Tableau in the World of Business Intelligence

Over the last 15 years, a lot has changed and a lot has stayed the same in the world of analytics.

I’m going to show my age a bit here. It was 2005 and I had recently been promoted to Director of Analytics at my company. I had two teams. The first was a business intelligence (BI) team tasked with serving the business with traditional operational reporting, ad-hoc analysis, and business forecasting. The second was an “operations research” team–essentially a modern data science team.

The BI team (which we had coined the Business Consulting Team to better capture its full remit) had quite a few stakeholders for reports and dashboards, and we had recently upgraded from SAS v6 to SAS v9. With these changes, we were looking at governance, standardization, automation, building trust in data, and determining what we needed to keep doing while balancing new requests.

Meanwhile, my data science team was focused on improving operational forecasting models, enhancing algorithms for dynamic pricing, and also identifying use cases to prove out business value so that we could balance short term wins while studying more complex problems that required a longer time horizon.

We’ve seen the big data revolution, faster processors, cheaper storage, migration to the cloud, and software vendors rise and fall. As more data has become available and technology more accessible, we’ve seen more data analysts and a bigger investment in data science. However, the challenges of data engineering and establishing trust with end users continue. Companies still struggle trying to find the right balance between governance and empowerment.

Through all these changes (and constants), there is one application that clearly stands out as the winner of the last 15 years: Tableau.

Tableau has turned the world of analytics on its head with their mission to help people see and understand data. The days of IT teams (or the rare “analytics teams” of 2005) controlling reporting are gone. In fact, they were really always a myth. Business teams have found ways to do their own data analysis and reporting since the days of the green bar report. It’s just become easier and cheaper for them to do so. While many data-driven business users still use Microsoft Excel for most of their data and business analysis (from reports and dashboards to executive decks and ad-hoc analysis), Tableau has emerged as an “Excel-on-steroids” that empowers business users to take their reporting and analysis to new frontiers.

But, most of us know the Peter Parker principle: “With great power comes great responsibility.” Tableau has given great power to business users, yet the questions of governance still remain. It is a shared enterprise responsibility to maintain governance while applications like Tableau have placed massive “data power” in the hands of a wider set of users.

The same principles that applied in 2005 still apply today when it comes to data governance. Our BI team was rapidly expanding its internal customer base for reporting and insights. Our data science team was exploring new use cases and unlocking silos of data to provide new solutions to complex business problems. But, we weren’t in IT; we were business users. It became critical that we needed to establish a joint partnership to tackle data governance. In the same manner today, it is important that a holistic approach is taken across all stakeholder groups to continuously evolve data governance strategy for the business.

Tableau provides excellent tools to put structure around your data strategy. Data can be curated and made available to the entire business with proper master data management (MDM) and definitions. This creates a single source of truth and builds trust in data and metrics across teams. We recommend that these processes be centralized and governed with strong change control processes. While the execution of this process should likely be centralized, it is recommended that a cross-functional team of key stakeholders and power users have a voice in establishing the infrastructure.

From an end-user perspective, Tableau can meet the needs of many personas. Here, de-centralization is important to help unlock the power of data across the organization. Power users should be empowered to author Tableau Projects and administer Sites. At enterprise scale, we’d recommend that each Site owned by a business team has an associated champion and support framework provided by the centralized team (usually in IT). It is important that this is a partnership that helps empower and not a layer of bureaucracy that stagnates data innovation.

We appreciate that every organization is different. As you think about where you are at in your journey with Tableau, it is important to understand the history of data governance in your organization, the culture of teams (silos, matrixed, politics), and your goals for BI and analytics. There is not a one-size fits all approach. SpringML brings team members with decades of experience and a customer base of hundreds that can help you design and optimize the next steps of your Tableau journey.

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