{ "media_type": "text", "post_content": "Data strategy question:\n\nMy Fintech organization has 2 banking products, both of which have similar features, but one caters to smaller banks and credit unions while the other focuses on the top 100 banks in the US. The product and engineering teams for both these products operate independently and rarely use any common technology vendors (except AWS, our common infra partner).\n\nDoes it make sense to have a common data strategy and a single data lake across this products? What are the benefits?", "post_id": "612e3bb5aaa99f002b0a6a32", "reply_count": 2, "vote_count": 2, "bowl_id": "55375ce690f5eebe1d2a0f88", "bowl_name": "Tech" }

Data strategy question: My Fintech organization has 2 banking products, both of which have similar features, but one caters to smaller banks and credit unions while the other focuses on the top 100 banks in the US. The product and engineering teams for both these products operate independently and rarely use any common technology vendors (except AWS, our common infra partner). Does it make sense to have a common data strategy and a single data lake across this products? What are the benefits?

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I would say so, but then again you have to consider would it be worth putting everyone through the implementation to get aligned and would it have any measurable impact on the product. Are there learnings that would be substantially valuable that would come from aligning?

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You are right. That's a factor I am definitely considering. It would be great to gain insights in areas like transaction data and customer behavior from clients across both these products. But, to get there we'd need a common data dictionary, processes and a firm overall strategy. The effort to get all of this in place may outweigh the benefits of those overall insights.

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