How to Create a Data Analytics Plan

Kate Kerrigan

Kate Kerrigan

Senior Insights Analyst

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AI-powered analytics tools are revolutionizing the way we use data. Now, almost anyone in your organization can track performance in real-time. But beware: having a ton of tools and data doesn’t guarantee success.

Without a clear strategy, a firm grasp of your data’s limitations, and the right infrastructure and governance in place, you risk drowning in a sea of information. And that means missing out on the valuable insights that could drive your business forward.

This article offers a simple framework for data analytics planning. It’s your roadmap to making smarter decisions, backed by the power of technology.

What is data analytics?

Data analytics is the process of transforming raw data into actionable insights. It’s about uncovering patterns, trends, and correlations that help you understand your business, customers, and market better. These insights empower you to make informed decisions, solve problems, and drive growth.

​​Ensure you know your data before diving into analysis

Strategic planning is a cornerstone of a sound data analytics plan. Instant access to detailed data is great, but it doesn’t replace good old-fashioned planning. Before you dive into analysis, take a step back and think strategically.

A strategic approach helps your team focus on the right questions and align on tactics to tackle your company’s biggest challenges and measure the success of your initiatives.

A better approach to data analytics planning

As you begin creating on an analytics plan, consider the following:

01. Stakeholder Management: Who is responsible for and impacted by this initiative.

02. Market and Audience Context: What changes and innovation are happening in your industry, how are your competitors (direct and indirect) adapting to industry changes, and what target audience are you intending to serve.

03. Business Objective and Desired Outcomes: Why is your company investing in this initiative and how will you measure success.

04. Analysis Methods: What approach will you leverage and which quantitative and qualitative metrics are relevant to the business objective.

  • Types of Indicators: Financial, product, and consumer metrics
  • Defining Benchmarks: Key Performance Indicators, segments, and industry standards
  • AI-Powered Analysis: Factors to consider when implementing AI-enabled tools

05. Dissemination and Utilization: How will you communicate insights to stakeholders and promote accountability for leveraging insights and recommendations.

  • Data storytelling
  • Data visualization

06. Data and Analytics Maturation: When will you revisit your plan and adapt your approach to enterprise, industry, and market changes.

A strategic approach helps your team focus on the right questions and align on tactics to tackle your company's biggest challenges and measure the success of your initiatives.

A high-level look at a data analytics plan

01. Stakeholder Management

Figure out who needs to be involved in your analytics planning and implementation process. This includes the folks who’ll use the data and results, the decision-makers who need those insights, and anyone else involved in the process.

Connect with them to get a handle on the big picture: how, why, and when data is collected, what burning questions need answering, and how they plan to use the analysis.

Getting everyone’s input up front helps you build a plan that tackles the most urgent problems and defines what success really means for your team.

02. Market & Audience Context

To understand what you need to analyze within your own organization, take a good look at what’s happening in the market, who your competitors are, and how consumers are behaving.

This means really getting to know your target audience: their profiles, needs, and pain points. Once you know what makes them tick, you can figure out which metrics matter most for understanding how well your solutions are working and how they impact your bottom line.

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03. Business Objectives and Desired Outcomes

Next you need to establish the big-picture business goals driving your analysis. What are you trying to achieve, and why does it matter?

Then, make sure your plan aligns with your company’s overall strategy. Think about short-term wins, mid-range milestones, and long-term objectives. Remember, a strategic approach to data analysis—one that’s integrated across your organization—is far more powerful than one-off projects.

04. Analysis Methods

Types of Indicators

To measure success, you’ll want to look at both numeric data (quantitative indicators) and rich descriptive information, like customer feedback and brand perception (qualitative indicators). Together, they’ll give you a complete picture of how your financial, product, and customer goals are shaping up.

Financial metrics include market intelligence and competitive intelligence indicators.

Market intelligence:

  • Quantitative metrics can reflect market categories/sizes/dynamics, market segments/sizes/growth rates, and market trends.
  • Qualitative indicators can reflect segment issues and business needs.

Competitive intelligence:

  • Quantitative metrics include market share and growth, wins and losses, and offering comparisons.
  • Qualitative indicators include your competitive landscape, strengths and weaknesses, competitive messaging, and industry presence.

Product metrics:

  • Quantitative metrics include adoption, engagement, churn, recurring revenue, and satisfaction.
  • Qualitative indicators include perceptions of product ease and usefulness, impact of product features, and opportunities for product development and optimization.

Consumer metrics:

  • Quantitative metrics include account information, buying drivers, and preferences/selection criteria.
  • Qualitative indicators include buying group, buyer roles, and customer pain points, challenges, and needs.
Checking the analytics plan

Defining Benchmarks

After figuring out which indicators matter, it’s time to set some targets. What key performance indicators (KPIs) will you use to track progress toward your goals? In each area, you’ll likely need a few different KPIs to get the full picture. For example, you might track monthly revenue growth, click-through rates on offers, or customer churn.

Next, decide how to measure each KPI. Will you use off-the-shelf tools like Google Analytics, custom code built by your developers, user surveys, or something else?

To get even more granular insights, consider breaking down your data into segments. This could mean looking at things like location, operating system, device type, or even customer demographics and interests. The more specific you get, the better you’ll understand what’s driving your results.

AI-Powered Analysis

To get the most out of any analytics plan, you need accessible data and a solid system for cleaning, storing, and managing it. This becomes even more crucial if you’re considering AI-powered tools.

AI can be a game-changer, using natural language models to simplify data analysis. It can help non-technical users quickly uncover insights, spot unusual trends, build queries and visualizations, and even forecast key metrics. AI can also transcribe and summarize qualitative data like interviews and focus groups.

But remember, AI isn’t perfect. It’s still learning and can make mistakes. That’s why it’s crucial to verify, trace, and double-check AI-generated results. Think of AI as a powerful assistant, not a replacement for critical thinking.

AI has its limits, especially when it comes to understanding context and prioritizing what’s truly meaningful in your analysis. That’s where you come in – to review, interpret, and refine AI’s output.

Before jumping on the AI bandwagon, make sure you have the data infrastructure to support it. And always review the tool’s security measures and terms to ensure your data is protected. Laws and regulations are still catching up with the rapid pace of AI development, so it’s important to be extra vigilant.

07. Dissemination and Utilization

Planning your analysis is just the beginning. You also need to figure out how to share your findings with the key players. This is where data storytelling and data visualization come in – they’re the dynamic duo that brings your data to life and helps everyone understand what it all means.

Data storytelling:
Data storytelling is all about weaving your key insights into a clear, simple narrative. Highlight the most important information so your audience can quickly make decisions and take action.

To tell a compelling story with your data, tailor your message to your specific audience, provide context so they understand the bigger picture, and keep them engaged throughout.

Data visualization:
Data visualization takes things a step further. Use charts and infographics to make complex information easy to digest and understand at a glance.

But choose your visuals carefully. The right chart or graph can make all the difference in communicating your message accurately and effectively. Always review your visualizations to make sure they’re on point and resonate with your audience.

08. Data and Analytics Maturation

Before diving in, take a good look at where your organization stands in terms of data maturity. Do you have the resources and know-how to put your analytics plan into action and keep it going?

As you invest in your data infrastructure, don’t forget to regularly revisit and update your analytics roadmap. Things change fast – within your company, your industry, and the market as a whole. Make sure your analytics approach evolves along with them.

Transform Your Business with Data-Driven Insights

The age of AI is transforming how we gather and analyze data. By following this framework and embracing the power of data-driven decision-making, you can create business value and achieve lasting success. Remember, it’s not about having the most data, but rather about using the right data strategically to reach your goals.


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