Technology isn’t enough when it comes to harnessing your business data to set your organization on a path toward growth.

Anyone can spend the money needed to bring the latest in cloud-based storage, data warehousing, and analytics to your doorstep. But without much-needed structural changes to how your organization operates, your modernization efforts won’t be sustainable in the long-term – and can’t deliver the results your business needs.

To get the most out of your analytics team’s efforts, you need to provide an internal structure that allows them to best serve your business needs. In addition, you need to establish a centralized resource the ensure the stakeholders across your organization receive the support and training they need to allow a data-driven culture to take root in your business.

Go beyond technology and build a data organization for business success

Modernizing your business to capitalize on the value of its data is a monumental undertaking, especially from a technological standpoint. In addition to adapting to a cloud-based storage environment, your team faces an array of new and complex tools that aim to revolutionize how your organization functions. Though a complicated process, these new systems establish a foundation that’s capable of carrying your business in new directions and, ultimately, the future.

But adopting all the right technologies isn’t enough to deliver the right results from a digital transformation. The various platforms that store, manage, and analyze your data only enable the changes your business needs to stay competitive. You must also ensure your data is being consistently updated, maintained, and documented with the right training and change management protocols in place for all of your internal teams. That way, you gain much-needed assurance that the systems you implement will evolve and improve as your business grows.

To effectively shift how your business accesses and analyzes its data, you need to fundamentally change its internal structure as well. In addition to establishing an operating workflow for your analytics teams, you also have to determine how they will support the way your business functions. Plus, you have to establish how infrastructure needs like data science, data governance, and data engineering will support your analytics team (and vice versa).

Preparing your organization for growth through a holistic and sustainable digital transformation is a two-way street. Through effective management of how your teams function, collaborate, and communicate, your business will reach its destination.

An effective analytics workflow ensures access to your data

Establishing a modern data stack for your business is a complex, large-scale undertaking. It’s also expensive. Implementing changes to how your organization accesses and generates reports doesn’t just ensure you’re maximizing your data’s capabilities; it also protects your investment.

Stakeholders in marketing, finance, sales, and the C-suite are all counting on the insights an effective data program delivers. If structured properly, your new analytics department will deliver on that promise. Depending on how your organization is structured – and, crucially, its size – there are two choices for how to maximizes the capabilities of your data analysts: Centralized or matrixed.

A centralized analytics team streamlines communication – to a point

When businesses modernize their operation, the most common structural shift is establishing a centralized data team. If, for example, you hired 10 data analysts to form a centralized team, those 10 specialists would field project requests from your finance, marketing, and sales departments and deliver their results. Any question or communication about data issues travels to a single source toward a streamlined, centralized analytics team.

However, assigning one team to be responsible for your data requires a significant level of care. If one small team is responsible for retrieving data, generating reports, and resolving bugs within your organization, your entire organization will encounter delays from its data requests. As this single data bottleneck results in more lost time, your teams will lose confidence in your new data program and turn away from its use. In the process, your overwhelmed data team will also lose its most in-demand analysts to more rewarding jobs elsewhere.

To avoid creating a backlog, your teams must be empowered to generate their own insights through a business intelligence platform like Looker. DAS42 has experience implementing effective internal training and enablement programs and data governance practices. Once individuals in various teams recognize how their data is defined, they can pursue answers driven by their expertise. Then, your data team is free to focus on big-picture insights about your core business functions. By operating at the peak of their abilities, your analytics team delivers more growth opportunities for your organization.

The communication gains made possible through a centralized approach to your data also come at a cost, however. As requests arrive from across your organization, every analyst becomes a generalist with little opportunity to build a rapport with your stakeholders or their domains. As a result, there’s little room for in-depth, department-specific analysis that comes with consistent focus from a single data analyst.

If your business is comparatively small, even up to a few thousand employees, any loss in domain expertise is offset by the efficiency of a centralized data team. However, as your company scales, its data needs will expand in the process.

Venture deeper into the specifics of data with a matrixed team

If your organization has experience with software development processes, you may already be familiar with how a matrixed team functions. In this model, a representative from your analytics team joins every product area or department in your organization. Directly involved with the functions of these teams along with dedicated project managers, engineers, or developers, these analysts gain a thorough understanding of the issues and applications of your data as it applies to their team.

