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Month: January 2026

Your Enterprise Data Isn’t Aligned and It Shows 

Posted on by Luka Jasionyte

Luka Jasionyte, from the marketing team at Snap Analytics, catches up with Raj Shah, Director of Strategic Accounts, to dive into his journey in data and analytics and explore the key challenges facing enterprise businesses today. Raj shares his insights on making sense of enterprise data, tackling data silos, the importance of strong governance, and how AI and high-quality data engineering are shaping the future. 

What inspired you to get into data and analytics, Raj? 

I was inspired by the opportunity to work across a variety of clients and industries, as data exists in every organisation. The business value that proper analytics can deliver feels almost magical, and although I didn’t enjoy programming, I found SQL surprisingly easy to write and understand.

What is the most exciting trend right now? 

AI, without a doubt. It has really focused everyone’s attention on getting their data right. It’s no longer something organisations can put off while continuing with manual or Excel-based analytics. AI depends on high-quality data, and ‘human-in-the-loop’ AI is the stepping stone for most organisations. Those that fail to embrace it risk being left behind.

What are some common data-related challenges that large enterprises typically face? 

Large enterprises often face several recurring data challenges that impact their ability to deliver reliable analytics. One major issue is the lack of a common data definition or language across the organisation. For example, a term like ‘margin’ can mean different things to different departments, leading to inconsistent reporting and decision-making. Another challenge is the reliance on flat files and manual data processing methods. These approaches are time-consuming, error-prone and make it difficult to scale analytics effectively. Excessive data transformation and manipulation often happens within front-end tools rather than being pushed down to the data warehouse. This creates inefficiencies, performance bottlenecks and governance risks.

What are the top client priorities for those looking to drive successful outcomes in data and analytics? 

For clients aiming to achieve successful outcomes in data and analytics, several priorities stand out. First, making data a true business asset is essential. This involves consolidating information from multiple systems into a clean, unified data warehouse that provides a single source of truth. Second, building data literacy across the organisation is critical. When teams understand and trust the data, they can make informed decisions and fully leverage analytics capabilities. Finally, reducing reliance on manual processes and Excel-based reporting is a key step towards scalability and efficiency. Moving to automated, integrated solutions not only saves time but also improves accuracy and enables advanced analytics. Together, these priorities create the foundation for delivering actionable insights and driving measurable business value.

Raj, why Snap Analytics? 

Clients choose Snap because we combine deep industry expertise with technical excellence. Our consultants have broad experience across the modern data stack, enabling us to design and deliver solutions that meet diverse business needs. We also bring specialised knowledge of complex systems such as SAP, supported by proven frameworks that reliably extract data and integrate it into modern cloud platforms. This ensures accuracy, scalability and speed. Snap focuses on delivering real business value. Every project is driven by outcomes that matter, whether that’s improving decision-making, reducing costs or accelerating innovation. Our approach uses lean teams, automation and reusable frameworks to achieve efficiency, standardisation and strong governance, giving clients confidence that their investment translates into measurable results.

AI Isn’t the Hero, Your Data Is.

Posted on by Luka Jasionyte

Luka Jasionyte, from the marketing team at Snap Analytics, catches up with Mark Todkill, Head of Delivery, to dive into his journey in data and analytics and explore the key challenges facing enterprise businesses today. Mark shares his insights on making sense of enterprise data, tackling data silos, the importance of strong governance, and how AI and high-quality data engineering are shaping the future.

What inspired you to get into data and analytics? 

I’ve always been drawn to problem-solving, finding patterns, breaking down complex challenges, and using maths and logic to uncover solutions. That curiosity has been a constant thread throughout my life, though I never set out thinking data and analytics would become my career. But after graduating, I landed in a role that aligned perfectly with my strengths, and that’s where everything started to click. 

