Data-Driven Dynamics: What's new?
What are the specific challenges organizations face in transitioning to more agile data architecture? How can organizations effectively manage their data product portfolios and ensure their data is trustworthy? What are some successful collaborations between industry players in driving innovation in the data-driven era?
In today's rapidly evolving digital landscape, data-driven organizations stand at the forefront of innovation. They leverage data not just as a resource but as a strategic asset, fueling applications, artificial intelligence (AI), and meticulous planning. As a result, many organizations face the challenge of managing 10K data pipelines and 40K tables over multiple databases while leveraging 10+ vendor solutions.?
That said, as the world evolves, the actual challenges stay the same. The innovation lies in the solutions, making it easier for practitioners with different skills to take advantage of the systems. There is something for everyone, from experienced Java and Python engineers to data analysts.
The journey from raw data to actionable insights is the same. It includes sourcing, extracting, and reconstructing data. Sounds simple. Well, not exactly. Today, systems are still fragmented across various siloed environments.
So, how can businesses contextualize and trust their data?
You might know this already, but at the heart of every data-driven endeavor lies the need for context and trusted data. Businesses recognizing more than ever the importance of metadata and the broader business context in making data valuable. But how do you make sense of all those different tables over an extreme volume of data processing pipelines? This can be scary.
Well. Actually, the bedrock of innovation is a proper context. AKA. Knowledge graph. With a knowledge graph and a semantic layer, you can finally know where to look and what to look for. Not so scary anymore, ha??
And here again, AI's promise looms large, aiming to revolutionize productivity and core business functions. How about leveraging AI to make sense of those 40K tables? Provide context and help us better surface insights? If you listen closely, you will learn that the traditional business operating model is gradually making its way for machine learning algorithms, statistics, and AI. An in-analytics DWH copilot is a repeatable product feature available in many, from Databricks solutions to others revolutionizing this space.?
Today, SAP had an open virtual event, SAP Data Unleashed where they discussed their latest innovations for the business data fabric architecture,? such as the semantic layer, knowledge graph, and SAP generative AI assistant Joule . Your copilot for your data and analytics. They also shared their proven success with some large enterprises and how those enable organizations to bridge the gap between departments and leverage data beyond their immediate organization. For example, I learned how a company finance team, which is responsible for the company's financial planning, benefits greatly from bridging the gap between financial and market planning. Two different data silos that, when bridged and analyzed together, can enable them to build a better plan that captures the market dynamics. This would be more accurate, timely, and specific, with a better chance of delivering the desired impact.??
If you think about it, it underscores the need for better data infrastructure and showcases that companies want to manage their data product portfolios effectively. From understanding data assets to navigating their lifecycle, the key lies in putting in place robust control mechanisms, copilot systems, and guardrails for success. here is a glimpse into the upgraded Data and Analytics world:
We are not done; LLMs paved the way for VectoreDBs. This further catalyzes the need for new capabilities in data infrastructure. As SAP mentioned in their event, their customers actively explore VectorDB capabilities such as search and similarities techniques to improve their data analysis and data-driven decision-making, which SAP offers out of the box.
Solving one problem opens the door to a new one. Unstructured and streaming data present novel complexities, necessitating sophisticated semantic models and real-time processing capabilities. The transition from legacy data platforms to more agile data space requires careful planning and execution, spanning years and touching every facet of the business. This is where innovation comes into play. Confluent is the leading company of data in motion, enabling many customers worldwide to benefit from highly accurate, real-time data at an extremely low latency. In partnering with SAP, they enable fresh, highly accurate data.
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You’d probably wonder, how those changes impact engineers today?
Practitioners need to keep developing their skills. You are not just responsible for setting up data systems anymore, but also for making sure data flows smoothly and is discoverable between departments. It's less about tweaking the JVM and squeezing yet another processing cycle. It is about improving data pipelines, connecting systems while managing cost, and building an agile, evolving data model.
Overall, the roadmap to becoming a truly data-driven organization is fraught with challenges but brimming with possibilities. Will companies be successful? Did the industry bridge the gap? Are there new mountains to climb? Is LLM truly the new revenue/ productivity engine for organizations?
Continue following. There is a lot of competition for greatness.
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Adi?
Disclaimer: The article was written in collaboration with the SAP team.