Is your Data Platform Sabotaging your AI Dreams?

Data teams are under immense pressure. Cost scrutiny is at an all-time high, and the value proposition of data & analytics in driving decision-making can be challenging to quantify in terms of ROI.

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Here's the kicker: the data landscape is incredibly fragmented, with over 500 vendors across 30+ categories. This forces companies to pre-select vendors and cobble together a platform. This doesn't help keep costs down or move quickly.

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The problem: Traditional "data readiness"—centralizing, cleaning, modeling, and structuring data for business use—was sufficient for BI, reporting, and basic data analytics. However, in the age of AI Data, data readiness takes on a whole new meaning.

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Why data readiness matters more than ever: Data readiness is now the cornerstone for powering the entire Gen AI and LLM world. Simply put, you can't have a successful AI strategy without a data strategy. If you don't understand your data, neither will your AI.

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So, how do you overcome this challenge and ensure your data platform is ready to support your AI ambitions? By making your data AI-ready!

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6-step process to get your data AI-ready

1. Automated Ingestion

Businesses of all sizes struggle with a growing mess of data – from internal apps to customer interactions. This scattered data makes it hard to collect and use effectively. That's why automating data ingestion is crucial. Here's why: manually handling all this data becomes a bottleneck as more sources pop up. Automation ensures everything runs smoothly and your AI projects get the data they need, on time.

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2. Engagement Data

For businesses with highly engaging products, there's another data goldmine: usage data! Product analytics or event trackers capture user behavior and store it in a central source. This data is super valuable for AI that personalizes experiences or predicts user behavior.

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3. Data Modeling

Using raw data is a bad idea. Its format can change unexpectedly, and it wasn't built for the business in the first place. That's where data modeling comes in. Clean, structure, and model your data so it's reliable and answers your specific business questions.

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4. Metrics & Iterations

Once your data is organized, it's time to define what success looks like. Set up a feedback loop with the business stakeholders. Reports for the right stakeholders validate that the metrics defined accurately reflect how the business is looking at them.

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5. Semantic Layer

Business stakeholders often lack the technical expertise to navigate complex data reports. A semantic layer can come in handy to standardize metrics definitions. It translates data into easily understandable terms, bridging the gap between technical data and business understanding.

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6. Business Process

Invest in a business process to maintain your golden data – a central data team to manage core data sets for consistency across the org and decentralized agile teams that work with departments or product teams to give them the speed to move quickly. 

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Our next-generation data platform is designed for one thing: making "data readiness" ridiculously easy. Get started in clicks, seamlessly integrate with your existing tools, and scale as your needs grow.