The Next Chapter of Enterprise Business Intelligence with Looker

Google Cloud and SquareShift are convening a session later this month to discuss a question many enterprises are now facing, how to modernize analytics without introducing unnecessary complexity or risk.

Migration from established BI platforms is rarely straightforward. Years of embedded business logic, diverse stakeholder requirements, and the need for uninterrupted reporting all make the process a matter of strategic importance, not simply technical execution.

This article provides the broader context for that discussion. It explores the challenges companies typically encounter in fragmented analytics environments, explains why Google designed Looker to resolve these issues at the semantic layer, and outlines how structured migration approaches can help organizations modernize without losing years of accumulated knowledge.

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After engaging with dozens of companies on their data problems, we've learnt something important.

Having more data doesn't make you smarter. What matters is having data you can trust and understand.

Most companies today are drowning in spreadsheets and reports that don't match up. Their data teams spend half their time just trying to figure out why the sales numbers in one dashboard don't match the numbers in another. Meanwhile, executives are making million-dollar decisions based on reports they're not sure they can trust. This isn't a technology problem. It's a strategy problem that touches every part of your business.

Why Google Built Looker

The business world moves fast now. Companies need more than just reports about what happened last quarter. They need data platforms that help them see what's coming next and act on it quickly.

That's why Google built Looker around something called LookML. This approach addresses a longstanding issue: when business leaders ask questions such as “What is our customer acquisition cost?” or “How many active users do we have?”,

The answer is derived from a single, consistent definition. The implication is significant. Analysts developing models and executives reviewing reports are aligned on the same numbers, which reduces time spent debating metrics and increases the focus on decisions.

What This Means in Practice

Independent analysis by Forrester has highlighted the impact of using Looker in combination with BigQuery. The study found that companies realized $2.05 in return for every dollar spent and typically reached break-even in less than six months.

The benefits occurred at several levels. Data teams reduced the amount of time dedicated to cleaning and reconciling inconsistent data, which led to an estimated $1.9 million in savings over three years. Business functions gained the ability to generate their own reports without depending on IT support, improving productivity by more than $6 million over the same period.

The study also observed broader commercial benefits. Companies using Looker and BigQuery reported a 3.75 percent increase in sales and $6.1 million in additional profit, largely because they could identify customer behavior patterns more quickly and reduce churn. These outcomes suggest that the value extends well beyond operational efficiency to measurable growth.

The Migration Challenge

Despite the benefits, migration remains a significant barrier. Existing BI platforms contain years of accumulated business logic embedded within reports and dashboards. Many of these assets are mission-critical, and organizations are understandably cautious about replicating them in a new environment.

Migration projects often encounter difficulties because they attempt to rebuild assets manually, a process that is time-consuming, costly, and prone to error. It is not unusual for such efforts to extend over many months, with limited success.

To address this, specialized migration accelerators have been developed to convert workbooks into LookML automatically. These tools can translate 60 to 80 percent of business logic programmatically, validate outputs against the original reports, and ensure accuracy levels as high as 99.9 percent. This reduces both the time and the risk involved, enabling organizations to move from planning to production in weeks rather than months.

Why This Matters Now

Your competitors are already doing this. 

The companies that move first get a real advantage.

They can test new products faster, spot market changes earlier, and respond to customer needs before their competitors even know what's happening.

This isn't just about having better charts and graphs. It's about building a data foundation that can grow with your business and give you the insights you need to stay ahead.

Why the Issue Is Timely

Competitive pressure reinforces the need to modernize analytics environments. Companies that adapt quickly gain the ability to test new products, detect shifts in market behavior earlier, and respond to customer needs more effectively. Those that delay often find themselves at a disadvantage that is difficult to recover.

The transition to platforms such as Looker is not solely about improved visualization. It represents the establishment of a trusted data foundation that enables consistent decision-making across the enterprise. Organizations that invest in this foundation position themselves to compete more effectively in increasingly dynamic markets.

What Happens Next

We're running a private session with our partners at Google Cloud to show exactly how this migration process works. We'll walk through the roadmap, show you how to avoid the common pitfalls, and demonstrate the tools we use to make sure nothing gets lost in translation.

If you're serious about getting more value from your data, this is where you start.

The companies that figure this out first will have an edge that's hard to catch up to.

Your data should work for you, not against you.

Let us show you how to make that happen.

Click here to Register

Conclusion

Enterprise analytics is moving beyond isolated dashboards and fragmented reports toward a model built on shared definitions, consistent data, and scalable platforms. The experience of companies that have adopted Looker demonstrates that this approach not only reduces inefficiencies but also supports measurable business growth.

For organizations considering migration, success depends on approaching the process methodically: understanding the limitations of current systems, leveraging automated conversion tools where appropriate, and validating results to build trust from day one. The evidence suggests that those who make this transition effectively are better positioned to realize the full potential of their data assets.