How to Survive Corporate Politics as a Data Scientist
30 tips to make sure you stay on the right track
Whether you’re a new data science team or an experienced one, we’ve created a list of the top 30 points to help ensure your team is effective and succeeds on its projects. As data scientists, we want to spend our time analyzing problems and rather than dealing with corporate politics. How can we do that when there is often a rift between executives and their data science teams. This can lead to a lack of trust, poor implementation and support of projects and thus, failed data science teams. Let us know if you think we missed something in our top 30 list!
1. Get Executive Ownership
One of the key contributing factors for any project is getting executive buy-in. It is your job as a data science program manager, project manager or data scientist to get your executives to believe in your project. Without their approval and funding, your project will not go on. When executives see that your data science project will help drive their strategy they will be all in.
How do you get data science and business to play nice?
2. Be Bold, Tell Your Manager They Are Wrong with Data
Managers and executives are humans, they can only process so much information at once and see so many correlations. As a data scientist, you have access to the know-how and tools to process 100x that amount of data and accurately look at thousands of correlations. Use that knowledge and boldly show your manager where they are wrong. No, we don’t mean be a dick about it. Support your manager. Help them go into meetings with the right conclusions. They will thank you for it.
3. Gain the Trust of Your Peers
As we travel around helping different teams, we’ve realized that many managers don’t even trust their data. Yet, they want new dashboards, data science teams, the whole nine yards. What is the point though if you can’t even trust your data? Our favorite quote from Sherlock Holmes talks about how data is the foundation for the building blocks of thinking. If that is true, and you don’t trust the house you have built, it will fall on top of you. Get your managers to trust you and your data!
4. Successfully Implement a Simple Project First
Look we get it, everyone wants to develop the next Google or Facebook algorithm. They are cool, super powerful and rack up billions of dollars a year. However, if your team is just starting out and you want them to succeed, start small. Even something simple, if done well, can provide your executives with insurmountable value. Once you get that first win under your belt. Executives will be begging you to help them with everything.
5. Communicate the Value of Your Project
One way to get buy-in from executives is to be a salesman. Express why they need the project. Data science is still new and many executives don’t know why and where to use it. Show them how to take data science and turn it into saved money, resources, etc. That is your job!
6. Standardize Your Data Science Processes
Data science has tons of tools that allow for great insight. However, like software engineering, without processes, you can fall behind projects, make poor products, and fail to maintain finished projects. This means you need to document your processes. It seems like a waste of time until you start having internal breakdowns of projects. So make sure you implement amazing data science processes early on!
7. Don’t Box in Your Data Science Teams
Don’t limit your data science team to what you know. Challenge them, ask them what they think, ask for their input and don’t hold them back. Let them know you support them and trust in their abilities. Data scientists are smart people, they just need to know you believe in them (like tinker bell).
8. Plan, Plan, Plan.. but Don’t Plan Too Much
If there is anything we have learned from our different experiences and projects its that you need to plan. This ensures you don’t go out of scope and stay on track. You’ll have a good handle on all of the data sources and requirements and will ensure you succeed. However, things change quickly in business today, so you can’t take 1 year to plan a project. When a project enters the pipeline, you need to jump on getting requirements. Just don’t spend all your time planning and never developing.
9. Play Well with Other Departments
Business is a team sport. You have accounting, finance, operations, and sales departments that your team needs to work with. They all usually have their own data warehouse and you need that data! If you are lucky, one central team manages all the databases. Even so, I still need the expertise from multiple teams. Additionally, those teams will probably have some requirements for your projects. So make sure to play nice.
10. Learn from as Many SMEs as Possible
Like we mentioned in the last point, you want to gain as much expertise from different departments as possible. Data scientists aren’t pharmacists or doctors, they aren’t accountants or financial managers. We need to gain some insight into the business or subject from those who know it best. When starting a new project, make a list of the topics and data you need and seek out those SMEs.
11. Remove Company Bias
Even though you should get SMEs opinions, don’t allow their bias to block new insights. It happens all the time. Executives, managers, and other team members believe that business drives have always been “XYZ”. Then, your team comes in with new insights, but instead of bringing it to managers the team buries it because it would go against the status quo. That is not your job as a data scientist. Your job is to challenge the status quo!
12. Challenge the Status Quo
Look, as a data scientist, you have data on your side. That means, when you are right, you are right. We don’t mean be a jerk, we just mean don’t be afraid. Don’t let managers or executives back you out of your opinion. In all honesty, they want your opinion. They want you to give them information that they can go to their bosses with and stand confidently. At the end of the day, your boss has a boss. Guess what, they feel the exact same way you do when they talk to them. So tactfully challenge your bosses opinion using data!
13. Use Data to Drive Initiatives
We believe you are recognizing a theme now. Use data! There is so much power there. It isn’t even a new idea. People have been using data forever to prove things. Science relies on the methodology of repeatedly proving theories with data, even ones that we consider true today. Do the same with every initiative. Why are you doing it? What is driving it? It better be data.
14. Build a Prototype First for Early Buy-in
How do you get early buy-in? Build a prototype (sure, in python)! Show your team and manager what it can do. People want action, not just theories and words. Set up a prototype and if you can, get real data. If you can’t then pump it with some data but make sure the functionality is there. Make it tangible, interactive, and actionable!
