How to Effectively Co-Champion Technology Investments with Your Data & Analytics Business Partners

For those championing transformation and technology investments like Lily AI, there is a critical early stakeholder who can be your strategic partner in defining and proving its short and long-term value: your data & analytics team.

  • According to PWC, 53% of CFOs say they plan to accelerate digital transformation using data analytics, AI, automation, and cloud solutions.
  • Bring your data & analytics team into Lily AI’s process early, testing a hypothesis as you pilot, in order to measure impact and see how Lily fits into your CAO’s agenda.
  • Dual-track both short-term and long-term initiatives to quickly gain traction and prove value while building the foundation for higher impact outcomes.

In the evolving landscape of business technology, Lily AI stands out as a transformative tool that can significantly augment decision-making and operational efficiency. For those championing this innovative solution, it is crucial to effectively convey its potential to data and analytics professionals. 

Engaging with these experts from the onset ensures that Lily AI is seen not just as a new piece of technology, but critical to organizational success. In a world where 53% of CFOs plan to accelerate digital transformation using data analytics and AI, getting your analytics teams’ buy-in on Lily AI is a pivotal asset in the quest for data-driven excellence and optimal business outcomes for retailers.

Bridging Business Goals with Analytical Precision

Analytics professionals often categorize business initiatives on two axes:

  • The x-axis represents long-term strategic goals with the potential for substantial business impact. 
  • The y-axis focuses on short-term objectives with immediate benefits, which are relatively smaller in scale but easier to validate.

It’s equally vital to position Lily AI within both of these short and long-term perspectives. 

In today’s era of big data, marrying human intelligence with machine learning is essential. To someone immersed in data and analytics, Lily AI might primarily appear as a data service. The most straightforward approach to gain a data scientist’s support is to demonstrate the return on investments (ROI) of this data, ensuring its inclusion in ongoing budget plans. 

However, the conversation shouldn’t end there. Let’s dive into all of Lily AI’s other proof points to prove its value to data and analytics professionals.

Immediate Benefits & Strategic Longevity

Lily AI is uniquely positioned to address both the x-axis of long-term, strategic imperatives and the y-axis of immediate, short-term advantages. 

Consider the rapid wins achievable through digital use case enhancements, like site search optimization. This not only delivers quick benefits, but also sets the stage for wider e-commerce applications, such as assortment planning and demand forecasting

Lily AI excels in translating such targeted technologies into substantial, measurable business gains.

The ROI Imperative

For data scientists and business analysts, the value of a new tool lies in its ROI. Demonstrating Lily AI’s ROI is straightforward. Its advanced analytics capabilities translate into direct budgetary benefits. In fact, you can read several Lily AI case studies that demonstrate value that was directly added after adopting this technology.

Yet, the strategic narrative should extend beyond immediate ROI. It’s about embedding Lily AI in the long-term growth strategy of the organization, recognizing that it complements human analysis with machine precision—vital in an era where retail is inundated with data. 

Enhancing Collaboration With Data & Analytics Professionals

Address the Redefined the Role of CAOs

The role of the chief analytics officer (CAO) has evolved from being a technical position to a more strategic leadership capacity. The modern CAO is a master of turning data into valuable business decisions. The role demands making astute business and investment choices, constantly aiming to optimize operating costs, and doing so judiciously while fortifying the organizational structure. 

When seeking buy-in from CAO’s, understand that these individuals are looking at and interpreting a spectrum of data, including: 

  • Descriptive Analytics – What happened?
  • Diagnostic Analytics – Why did it happen? 
  • Predictive Analytics – What might happen?
  • Prescriptive Analytics – What should we do? 

By understanding these stages, you not only immerse yourself in the analytics mindset, but can tailor your pitch to resonate with these data-focused executives. In promoting Lily AI, it’s important to resonate with the CAO’s expanded remit, emphasizing how the platform supports a range of analytical tasks—from descriptive to prescriptive.

Pro Tip: Use the language of analytics to expedite understanding and alignment.

Crafting a Cohesive Data Narrative

When advocating for Lily AI, it’s essential to understand its role in the broader analytics landscape. A business analyst, who often has a broader business perspective, can be your strongest ally here. Navigating the transition from conceptual thinking to tangible results requires more than just a vision—it demands strategy, synergy, and a thorough understanding of the analytics landscape. 

Illustrate how Lily AI offers not only immediate performance improvements but also aids in scaling analytics for long-term strategic benefit. Major initiatives succeed when phased thoughtfully, balancing quick wins with sustained, strategic impact. It’s a balancing act between immediate achievements and long-term goals. Identify and speak to the immediate, tangible wins while concurrently crafting a broader, impactful long-term plan. 

While ROI is a crucial metric, it’s not always black and white. In technology investments, especially AI, the focus should shift from only ROI to broader, sustainable value. When speaking to data analysts, emphasize AI’s potential to deliver enduring, incremental benefits, validated through methodical analytical techniques that can be tested.

Pro Tip: Engage your business analyst early on. Collaborate to build a robust business case for data scientists.

Drive Insights with a Hypothesis-Led Approach

Lily AI thrives on its ability to merge raw data with human insights. Starting with a well-defined hypothesis, it facilitates a collaborative exploration of insights that inform cost-benefit analyses and drive business actions.

This marriage of data, situational context, and domain expertise is where profound insights can emerge. It’s critical to begin conversations with the data team to convey the importance of starting with a distinct objective and a hypothesis that can be confirmed or debunked. 

Pro Tip: Encourage a culture of data transparency to fuel a diversity of insights and foster innovation.

Charting a Data-Driven Future with Lily AI

Innovation can often be stifled when organizations aim to internally replicate technologies. Lily AI represents not just an addition to your technological suite, but a shift towards an integrated, data-fluent organization. Engaging your analytics team from the onset not only aids in pinpointing the right problems. This also crafts solutions that deliver on both immediate and strategic goals.

Lily AI is about understanding the full picture—how data can be leveraged for quick wins and how it can underpin more significant transformations. By approaching your data and analytics team with a blend of technological comprehension and strategic foresight, you pave the path to a future where data isn’t just a resource—it’s the backbone of business innovation.

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