unified foundation facilitates.

Example: An international bank used AI-powered systems to identify fraudulent activities by analyzing customer spending patterns and detecting outliers in transaction data. The system achieved a 40% increase in fraud detection accuracy after consolidating its data silos and implementing a unified data platform.

2. Personalized Customer Experiences

Today’s banking customers expect tailored services and personalized product offerings. AI can help financial institutions analyze customer behaviors, preferences, and transaction histories to deliver more customized solutions. For instance, AI can recommend personalized financial products, investment plans, or loan offers based on an individual’s financial situation.

Example: A U.S.-based credit union implemented AI to analyze its customer data, which was previously siloed across different product lines. By adopting a unified data foundation, the credit union launched a highly personalized marketing campaign, resulting in a 25% increase in loan applications and a 15% boost in customer retention.

3. Streamlined Lending Processes

AI in banking is transforming the credit and lending processes by automating credit scoring and loan approval workflows. Machine learning models analyze a customer’s financial history, credit behavior, and other factors to generate a more accurate and faster assessment of creditworthiness.

Example: A European bank implemented AI to automate its loan approval process. By integrating a unified data foundation, the bank could evaluate a borrower’s complete financial profile from multiple data sources. This resulted in faster loan approvals, improved customer satisfaction, and a 20% reduction in manual errors.

4. Risk Management and Compliance

AI enables banks to strengthen risk management and ensure compliance with ever-evolving regulations. By analyzing data from various sources, AI systems can identify potential risks and ensure that the bank is adhering to regulatory standards. A unified data platform allows these AI models to access all necessary data in real time, reducing compliance risks and improving reporting accuracy.

Example: A leading financial institution used AI to automate its regulatory compliance checks. By consolidating its data into a unified platform, the institution achieved a 35% reduction in the time spent on compliance reporting, while ensuring accuracy and regulatory adherence.

The Future of Banking: Unlocking Potential with AI and Unified Data

Financial institutions that embrace both AI and a unified data foundation are positioning themselves for success in a highly competitive market. As more banks adopt AI for critical functions—ranging from customer engagement to risk management—those with unified data systems will be able to fully harness the power of AI and make more informed decisions.

Data-Driven Insights:

Customer-Centric Growth: According to McKinsey, banks that adopt AI and data-driven approaches are 2.3 times more likely to grow their customer base compared to their competitors.

Cost Reductions: Financial institutions can reduce operational costs by up to 25% through AI-driven automation and a unified data approach, as reported by Accenture.

Faster Time to Market: A unified data foundation enables banks to deploy new products and services faster, giving them a competitive edge in the market.

Overcoming Challenges: Building a Unified Data Foundation

While the benefits of AI in banking are clear, many financial institutions still struggle with data integration and management. Building a unified data foundation requires careful planning and strategic investments in technology infrastructure. Banks must modernize their legacy systems, adopt cloud-based platforms, and implement data governance practices to ensure that the foundation is reliable and secure.

Conclusion: Unlocking the Future with AI and Unified Data

AI in banking is revolutionizing how financial institutions operate, from risk management and fraud detection to personalized services and streamlined lending. However, to fully unlock the potential of AI, banks must first eliminate data silos and build a unified data foundation that allows seamless data sharing and collaboration across the organization.

By investing in a unified data platform, financial institutions can leverage customer data more effectively, deliver superior services, and drive innovation across all levels of the organization. As the banking industry continues to evolve, those who prioritize AI and unified data will emerge as leaders in the digital age.

Browse Related Blog - Selecting the Best GenAI Model for Your Customer Service Strategy

AI for Data Management: A Game-Changer for Business Leaders

[jcmarchi]

Akhilesh Tripathi, CEO of Digitate – Interview Series

New Post has been published on https://thedigitalinsider.com/akhilesh-tripathi-ceo-of-digitate-interview-series/

Akhilesh Tripathi, CEO of Digitate – Interview Series

Digitate CEO Akhilesh Tripathi joined the company in 2015 to launch its flagship product, ignio™. Under his leadership, ignio became one of the fastest-growing enterprise applications, with a global customer base spanning many industries and Fortune 500 companies. Previously, Akhilesh  served as the head of Canada for TCS (Tata Consultancy Service), where he grew the entity from a small, relatively unknown firm to a perennial top 10 service provider. His 25-year career with TCS has also included serving as Head of Enterprise Solutions and Technology Practices for TCS in North America.

