32 Reasons A Data Science Project Will Fail
Data science projects fail all the time! Why is that? Our team of data science consultants have seen many good intentions go wrong because of failure to empower data science teams, locking away access to data, focusing on the wrong problem, and many other problems that could be avoided! We have written 32 of the reasons we have seen data science projects fail. We are sure there are more and would love to get comments on what your teams have seen! What makes a data science project team succeed?
1. The data scientists aren’t given a voice
Data science and strategy can play very nicely together when allowed! Data scientists are more than just over glorified analysts! They have access to possibly all the data a company owns! That means they know every movement the company has made with every outcome (if the data was stored correctly). However, they are often left in the basement with the rest of the tech teams forced to push out reports like any other report developer. There is a reason companies like Amazon, and Google continue to do so well! It is because the people with the Data have a voice!
2. Starting with the wrong questions.
Let’s face it. Most technology people often focus more on how cool a project is, not how much money it will save the company. This can sometimes lead to the wrong business questions being answered! This will lead to a team quickly either failing, or losing value inside of the company. The goal should be to do as much to hit high value business targets as possible. That is what keeps data science projects from failing or at least, being unnoticed.
3.Not addressing the root cause just trying to improve the effect of a process
One of the most dubious and hard to spot until it is too late is not realizing a data science team wasn’t even looking at the actual cause of the problem. When our data science team comes in, one of the things we assess is how a data science team develops their hypotheses. How far do they dig in the data, how many false hypotheses do they think of. How about other causations that could cause a similar output. An outcome can have a very deep root.
4. Weak stakeholder buy-in
Any project, data science, machine learning, construction, or any other department will fail without stakeholder buy in! There needs to be an executives to own the project. This gives a team acknowledgement for their hard work and it also ensures that there will be funding! Without funding, a project will come to a dead halt.
5.Lack of access to data
Slightly attached to the previous point. Locking access away from data scientists, whether it be tools or data is just a waste of time. If a data scientists is forced to spend all day begging a DBAs for access, don’t expect projects to finish any time soon!
Any data specialist (data engineer, analyst, scientist, architect) will tell all managers the cliche saying. Garbage in, garbage out! If the data science team trains a machine learning model on bad data, then it will get bad results. There is no way around it! Even if an algorithm works with 100% accuracy, if all of the data classification is incorrect, then so are the predictions. This will lead to a failed project and executives no longer trusting the data science team.
7.Relying on Excel as the main data storage….or Access
As data science consultants, our team members have come across plenty of analytics and data science projects. Often times, because of lack of support, data scientists and analyst have to construct make shift storage centers because they are not given a sandbox or server to work on. Excel and Access both have their purposes. One of them is not managing large sets of data for analytics purposes. Don’t do that to a data scientists. This will just get poorly designed systems and high turn over!
8. Having a data scientist build their own ETLs
We have seen ETL systems built from R because instead of getting an expert ETL developer a company was allowing the poor data scientists a crack at it. Don’t get us wrong, data scientists are smart people. However, you would much rather have them focus on algorithms and machine learning program implementations instead of spending all day engineering their own data warehouses.
9. Lack of diverse Subject Matter Experts
Data scientists are great with data and often a few subjects that revolve around the data they have worked with. However, data, and businesses are so very different. Sometimes this means a company needs to partner the data science experts with experts. Otherwise, they won’t have the context to better understand complex subjects like manufacturing, pharmaceuticals and avionics.
10.Poorly assessing a team’s skills and knowledge of data science tools
If a data science team doesn’t have the skills to work with Hadoop, why would you set up a cluster? It is always good to be aware of a teams skill set first. Otherwise they won’t be able to produce products and solutions at the highest level. Data science tools vary, so make sure you look round before you make any solid decisions.
11.Using technologies because they are cool and not useful
Just because you can use certain tools for a problem. Doesn’t mean it is always the best option. We wouldn’t recommend R for every problem. It is great for research type problems that don’t need to be implemented. If you want a project to get implemented into a larger system, than python or even C++ might be better(depending on the system). Same things goes for Hadoop, or MySQL, or Tableau and Power BI. They all have a place. Don’t let a team do something, just because they can.
12. Lacking an experienced data science leader
Data science is still a new field. That doesn’t mean you don’t need a leader who has some experience working on a data science team. Without one that has a basic understanding of good data science practices. A data science team could struggle to bring projects to fruition. They won’t have a roadmap for success, they will have bad processes and this will just lead to a slew of other problems.
