Analog Conversations for Digital Transformation

Discussions around harnessing an organization’s data to enable greater business impact through tools like AI and ML are often dominated by the technology itself. What the tech can do, how much time it will save us and all the bells & whistles at our finger tips. But it’s vital to understand the steps required to achieve those goals and, more importantly, why we’ve chosen these projects to spend our resources on in the first place. Chasing shiny objects can derail a roadmap quite quickly.

I recently had the chance to dive into this topic in a roundtable discussion with other marketing technology leaders, particularly around preparing data for AI. The big takeaway? We need to have detailed, team-based conversations to map out our data goals and roadmap long before we touch the tech.

Starting with the Why: Identifying Our Use Cases

Before we get into the nitty-gritty of data and AI, we need to start with a fundamental question: Why? Why are we investing in AI? What problems are we aiming to solve? This step is all about identifying our use cases, and it’s more important than it sounds. Without clear, defined use cases, our projects can easily become unfocused and unproductive.

Let’s say your company is looking into AI for customer service. That’s a broad goal. What specific issues are you trying to address? Is it reducing response times, improving customer satisfaction, or perhaps automating routine inquiries? Each objective will shape the direction of your AI initiative.

Unifying Use Cases in Context to the Business

It’s crucial to unify our use cases within the broader context of our business. This means aligning our data initiatives with our overall business strategy and ensuring they support our long-term goals. We want our data projects to be integral parts of our organizational roadmap, not just isolated experiments.

Here’s how we do it:

  1. Identify Business Objectives: Start by understanding your key business objectives. This helps in mapping use cases to these goals. For example, if one of your objectives is to enhance customer experience, you might focus on use cases like personalized marketing or customer service automation.
  2. Map Use Cases to Objectives: Once you’ve identified your business objectives, map your use cases to these objectives. This ensures that every project is contributing to your overall strategy. For instance, a use case aimed at reducing operational costs can be linked to goals like improving efficiency and profitability.

Ensure Alignment and Support: Make sure that your initiatives are aligned with your business strategy and have the necessary support from stakeholders. This involves regular check-ins and updates to ensure that the projects are on track and delivering value.

Priorities and Capabilities: Build Out

Setting Priorities: Goals and Objectives

Setting priorities is about figuring out what we want to achieve with our projects. Are we looking to boost operational efficiency, enhance customer experiences, or drive innovation? Each goal may dictate a different approach and set of priorities. It’s essential to ask:

  • What’s the primary objective of our project?
  • How will we measure success?
  • What’s our timeline?

For instance, if improving customer satisfaction is the goal, we might prioritize use cases like customer service automation or personalized recommendations. If operational efficiency is our main aim, predictive maintenance or supply chain optimization might take the lead.

Assessing Capabilities: What Can We Do Now?

At the same time, we need to look at our current capabilities realistically. This means understanding what our data looks like, what tools and technologies we have at our disposal, and what skills our team possesses. It’s about being honest about where we are now so we can plan how to get to where we want to be.

Key questions include:

  • What data do we currently have?
  • What additional data do we need?
  • What are our existing data management capabilities?
  • Do we have the right technology and tools?
  • Do we need to upskill our team?

Assessing capabilities often reveals gaps that we need to address before moving forward. For example, we might find that our data is siloed across different systems or that we lack advanced analytics tools for AI. Addressing these gaps is crucial for setting ourselves up for success.

Asking the Right Questions: The Nuts and Bolts

To effectively activate our use cases, we need to dig deeper and ask specific questions about our data. These questions help us understand the practical aspects of our data strategy and ensure we’re prepared for technical challenges.

What are the sources of the data you need?

Understanding where our data comes from is fundamental. Are we drawing from internal databases, external APIs, IoT devices, or social media feeds? Each source presents its own set of challenges and opportunities. For example, integrating data from IoT devices might involve dealing with real-time data streams, while social media data might require advanced text analytics capabilities.

What data types do you need?

Different use cases require different types of data. Structured data (like sales figures or customer records) is often easier to manage, but unstructured data (like emails or social media posts) can provide richer insights. Identifying the types of data needed helps us prepare the appropriate data processing and storage solutions.

How is the data connected?

Data rarely exists in isolation. Understanding the relationships between different data sets is crucial for creating a unified data strategy. This involves mapping out how data flows between systems and identifying any dependencies. For example, customer data might need to be linked with sales data to generate meaningful insights for personalized marketing.

How is the data prioritized?

Not all data is created equal. We need to prioritize data based on its relevance and impact on our use cases. This means identifying which data sets are critical for achieving our objectives and ensuring they are of the highest quality. For example, in a predictive maintenance use case, sensor data from machinery would be prioritized over less relevant data.

Keep the Conversation Moving

Digital transformation isn’t just about adopting new technologies; it’s about thoughtful planning and strategic discussions. By identifying our use cases, setting priorities, assessing capabilities, and asking the right questions, we can lay a solid foundation for our digital initiatives.

These pre-planning discussions also aren’t all about preparing the data itself; they’re about preparing our organization for a future where data plays a central role. They help us align our technology efforts with our business goals, address potential challenges proactively, and ensure that we’re ready to harness the full potential of incoming tools.

As we move forward in this data-driven era of exponentially increasing toolsets, these conversations will be instrumental in unlocking new opportunities and driving innovation. By fostering a collaborative environment and encouraging open dialogue, we can navigate the complexities of digital transformation and achieve our strategic objectives.

If your organization could use some help preparing data for your next step in innovationshoot us a message at

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