Business today thrives on data analytics and the crucial insights it brings, yet many organizations are still shackled by treating data analytics as supportive and secondary to their business initiatives. Some organizations have recognized the power of data and conceived data-driven business opportunities. Still, they lack a clear analytical strategy that inspires transformative thinking and are unable to orchestrate broad business achievements across departments, including in the office of finance.
But before any executive leaders tackle the problem head-on by throwing headcount or budget at the problem in the hope of narrowing the analytics divide, they should understand the following about running an effective analytics practice:
- It’s not about the dashboard; it’s about the decision.
- It’s not about turning everyone into a data scientist; it’s about asking smarter questions of the data.
- It’s not about a wall-to-wall advanced analytics technology platform; it’s about a prioritized, outcome-driven business strategy infused with data and analytics.
- It’s not about “working with IT”; it’s about connecting value streams across the organization.
Four Things to Consider to Begin Your Analytics Journey in the Office of Finance
In a holistic framework, strategists responsible for building out the data and analytics practices in the office of finance should prepare their organizations in the following four areas:
- Data-driven vision: Explain the importance of data analytics with a focus on business purposes.
- Stakeholders’ objectives: Focus data analytics on what stakeholders care the most about with specific use cases.
- Stakeholder value propositions: Manage stakeholder expectations so that the implicit value of data analytics becomes explicit.
- Execution: Determine capability gaps and deficiencies for data analytics execution.
Create a Data-Driven Vision Rather Than Just a Mission Statement
To initiate analytics adoption, future analytics leaders should provide an effective data-driven vision that’s more than a mission statement stuffed with buzzwords and motivational phrases. He/she should challenge the status quo to reveal how data analytics is integral to the office of finance rather than just a “nice-to-have.”
A good data-driven vision has the following characteristics:
- It's aligned with the company's mission and explains how data and analytics contribute to the success of the finance department.
- The vision extends beyond the purpose of internal decision-making. It focuses on internal and external stakeholders, customers, investors, and financial regulators and provides a broad, long-term data and analytics strategy.
- It's relevant, specific and distinguishes your department and organization by clearly defining the value your department and organization provide (further enhanced by data and analytics capabilities).
- It's inspiring, authentic, and reaches people on an emotional level.
- Data-driven visions have the power to elevate teams by giving team members a common purpose, which can lead to higher levels of innovation and engagement.
Identify Stakeholders’ Objectives
Future analytics leaders should identify internal and external stakeholders, their strategic goals, and in particular, the unmet needs that are new business opportunities. This sets the scope for your strategy.
For internal stakeholders look for opportunities to embed analytics into the business process. Use predictive and prescriptive analytics tools to transform processes, improve the performance of existing workflows, or design new ones from the ground up. Incorporate data-driven decision-making into the financial operation that traditionally relies on instincts or “gut feelings.” For cross-functional stakeholders and customers, it’s about the opportunity to use data analytics to create tangible benefits and optimal outcomes.
The following techniques can help you create data analytics use cases:
- Journey mapping: Catalog how customers engage with you and your partners, then use analytics to identify unmet needs, personalize interactions, and offer relevant offers and messages.
- Design thinking: Through empathy, brainstorming, iteration, and collaboration — “walk a mile” in the stakeholder’s shoes.
- Decision modeling: Explore the business processes and decisions related to stakeholder targets and how those relate to business applications and data.
Align Stakeholder Expectations with Tailored Value Propositions
Based on stakeholder objectives, a value proposition describes what you’ll create and deliver, expressed in terms of the receiving party. It’s not enough to deliver value — value must also be received and perceived. The value added from data and analytics isn’t always self-evident, so it can be easy for everyone to have different expectations, most of which are implicit views we just happen to believe.
The role of analytic leaders is to make sure value propositions become explicit and that all stakeholders reach a common ground of understanding. There are three types of value propositions that are common for data and analytics practices as follows:
- Analytics as a utility: Data and insights should be available to all.
- Analytics as an enabler: Analytics should always target a specific business goal and enable the business to make better decisions every day.
- Analytics as a driver: Use analytics to achieve new business goals. New tools can uncover new insights, and new data types can lead to new business questions; both drive new business ideas and revenue sources.
These different types of values may contradict each other, but there’s no one right or wrong path. Each value expresses a belief that enables data and analytics success. The coexistence of multiple types of value propositions within a single organization is not only likely but also preferable.
Determine Capability Gaps and Deficiencies for Execution
The final part of your preparation is to identify what capabilities you need for execution. Communicate with executive leaders, IT, and other stakeholders to facilitate a clear-cut evaluation of capability gaps, such as:
- Core analytics competencies: Availability of specialist skills, training, and data literacy programs
- Culture/organization: Plan for change management and analytics democratization
- Data and analytics governance: What principles, policies, responsibilities, and outcomes you’ll uphold
- Technological capabilities: How you’ll handle data integration, data management, and data access
- Processes: Tactics for life cycle management and priority management
Ready to Jump Start Your Analytics Journey?
Watch our Alteryx visionary panel on demand to see how leaders from eBay, ADL Digital Labs, and FirstCaribbean International Bank started their analytics transformation journeys.
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