Over time, my passion for data grew through hands-on experience. The field moves fast, with new problems to tackle and fresh innovations emerging constantly. What I love most is how it brings together two sides of problem-solving. On one hand, there is the technical challenge of making sense of vast amounts of enterprise data, structuring it, and ensuring it is usable. On the other, there is the business side, which focuses on turning that data into meaningful insights that drive smarter decisions. 

It is a space that keeps me engaged, constantly learning, and always excited for the next challenge. That balance between technical precision and strategic thinking is what makes it such a compelling field to be in.

What is the most exciting trend right now? 

One of the most transformative trends in data and analytics right now is the fusion of AI and high-quality data engineering. AI continues to revolutionise industries, from automation and predictive analytics to personalised customer experiences and real-time decision-making. However, its true power lies not just in the algorithms but in the enterprise data foundation it relies on. 

As AI adoption accelerates, businesses are increasingly recognising that structured, well-governed data is the cornerstone of effective AI-driven solutions. Without clean, reliable, and strategically modelled data, AI can produce inaccurate insights, biased recommendations, or inefficient automation. The success of AI depends on robust data pipelines, governance frameworks, and scalable architectures that ensure the right data is available at the right time. 

What are some common data-related challenges that large enterprises typically face? 

There are three key challenges I tend to see when it comes to enterprise data. 

The first is making sense of the data. Large enterprises generate huge volumes of information across different platforms and departments, but without a clear, unified view, it is difficult to connect the dots. That means businesses might be sitting on valuable insights but are unable to leverage them effectively. 

The second challenge is data silos and a lack of strategy. Different departments often have their own databases and tools, creating a fragmented data landscape. Without proper integration, businesses end up with incomplete insights and decisions based on limited information. On top of that, many companies invest in analytics without a clear roadmap, meaning data initiatives do not always align with business goals or deliver meaningful results. 

The third challenge is weak data governance. If there is no clear ownership and accountability, businesses face data quality issues, compliance risks, and accessibility problems. Governance is essential for keeping data reliable, secure, and usable across the organisation. Without it, even the best analytics tools cannot provide accurate insights. 

Businesses that take a proactive approach to these challenges will gain deeper insights, make smarter decisions, and fully unlock the value of their data. 

What are the top client priorities for those looking to drive successful outcomes in data and analytics? 

The main priority for large enterprises is ensuring that data is structured in a way that makes sense to business teams. Data must not only be available but also organised, accessible, and aligned with business objectives so that decision-makers can extract meaningful insights. Companies are increasingly recognising the importance of empowering teams with well-governed, well-structured data models that enable faster and more informed decision-making. Without a clear structure and governance framework, even the most advanced analytics tools will not deliver real business value. 

Another critical factor is showing value quickly. Early wins are essential for proving the impact of data initiatives and securing buy-in across the organisation. Businesses need to see tangible results fast, whether through automation, streamlined reporting, or AI-powered insights. Strong collaboration between data teams and business teams plays a key role. When data professionals work closely with stakeholders, they can better understand challenges, refine solutions, and make enterprise data more impactful. 

Lastly, cost management remains a top priority. While cloud technologies provide unmatched scalability, they also introduce the risk of rising costs if not properly managed. Enterprises must strike a balance between leveraging cloud flexibility and maintaining cost control by optimising storage, processing, and data usage. 

Mark, why Snap Analytics? 

What sets Snap apart is our ability to deliver high-quality solutions that drive real business impact. While technical expertise is at the core of what we do, our approach is always business-first, ensuring that every solution we develop has a tangible, measurable outcome for our customers. Data and analytics should not exist in isolation, they should directly support business strategy, streamline operations, and enable smarter decision-making. That is exactly what we focus on. 

But more than that, we foster a collaborative, open partnership with our customers. Every project is built on trust, transparency, and shared expertise, making Snap a long-term, strategic partner rather than just a service provider. 

Real-Time Data Is the Revolution We’ve Been Waiting For

Posted on by Luka Jasionyte

Luka Jasionyte, from the marketing team at Snap Analytics, catches up with Jan Van Ansem, Co-founder and Head of SAP at Snap Analytics, to get the lowdown on his journey through data and analytics, and to share insights on the transformative power of Generative AI, the shift towards real-time data warehousing, and strategies for overcoming enterprise data integration and governance challenges.

