Incomplete data? That's why you still need to do analyses!

Many SMEs stick to intuitive decision-making simply because they think their data isn't complete enough to rely on. Feeling like you're basing decisions on an incomplete picture can be paralyzing. But what if we tell you that this is the perfect time to start analysing? The question is not whether your data is complete, but what you can do with the data you have.

Perfect data doesn't exist

In this article, we explain why starting analyses, even with incomplete data, is not only useful, but also essential for improving data quality and making better decisions.

The illusion of perfect data

Let's face it: perfect data doesn't exist. Every company, big or small, faces data gaps, missing fields, or inconsistent sources. This is no reason to delay analysis. In fact, waiting for “perfect data” often leads to missed opportunities. Data only becomes valuable when you work with it. Analyses can help you discover what information is missing and where improvements are needed.

The dangers of gut decision making

Gut feeling can sometimes be quick and convenient, but it's rarely objective. It lacks the consistency, scalability, and reliability needed for strategic decision making. Relying on intuition alone can lead to:

• Unexpected errors: You're missing critical patterns that only data can reveal.
• Inefficiency: Decisions based on wrong assumptions can waste your time and resources.
• Competitive disadvantage: Companies that work in a data-driven way have a significant advantage when it comes to innovation and growth.

In short, avoiding analysis due to “incomplete data” can limit your growth and make you vulnerable in a competitive market.

The question is not whether your data is complete, but what you can do with the data you do have.

Start with what you have

Even with incomplete data, you can gain valuable insights. Here are some of the benefits of starting analytics regardless of data quality:

• Insight into data deficiencies: Analyses can reveal where your data is missing or inconsistent. By identifying patterns in what you have, you'll discover what information is critical to better decision making.
• Improving data quality: By analyzing, you can set priorities when collecting and cleaning data. This ensures an iterative process where your data quality continuously improves.
• Faster decision making: Even with limited data, you can recognize trends, conduct experiments, and refine your strategies. Small improvements can make a big difference.
• Identification of quick wins: You don't always have to have a complete picture to take concrete steps. Sometimes a partial analysis can already lead to quick wins that you can implement immediately.

Data-driven work doesn't have to be perfect from day one

How analytics improve your data quality

When you start analysing, you'll recognize imperfections in your data more quickly. Here are some of the ways analytics contribute to better data quality:

• Detecting errors: Duplicate records, incorrect entries, or missing values become visible more quickly.
• Data structuring: Analyses force you to bring consistency to your data architecture.
• Prioritizing data collection: You'll discover which data is really valuable and where your investments have the most impact.
• Creating feedback loops: By performing regular analyses, a culture is created in which data quality is continuously monitored and improved.

Practical examples

Imagine running a logistics company. You know that some of your delivery dates are incomplete; some packages have no tracking and not all customer feedback is recorded. However, you can start with the data you do have:

• Pattern recognition: Which routes are causing the most delays?
• Focus on priorities: How can you optimize the top 10% of your most profitable routes?
• Exposing data gaps: Where is critical information missing, such as timings or customer satisfaction?

Analyses are not an end point, but a means to achieve your goals

An analysis shows, for example, that a particular distribution center consistently causes delays. With this knowledge, you can specifically collect additional data, such as internal process data or external traffic information, to address the problem.

The power of an iterative process

Data-driven work doesn't have to be perfect from day one. It's an iterative process where you're constantly refining and improving. Here are a few steps to get started:

1. Start simple: Focus on the key question you want to answer with your data.
2. Analyze what you have: Use available data to gain preliminary insights.
3. Make gaps visible: Identify what data is missing and set priorities for improvement.
4. Collaborate: Involve your team to set common goals and take responsibility for data quality.
5. Continuously optimize: Repeat the process and build on previous insights.

Make data work for you

The most important thing to remember is that analytics is not an end point, but a means to achieve your goals. Even with incomplete data, you can already take valuable steps to make your company more efficient, innovative and future-proof.

At InsightData, we believe that things can always be better. Our tools and experts are here to help you start analytics, no matter the state of your data. After all, starting with analytics is not only an investment in better decisions, but also in the quality of your data and the growth of your business.

Definitely Better, even with incomplete data.

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