Digitate uses machine learning and artificial intelligence (AI) to manage IT and business operations. Its product, ignio™, is a cognitive automation solution designed to help IT teams identify and address outages quickly. Ignio includes pre-built knowledge aimed at enabling faster adoption of AI compared to other solutions. It connects various business applications, processes, and infrastructure to support decision-making and perform actions autonomously.

What was your vision for Digitate when you first joined in 2015, and how has that vision evolved over time?

When I first joined Digitate in 2015, my vision was to push forward a new way of thinking that shifts enterprises from a people-first model to a technology-first approach. By leveraging AI and automation, we would allow machines to become the initial handlers of tasks while humans became the handles of exceptions.  Over time, this vision has evolved to encompass a broader goal: helping enterprises achieve what we call the “autonomous enterprise” journey. This involves leveraging unified observability, AI-driven insights, and closed-loop automation to ensure that our customers can manage their increasingly complex IT environments with minimal human intervention. Today, Digitate is all about empowering enterprises to not just react to problems but to proactively prevent them, ensuring operational resilience and continuous value creation.

How do you foresee the future of AI-driven enterprise solutions, particularly in the context of automation and autonomous operations?

The future of AI-driven enterprise solutions is incredibly promising. We’re on the brink of a transformative shift where AI doesn’t just assist with tasks but fundamentally changes how enterprises operate at a core level. We’re already seeing AI-driven solutions becoming even more integrated into every facet of business operations. The goal is for enterprises to use AI and automation not just for automating routine tasks, but for making real-time decisions, optimizing operations across diverse environments, and predicting and preventing issues before they arise.

This shift towards autonomy is particularly exciting. As AI continues to evolve, we’ll see more systems that can self-manage, self-heal, and even self-optimize without the need for constant human intervention. This is already at play in our closed-loop model, allowing teams to focus on more strategic tasks rather than being bogged down.

What are the key challenges you’ve faced in scaling Digitate globally, and how did you overcome them?

Digitate is pioneering a new category, and as we scale globally, it’s important to build interest in our vision of the autonomous enterprise and communicate the value we offer. Many people still think that data silos and automation are the status quo, but we believe they don’t have to be. To tackle this, I’ve instructed my team to focus on what I call the 3Es: excite, educate, and execute.

Education is crucial because we need businesses to be open to taking risks, and this often requires a leadership mindset that embraces new technology and innovative perspectives. After we have educated and inspired our audience, we must follow through during the implementation phase. It is essential that we keep our promises – our goal is to deliver on what we commit to.

What inspired the development of Digitate’s flagship product, ignio™, and what sets it apart in the market?

ignio™ was developed with a vision to revolutionize how businesses approach IT operations by embedding intelligence and automation at its core. The inspiration came from our deep understanding of the pain points that IT teams face daily: lengthy resolution times, fragmented visibility across systems, and the sheer volume of alerts that overwhelm human operators. We wanted to create a solution that could not only detect and resolve issues faster but also predict and prevent them from occurring in the first place. This led to the concept of an autonomous enterprise, where ignio™ acts as the digital brain, continuously learning from the environment, correlating data, and taking automated actions to ensure smooth, uninterrupted operations.

What sets ignio™ apart in the market is its ability to combine unified observability, AI-driven insights, and closed-loop automation into a single platform. Unlike other solutions that focus on individual aspects of IT management, ignio™ offers an integrated approach that addresses the entire lifecycle of IT operations.

Can you share how Digitate is leveraging AI to enhance predictive analytics and proactive problem management in IT operations?

