Hi and welcome to data futurology a podcast for that enthusiasts data scientist and upcoming data science leaders to learn the skills required to take their careers your careers to the next level. My name is Felipe flurries. I am your host. Thank you so much for being here for tuning in for listening for your feedback and your support. I really really appreciate it. Thanks for coming back for another episode and today's episode is a different type of episode which means it's a rant from me instead of an interview. So one of the feedbacks that I've been getting overtime is people would like to hear more about my opinions and perspectives and things. So this is an effort along those lines. Let me know what you think if it's working for you. I'm happy to continue. If not, I'm happy to take to essentially drop this type of episode and introduce something else very very open. I'll definitely keep going with the interviews one a week with the executives with the leaders now space making a significant contribution. And changes in different types of organizations always say we've spoken with people that are in high tech. So we've had a couple people from Uber for example, one of them from the traditional over side and other one from the Advanced Technologies Group, which is a self-driving car division. We've had people very technical creating deep learning platforms for software engineers, and we've had people from government from Consulting from Health from retail, etc. Etc. Etc. Those are really great. I'm definitely enjoying Doing the interviews and from what I'm hearing is that they've been helpful in people's careers. There's a few themes that obviously come out through the interviews about what makes great leaders in this space. One of the ones that I hope you realize is the empathy the human communication or human understanding more than communication. Sometimes I start a scientist we feel correctly or incorrectly that our communication isn't the best but I think as an industry we are definitely trying to improve that on a continual basis, but Empathy there is key a caring for people and wanting to help them. So I wanted to spend a little bit of time talking about the empathy and I'll cover some of the other big things that have come out of the interviews. I cover all cover those in other episodes, but to talk about the empathy is obviously the core of Human Relationships. And in my opinion to be a leader in this space at least half of what you need to do or probably it touches all areas, but at least half is deeply rooted on the people side arguably more. Or you but having the empathy is the first key step to developing great relationships and having excellent communication as a data scientist. So today I wanted to talk about how to build relationships from that empathetic point of view and we'll focus on the stakeholder relationships on the stakeholder management. We've done a previous episode on the one-on-one mentoring side and will do future ones on the management and Leadership side outside of the mentoring relationship, but this one will be around the I called her an engagement stakeholder management and communication as a data scientist. The first thing that we need to understand is the people are scared our stakeholders think of somebody in your mind somebody else how to your department that you work with and I hope that you have lots of examples in your mind at the moment pick one pick one of the main stakeholders that you have and think about how you engage with them in the communication how you present things one of the first things that I would like to highlight and would like to acknowledge or I realize if you haven't done so yet is that people are really scared about this space. It's essentially a high school equivalent would be maths. If you remember back to high school, some people were all formats and others were totally against totally scared of maths and at least back in my day. He might have scope the majority of people were afraid of math somewhere sort of against mats, and they don't want to think about it. They don't want anything to do with it. They don't want to touch it with a ten-foot pole and now fast forward 20 years. What's happening today is that we have this very math heavy set of skills that have taken over business an area where before the sales is really important. The marketing was really important the operations management processes the things that you needed less maps for now, there's bit discipline sweeping through organizations. Obviously, not only business but government non-for-profit. There's this very Matt's heavy discipline sweeping through trying to tell people what to do really if you think about it. We want to enable people to make better decisions. We want to be automating a little man you work and decisions and we want to be uncovering new opportunities for our organizations. And what happens is that there's a big gap between where we're coming from where we're thinking about how we see the world and how we approach problems and the way that our stakeholders do those things. So you have to acknowledge. This is a scary area for a lot of people it's a complex area and you have to be Mentors are completely non-judgmental and you know, sometimes you're dealing with stakeholders are very senior in organizations and regardless of who you're dealing with people don't want to feel incompetent. They want to feel like they're doing a good job. Like they're able to add value assist the useful and part of your job is to find how they can help because people in a business will be able to help you as a data scientist definitely will be able to help you. People are they're doing a job which means solving a problem. Vroom day in day out we're looking at an abstraction of how the organization Works through the data, but they're your stakeholders are there every day in the coal face or getting their hands dirty or leading teams that get those hands dirty and having a completely different perspective on the customers organization almost like the blind people seeing touching the elephant and describing it is completely different. Some people are like sort of a typical stories that there's a group of blind people touching an elephant and trying to describe it and one of them says touching its leg. Saying it's like a tree it's a wide and strong to tree the other one goes. No, it's big and floppy like a fan touching its ear the elephant's here is his not big and floppy it like a fan the other one goes. No, it's like a big worm. I think it's trunk was like has all dexterity. I like touching the tail. It's like no it's skinny and Harry at the end. Obviously, everyone has a different perspective in as much as possible extend the Olive Branch breach the Gap the the Kinder person remove the complexity drop any judgments or any expectations that oh, I only deal with people that are like this that can talk to me like this that can explain