15. Design for robustness and maintainability
We can’t stress this enough. Make sure whatever dashboard you build, process you set in place, or algorithm you develop is maintainable. If you leave the company tomorrow, will the project still work or will people curse your name? Seriously! Don’t be the employee who leaves behind no documentation and never shares their code.
16. Work to Automate Yourself Out of Boring Work
Stop doing boring data munging and QA manually. Just stop. When you first design your system, make sure as much of the boring, basic work is automated. Don’t worry, your company will have plenty of more data science projects when you are done. You will be much better off putting the basic stuff at the will of a computer than having to spend 2 hours a week uploading data.
17. Get a Data Science Guide
There are a lot of data science consulting companies that will develop a data science guide of good business practices for your team. They assess your team’s current status and work alongside them to realize where your team could be more effective. Often times, this is skipped by most teams, but it is helpful to bring in outside help.
18. Write Your Own Data Science Guide
Maybe you have an amazing data scientist on your team that can do their work while developing a good handbook for your team. That means onboarding, coding practices, system documentation, etc. If so, get them to build it. Trust us, nothing is more helpful than walking into a company and getting documentation. Then you can assess early on what is going on, develop a new solution and get out fast. This saves your company money and makes data science consultants happy.
19. Learn to Creatively Gather and Classify Data Before You start
Do you know what is a bad idea? Having 20 analysts classify 50,000 data points for one month. This is a terrible waste of both resources and finances. When you develop an initiative, try to develop a method of data classification that doesn’t require analysts. Try to crowdsource public opinion, offer a service, design a new product, whatever it has to be. But try to avoid getting your teams involved in tedious work.
20. Build Your Systems to Properly Gather Data First
Sometimes, you are lucky enough to work on a project that is the first of its kind. The system itself will be gathering data for your analysis in six months. Build it with the end in mind. Think about how you want to use the data, the other systems you want it to interact with, etc. Don’t merely build a functional system, and then add in the data gathering component as an afterthought.
20. Collect as much clean data as possible
Data comes from all different sources. You can get it from internal warehouses, external APIs and just about everywhere else. Gather as much of it as you can, and make sure it is managed and clean.
21. Be a Great Story Teller
When it comes down to it, we all have bosses that we need to convince. As a data scientist, you have to do this all the time. Why do we need to tell great stories? Because you have a data-backed opinion that could get really boring otherwise. You could start talking to executives about the different percentages and standard deviations and watch their eyes glaze over. Instead, develop an infographic, presentation, or anything other than a bunch of numbers. You want to elicit passion and emotion for your cause. More executive buying in means more funding and an increased likelihood of success.
22. Communicate the Internal and External Values of Data Science to Management
Data science has the ability to affect both customers and employees. Tell your managers how it will work and its value. This point is somewhat repeated through this list, however, the more angles you can communicate to management about your project’s value the better. Data scientists love to talk about how their algorithms are calculating the probability a person is scratching their nose when they are scrolling through Facebook but, the business teams only care how much money they can make from that knowledge. Otherwise…why?
23. Learn Management’s Processes
Your team needs to know how management works. How many committees does your project have to go through, how often do they meet, what do they like to see? The better you know how upper management works, the better you can help drive their processes in the right direction. Also, the easier it will be to get your funding and projects through oversight.
24. Understand Executives Strategies and Why They Make Decisions
Executives have their own politics going on, their own processes, and their own strategies. Many employees don’t even know what is going on behind closed doors. One of the things we push for is a more open discussion between data scientists and their executives. Once you have an idea of what is going on at a higher level, it is much easier to start developing projects and programs that more closely align with that strategy.
25. Be Able to Explain to Your Managers and Leadership Your Failures
Failures happen all the time in the world, especially in the data science world. Make sure you can tell your manager why and ask for help when you need it. Don’t let a project sit half finished because you are stuck on a small problem that requires outside intervention. That makes everyone lose.
26. Seek Outside Intervention When Necessary
Sometimes seeking outside intervention becomes necessary. This may mean hiring a consulting team or new employees. There are times projects grow or there is a temporary influx in the number of projects. Getting some temporary employees to meet timelines is not a bad thing. Spending a little on a project that could save millions makes sense.
27. Read Just Enough Outside News to be Inspired
Too much outside news can bog down the mind. It may cause a fear of falling behind, as your projects may not be where these other titans of industry are at. Don’t worry, just read enough to inspire you to move forward, but not so much that you think you can never compete.
28. Question Every Project You Do
If a manager comes to you with a project, even if it has support from other senior VPs or executives, question it. Why are you doing it? Who will it affect? How much will it save? You might be the one finding these answers too, but make sure you know. Otherwise, you might be working on a dead project.
29. Be Positive!
Corny, but true. It is really easy, especially if you are a data science project manager, to lose hope. Maybe the insights your team has come back with are not very valuable or maybe they haven’t found anything at all. Guess what, that is much more common than you think. Not every project will lead to instant success. Be patient and be positive. If your data is clean, and your data science practices are solid. Something will eventually shake out.
30. Make a Decision, Give an Actual Opinion
As a data scientist, you have power. You have data, that means you can make conclusions with confidence. Don’t forget that. Say things like:
- The best decision would be to…
- I propose we…
- I know that…
- Let’s try solution x because….
If you want to learn more about data science, or need a data science consulting team! Feel free to contact us! Also, comment below or connect with the writer on Linkedin to ask any more questions