As the buzz around GenAI continues to captivate the tech industry, it’s easy for enterprises to get swept up in the excitement and rush into implementation. However, in this enthusiasm, there is a real risk of overlooking foundational principles and best practices, which can lead to significant challenges down the road.

To navigate this, we emphasize the importance of data readiness and governance. We know that AI, no matter how sophisticated, is only as good as the data it operates on. Our ignio™ platform, for example, leverages AI to enhance predictive analytics and proactive problem management in IT operations. However, these capabilities are only fully realized when they are supported by high-quality data and robust methodologies. This strategic focus allows us to harness the power of AI effectively, driving true digital transformation while minimizing risks associated with the hype cycle.

How does Digitate ensure that ignio™ stays ahead of the curve in a rapidly evolving tech landscape?

At Digitate, we ensure that ignio™ remains at the forefront of the rapidly evolving tech landscape by continuously innovating and refining our platform to meet the dynamic needs of modern enterprises. We do this by leveraging a combination of advanced AI, machine learning, and a closed-loop automation approach to keep our systems ahead of the curve.

Our ignio™ AIOps platform is designed to tackle a wide range of problems enterprises face in IT and business operations across industries. “We use AI and automation to predict and solve issues before they impact key business KPIs, such as revenue assurance and customer satisfaction. Our proactive approach transforms IT from reactive to predictive, creating an environment where AI and ML systems solve errors automatically in real time, eliminating the need for tickets. With GenAI, we accelerate innovation and reduce manual effort in finding and solving issues, leading to faster time to value.”

In your opinion, what role will AI and automation play in shaping the future of digital operations across industries?

As we look towards the future of AI, we’re entering an era where human-AI collaboration is set to become more seamless and intuitive. The advancements in AI capabilities are leading us towards a new paradigm of augmented intelligence, where AI doesn’t just automate tasks but works alongside humans, enhancing our abilities through continuous learning and real-time insights. We’re particularly focused on how AI can mimic and adapt to human behaviors, making interactions more natural and conversational. This shift is crucial as it allows AI to fit more organically into daily workflows, whether it is through decision-making processes, predictive analytics, or even customer interactions.

However, with these advancements come significant challenges. For one, the opacity of AI systems, often referred to as “black boxes,” makes debugging and maintenance more complex than traditional software. This requires us to develop new skills and processes to ensure that AI systems are reliable and trustworthy. Change management is another critical area. As AI becomes more embedded in our operations, there is a natural resistance that can emerge, both from individuals accustomed to traditional workflows and from regulatory bodies concerned about the implications on employment and job roles. Addressing these concerns requires a thoughtful approach that balances innovation with empathy and strategic foresight. Cybersecurity and privacy risks are also escalating as AI systems become more pervasive. The more we rely on AI, the more attractive these systems become to malicious actors, including potential state-sponsored threats.

Despite these challenges, the potential for growth and innovation in AI-driven collaboration is immense. The market is ripe with opportunities, and businesses that invest in integrating AI with a focus on transparency, augmented intelligence, and seamless human interaction will be well-positioned to lead in this evolving landscape. At Digitate, we’re excited about the role our technology will play in shaping this future, driving both operational efficiency and transformative business outcomes.

How is Digitate addressing the growing demand for AI-driven solutions in sectors like retail, manufacturing, and financial services?

Digitate is addressing the growing demand for AI-driven solutions by developing industry-specific offerings that meet the unique needs of sectors like retail, manufacturing, and financial services. In retail, for example, ignio™ helps optimize supply chain operations and enhance customer experiences by predicting and preventing disruptions. In manufacturing, we enable smarter production processes through predictive maintenance and automated quality control. In financial services, our AI-driven insights support fraud detection, compliance, and risk management. By tailoring our solutions to the specific challenges of each industry, we help our customers drive innovation and maintain a competitive edge.

What are the most significant industry trends you’re seeing right now, and how is Digitate adapting to them?

One of the most significant trends we’re observing in the AI industry is the rapid advancement of Large Language Models (LLMs), particularly their evolving specialization and multimodal capabilities. These models are not just becoming more powerful in a general sense. They’re also increasingly tailored to specific industries and tasks, which opens up new possibilities for AI-driven solutions across various domains.

