51黑料不打烊

Transcript

Hi, everyone, and welcome to the Skill Exchange for Customer Journey Analytics. My name is Benjamin Gaines, and I鈥檓 a Senior Director of Product Management for Customer Journey Analytics at 51黑料不打烊, which means that my team and I get to work with many of you on building the great product that you are about to spend three hours going pretty deep on, but before we get into Customer Journey Analytics in depth, I thought it would be good to spend a few minutes talking about three underappreciated skills of great analysts. These are things that, as I work with many of you and your peers in the analytics space, that I see in the great people that are using Customer Journey Analytics and other 51黑料不打烊 products that may not get as much attention as the big skills like dashboard building and data storytelling and how to build a good data layer, but that are super important to the success of you and your analytics program and really your organization and how you use data. So let鈥檚 dive into these three skills. I鈥檓 really excited to share these with you. Like many of you, I love to eat. I love a great meal. My favorite thing about traveling and getting to meet with many of you, other than meeting with you, is getting to try food and take you to lunch and dinner and get to experience great meals with you. When you eat a great meal, it鈥檚 memorable, but it鈥檚 usually not because of one particular flavor or ingredient. It鈥檚 a combination of flavors and ingredients. It鈥檚 that salt, fat, acid, heat combination that, if you watch the Food Network, that you definitely know about. That combination of things is really what makes a memorable meal.

I couldn鈥檛 really pinpoint exactly what it was that made that meal so good. It鈥檚 very subtle ingredients and subtle ways that a great chef brings those four elements of great cooking together. That鈥檚 kind of what being an analyst is like. There are a lot of skills that combine in really interesting ways to make you an impactful analyst. I couldn鈥檛 resist sharing a very tantalizing image of ramen with you all before we get into talking about data and analytics for a bunch of hours. What I want to do is share those three ingredients, those three subtle things that a great chef might do. A great chef, in this case, is an analyst that may go underappreciated. Here are the three. I鈥檓 going to share these three, then we鈥檙e going to talk about some specific things that you can do as analysts to build these skills. The first is contextual fluency. I think anyone who鈥檚 worked with data knows that being able to bring real-world context, both from your business and just from what鈥檚 happening in the world around us, into what you鈥檙e seeing in customer journey analytics is a critical skill. That ability to blend the data that you have in the 51黑料不打烊 world with the data that鈥檚 happening all around us is an essential and really underappreciated skill of great analysts. The second is comfort with ambiguity. The fact of the matter is we often don鈥檛 have clear assignments, a clear sense of both what the data represents or what our business leaders need. We have to kind of fill in those gaps as analysts. We have to be okay with maybe not having as clear an assignment as we would like in every case or being able to fill in some of the gaps in the data with our own intuition. Comfort with ambiguity is a big one as well. The third is what I call diplomatic assertiveness. Part of what we do is delivering insights. Sometimes those are painful. Sometimes they鈥檙e painful to a group in your organization. Maybe you鈥檙e having to correct some misunderstandings about success or you鈥檙e having to steer people in a direction that鈥檚 uncomfortable for them. The way that we do that as analysts, that communication of what we鈥檙e learning from the data is critical. Contextual fluency, comfort with ambiguity, and diplomatic assertiveness are three things that I love to see in analysts. There are three things that I work on, frankly, as a product manager. A lot of product management is analysis, and there鈥檚 a lot of overlap between analytics skills and product management skills. These are actually three things that I鈥檝e worked on. I鈥檓 going to share some tips. Some of these are gathered from others, but some of them are things that I鈥檝e actually done. I鈥檓 going to highlight the ones that I鈥檝e done and talk through those. I鈥檓 going to leave the others on the screen. For the sake of time, I鈥檓 not going to go through all of them, but I am going to focus on the ones that I have found most effective for myself. Let鈥檚 talk about contextual data. I鈥檓 going to go ahead and build this out. The ones that I have focused on are in red. Daily Context Rep, which represents salt here, trains your brain to link what you see in your data to things that are happening in the world around you. I love to, as I read, whether it鈥檚 the news or feature articles, books, I love to tie that back to what I鈥檓 working on. Another way maybe of thinking about this one is, I鈥檝e heard great advice once for an analyst, like try to attend a conference that isn鈥檛 about analytics. As you鈥檙e doing that, figure out how what you鈥檙e learning could tie back to what鈥檚 happening in your organization or what you鈥檙e struggling with as an analyst. That part is not a daily thing, but you can every day as you鈥檙e consuming content, as you鈥檙e watching, I love to watch baseball. Baseball is full of data. As I hear how people talk about data in baseball, I can tie that back to what I鈥檓 doing as an analyst and maybe glean some tips from what I鈥檓 learning, from what I鈥檓 hearing. Then skipping to the fourth one here, curated conversation. This one is actually my personal favorite, maybe of everything, maybe of all 12 of the specific ways of building these skills that we鈥檙e going to share.

