Tag: managing priorities

Posted on in High Performance Data Teams

Reducing Rework in a Data Team

As much as we’d all like to get things done right the first time, with analysis and modeling it’s not always possible.

When delivering results, it’s fairly common to receive requests for minor revisions – and most of that we can all handle. But every so often the situation catches you by surprise. You’re delivering what you think is a great piece of work only to learn that it missed the mark completely. You hear statements like “This isn’t what I asked for!” or “You misunderstood what I asked for!” and you wonder where things went wrong.

Sometimes you can rightfully blame the person who requested the analysis, and then conveniently changed their mind. But more often the breakdown happens around communication and agreeing on expectations.

Final version

So what do you do? Here are some coping strategies:

1) Ask the question “What does a job well done look like?”
The next time you’re asked to run a major analysis where you feel that you don’t have an adequate understanding of what is being asked, try this script:

“I want to make sure that I give you what you want. Would you mind if I grabbed a couple of minutes to clarify a few things?”

Then ask your clarifying questions. For example:

  • What’s the business question that this analysis is supporting you with?
  • Do you just want the summary, or did you want the supporting details?
  • Is this analysis just for your reference, or is it going to be distributed?
  • How accurate does this need to be?

The answers to these questions can make a big difference in determining the final deliverable. If you only have time for one question, the first question is the best one to ask.

If you’re lucky enough that the person making the request is willing to spend more than a couple minutes with you, then you can try to get crystal clear on “What does a job well done look like?” The following are some of the statements that you might hear:

  • It will help me answer this questions …
  • The numbers will be consistent with our annual report
  • The summary of results will be jargon-free
  • The results will be delivered by Friday morning at 10 am, both by email as well as a color print out on my desk

2) Put your understanding in writing
Now, with your heightened clarity you can now put it into writing. A short follow up email of the form “Thanks for clarifying. So, just to recap I will …” will provide one more opportunity for corrective feedback.

In many situations you won’t be able to do the first step (getting clear on “what a job well done looks like”) because the person making the request is too busy. But even in these situations it’s still worthwhile putting into writing. You can write the same short email, but this time it will have an opening line of the form “I know you’re too busy to discuss the analysis, so I’ll make the following assumptions when I do it …” And then, you can add a closing line “Hopefully that captures it. If I don’t hear otherwise from you, I’ll deliver results based on this understanding.”

3) When delivering your result, include the original request
You’ve done the hard work of clarifying expectations, you’ve done the analysis, and now this is the easy part. When summarizing the results, make sure that you attach your analysis to the clarifying email. If you’re delivering it in hard copy, you can attach a print out of the clarifying email to the top.

Using this approach the person making the request will be able to see their role in the entire process. It won’t take long for people to see the value of slowing down and spending a few minutes getting clear on the request.

4) Follow up after the fact
The worst situations are when you’ve put in the hard work, but it wasn’t really what the requester wanted, and so they don’t use it. They’ve wasted their time, your time, and they still didn’t get what they want. Because they feel embarrassed about not using the work, they will often not bother giving you feedback.

So, it’s up to you to solicit feedback after each major deliverable. A brief check-in after the fact can yield great feedback. If you’re not getting rave reviews about the great work you did, you can ask “What could I have done to make it even better?” This seemingly innocent question prompts the requester to give candid feedback, and demonstrates that you really care about the value of your work.

How's my analysis?

These coping strategies are not for everyone, and are not needed in every situation (especially the quick and easy analyses). But it’s the times when we get it wrong where we really appreciate the value of clarifying expectations. If you have your own coping strategies, please weigh in.

Note: What is a Data Team?
When we refer to “Data Teams” it’s a catch all for groups of technical, statistical, and subject-matter domain experts that are involved in providing information to support their organization. These teams are sometimes called “Business Intelligence”, “Decision Support”, or “Information Management”, but they can also be internal consultants such as “Operations Analysts”, “Strategic Information” or “Research”. Many of these concepts equally apply to teams of Data Scientists.