What inspired you to get into data and analytics, Jan? 

My passion for coding started early – I was just 12 when I bought my first home computer, eager to dive into programming with Basic. That initial curiosity soon turned into a career in software development, where I honed my skills in building applications and systems. At the time, data & analytics wasn’t recognised as a distinct field; rather, it was an underlying component of software engineering and database management. 

The landscape shifted dramatically when Ralph Kimball published The Data Warehouse Toolkit, introducing methodologies that sparked widespread discussions about data modelling and architecture. This led me to explore the contrasting philosophies of Kimball vs. Inmon, each offering unique approaches to structuring enterprise data. That debate ignited a deep interest in understanding how businesses could best harness their data and analytics to drive decision-making. 

Since then, I’ve remained captivated by the challenge of designing optimal enterprise data models, ones that not only store information efficiently but also empower users with actionable insights. Whether it’s crafting intuitive data warehouses, refining business intelligence strategies, or integrating modern analytics tools, I’ve always been driven by the pursuit of solutions that transform raw data into meaningful knowledge. 

What is the most exciting trend right now?

AI is the obvious one. It’s completely reshaping how we work and live. From automation to predictive analytics, personalised experiences, and smarter decision-making, it’s changing the game across industries. And the exciting part? It’s not some distant futuristic concept anymore. AI is already making businesses more efficient and innovative every day. 

But for me, the real breakthrough is something that has been talked about forever but never fully realised at an enterprise data scale. Real-time data warehouses. For years, this has been the holy grail of data warehousing. Something that’s always been promised but never quite delivered in a way that works seamlessly for large businesses. The problem has always been the reliance on batch processing, which means companies are stuck making decisions based on outdated data instead of seeing what’s happening right now. 

Now though, we’re finally seeing real-time analytics become a reality. Thanks to advancements in cloud computing, AI-powered data processing, and cutting-edge architectures, enterprises can move beyond traditional reporting and actually act on insights as they happen. Whether it’s fraud detection in finance, predicting stock levels in retail, or optimising supply chains in real time, these innovations are making data warehouses more powerful than ever. 

We’re not quite at full-scale adoption yet, but we’re closer than ever. And soon, real-time enterprise data won’t just be a competitive edge. It’ll be the standard for businesses that want to stay ahead. 

What are some common data-related challenges that large enterprises typically face?

One of the biggest challenges is integrating missing pieces of data quickly. Despite our best efforts, business users almost always require more detail than what is readily available in a data warehouse. Closing the gap from 90 per cent complete to 100 per cent often demands disproportionate effort, consuming significant time and resources. 

A major reason for this difficulty is that enterprise data is typically spread across multiple platforms, legacy systems, and departmental silos, making it difficult to locate and consolidate. Additionally, data governance policies and security restrictions can further delay the process, adding layers of complexity when accessing specific datasets. 

More agile tools and processes, along with improved visibility of where data resides, can help enterprises tackle this issue more effectively. Solutions such as real-time data integration, AI-driven analytics, and self-service data platforms are making it easier for business users to retrieve insights on demand without needing to rely entirely on IT teams. As technology progresses, organisations that prioritise agility and seamless data access will gain a competitive advantage in leveraging their information for strategic decision-making. 

What are the top client priorities for those looking to drive successful outcomes in data and analytics? 

One of the biggest priorities for clients is improving customer engagement, understanding what truly drives customer behaviour through data. Businesses don’t just want surface-level insights; they need a deep understanding of how customers interact with their products, services, and digital channels. Customers now expect personalised experiences, but delivering them at scale is only possible with smart enterprise data systems that can identify patterns and preferences in real time. Whether it’s AI-driven recommendations, predictive analytics, or automated insights, businesses are looking for ways to refine their offerings and strengthen customer relationships. 