We’re closely following these developments, particularly the trend towards domain and industry specialization in LLMs. As companies look to maintain their competitive edge, they’re investing in LLMs that can understand and operate within the specific contexts of their industries. This means that LLMs are being customized to handle industry-specific jargon, concepts, and challenges with a level of precision that was previously unattainable. We see this as a crucial area for us to integrate into our own offerings, especially as we aim to provide more targeted, actionable insights for our clients across different sectors.

Commonsense reasoning and factual grounding are also critical areas where LLMs are making strides. As these models become better at understanding real-world contexts and maintaining factual accuracy, the reliability and usefulness of AI in enterprise settings will grow exponentially.

With over 20 years in the IT industry, what key leadership lessons have you learned, particularly in leading innovative tech companies?

In my 20 years in the IT industry, I’ve learned that having a clear purpose and a sense of curiosity is crucial for leading innovative tech companies. A strong purpose drives passion, creating an ongoing cycle of innovation. When innovation is fueled by a compelling purpose, it has greater staying power, enabling companies to overcome challenges and stay competitive in the long run. It’s important to note that each person’s purpose may differ, and as a leader, it’s vital to align an individual’s purpose with the overall organizational goals to maximize their potential.

Curiosity is equally important. The drive to learn, explore new ideas, and create something new is what pushes a company forward. The real magic happens when purpose and curiosity come together. This is where innovation and creativity thrive, allowing us to make breakthroughs and lead in the industry.

Thank you for the great interview, readers who wish to learn more should visit Digitate. 

[garymdm]

Multidomain Master Data Management for Real-World Results

Imagine trying to empty the ocean with a bucket. That’s the Sisyphean task businesses face when they attempt to govern, consolidate, measure, and optimize all their enterprise data. It’s overwhelming and ultimately ineffective. Focus on Processes, Not DomainsThe Power of Process-Oriented MDMFocus on the Flow, Not the Bucket The domain-oriented approach – focusing on specific areas like…

[appseconnect]

Modern-day businesses utilize different systems and applications for various operations. And while such systems are essential to streamline operations, they can also quickly turn into data silos. This is becoming a stark reality – countless businesses struggle with breaking down data silos which is becoming evident from industry statistics as well.

[ipervi]

From Chaos to Clarity - How Data Mesh is Taming Data Silos for Modern Businesses

In today's data-driven world, businesses are generating more data than ever before. From customer transactions and marketing campaigns to website analytics and social media interactions, the volume of information is truly staggering. However, this abundance can be a double-edged sword. Often, valuable data gets trapped within departmental "silos," making it difficult to access, analyze, and leverage for strategic decision-making. This fragmented data landscape leads to frustration, hinders collaboration, and ultimately, restricts a company's ability to unlock the full potential of its information assets.

The Silo Effect: How Data Fragmentation Hinders Progress

The culprit behind this data chaos? Data silos. These arise when different departments within an organization collect and manage their own data independently. Departmental ownership of specific data sets, lack of standardized formats across departments, or simply a culture of information control can all contribute to silo formation. Regardless of the cause, the consequences are far-reaching.

Limited Visibility: Without a unified view of all relevant data, gaining a holistic understanding of the business becomes challenging. This can lead to missed opportunities, inefficient resource allocation, and flawed strategic planning.

Data Inconsistency: Fragmented data management can lead to inconsistencies in quality and format. This makes it difficult to trust the accuracy of insights derived from the data, hindering data-driven decision-making.

Reduced Agility: When valuable data is locked away in silos, it takes longer to access and analyze, hindering an organization's ability to respond quickly to market changes or customer needs.

Traditional approaches to data integration, such as building centralized data warehouses, often prove cumbersome and expensive. They require significant upfront investment and ongoing maintenance, making them both time-consuming and resource-intensive.

