Implementing Business Intelligence (BI)—performance-based data, often acquired through software—enables organizations to access constant reporting, meet reporting requirements faster and easier, and foster a broader culture of accountability and data-driven decision-making.
BI data can help you from an operational perspective, fundraising and revenue-hitting goals, and organizational efficiency. Develop and analyze prospects and closely measure your team’s performance with BI data—making decisions regarding things like Key Performance Indicators (KPI), budget forecasting, and other metrics-oriented decisions.
Advanced analytics is a complex undertaking that presents strategic opportunities but also data challenges. But it all starts with an effective business intelligence team. Here’s how to develop yours in five steps.
Step 1: Define Your Vision and Strategy
BI’s main benefit is to help make better-informed decisions supported with accurate data—helping to uncover new business opportunities, cut costs, remain compliant, hire more effectively, or identify inefficient processes that need reengineering.
BI can help make better decisions by showing up-to-date and historical data within the business context.
A few ways that BI can help organizations make smarter, more data-driven decisions include:
Identify ways to increase profit
Analyze customer behavior
Compare data with competitors
Recognize market trends
Discover issues or problems
Before you begin, you’ll need to define your vision and strategy. Too often, organizations dive into BI with high aspirations and little planning to get there.
To define your data roadmap, you’ll first need to identify things like:
Mission, vision, and competitive strategy
Goals for the data team to achieve
Infrastructure you need to accomplish
Resources to prioritize
Step 2: Structure Your Analytics Organization
Building your BI team requires structure that supports the vision and strategy you’ve defined. Misaligning your team structure can result in subpar results, lost revenue, and team burnout.
There are several ways to structure your team.
A centralized team structure is often independent of the Information Technology (IT) department in larger organizations. A centralized team may have its own budget and more autonomy. Frequently, the analytics team reports directly to an executive.
The main benefit of the centralized team structure is the autonomy it presents. The BI team can apply analytics and data as they deem necessary—providing value and support to your overall strategy.
On the contrary, the centralized team can become siloed and get buried in overly complicated processes—keeping them from being productive and impactful. Not to mention, everyone is accessing the same data sets which can result in a duplication of effort.
A decentralized team model functions similar to marketing a product. You hire a dedicated team member to create and control an internal team effectively. The leader of this team would need to be someone with skills in the hybrid domain and analytics.
The decentralized teams works independently of other teams, and priorities are decided by the C-suite. This structure is useful in the early stages of data science, providing “quick wins” in pilot projects—it often proves less risk.
This team structure is not effective unless an organization has robust data governance and master a data management model. Otherwise, people within different business units and functions may have conflicting information or require more time to verify analytics conclusions.
A hybrid team structure is a combination of a centralized and decentralized team. It functions similar to a centralized unit, except the team is placed under a business function—using analytics to drive strategic results.
In this structure, the team reports to the head of the department. Hybrid teams are useful for organizations with less mature analytics strategies or limited resources.
Step 3: Define Roles
After you’ve defined your vision, strategy, and structure, you can now identify individual skills and team roles needed to achieve these results.
A great place to start is leadership.
The Business Intelligence Director is a high level and expensive position—it is the most sought-after position in Advancement. This position directs and oversees high level strategic and tactical decisions for BI tools and applications—they are responsible for leading the design and maintenance.
After you’ve established leadership, create job roles that help fulfill the skills and resources you defined in your data vision and strategy. Avoid getting stuck on the right job title, but instead, focus on understanding the skills required and how you might leverage or complement those skills—whether through existing team members or outside vendors.
Step 4: Recruit and Assess
A successful data-driven organization isn’t derived solely from data, but from the people. Hiring the best resources is where the value from data is created within your organization.
You can choose to hire talent to fulfill an internal role or opt to partner with an outside vendor. From inception to operational stability, leveraging outside consultants to jumpstart your effort depends on the types of resources available.
If your needs are in advanced technology or machine learning, consider looking to outside vendors to fulfill your needs. Currently, this role is exceptionally specialized and could cost your organization upwards of $250,000 annually. In this instance, working with a vendor is more affordable than hiring someone internally.
Step 5: Develop Data Skills
Data skills should not remain siloed in the BI team if organizations hope to add value and create a competitive advantage from their data. Data democratization—enables the average end-user to evaluate data without requiring outside help—is essential for companies who embrace it, leveraging it to create a data-driven culture.
To ensure a data-driven culture, consider taking steps toward developing employee data skills.
Cross-pollination: allows team members with diverse and complementary skills to develop new ideas and concepts.
Cross-functional collaboration: similar to cross-pollination—directly related to organizational structure—and encourages team members to work closely together with the BI team.
Hybrid career paths: creates opportunities so employees can shift into other data related operations like management, digital marketing, or customer relationship management.
Provide professional development: offers monthly presentations from internal or external experts, inviting staff to attend data analytics conferences and workshops.
Building your BI team is a complex process, requiring a long-term view and a clearly identified mission and vision for your data goals. The ability to get data faster depends on your relationships with centralized resources—accelerating with the right hires, whether internal or external.