things like nice. Sometimes I've seen people unconsciously approach. They called us in a way that says you certain stakeholders. You have very good logical reasoning skills. So you are my preferred staple stakeholders and the rest of my stakeholders. You guys are a bit all over the shop and don't have very clear thinking so you're my least preferred stakeholders and what we say people realize they feel That comes across and as data scientist most in most organizations today are quite immature in this space. So is that a scientist? We need to be empathetic and non-judgmental and be able to work with people that are have complementary skills. So if we're coming in and trying to help them make data-driven decisions evidence-based decisions, then we definitely will have an upper hand on the logical reasoning and the sometimes the critical thinking so, let's see what we don't have and how we We can work with those skills that our stakeholders have but definitely keep in mind that people are scared. This is a complex area. People don't want to seem incompetent. They don't want to be judged and the sooner you can get rid of that judgment, which is difficult when you're busy and you're trying to deliver the sooner you can get rid of that drop that then the sooner you'll be able to make a difference in more people's lives which means in more parts of an organization, which means you have a bigger impact to do this. You will have to meet people where they are. Not only On their level of knowledge. Obviously. It's important to meet people at the level of knowledge that they're at and be able to speak in a way that they understand and they will be doing the same for you. I don't think that it's I go one way that although we have to go down and speak to people in simple terms. They have to do it to us as well. Like if you sit down with their operations expert or somebody who does credit assessment or something from marketing or they all know a lot of things that we as that a scientist won't so they'll be speaking in a way that they can share. Are the knowledge give you the knowledge in a way that then you can use it and act on it. So if you're coming out of discussions and meetings Having learned something then that means that they've done a good job. And that's the type of job. Do you want to be doing you want to meet people where they are explain things to them? Some people like to be educated. We should go into teaching mode in those situations or with those people for others. They want things explained because they're more interested in the outcomes in the results in the difference that you and your work can make And that's also a great relationship to have with stakeholder. So meet them where they are explain things to them or teach them if that's your preference and they'll surprise you make sure that your learning throughout the interaction as well be open and encouraging simplify the complex or the complexity demystify this space for them when people start to understand that a model is kind of like a rule of thumb or it's kind of like a map of the world. That is an imperfect abstraction of what's Happening. Young and but it helps us make better decisions because of the evidence make it described in ways that they can resonate with so you can make a difference in their lives in the organization the key there is that to have an impact on anything you have to be able to have an impact on an individual to help them see and understand and get motivated to change or to adapt or to try something new if you can do that with one person and then another one and another one and another one then you'll be able to create change in an organization. Whatever. Organization that you rain and that's how you have an impact how you can leave a place better than how you found it by leaving people better than how you found them. So definitely be open encouraging simplify the complexity of our space and the other one is so besides feeling good that having empathetic communication and relationships with your stakeholders. The other side is to focus on outcomes on business value that comes out of the decisions instead of focusing on the Teams on the approaches on the coding on the techniques don't focus on that. That's just the means to the end. And the end is the outcomes. The decisions that are made differently as a result of the work the additional value that is able to be captured because of the Insight the predictions or the experiments that you're helping to create the definitely a focus on the human side and the human connection and be patient and have time for people and then focus on A shared goal on the shared outcome that you are together able to create if you both keep an eye or you all keep an eye on the difference that you can jointly make on the outcome on the value. If you all focus on that you'll be able to get there faster and make it much better the other tip that I wanted to say around this is that when you're thinking about your communication when you're communicating with non-technical people either if you think about Executives communicating with Executives the way that we normally provide Present our work or the way that we normally like to talk about our work because they decide to test is in a very workflow based manner. So we essentially say a first I got this data then I got this other data and then I have to clean it like this and prepare like this and then use this type of model and I got this output very workflow based. So here's my recount of the steps that I took chronologically Step 1 Step 2, Step 3 step 4 now for people that are not in this field. When you take them through that process and I should say when you drag them through that communication style. They are dying inside because they're not practitioners didn't do this type of work, which means they don't care about this work. They don't care about the work at all. They couldn't care less they care about the outcome they care about what it does for them. But in terms of the steps required to do something they couldn't care less. So what you need to do is to flip a coin ocation around and to start with the objective bijective. I mean we We aim to improve conversions on the website for this product a title objective. We aim to improve retention for this product to make it an objective which is in business terms when our language of the organization and that people can get excited by right short. This is like an elevator pitch for your work short sharp objective. And then you go to the punch line with our efforts. We improved uptake of this product by 50% or over the month. March we got 50 percent more sales of this product through our efforts. So that's the outcome. That's the punchline. So you go objective. What are we aiming to do? Obviously as a one sentence one sentence objective one sentence outcome well with the results what happened and then you give a little bit of detail such as we worked