Data Doubtful to Data Lovers

By Hannah Wheatley, Data Specialist, CIVICUS

Shifting from Leaving Data for Later

I have spent five years testing ways to improve civil society’s ability to use data at scale. Through research on best practices and iteration, I have found a method championed by those usually the most difficult to reach: the data doubtful. They are data doubtful for many good reasons. Most often, data has been something they have experienced after the program is over – often needing a large amount of effort yet yielding marginally useful data for the donor, the program and internal monitoring and evaluation (M&E) systems. Then there are the inherit bias in data, the technical elitism, and the struggle to capture the data that matters. Too often the message is that numbers are the objective and reliable data and therefore the “important” data. However, for many programs, it is not numbers alone that explain the work. Instead, it is the stories – also known as qualitative data – that explain the work best. Yet, qualitative data may be messy and not collected and analysed with a standard and regular methodology to make it useful and meaningful. With these frustrations and barriers, most have good reasons to leave data to someone else, leave data for later, and invest as little as possible.

Qualitative Data = “Real” Data

However, we’ve found another way at CIVICUS. We’ve found a way to support program managers, their teams and their participants to capture data in a way that is easy, useful and motivating. It’s a template, a practice and a learning approach. It was developed to be useful for a wide range of topics, needs and projects, and it’s a great way to operationalize Developmental Evaluation at the program level. It is especially useful for capturing qualitative data, but it is flexible enough also to include quantitative data (numbers), media (photographs and screenshots), and links to additional evidence. Expert practitioners can also leverage the programmatic-level data for larger organizational strategic goals. For example, when holding reflection sessions, such as CIVICUS’ regular Impact Reflection sessions, the written record helps the sessions begin with a rich data set of insights, successes, and challenges allowing participants to focus more on the analysis and synthesis stage – arguable the most important part of a reflection process. In addition, it provides a more accurate data set to use for the reflections because it has less bias on the most recent activities. In many cases, it also increases the diversity of viewpoints because project individuals are given many opportunities to contribute to the record from their unique project experience – rather than a sometimes intimidating organizational-wide session. However, today is about the basic template, practice and approach.

Grow Log Template - Celebrating the Small Wins

The basic Grow Log template is a table in Word or spreadsheet with the following columns: date, name, comments, follow-up needed and other. It’s completed weekly, bi-weekly and/or after significant events. It should only take about five minutes to complete. Every team member contributes to the Grow Log. The objective is to record successes, challenges, surprises, insights, questions that need answered or something not to forget. Sometimes several of comments might be recorded at one time, sometimes each person may contribute their own, and/or sometimes the team members may build upon another team member’s comment. The key is to record these thoughts in a timely manner such as the first five minutes of a regular weekly or bi-weekly meeting or immediately after a significant event. The strength of this approach is that it makes it easy to record the project work because it is easy to remember when it is fresh on the mind. 

But perhaps most importantly is the motivation teams feel by taking these mini moments of reflection to document what they are learning and have accomplished. Too often, we miss moments to celebrate our efforts, challenges overcame, hard lessons learned through mistakes, and the small wins that add up to much bigger wins. Capturing them on the Grow Log become moments to appreciation the team’s effort and be grateful for what has been accomplished to date – even as there is more to do. And gratefulness and appreciation bring positive emotions and wellness to the team while boosting engagement and performance. We collect data to improve programs, so it only makes sense to use a data approach that also adds value to the program and its team.

Grow Log: Comment column

The comment column should be completed with enough detail and context that it can still be understood six months later. It’s OK if one doesn’t remember all the exact details at the moment of capturing the comment. For example, we may not have a name, a number, or other relevant information for the comment. In these cases, just mark an X or highlight yellow the data that needs to be added or verified. The table is not the final record – it is a place to capture what might be forgotten if more time passes. We prefer to have imperfect captured data that can be edited and improved rather than wait until we have it perfect or are certain. The table is capturing our observations as we go, and we may decide later that an observation was incorrect or inaccurate. That is how we learn and to be part of a learning culture. It also becomes a positive way to capture the effort one undertakes to either verify or disprove an observation. These become evidence for the team, for the donors, and others that learning is happening and when shared may help others in their own learning journeys.

The table entries however shouldn’t be so long that it becomes difficult to serve as a quick reference. If the team is finding the comments becoming very long, it may be because several different thoughts are being recorded as one comment. If this is the case, simply divide the comment so that each row contains a unique point. Other times, teams may start using the comments more as a notetaking. If this is happening, start a separate space for notes below the table. In the notes section, everything can be captured. Then, just capture the main points in the Grow Log itself. 

The take-away message for this column – there is no wrong or right kind of comment to record. The only wrong way is not to get started documenting the wonderful successes, insights, and challenges each person in the project can contribute. 