A major challenge is connecting scattered data sources. Many organisations struggle with fragmented data spread across multiple platforms, making it difficult to get a complete picture of customer behaviour. By integrating data from different systems and applying machine learning models, companies can go from reactive decision-making to proactive, tailored engagement. 

Jan, why Snap Analytics? 

At Snap, delivering great solutions isn’t just about technical expertise, it’s about teamwork, learning, and making a real impact. Our consultants work closely with customers’ teams, sharing knowledge and refining strategies to create the best possible outcomes. 

We also have strong relationships with vendors, helping shape product roadmaps and drive innovation that benefits our clients. But beyond the work itself, what really sets us apart is the people. We have a talented, forward-thinking team that’s not only skilled but also genuinely passionate about problem-solving. And most importantly, we create an environment where people actually enjoy working together, making Snap a great place to collaborate and grow. 

SAP and Snowflake Unite: Zero-Copy Data Sharing Transforms the Business Data Fabric 

Posted on by Jorel Digman

The enterprise data landscape has reached an inflection point. For decades, organisations have grappled with a fundamental tension: SAP systems hold their most critical business data, yet extracting that data for advanced analytics has meant choosing between preserving semantic richness and achieving cloud-scale performance. SAP and Snowflake’s newly announced partnership fundamentally changes this equation by enabling organisations to seamlessly leverage Snowflake’s AI Data Cloud and SAP Business Data Cloud with semantically rich data through zero-copy sharing. 

The Hidden Tax on SAP Data 

Every organisation running SAP knows the story. Your most valuable business data (financials, supply chain operations, customer relationships) lives in SAP. But when you want to combine that data with external sources, build advanced analytics, or train machine learning models, you face a painful reality: traditional integration approaches strip away the business context that makes SAP data valuable in the first place. 

For many customers, integrating data across multi-cloud and hybrid environments adds complexity, especially when bringing transactional and analytical workloads together, and that process comes with a hidden data tax: it strips away the business context and semantics that give data its meaning. 

This isn’t just a technical inconvenience. When you lose SAP’s semantic layer (the carefully defined business logic, KPIs, and relationships that took years to build), your analytics teams spend months reconstructing it. Different business units develop their own versions of truth. Data governance becomes a nightmare. And the promise of AI-driven insights remains perpetually out of reach. 

Two Products, One Unified Vision 

The partnership introduces two complementary offerings: SAP Snowflake, a solution extension for SAP Business Data Cloud that brings Snowflake’s data and AI platform directly to SAP BDC customers, and SAP Business Data Cloud Connect for Snowflake, which enables bidirectional, zero-copy data sharing with existing Snowflake instances. 

SAP Snowflake: Extending SAP BDC with Cloud-Scale Analytics 

SAP Snowflake unites SAP’s deep expertise in mission-critical business processes and semantically rich data with Snowflake’s unified platform capabilities for building AI and machine learning solutions. For organisations already invested in SAP BDC, this means gaining access to Snowflake’s full ecosystem (advanced analytics, AI capabilities through Cortex, the Snowflake Marketplace, and data collaboration features) without sacrificing the semantic richness of their SAP data products. 

The architecture enables something previously difficult to achieve: customers can harmonise SAP and non-SAP data while optimising total cost of ownership across workloads and build agents and AI applications fueled by trusted SAP data products. 

SAP BDC Connect: Meeting Customers Where They Are 

Many enterprises have already made significant investments in Snowflake. For these organisations, SAP BDC Connect for Snowflake enables integration of existing Snowflake instances with SAP Business Data Cloud for more seamless, zero-copy access, providing Snowflake users with real-time access to semantically rich SAP data products without duplication. 

This is where the partnership shows its strategic sophistication. Rather than forcing a rip-and-replace approach, SAP recognises that customers have diverse technology stacks and provides a path forward that respects existing investments. 

