When it comes to tracking change adoption, identifying levels of change support or uncovering pockets of change resistance, data may be king, but information should rule your decision-making process.
In my last article, I introduced a way for Change Agents to gather and apply accurate data related to change support, adoption and resistance.
It all starts with getting good data.
The next step in this fact-driven process is to analyze the data to find useful information. That information will then be used to generate
options that address the needs of your stakeholders as they consider their approach toward the change.
Change Agent Tip #61: Know the Difference Between Data and Information.
Let’s start by looking at some of the differences between data and information:
What’s Data? Characteristics of data include:
- Data is made up of raw text, measurements, facts or numbers.
- It’s unfiltered and unorganized – it hasn’t been manipulated, studied or analyzed (yet).
- It hasn’t been considered within any specific context.
- It may look random and useless until it’s analyzed.
- Data “is what it is” and it isn’t someone’s opinion or interpretation of what it means.
OBTW: “Data” is plural. The singular form is “datum” – but only geeks like us really care about that sort of stuff… :o)
Examples of data include: “730”, “blue”, “4 out of 6” and “Flight 1180 is on time”.
A. Information has context. It has meaning relative to the area in which it is being considered. For example: “121412” is raw data that could mean anything from a padlock combination to a parking space label. When expressed as a 12/14/12 – in the format and context of a date on the calendar, it’s more useful information.
B. Information is often made up of multiple bits of data that have been studied together to draw out some overall meaning. For example, I can combine these three bits of data to get this fourth bit of information:
- I’m on Delta flight 1180 which lands in 60 minutes.
- It takes 30 minutes for my wife to get from our house to the airport.
- It takes me 10 minutes to get from the plane to the curb.
- She should leave the house in about 40 minutes to pick me up. (60-30+10=40)
- Trends or patterns that have been identified after looking through a large group of data points.
- Specific outliers that exhibit distinct contrasts in one or more ways from the other data points around them.
- Gaps in the data that become information mostly because they help us identify areas where there’s a risk of making false assumptions.
What This Means for Change Agents: As a Change Agent, you should gather accurate data to better understand the level of change adoption, support or resistance within each group of stakeholders, then turn it into information that can drive action.
As you sift through your stakeholder data, here are a few things to look for:
- What trends & patterns do you notice across all stakeholders and which trends do you only see within certain groups of those who are impacted by your change? For example, is everyone indicating a lack of information about the change, or just the people in a few specific stakeholder groups? The first trend could indicate a need to re-accomplish broad messaging while the second pattern indicates that you may need to address a few isolated communication gaps or remind a few specific communicators to step up their game.
- What looks just about right? Validate assumptions as you scrub the data. Verify the things you were expecting to see are actually backed up by the data. For example, if the survey feedback tells you that people feel ready for the change, try corroborating this finding by comparing it to interview data. Hopefully, things synch up and you can continue guiding your change as planned. If not, you have some work to do.
- What outliers do you notice? Does one group jump out at you because it doesn’t fit the trend you’ve seen in the other data? Look for surprises such as a mismatch between what people told you on a confidential survey and what they’ve said in face-to-face conversations or public meetings. Look for oddities. They may need to be addressed one at a time. Outliers shouldn’t be ignored – they may indicate your change has underground resistance or it doesn’t have the levels of support or acceptance you initially thought it did.
- Finally, ask yourself what gaps appear in the data? Be careful not to draw broad, inclusive findings (positive or negative) about people, groups or organizational units that cannot be backed up with data. Diligently look for holes. The obvious answer to filling the gaps will be to go dig for more targeted data that either verifies your findings or generates more information.
Next Steps: Once you’ve translated your raw data into information, you’re now ready for the next part of the process which is to generate possible responses to keep your change on track. That’s the topic of our next discussion.
Questions for Chatter:
- What could go wrong if a Change Agent prematurely acted on raw data as if it were valid information? (…for example, if they reacted to a single data point as if it reflected the opinions or needs of everyone…)
- In what other ways have you used data to derive valuable information for measuring change readiness, adoption or resistance?
Incoming search terms:
- data vs information