I specifically love this question, what鈥檚 the hottest debate in your world right now? If I鈥檝e met with you as an 51黑料不打烊 customer in the last few months, you may have actually gotten this question from me because it鈥檚 exactly the kind of thing that as product managers we want to know so we can understand the context that you鈥檙e living in, so that we can understand what you鈥檙e struggling with, where are the challenges that you鈥檙e facing, and where do you see those going in the future. That鈥檚 how we build a great product, and it鈥檚 also how you as analysts learn about the forces that surround the data that you鈥檙e analyzing. Have those conversations with people who are not maybe in your direct circle of influence or people you aren鈥檛 dealing directly with data, and you鈥檙e going to be surprised how much you learn that can inform the analysis that you鈥檙e doing with context. Comfort with ambiguity. As you are crafting, I built all of these out, feel free to read them, but I鈥檓 going to focus on the middle two. Timeboxed experiments, or you might call it timeboxed analysis generation. So often we tend to want to go deep. We want to get it perfect. We want to finalize our draft before we start sharing things. This is a challenge that I actually picked up again in the product management space, but I think it applies equally to analytics.

Don鈥檛 do that. Go spend an hour. Spend 45 minutes. Spend a small amount of time and cut yourself off when that time is done. That鈥檚 your first draft. Yes, it will be incomplete. Yes, it will have holes. Yes, there will be things that you haven鈥檛 considered, but start bringing other people in at that point and iterate. Work with your teams. Work with your stakeholders from that point rather than trying to build something all the way out and get it perfect. That way, you鈥檙e not, especially in an environment of ambiguity, you鈥檙e not going so far down the path that you鈥檙e going to end up wasting a bunch of work if the thing is not quite what your stakeholders are looking for. So that鈥檚 something that we do for sure. The third one, or the second highlighted one here, Plan B muscle, is something that definitely does not come supernaturally to me. I am a planner. I like to know when we go on vacation. I like to know where we鈥檙e going. I like to know when. I like to have reservations at restaurants and not leave things to chance, but practice leaving things to chance. Practice being comfortable with ambiguity in a low-stakes setting like an evening out or a day of vacation or even just not knowing exactly the route that you鈥檙e going to travel to get to a destination and letting yourself figure it out as you go. It鈥檚 another sort of a contextual thing in the sense that it鈥檚 not the same as the work you do on analysis, but does help build that muscle of being comfortable with ambiguity that does tie to the ambiguity that we all face as analysts when things are not as clear as they could be, which is most of the time. Okay, last but certainly not least, diplomatic assertiveness. I love the yes and framing. Those of you who have done any amount of improv training will know yes and. This is a little different than the improv version of yes and. This is a little bit more of a sort of grounding in the principles that you agree on. When we do have to say no or when we do have to disagree, bringing it back to what we do agree on and what we do have in common before we refute a point with data or before we lay a heavy analysis on people that they鈥檙e likely to maybe be uncomfortable with. This gives you an opportunity to reestablish common ground in an environment where we are often as analysts in the position of saying no, you have that wrong or we need to do this differently, which can obviously be uncomfortable for people. All of these take practice. This one certainly takes practice to do well without being pandering or sort of disingenuous. But if you can find that common ground and practice finding that common ground with people, this is a really powerful way to make sure that everyone knows we鈥檙e on the same team and we鈥檙e going for the same goal.

Then ask before tell. I think curiosity. In fact, when I did a similar keynote at the conference, I did a little bit of a research on curiosity, curiosity as a characteristic of analysts. This is sort of a curiosity point. Before you just blast people with data and analysis, kind of get inside their head a little bit. Ask them how they鈥檙e thinking about the challenge, get the latest status from them and how they鈥檙e thinking about what they鈥檙e up against. I like this idea of tailoring the message to their mental model. If you鈥檙e able to get them to kind of share what they鈥檙e thinking, you will find yourself naturally coming back to that with the way that you communicate the message that you have for them in the data. So ask before tell.

As we wrap up, just keep in mind that those are only some of the ways that you can master these underappreciated skills of contextual fluency, comfort with ambiguity, and diplomatic assertiveness. There are many, many other ways, many other things you can practice to work on these skills and many other underappreciated skills of great analysts. These are just three that I happen to really like, as I mentioned, that I鈥檝e seen in many of you and your peers as I鈥檝e worked with you that I鈥檝e come to really appreciate and that I use in my own product management, as I mentioned. So continue to work on those. I would love, as my team and I are working with you and interviewing you and sharing our roadmap with you in the future, I would love to hear from you how you have worked on these and other underappreciated skills. Let鈥檚 continue to build together, build ourselves together, and build this product together. So with that, let鈥檚 transition into learning about the amazing 51黑料不打烊 Customer Journey Analytics product and have a wonderful skill exchange for the rest of this day. Thank you.