Grow Log Table

Date Recorded Name Recording Comments
(E.g. Success, challenge, surprise, insights, question, something not to forget)
Follow-up Needed
(Based on this comment, what do we need to do? Or, how can we take this learning forward?)
(E.g. Completed? Share story? Strategic goal? Additional reflections or links)

Grow Log: Follow-up Needed Column 

This column can be completed at the same time as the comment, but also, it’s ok to do at a different time. What usually works best is to complete this when the team has 15-30 minutes to review the comments and decide together what needs to be done or how to take the learning forward. Some groups do this at their regular bi-weekly meeting while others do this more on a monthly or quarterly basis. What is encouraged is to focus on what the team can do to incorporate the learning based on the existing resources (time, energy and funding). Sometimes the follow-up needed isn’t clear until later. Sometimes, there will not be any follow-up needed. The column is there to use if needed, it is not mandatory to complete. There are also times that an issue is raised that is best suited to escalate to a different forum, decision-maker, or future time. In those cases, I encourage teams to do what they can now within their control, but also note in the follow-up that escalation is needed.

Grow Log: Other Column 

This is an optional column to complete. Some teams may want to mark the status of the comment e.g. completed. Others however prefer to create a color code for the status of the comments recorded e.g. red for action needed in 48 hours, green for a comment that is significant and should be shared with others, and/or yellow for a comment that needs verified or completed. Some teams do neither. Depending on the work and the team, different ways will be the best way. 
Other times this column can be used for linking to documents, documenting what ’s happened since the follow-up was completed or screenshots of evidence for the comment. 
For advanced users, they may use this column to link the comment to overall organization goals, learning questions or objectives.

Sharing the Stories 

Others may use the other column to mark learnings to share with other teams within the organisation, participants in the project, or on social media. Firstly, the table is used to capture project data in real time to improve upon the program itself as it happens. However, the same data can be used in other ways by sharing with others. Many teams find that taking time to share the learnings with others brings increased satisfaction with the work. For example, frequently our own participants and colleagues are unaware of the challenges overcame or successes of the work. Sharing supports a learning organisational culture as well as another way to recognize the work accomplished, which helps motivate the team, the participants, and the organization. Again, finding the small success to celebrate along the way helps build support and energy for the work and the organisation.

And while it may be frustrating and feel worthless to have misjudged a situation and invested resources in a way that doesn ’t deliver the outcomes expected, that disappointment can be mitigated by being able to share that learning with others, so they can avoid the same misstep. It’s a way to transform a mistake into “advice”. This step of sharing of what didn’t work is much easier when the stories of successes have already been shared. So, I encourage starting with the positive shares to build momentum and trust and once that is established, the safety to share what hasn’t worked will usually be there.

We generally find it takes about three months of capturing comments before we have stories that are ready to start sharing.

Pic 1) Example of sharing a story within the organisation about the application process for youth.

Data Lovers

Once teams have been using the Grow Log for three months, they usually have fallen in love. What started out as something mechanical and sometimes hard to remember to do, has become something that helps bring the team together, make progress and motivate themselves and those around them. From capturing what was learned and how they changed, they have the evidence to show how they are growing. To my surprise as well as theirs, they often to find themselves now loving data and sharing the methodology with others. 

However, the perks aren’t over yet. Those that embrace the approach find that writing the final report is mostly easy because they have been writing that final report along the way – but in a way that brings value to the work as it happens and energizes the work. The “raw” data in the comments is there for us to review and analyse for themes and trends, both at a program level as well as across different programs to support organisational learning. The shared stories can often be re-used for the program reports as well as other communications across the organisation. Programs can combine the output data captured from their program – number of events, participants, grants, countries, and other quantitative data required for reporting – with the successes and learnings from the Grow Log. The two types of data support each other and help reduce bias. 

The numbers provide easy points to compare and explain the scale or depth of a program, and the stories help explain the meaningfulness of those numbers and the work. Using both, we can deepen our understanding of our program’s successes and challenges and what that means going forward for the program and the organisation. For many, the Grow Log provides the guidance and confidence to capture and share stories as meaningful data that expands and explains the quantitative (numbers) data. It’s another reason that many go from Data Doubters to Data Lovers using this simple template, practice and learning approach. And the more Data Lovers we have, the more appreciation for the small wins along the way, the more engaged our program team, the better we can embed an evidence-driven learning culture that improves our programs for the reason that matters – improving our communities. 

Example 2) Diversity and Inclusion at CIVIUS were able to take their yearlong documentation of the organisation’s

Diversity and Inclusion Diaries

Recent Posts

No item found!

About DataShift

Learning Zone

Direct Support