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      "body": "It’s that time of year again in America, spring break time. When everyone takes a break from the cold and goes on a little vacation because their kids get a week off from school. It’s a great time for me to take a break from my usual as well. Normally I talk about data and its various aspects but something has really grabbed my attention lately. That’s the need for change in the corporate world. It’s not just an American thing either, it’s going on in China, Australia, Russia, the EU, Brazil, you name it. What is it? The need for “change.”\n\n \n\nI am quite blessed that I get to talk to many companies and get interviewed to help companies to make changes. Although I don’t like the term change agent, it is a term that most know. Lately I have been approached by a few companies and recruiter for roles in those companies looking for change. Life changes, it is the only constant we have. And for a variety of reasons, companies stop changing and then go looking for someone like me who has created change in the past. Now I love my work, it is fun and rewarding. Creation of new ideas and making them a reality is probably the most fun I can have and it is great that I get paid for it too! But I want to point out that it isn’t always fun. Sometimes I run in a wall of change but not change. Companies know they need to change but they don’t really want to change. And this can be solved with a few simple things I would like to cover here.\n\n \n\n \n\nChange is Not Easy\n\nI have worked in a number of companies and different industries. I know that many people don’t like change because often past experience have been negative. But when the opportunity for change comes along, it doesn’t have to be like watching a bull in a china shop. Change, when done right, can be a very exciting and fulfilling experience for leadership and employees alike. Too often I see leadership talking positive to the team and then thinking about who gets laid off. Nobody gets fooled by that. \n\n \n\nBe honest, be open and most importantly, be engaging. This is a journey and a great leader knows they must bring in the team, not some outside consulting firm to do the work and come up with some change management process. The best change is done from the inside and led from the inside. \n\n \n\n \n\nChange Means You Too C-suite\n\nRecently a few companies have approached me and talked about changing their team because, Amazon or something similar. They want to go in a different direction yet the leadership doesn’t actually want to do any changing themselves. Sorry guys, but the ones that really need the most changing is often at the top. Who makes the decisions that impacts the whole company? Change always needs to be from the top, change yourself and become friends with change.\n\n \n\n \n\nDon’t Hire the Same Person!\n\nThis one always makes me roll my eyes. A company wants change, they want someone who can bring in real change and stir up the company and the industry. So, what do they do, hire someone who is an industry insider. Honestly, how much change do you really think will happen. Hey, I got coffee with one sugar instead of two, check me out, I’m being all cool and innovative right? No.\n\n \n\nI don’t deny that industry experience is important, it is! However, when you want change and disruption to take place, someone who has done it different at your competitor is not going to give you that, they will give you a coffee with a little less sugar in it when you really wanted a totally different drink all together.\n\n \n\nChange is About People\n\nI always find it interesting how everyone is so focused on tech as the change. Data is about human activities. Tools are meaningless, they change all the time. Fundamentals of how people engage technology and other people to create data is far more important. Can your people talk personas? Can they talk about the fundamentals of data: Uncanny valleys, day parting, Aspect to Aspect? No, they you need a different change agent!\n\n \n\nChange is a great thing for a company. Make sure you get the right person to make changes. That starts with your leadership. Stop chasing the fads and go for real substance change. It is a far more rewarding journey.",
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      "body": ">  to get superseded. It is shocking\n\n![](https://steemitimages.com/0x0/https://steemitimages.com/DQmSRdJf6PfTRu7r3oqyywzgpVcxzMwY4dPQgaWh17qUjCg/mildshock.gif)",
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      "author": "quantum999",
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      "body": "![sadfsd.jpg](https://steemitimages.com/DQmXWws9MBrCsPPnRRmiFJTfZ2NTj84UCQf7i37sTeec8CJ/sadfsd.jpg)\n\nI've had several conversations recently with people I know in the data science space that always start out about business and then drift to the state of data science as a whole. One theme constantly comes up in these conversations: There are a lot of people currently running data science teams at large organizations and the vast majority of them — I believe we are talking about 80-90% — want to leave their jobs. Why is that? Within smaller organizations, the number isn’t as great. So what is going on in larger organizations that is causing such a mass exodus? Having worked in and with many large organizations in a data science leadership capacity, I have a few theories. \n\n\nAcademia Can't Do For-Profit\n\nMany large companies have fallen into the trap of believing that you need a PhD to do data science. You don’t. Of the top five data scientists I have ever worked with, only one had a PhD and it wasn’t even in stats or data science — it was in biophysics. I call this the \"academia trap.