Why Zero-Copy Matters 

The term “zero-copy” might sound like technical jargon, but it represents a fundamental shift in how enterprise data integration works. Traditional approaches require copying data from SAP into Snowflake, creating multiple versions of truth, introducing latency, and multiplying storage costs. Worse, they break the connection to SAP’s semantic layer. 

The integration happens in near real time through a zero-copy connection, allowing organisations to build AI applications that combine SAP and non-SAP data while maintaining unified governance and performance. This means your data teams can work with live SAP data products in Snowflake, complete with all the business context and definitions SAP has built up over decades. 

What This Means for Your Data Architecture 

If you’re running SAP and contemplating your analytics strategy, this partnership addresses several critical pain points: 

Semantic Preservation: Your SAP data products arrive in Snowflake with their business meaning intact. The KPIs, relationships, and definitions your organisation has invested years developing don’t need to be rebuilt. 

Real-Time Access: Near real-time data availability means your analytics, reports, and AI models work with current business data, not yesterday’s snapshot. 

Unified Governance: A single governance framework spans both platforms. You define access controls, data quality rules, and compliance policies once, and they apply across your integrated environment. 

Flexibility and Choice: Whether you’re starting fresh with SAP BDC or have years of investment in Snowflake, there’s a path forward that respects your current architecture. 

Cost Optimisation: Zero-copy sharing eliminates redundant storage costs and the compute overhead of traditional ETL processes. You can right-size your analytics infrastructure based on actual usage patterns. 

The Competitive Context 

This partnership follows integrations with Databricks in February 2025 and Google Cloud Platform BigQuery, making it the third such partnership SAP has announced in recent months. The pattern is clear: SAP is building an open data ecosystem where customers can choose the best tools for their specific use cases while maintaining the integrity of their SAP data. 

Industry analysts have taken note. Scott Bickley, an advisory fellow at Info-Tech Research Group, observed that Snowflake was the missing link SAP needed to enable bi-directional, zero-copy data sharing with non-SAP data sources. Sanchit Vir Gogia, chief analyst and CEO of Greyhound Research, noted this partnership feels less like a technical upgrade and more like SAP finally recognising how its customers actually work. 

Looking Ahead 

SAP Snowflake is planned to be generally available in Q1 this year, with SAP BDC Connect for Snowflake expected in H1 2026. These timelines give organisations the runway to assess how this partnership fits their data strategy and begin planning for integration. 

The implications extend beyond immediate technical capabilities. This partnership signals a broader shift in enterprise data architecture, away from monolithic, vendor-locked systems and toward flexible, semantically rich data fabrics that can adapt to changing business needs while preserving the institutional knowledge embedded in business data definitions. 

How Snap Analytics Can Help 

Snap Analytics was born from the SAP data and analytics space. Our founders all came from SAP-specialist Bluefin, and we’ve spent years helping organisations navigate the complex intersection of SAP data, ecosystem data, next generation data teams, and cloud analytics platforms. The SAP-Snowflake partnership creates new opportunities to unlock value from your SAP investments but realising that value requires careful planning and execution.

Complimentary 1-Hour SAP BDC Roadmap Session 

Snap Analytics is offering a complimentary 1-hour roadmap workshop to help you explore your SAP BDC options and chart your path forward in 2026. During this session, our experts will work with your team to: 

  • Evaluate your current SAP and analytics landscape 
  • Review SAP BDC integration options (SAP Snowflake vs. SAP BDC Connect) 
  • Identify quick wins and strategic priorities 
  • Develop a phased implementation roadmap aligned with the current 2026 availability timelines 
  • Assess resource requirements and budget considerations 

This is an ideal opportunity to understand how the SAP-Snowflake partnership fits your organisation’s specific needs now that general availability is approaching.  

