Making Sense of Your RALI Data

RALI can bring together information from connected devices, sensors, and things you enter yourself.

On their own, individual measurements or events can be hard to interpret. Looking at them over time — and alongside other information from the same period — can make them much more useful.

Start With the Measurement

A single measurement is one moment in time.

Before reading too much into it, look at what came before and after it.

It can help to ask:

• Is this different from what I usually see?

• Has something similar happened before?

• Was there anything unusual happening at the time?

• Is this part of a larger trend?

One number can be useful, but a little context usually tells you more.

Look at the Bigger Picture

Your data becomes easier to understand when you stop looking at each point in isolation.

A change may seem important when compared with the previous measurement, but much less unusual when you look at the last week or month.

Try looking at:

• recent measurements;

• longer-term history;

• repeated changes;

• periods where the data stays relatively stable; and

• anything else recorded around the same time.

This can help separate an isolated change from something that keeps happening.

Use Different Sources Together

Not all information comes from the same place.

Some information may be collected automatically by a RALI device. Some may come from sensors. Other details may come from something you entered yourself.

Those sources can be more useful together.

For example, if you notice a change in a measurement, you might also look at what happened around that time and whether you entered anything that adds context.

That extra information can make the original measurement easier to understand.

Pay Attention to Timing

Timing can be one of the most useful pieces of context.

If two things happened around the same time, it may be worth looking at them together.

You can ask:

• What happened first?

• What happened shortly after?

• Did a similar sequence happen before?

• Was the same kind of information recorded at another time?

Seeing the order of events can help you understand how different pieces of information fit together.

Watch for Patterns

Patterns often become clearer as more information builds up.

You may notice that certain changes happen around similar times, under similar circumstances, or alongside the same kinds of inputs.

When you see a possible pattern, look at more than one example.

Check whether it appears consistently, whether there are exceptions, and whether the same surrounding information shows up each time.

A pattern can be useful even when it does not explain exactly why something happened.

Add Your Own Context

Information you enter can make automatically collected data more meaningful.

Something you recorded may help explain what was happening around a particular measurement or event.

Over time, this can make it easier to compare similar situations and understand whether a change looks familiar or unusual.

Think of your own inputs as another layer of context, rather than a replacement for what your devices and sensors collect.

Ask Better Questions

When something in your data stands out, asking the right questions can be more useful than focusing only on the number itself.

You might ask:

• Is this normal for me?

• Has this happened before?

• What changed around the same time?

• Does this look different over a longer period?

• Is there anything I entered that adds context?

• Do other measurements point in the same direction?

These kinds of questions can help turn a collection of data points into something easier to understand.

Automated Explanations

RALI may also use automated tools to help summarize or explain changes in your data.

These explanations can make it easier to spot trends, compare periods, or notice information that may be worth reviewing.

When you see an automated explanation, it can still be useful to look at the measurements and surrounding context behind it.

The explanation is most helpful when you understand what information it is based on.

Over Time

Data becomes more useful as context builds.

A single measurement gives you one point.

A few measurements let you compare.

More history can reveal patterns and trends.

Information from different devices, sensors, and your own inputs can add even more context.

The goal is not to collect as much information as possible. It is to make the information you already have easier to understand and use.