Expanding the scope of your analytics team into a matrixed model results in multiple, self-contained departments generating insights tailored to their specialized needs. In addition, these analytics specialists can approach any data issue or project with complete focus rather than also serving the needs of a centralized data department.

However, this model also introduces additional complexity. You must maintain communication systems to prevent any duplication of effort. Analysts embedded with your finance team could be working to resolve a data issue that their counterpart in sales also discovered. Plus, you will need to allow for some centralization to resolve outages and issues in your data infrastructure.

As your organization grows, the role of your analytics team also expands. To be most effective, your analytics specialists must also be engaged with the supporting functions your organization’s data requires as well.

Data science and data engineering must remain coupled with analytics

One of the core issues with establishing an organizational structure to support your data is a natural divide that often appears in its focus.

Typically, data engineering and optimizing the connections between databases is organized under IT teams. Data analytics is often viewed as part of an organization’s finance team. As a result, data analysts often present requests to IT teams with data engineering requirements for a specific use case. But without an analytics background, IT doesn’t truly understand how the data will be used. Then, once IT resolves their request, the analytics team doesn’t truly know the technical nuances behind how the data supply chain was used.

An effective organization can’t have these two critical pieces of your data puzzle divided and talking past one another. At DAS42, we know both sides of this equation. With a richly experienced mix of technical expertise and business intelligence understanding, we bridge the gap between your data’s infrastructure and the analytics needs of your business to prevent these communication breakdowns.

Clear the silos within your data to build trust and ownership

However your analytics team is structured, you must incorporate a process for effectively resolving the infrastructure needs of your data. Consequently, you’ll need an organizational layer responsible for maintaining the tools attached to your cloud-based storage service and a warehousing platform like Snowflake.

Instead of keeping data engineering skills under your IT group, we recommend establishing something more like a data platform team. Once implemented, these teams won’t have anything to do with the insights or reporting found within data systems. Instead, they maintain the system’s infrastructure and increase efficiency through building automated processes through DevOps so analysts have the tools they need. If something breaks, the data platform team fixes it. But the analytics team then takes greater ownership of the data engineering and data science that’s crucial to the reliability of their reports.

By gaining a greater understanding of the “last mile” between where your data is stored and its delivery to your insights dashboard, your analytics team becomes more involved with your data’s infrastructure. The people who present insights to your stakeholders must have a full understanding of your data supply chain. If your analytics team has complete trust in their reports that bring your business closer to its goals, so will you.

A center of excellence encourages data literacy across your business

The long-term success of your data program and transformation effort depends on establishing a data-driven culture. Creating a team that functions as a center of excellence provides a single resource to respond to data issues. Fielding enhancement requests, support questions, and bug fixes, a center of excellence establishes a lasting internal connection between your data program and your internal teams.

Once your organization has outgrown a centralized approach to its data, a center of excellence becomes that much more important. With analysts spread across every team, you must establish a source to determine and distribute standards for working with your data. Rather than a formal layer within your organization, this group is really a loosely formed team drawn from multiple stakeholder departments across your organization. A representative from each department should join the center of excellence, which ensures everyone’s department is invested and your data needs become a shared responsibility for the entire company.

This working group also provides training to employees with your various data platforms. Establishing an on-boarding process for new hires also ensures your organization is well-positioned for growth while providing user support. If someone has a question about how to use Looker, for instance, your center of excellence can answer it while still collaborating on new features and datasets that carry company-wide benefits.

Though formed in an unofficial capacity, this working group defines and documents best practices for working with your data stack. That way, if someone in your marketing team encounters an issue that someone in sales already encountered, no one has to reinvent the wheel to find a solution. A center of excellence allows for a cross-functional view of what’s going on with the analytics efforts within your organization.

DAS42 prepares your organization for a data-driven culture – and the future

Experienced with the technical and cultural changes of a digital transformation, DAS42 is your ideal choice to ensure your organization keeps up in a challenging, fast-paced environment. We’ll help you navigate the challenges of implementing a cloud-based platform that offers flexible storage and self-serve analytics. And with our organizational expertise, we can ensure your teams have the systems they need for long-term, sustainable success. Across every industry, we’ve provided businesses with what they need to grow into the future.

Modernizing your operation to accommodate this level of structural change demands a holistic, multi-disciplined approach. But it’s not one you need to try and resolve alone. Contact us when you’re ready to find out more.