3 Frameworks For More Business Impact With Data

How to increase the chances your data projects drive real business impact

Read time: 4 minutes

Hey there,

Today, I want to share with you three frameworks that I have used repeatedly to ensure that my data projects deliver tangible business impact.

We've all experienced it: "Oh, that was a great presentation!" And then nothing happens. If this occurs too often, you'll appear to be wasting valuable resources. That's why maintaining business alignment is vital.

Unfortunately, many data analysts struggle to achieve this alignment, but it's crucial to get it right.

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Business impact doesn't happen by chance - it requires systems

Driving business results with data requires frameworks that deliberately anticipate success.

Data analysis projects often fail in business due to:

  • Not addressing real pain points

  • Lack of clear outcomes

  • Poor communication

  • Fuzzy processes

The good news is that systems can address these issues. Today, we'll explore three frameworks together.

They don't guarantee success, but they do increase the odds. At least they did for me.

So let's dive into them one by one:

Framework 1: Jobs to be Done (JTBD)

You may find the Jobs to be Done (JTBD) framework unconventional in data analytics. But it's popular in product development, where it's been wildly successful.

Here's the trick: Treat each data analysis as a small data product. There's a user, a need, and a solution. Addressing all three is essential to a great product.

So how does JTBD work?

JTBD helps identify users' real pain points and desired outcomes.

At its core is the Customer Job Statement, which explains what the customer wants to achieve and their ultimate goal.

Here's a good template for a customer job statement:

You can easily adapt this to data analytics to pinpoint what you're building, for whom, why, and the expected outcome.

Here’s an example of the JTBD framework applied to a data analytics project:

"As a marketing manager, I want to identify the root cause of declining customer retention so that I can develop a new retention strategy using a Power BI dashboard that will help me understand the patterns and behaviors of customers who have churned and those who have remained loyal."

Being able to create customer job statements like this in your role as a data analyst, will greatly improve your effectiveness: It keeps you focused on solving real problems for your users, rather than just analyzing data for the sake of it.

Framework 2: SMART Problem Statements

Identifying your customer's job to be done is just the beginning.

The next step is to create a SMART problem statement, which helps you become more specific about the problem you're trying to solve and develop a clear, concise roadmap to address it.

The JTBD framework provides the "why" of the problem, while the SMART problem statement defines the "what".

A SMART problem statement must be:

  • Specific

  • Measurable

  • Actionable

  • Relevant

  • Timebound

Here's an anti-pattern of a bad example:

"Company X is only converting 30% of all leads on their platform and misses their goal of selling 1,000 products per quarter."

This isn’t a problem statement. It’s a factual statement.

Let's turn it into a SMART problem statement:

Now this sentence clearly identifies the problem (increase lead conversion), quantifies the focus (45%), provides actions (improved marketing strategy), ensures relevance (aligned with business objective), and establishes a clear timeframe (next 3 months).

In practice, it may not always be possible to craft a perfect SMART problem statement. However, it can even be beneficial to acknowledge the facts you don't have.

This awareness alone can help you set a much clearer scope and expectations for your project.

Framework 3: CRISP-DM

CRISP-DM is a widely-used framework for data mining and analytics projects. First introduced in 1996, it remains relevant today.

If JTBD is the "why" and the SMART problem statement is the "what", then the CRISP-DM model gives you the "how" for your data analytics project.

CRISP-DM offers a structured approach to data analytics projects through six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

What makes CRISP-DM so appealing is that it starts with the business and allows for iteration between the different phases.

The emphasis here is on iteration, because new discoveries in your data analysis may lead you all the way back to the problem definition:

The CRISP cycle is based around exploration […]. Outcomes are far less certain, and the results of a given step may change the fundamental understanding of the problem.

There's a 50+ pages free PDF guide documenting the CRISP-DM methodology step by step, so I spare you the details here.

If you've never heard about CRISP-DM before, I strongly encourage you to try it out.

As a result, you'll gain a clear and structured approach to your data analytics projects with excellent transparency that will not only help solve but also communicate your next project much more effectively.

Conclusion

In data analytics, frameworks play an essential role in aligning with business goals and achieving impact.

Applying the JTBD framework helps identify user pain points and desired outcomes, while SMART problem statements provide clear, focused goals.

From there, frameworks such as CRISP-DM can provide a structured approach to solving the problem in a clear, transparent, yet iterative manner.

By incorporating these systems, you'll improve your ability to align with business goals, communicate effectively with stakeholders, and ultimately achieve greater business impact.

As always, thanks for reading.

Hit reply and let me know what you found most helpful this week — I'd love to hear from you!

See you next Friday,

Tobias

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