Our Full Range of Services 

Beyond the roadmap session, we can help you: 

  • Assess Your Current State: Evaluate your existing SAP and analytics architecture to identify opportunities for optimisation through the SAP-Snowflake integration. 
  • Design Your Target Architecture: Develop a blueprint that leverages SAP BDC and Snowflake while preserving your semantic models and governance frameworks. 
  • Execute Strategic Migrations: Implement the integration with minimal disruption to ongoing operations, ensuring data quality and business continuity throughout the process. 
  • Optimise for Performance and Cost: Right-size your infrastructure, configure zero-copy sharing for optimal performance, and implement cost management strategies. 
  • Enable Advanced Analytics and AI: Help your teams leverage the combined capabilities of SAP BDC and Snowflake for sophisticated analytics, machine learning, and AI-driven insights. 

The convergence of SAP’s mission-critical business applications with Snowflake’s cloud-native data platform represents more than a technical integration. It’s an opportunity to fundamentally rethink how your organisation leverages data for competitive advantage. 

Conclusion 

The SAP and Snowflake partnership addresses a challenge that has frustrated enterprise data architects for years: how to preserve the semantic richness of SAP data while achieving cloud-scale analytics performance. Through zero-copy data sharing and bidirectional integration, organisations can finally have both. 

Whether you’re already running SAP BDC or have significant investments in Snowflake, this partnership offers a pragmatic path forward that respects your existing architecture while opening new possibilities for advanced analytics and AI. 

The question isn’t whether your SAP data should power your next generation of analytics and AI initiatives. The question is how quickly you can architect an integration that preserves your business context while delivering cloud-scale capabilities. With the SAP-Snowflake partnership, that timeline is now dramatically shorter. 

Ready to explore how the SAP-Snowflake partnership can transform your data architecture?

Contact Snap Analytics today to schedule your complimentary 1-hour SAP BDC roadmap session and develop a strategy for success in 2026.

Book your discovery call now.

Primary Sources

Primary Sources on the Partnership: 

  1. SAP News Center – Official announcement of the SAP and Snowflake partnership, detailing the two product offerings (SAP Snowflake and SAP BDC Connect for Snowflake), zero-copy data sharing capabilities, and planned availability timelines. 
  1. Snowflake Blog – Partnership announcement covering the integration details, technical architecture, and the vision for combining SAP’s semantic data models with Snowflake’s AI Data Cloud capabilities. 
  1. CIO Magazine – “SAP and Snowflake add zero-copy sharing between their systems” – Comprehensive coverage of the partnership announcement at SAP TechEd Berlin, including executive quotes from Irfan Khan (SAP’s data and analytics president and COO) on preserving semantics and high-fidelity data exchange, Christian Kleinerman (EVP of Product at Snowflake), and technical details on the differences between SAP Snowflake and SAP Databricks CIO. 
  1. AstraZeneca Customer Quote – Russell Smith, Vice President of ERP Transformation Technology at AstraZeneca, provided commentary on the value of real-time data access and AI capabilities enabled by the partnership CIO. 

Industry Analyst Perspectives: 

  1. Info-Tech Research Group – Scott Bickley, Advisory Fellow, commented on Snowflake being the missing link for SAP’s bi-directional, zero-copy data sharing strategy CIO. 
  1. Greyhound Research – Sanchit Vir Gogia, Chief Analyst and CEO, provided analysis noting the partnership “feels less like a technical upgrade and more like SAP finally recognizing how its customers actually work” CIO. 
  1. Moor Insights & Strategy – Robert Kramer, VP and Principal Analyst, discussed how the joint solution preserves contextual meaning and maintains governance controls while shifting the relationship from informal integration to formal alignment CIO. 

Context on Related Partnerships: 

  1. Previous SAP Integrations – References to SAP’s earlier partnerships with Databricks (February 2024) and Google Cloud Platform BigQuery, establishing the pattern of SAP building an open data ecosystem CIO. 

The Big 3 Data Problems Holding Enterprises Back

Posted on by Luka Jasionyte

Luka Jasionyte, in the marketing team at Snap Analytics, catches up with Deepam Biswas, our Head of Technology and Delivery in the India office, to get the low down on his journey through data and analytics, and to share insights on what he thinks are the most important trends, Generative AI in business, and challenges facing enterprise businesses today. 