Breaking Down the Walls: Introducing Data Mesh

The Data Mesh architecture offers a revolutionary solution to the challenge of data silos. It promotes a decentralized approach to data management, where ownership and responsibility for data reside with the business domains that originate it. This approach empowers data domains, such as marketing, sales, or finance, to own and manage their generated data. This fosters accountability, ensures data quality, and instills a sense of stewardship within the organization.

One of the key features of Data Mesh is self-service data availability. Domains are responsible for preparing their data as easily consumable products, allowing other departments to access and utilize it without relying on centralized IT teams. This self-service approach democratizes data access, facilitating collaboration, and enabling faster decision-making.

Data governance remains a vital component of Data Mesh, but it is implemented at the domain level. Each domain is responsible for ensuring the quality and consistency of its own data set. This decentralized approach to data governance aligns with the principles of Data Mesh, promoting agility and empowering data domains to take ownership of their data management practices.

Lastly, Data Mesh fosters a culture of data collaboration and innovation, enabling data sharing across domains and unlocking new opportunities for innovation.

Building a Data-Driven Future: Implementing Data Mesh

While Data Mesh offers a promising solution, its implementation requires meticulous planning and execution. The initial step involves identifying data domains within an organization and assigning clear data ownership responsibilities. Establishing data governance frameworks and standards is crucial, encompassing standardized data models, access controls, and quality assurance measures across all data domains.

Moreover, investing in data interoperability tools, such as APIs and data catalogs, facilitates seamless data exchange and integration between domains. Encouraging a data-driven culture is pivotal, entailing training employees on data literacy and highlighting the benefits of Data Mesh.

Conclusion

Data silos present a significant challenge to businesses, hindering their ability to leverage the full value of their data assets. Traditional approaches to data integration are time-consuming and resource-intensive, but the Data Mesh architecture offers a revolutionary solution. The Data Mesh promotes a decentralized approach, empowering business domains to own and manage their data, fostering accountability, data quality, and stewardship.

If you're seeking effective data solutions, consider exploring services from Cipherslab. With a team of skilled and experienced data analysts, they offer a wide range of solutions to address your data management needs. Visit CipherSlab to unlock the power of your data assets today.

[josephkravis]

The Future of Our Personal Data: A Guy's Thoughts on AI, Blockchain, and Digital Identity

Hey everyone, The rise of AI and blockchain technology is transforming the way our personal data is collected, stored, and used, raising important questions about privacy and control. I’ve been thinking a lot lately about how our personal data is being collected, stored, and used in today’s digital world. It’s crazy to see how quickly things are changing with the rise of artificial intelligence…

[kariniai]

Unified Data: Bridging the Gap between Silos

In an era where data is the new gold, businesses have grappled with the challenge of data silos - isolated reservoirs of information accessible only to specific organizational factions.

This compartmentalization of data is the antithesis of what we term 'healthy' data: information that's universally comprehensible and accessible, fueling informed decision-making across an enterprise. For decades, enterprises have endeavored to dismantle these silos, only to inadvertently erect new ones dictated by the need for efficient data flows and technological limitations.

However, the landscape is radically transforming, thanks to Generative AI (Gen AI) and its groundbreaking capabilities.

The Transformational Shift with Gen AI:

The advent of Gen AI heralds an unprecedented shift in data management and accessibility. With the advent of Retrieval Augmented Generation (RAG) and its integration into infinitely expandable vector data stores, the once-unthinkable is now a tangible reality. Karini.ai stands at the forefront of this revolution, harnessing Gen AI to bridge the gaps between disparate data stores, file repositories, and databases, turning unconnectable into a seamlessly interconnected web of knowledge.

The Dawn of a New Data Era:

For the first time in the annals of corporate history, every line of business has the key to unlock the treasures within all available data, regardless of its domicile. The power of Large Language Models (LLMs) further revolutionizes this landscape, enabling users to query complex data pools through intuitive, natural language. The beauty of this innovation lies not just in its technical prowess but in its adherence to the intricate tapestry of governance and compliance that underpins the corporate world.