with marketing to roll out this experiment that led to the fifty percent increase in sales and then you wait and then you pose and you wait and you see whether your stakeholder your audience has more questions. As whether they want more detail or if they're happy with the outcome and then the additional detail that they'll want is usually around the what data you use. How did you roll it out? How did you measure so then you answer questions around the mental process that they have on how the work is done. Sometimes the last few around that sometimes I'll ask you about how the engagement was who started the project who was involved who verified who acted on it more on the people side. And usually that's Two big pots. Sometimes you get a bit of a curveball and different questioning line, but overall that sort of the ones that most people follow but for you you original communication is objective one line in what the objective of the project or the effort or the analysis the experiment but the objective in one line type something that is understandable in non-technical terms and attractive one line then the outcome also in one line and then I'll say details and inverted quotes like details, but it's also a line or or two lines Max. So obviously like for you for the data scientist doing the work, it's quite difficult to summarize so much effort into something so short the reason why it's difficult is because we had to go through a little pain to get there where you want to share that pain room to say look. This is a journey that I took but more successful Communication in organizations is the other way around. What are we aiming to do? How do we do what were some of the steps overview and then let the stakeholder or your audience ask you the questions that they I would like to know and these questions knowing that they will go down the line of what did you cover these steps in your workflow and obviously dumb questions won't be exhaustive compared to your workflow. They'll have the questions from their perspective. Sometimes they'll point you in new things new directions that you didn't think or expect or one of them is around will be around workflow. And then the other one will be around people how and who so definitely remember after your communication objective outcome details all three short of them and then have a broader discussion with coming from this place of of empathy and focus on business value and outcomes, which is shown by you three step communication outlined great. I hope this was useful in terms of building relationships and communication as a data scientist. Think about empathy think about connecting with people be kind and patient with the side of empathy and also think outcomes. What is the difference that you're making as a person and as a group the difference that you make in your organization, which often means not using the fanciest techniques and Excedrin will do an episode on that, but definitely Outcome and then the third piece of this episode was the communication three-step outline. So empathy focus on Art come and have a 3-step Punchy communication style that every project is described as a as an elevator pitch. And with that you will do really great. Thank you for listening. Let me know how you go with this. I hope it was useful to you soon. That is so Services is Australia's leading executive Search and recruitment provider to the data and analytics industry data source is chosen by many of Australia's most successful and Innovative analytics team working closely to understand customer needs and deliver the top-performing candidates in the Australian Market from Executives and directors through the project managers Bas and Technical Specialists are deep networks allow us to Source the highest caliber of candidate our consultative and personalized approach to the recruitment process. Choose the highest level of service and care across both Contracting and permanent roles, whether you're looking to hire or searching for your next career move. Please contact will at datasource services.com dot EU for more information exciting news listeners University of New South Wales has launched a new master of data science and it's a hundred percent online. They have designed this program to deliver the skills that are in the highest demand and most difficult to find it covers the advanced stats programming machine learning and strategy are has you need to be able to call yourself a true data scientist to find out more visit study online dot unsw dot edu dot a you That brings this episode to conclusion. Thank you so much for listening. Please find us on data futurology.com or on Facebook Twitter LinkedIn or Instagram as that of mutuality. Also go to data futurology.com forward sighs podcast to find the show notes for this and any other episodes if you like this episode it would mean a lot to us if You could leave us a review wherever you listen to our podcast. I hope you enjoyed this episode and that it was helpful and valuable for you. Thanks again and see you next time.
In this episode, Felipe spends time discussing empathy as the core of human relationships. Felipe believes to be a leader in the data science space; half is deeply rooted in the people side. Having empathy is the crucial first step to building relationships with stakeholders. Our stakeholders are scared; today data science is math heavy and dominating the business side of operations. The discipline of data science is taking over organizations and trying to tell people what to do. Enabling people to make better decisions, automating work, and uncovering opportunities are goals of data science. However, stakeholders may not understand the complicated methods of data science and will feel incompetent about this new method of solving problems. Building relationships with stakeholders will require the data scientist to meet people where they are. These conversations will allow each party to gain knowledge from the other and use it to give back to the business. Instead of starting with the steps you took to get the results, start with the objective and give something people can get excited for. Then, go to the punchline and say how you improved and present them with the outcome. Lastly, provide a few details about the process and wait to see if the audience has more questions, or if they are happy with the result. Enjoy the show! We speak about:   [03:05] Empathy: people are scared   [08:00] Outcome: meet the stakeholders where they are   [12:00] Communication: taking stakeholders through the process   Quotes:   “As data scientists, we need to be empathic, non-judgmental, and be able to work with people that have complementary skills.”    “The sooner you can get rid of the judgment, the sooner you can make a difference in more people’s lives.”   Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information  And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Enjoy the show! 