What inspired you to get into data and analytics, Deepam? 

Data has always fascinated me, not just as numbers, but as the foundation of every decision, strategy, and transformation. What truly drew me into the field was the recognition that data is only as valuable as its accuracy, structure, and data governance. Without trust in the data, businesses can’t make informed decisions, drive efficiency, or unlock meaningful insights. My curiosity for uncovering patterns, validating assumptions, and refining raw information into something truly usable has been a constant throughout my journey. 

What is the most exciting trend right now? 

In my opinion, the most exciting trend in data and analytics right now is the integration of Generative AI in business. With Generative AI, we can leverage natural language to both analyse data and generate actionable, prescriptive strategies. This means that we can ask complex questions in plain language and receive insightful, data-driven responses that guide decision-making.

Additionally, modern BI tools have evolved to automatically identify patterns, anomalies, and correlations in data that might be missed by human analysts. These tools present insights in an easily digestible format, making it simpler for businesses to understand and act upon the data. Another fascinating development is edge computing, which allows for the processing of sensor data almost in near real-time. This capability enhances efficiencies in business processes such as warehouse management and production management by providing timely insights that can significantly improve operations. Overall, these advancements are pushing the boundaries of what we can achieve with data and analytics, making it an incredibly exciting field to be a part of. 

What are some common data-related challenges that large enterprises typically face? 

One of the most significant challenges I’ve seen in large enterprises is dealing with data silos. Data is often scattered across various systems like CRM, OMS, Billing & Invoicing, and legacy systems, making it incredibly difficult to get a comprehensive 360-degree view for data analytics. This fragmentation can lead to inefficiencies and missed opportunities for insights. Another major issue is the persistent skill shortage in the data field. There’s a high demand for skilled professionals such as data engineers, data scientists, data analysts, and data governance specialists, but the supply just can’t keep up. This gap can hinder the ability of enterprises to fully leverage their data. Additionally, many large enterprises still rely heavily on batch processing of data, which results in considerable latency in generating insights. This often forces upper management to make business decisions based on outdated data and gut feeling, rather than real-time analytics.

Near real-time data analytics is still complex and costly, but it’s crucial for making timely and informed decisions. Generative AI in business is helping bridge this gap by automating data analysis and providing real-time insights without requiring deep technical expertise, enabling enterprises to make smarter, faster decisions. These challenges can significantly impact the efficiency and effectiveness of data-driven strategies in large enterprises. 

What are the top client priorities for those looking to drive successful outcomes in data and analytics? 

When it comes to driving successful outcomes in data and analytics, clients have some top priorities that are pretty clear. First and foremost, they want to know how this data is going to make them more money or save them money. It’s all about ROI and tangible business values. It’s no longer enough to just get reports or dashboards that tell them what happened. Clients want to understand why it happened and, more importantly, what they should do next. They seek prescriptive analytics that guide decision-making. And let’s not forget about protecting sensitive data. With the growing number of data privacy regulations like GDPR and CCPA, adhering to these rules is non-negotiable. It’s all about making sure their data is secure while still being able to leverage it for business success. 

Deepam, why Snap Analytics? 

Snap Analytics has extensive experience with diverse and complex client projects across industries such as FMCG, Finance, and Supply Chain. This means clients benefit from the exposure to a wide range of real-world business problems and data challenges, and we provide effective solutions to tackle them. Our core mission is to help clients ‘make sense of data’, by connecting their data, technology, and teams to drive more effective decision-making. This approach ensures we deliver tangible business value, not just technical solutions. 

Also, we proudly work with the most progressive technologies and leading cloud vendors like Snowflake, Databricks, and Matillion, providing clients with hands-on experience with in-demand tools and platforms. I would say that one of our key differentiators is our specialisation in SAP Data. We excel in connecting complex SAP landscapes to modern cloud data platforms, providing seamless and efficient data integration.