Case Studies: The Infinite Horizon of Use Cases:

Karini.ai, armed with Gen AI, is not just transforming businesses; it's redefining them. From marketing insights derived from an ocean of consumer data to predictive maintenance in manufacturing powered by real-time IoT data - the use cases are as limitless as the human imagination. In finance, risk assessment models become more nuanced and robust, drawing from a richer, more diverse set of data points. Patient care personalization reaches new heights in healthcare as medical histories and research data converge to offer bespoke treatment plans.

Karini.ai: Your Navigator in the Gen AI Odyssey:

Navigating the vast seas of data with Gen AI is a venture fraught with challenges, from ensuring data integrity to maintaining privacy and compliance. Karini.ai does not just provide the tools for this journey; it offers the compass and the map. With our expertise, your enterprise can chart its unique course through this brave new world of unified data. We provide the guardrails to ensure your voyage is innovative, secure, compliant, and aligned with your corporate ethos and objectives.

Conclusion: A Call to Pioneer the Future:

The amalgamation of siloed data through Gen AI is not just an operational upgrade; it's a paradigm shift in how businesses perceive and utilize information. It's an invitation to pioneer a future where data is not just a resource but a beacon that guides every strategic decision, every innovation, and every customer interaction. Karini.ai is your partner in this transformative journey, fortified with robust governance and a deep understanding of your business landscape, bringing your business the prowess of Gen AI.

(करिणी) - We are with you on your entire journey…

About Karini AI:

Fueled by innovation, we're making the dream of robust Generative AI systems a reality. No longer confined to specialists, Karini.ai empowers non-experts to participate actively in building/testing/deploying Generative AI applications. As the world's first GenAIOps platform, we've democratized GenAI, empowering people to bring their ideas to life – all in one evolutionary platform. 

Contact:

Jerome Mendell

(404) 891-0255

sales@karini.ai

[brillioitservices]

Data Supply Chain Transformation and Innovation.

Explore the journey of data supply chain transformation, breaking down data silos to unlock valuable insights and foster innovation. Discover how businesses are leveraging advanced technologies and strategic initiatives to streamline data flow, enhance collaboration, and drive impactful decision-making. Embrace the era of data innovation and harness the power of interconnected data ecosystems for sustainable growth and competitive advantage.

[atozofsoftwareengineering]

Unlocking the Benefits: How Cloud-Based Data Warehouses are Revolutionizing Data Management

A data warehouse is a centralized repository of data that enables businesses to store, manage, and analyze large amounts of structured and unstructured data from various sources. Data warehouses have traditionally been on-premise solutions, but with the advent of cloud computing, businesses are now able to deploy data warehouses in the cloud. Cloud-based data warehouses offer many benefits over…

[jcmarchi]

Messy Data Is Preventing Enterprise AI Adoption – How Companies Can Untangle Themselves

New Post has been published on https://thedigitalinsider.com/messy-data-is-preventing-enterprise-ai-adoption-how-companies-can-untangle-themselves/

Messy Data Is Preventing Enterprise AI Adoption – How Companies Can Untangle Themselves

Health startups are saying that unclear regulations are stifling AI innovation in the sector. Of course, such precautions are necessary in the healthcare industry, where it’s literally a case of life or death. But what makes less sense is the sluggish adoption of AI across enterprise SaaS – a space that isn’t being held back by red tape like other sectors are.

So what’s stopping enterprises from adopting AI to streamline and optimize their processes? The primary culprit is the hoards of messy data that accumulates as companies grow and add new tools and products. In this article, I’ll delve into how messy data is a blocker to AI innovation in enterprise, and explore the solutions.

Welcome to the data jungle

Let’s start by looking at a common data challenge that many modern businesses face. Initially, when businesses offer a limited range of products, they typically have clean revenue data that’s all housed within a single system. However, as they expand their offerings and adopt a range of revenue models, things quickly get messy.

For example, a business might initially employ a one-time purchase model, but later introduce additional options such as subscriptions or consumption-based pricing. As they expand, they’ll likely diversify their sales channels, too. A company that starts with 100% product-led self-serve sales may realize over time that they need the help of sales teams to up-sell, cross-sell, and land larger clients.