Posted on in High Performance Data Teams

Tips for Managing Priorities in a Data Team

We work with a lot of different Data Teams, and most of them are faced with the same challenge:

How do you handle all of these competing requests for information?

Below are some relatively easy-to-implement tips for dealing with this situation, but first let’s see why this can be so hard. The following are some of the more common reasons we’ve seen in the field:

  • Every request seems to be urgent. Most Data Teams are all too familiar with the expression “we need it yesterday”.
  • Every request seems to be very important. How can a Data Team not give priority to a request that comes from the CEO’s office or from the Board? What about situations where Public Relations needs good information to handle an emerging PR issue?
  • Requests for information are “free”, meaning that in most situations, the people requesting the information don’t have to pay for it. As a result, demand for information grows much faster than the capacity of the Data Team.

Overloaded Inbox

Here are some tips for Managing Priorities in a Data Team:

1) Keep a log of all active requests
As simple as it sounds, keeping an up-to-date log of all active requests is a “must have” enabler for managing competing requests in a Data Team. Many Data Team leads feel that they don’t need such a log, citing that they have it all under control, and that they are too busy to keep another list up to date. But such a log can help identify the capacity needed in the Data Team, and the skill mix that’s required. At minimum the Active Request Log should include the following information for each information request:

  • Who is asking for the information?
  • What are they asking for?
  • When did they ask for it?
  • Who in the Data Team is handling the request?
  • When did we promise to get it done?
  • What’s the status of the request (not started, active, completed, cancelled)?

In addition, the following information can be very helpful for planning purposes:

  • When was the information delivered?
  • How many hours of effort were involved in preparing it?
  • Was the due date pushed back? If so, how many times and by how many days?
  • Was there any feedback from person who requested the information?

This list can be as simple as a whiteboard, a shared spreadsheet, a SharePoint list, or a Google Doc. The hard part is having the discipline to keep it up to date.

2) Review the log as a Data Team every day
Having a daily 5 minute meeting as a Data Team may seem like a big burden. Who needs another meeting in their already-too-busy schedule? But if done right, a daily 5 minute meeting to review the Active Request Log can help a too-busy Data Team work together to make sure that the most important things are being worked on every day. Specific things that can be clarified during this 5 minute check-in include:

  • What must we get done today?
  • What must we get done in the next couple of days?
  • Who has the lead on each piece of work?
  • What requests need more support?
  • What counts as “good enough” for the requests that we’ll be working on today and tomorrow?

This quick meeting can set the entire Data Team in the right direction at the start of each day, and in doing so, go a long way to reducing the last-minute scramble, and make sure that the Data Team works to it’s full potential as a team.

3) When handling new requests, use the active request log to set expectations
If you have the discipline to do the above 2 steps, then after not too long you will have great information for managing expectations with new requests. For example, if there is a last minute urgent and important request for information, then at minimum you will now know:

  • How long will this really take us to complete?
  • Are there any recent requests for information that are similar to this one? If so, can that requests be modified to meet this urgent need?
  • Will any active requests not be completed on time, as a result of this new urgent request? If so, is the person making this new urgent request willing to take the heat?

In a lot of respects, most Data Teams are carrying out all of these three functions, but often it’s in people’s heads. By adding a little bit of tracking and daily discipline, the Data Team can significantly improve their work effectiveness, and at the same time better meet the needs of their customers.

We’re sure you have perspectives of your own on this subject. If you so, please share your thoughts and ideas.

Note: What is a Data Team?
When we refer to “Data Teams” it’s a catch all for groups of technical, statistical, and subject-matter domain experts that are involved in providing information to support their organization. These teams are sometimes called “Business Intelligence”, “Decision Support”, or “Information Management”, but they can also be internal consultants such as “Operations Analysts”, “Strategic Information” or “Research”. Many of these concepts equally apply to teams of Data Scientists.