\" There are some smart people who know a lot about a very narrow field, but data science is a very broad discipline. When these PhDs are put in charge, they often discover they are out of their depth. They were never taught how to run a P&L, manage a team, or deal with people, competitive intel, market assessments, building a business case, etc. \n\nAdd to this the world they came from: Many peer-reviewed papers in the academic world that are really good don’t see the light of day. Why? The reviewers may have a competing theory and don’t want their ideas to get superseded. It is shocking how often this happens in the data science space. I have always found the academic world to be more political than the corporate one. When your drive is profits and customer satisfaction, the academic mindset can be more of a liability than an asset. I have yet to see a data science program I would personally endorse. In my opinion, most are run by people who have never done the job of data science outside of a lab. That’s not what you want for your company.\n\nIf you’re not willing to give data science managers the training and support they need — or create an organizational structure that brings people with business backgrounds into data science strategy — what will happen is your budget will grow and your results will drop. I’ve seen leadership teams spend $10 million plus a year with very little return. I have never spent that kind of money, yet I have built billion-dollar data products. I’m also not a PhD. The difference is, I know business. PhDs do great work, but the type of work that they excel in isn’t what most companies are asking them to do in this new age of enterprise data science. Don’t make this mistake.\n\n\nWrong Expectations\n\nDoing data science and managing data science are not the same, just like being an engineer and a product manager are not the same. There is a lot of overlap, but overlap does not equal sameness. Sometimes, I envy data scientists — much of their time is spent cleaning data sets, testing algorithms, and researching new methods. Compared to the job of someone who needs to run the practice, a data scientist has it pretty good.\n\n    \"Doing data science and managing data science are not the same.\"\n\nThe leader of a data science practice needs to focus on data governance, MDM, compliance, legal issues around the use of algorithms, and documentation just in case someone sues for wrongful use. There are hiring issues and staffing problems to deal with, budget and funding to gain, P&L to run, business cases to build, market research to conduct, vendor meetings to hold, tech life-cycle management, the evangelizing of projects (both internal and external), and turning that work into data products that sell — all this while trying to ensure profit for the company. Big difference.\n\nMost data scientists are just not ready to lead teams. This is why the failure rate of data science teams is so high right now. Often, companies put a strong technical person in charge when they really need a strong business person in charge. I call this person a data strategist. Right under that data strategist is the strong technical person; there needs to be a very solid and strong interpersonal relationship between the two. If they are competing, it will cause friction and less-than-desired results.\n\n\nBad Methods\n\nAgile has taken the tech world by storm. It works fairly well for software development and, as a result, many companies enforce it on data science. But data science is not software development — it’s really a field of discovery, whereas software development is about assembly. I have worked with companies that demand agile and scrum for data science and then see half their team walk in less than a year. You can’t tell a team to solve a problem in two sprints. If they don’t have the data or tools, it won’t happen.\n\n    \"You can’t tell a team to solve a problem in two sprints. If they don’t have the data or tools, it won’t happen.\"\n\nData science is a discipline that requires its own methods. In addition, most companies are still treating data products like they do physical products — but the economics are not the same. When I build a recommendation engine, my cost per unit is pretty much zero, unlike a physical product which certainly has a per unit cost. I can make a million product recommendations with that engine or just one and, other than the electricity, my cost per recommendation would be the same. The cost of a physical product would be a lot more and involve many different variables if I wanted to increase the number from one to million.\n\nWe have to understand that the economics of data products are different. A lot of large companies don’t even have this conversation, which can cause a lot of frustration for those in charge of data products. In essence, they have one hand tied behind their back.",
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      "title": "Why Are Data Science Leaders Running for the Exit?"
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