During rapid growth stages, many businesses simply stack new sales systems onto existing ones. They’ll procure a different SaaS tool to manage each different motion, pricing model, purchasing process, and so on. It’s not uncommon for a company’s marketing department alone to have 20 different SaaS tools with 20 different data silos. 

So while companies generally start with clean, integrated data, growth causes data to quickly spiral out of control, often well before businesses recognize it as an issue. Data becomes siloed off between billing, fulfillment, customer success, and other systems, meaning companies lose global visibility into their inner workings. And unfortunately, manually reconciling data is often so labor-intensive and time-consuming that insights can be outdated by the time they’re ready to use.

AI can’t fix your messy data for you

Several prospective clients have asked us – “well if AI’s so great, can’t it just solve this messy data problem for us?” Alas, AI models are not the panacea for this data problem.

Current AI models require clean datasets to work properly. Companies relying on diverse sales motions, SaaS platforms and revenue processes inevitably accumulate disparate and fragmented datasets. When a business’s revenue data is scattered across incompatible systems that can’t communicate with each other, AI can’t make sense of it. For example, what’s labeled as “Product” in one system could be very different from “Product” in another system. This subtle semantic difference is difficult for AI to identify and would inevitably lead to inaccuracies. 

Data needs to be properly cleansed, contextualized and integrated before AI comes into the picture. There’s a longstanding misconception that data warehousing offers a one-size-fits-all solution. In reality, even with a data warehouse, data still needs to be manually refined, labeled, and contextualized, before businesses can use it to produce meaningful analytics. So in this way, there are parallels between data warehousing and AI, in that businesses need to get to the root of messy data before they can reap the benefits of either of these tools.

Even when data has been contextualized, AI systems are still estimated to hallucinate at least 3% of the time. But a company’s financials — where even a decimal point in the wrong place could have a domino effect disrupting multiple processes — require 100% accuracy. This means human intervention is still essential to validate data accuracy and coherence. Integrating AI prematurely may even create more work for human analysts, who have to allocate additional time and resources to correcting these hallucinations.

A data catch-22

Nevertheless, the proliferation of SaaS solutions and resulting messy data does have several solutions.

First, companies should regularly assess their tech stack to ensure that each tool is strictly necessary to their business processes, and not just contributing to the data tangle. You may find that there are 10 or even 20+ tools that your teams are using daily. If they’re truly bringing value to departments and the overall business, don’t get rid of them. But if messy, siloed data is disrupting processes and intelligence gathering, you need to weigh its benefits against switching to a lean, unified solution where all data is housed in the same tool and language. 

At this point, businesses face a dilemma when choosing software: all-in-one tools can offer data coherence, but possibly less precision in specific areas. A middle ground involves businesses seeking out software that offers a universal object model that is flexible, adaptable, and seamlessly integrated with the general ecosystem. Take Atlassian’s Jira, for example. This project management tool operates on an easy-to-understand and highly extensible object model, which makes it easy to adapt to different types of project management, including Agile Software Development, IT/Helpdesk, Marketing, Education, and so on.

To navigate this trade-off, it’s crucial to map out the metrics that matter most to your business and work back from there. Identifying your company’s North Star and aligning your systems towards it ensures that you’re architecting your data infrastructure to deliver the insights you need. Instead of focusing solely on operational workflows or user convenience, consider whether a system contributes to non-negotiable metrics, such as those crucial to strategic decision-making.

Ultimately, it’s the companies that invest time and resources into unjumbling the data mess they’ve gotten themselves into who will be the first to unlock the true potential of AI.

[garymdm]

Why Invest in an Internal Data Marketplace?

In today’s data-driven world, organizations are sitting on a goldmine of information. But much of this valuable resource is locked away, siloed within departments and difficult to access! This is where data marketplaces come in, specifically private data marketplaces, also known as internal data marketplaces. What is a Private Data Marketplace Reaping Internal Rewards: Stepping Stone to…

[ahrencmeptn]

I could only resist for so long.