13. Hiring a scientists with limited business understanding
Technology and business are two very different disciplines and sometimes this leads to employees knowing one subject really well and failing to know the other at all. This is ok if a small percentage of the data science team are built up of purely research based employees. It is important to note that some of them should still be very knowledgable of how to act in a business. If you want to help them get up to speed quickly. Check out this list of “How To Survive Corporate Politics as a data scientist”.
14. A boss read one of our blog posts and now thinks he can solve world hunger
Algorithms can’t solve every problem, at least not easily! If this were true, a lot more problems would be solved by now. Having a boss who simply went to a data science conference and now believes he or she can push the data science team to solve every business gap is not reasonable. Limited resources, complexity of subjects, and unstable processes can quickly destroy any project.
15. The solutions are too complex
One mistake executives and data scientists make is thinking their data science models should be complex. It makes sense right, data science is a complex, statistics based subject. This is not true all the time! The simpler you can build a model, or integrate a machine learning solution means a data team will have an easier time maintaining the algorithm in the future.
Most technology specialist dislike documentation. It takes time, and it isn’t building new solutions. However, without good documentation, they will never remember what they did 1 month ago, let alone a year ago. This means tracking bugs, tracking how programs work, common fixes, play books, the whole nine yards. Just because data science teams aren’t technically software engineering teams, it doesn’t mean they can step away from documenting how their algorithms work and how they can to their conclusions.
17. The Data science team went with every new request from stakeholders(scope creep).
As with any project, data science teams are susceptible to scope creep. Their stakeholders demand new features every week. They add new data points, and dashboard modules. Suddenly, the data science project seems like it can never be finished. You have half a team focused on a project that managers can’t make their minds up on. Then it will never succeed.
18. Poorly designed models that are not robust or maintainable :
Even well documented bad systems lead to quick failures. Data science projects have lots of moving pieces. Data flowing through ETLs, dashboards, websites, automated report, QA suites, and so one. Any piece of these can take a while to develop, and if developed badly even longer to fix! Nothing is worse then spending an entire FTE on maintaining systems that should be able to run automatically. So spend enough time planning up front that you are not stuck with terrible legacy code.
19. Disagreement on enterprise strategy.
When it comes down to it. Data science offers a huge advantage when implemented well for corporate strategy. That also means the projects being done by some of the more experienced data scientists need to closely align with a directors and executives strategy. Strategies change, so these projects need to come out fast and be focused on maximizing the decisions making of executives. If you are producing a dashboard focused on growth, but an executive team is trying to focus on rebranding, you are wasting time and money!
20. Big data silos or vendor owned data!
You know what is terrible. When data is owned by a vendor. This makes it so hard for data science teams to actually analyze their companies data. Especially if the vendor offers a bad API, none at all or worse, they charge you just to use it. To get a company’s data! Imagine, a poor data science budget going to buy back the data! Similarly, if all the data is in silos. It is almost impossible for a data scientists to bring it all together. There are rarely crosswalks or data standards so they are often stuck hopelessly starring at lots of manual work to make data relate.
21 . Problem avoidance(Ignoring the elephant in the room!)
We have all done it! Even data scientists! We know the company has a major problem, it’s the elephant in the room and it could be solved. However, it might be part of company culture, or a problem that no one discusses because it is like the emperor with new clothes. This is sometimes the best place for a data science team to focus.
22. The data science team hasn’t built trust with stakeholders
Let’s be honest. Even if a team develops a 100% accurate algorithm with accurate data, if a team has not been working to build executive trust the entire time, then the project will fail. Why, because every actionable insight a project provides will be questioned, and never implemented.
23. Failing to communicate the value of the data science project
One of the problems our data science consultant team has seen is teams failing to explain the value of a project. This requires…data! You have to use financial numbers, resources saved, competitive advantage gained, etc. To prove to the executives why the project is worth it! The data scientists, use that to help prove their point!
24. Lack of a standardized data science process
No matter how good the data scientists are, without some form of standardization, a team will eventually fail. This may be because a team has to scale and can’t or because a team member leaves. All of this will cause a once working machine to fail.
25. If You Failed To Plan, Plan to Fail
When it comes down to it. There needs to be some amount of planning in the data science projects. You can’t just attempt to find some data sources, make assumptions, attempt to implement some new piece of software without first analyzing the situation! This might take a few weeks and the executives should give you this. If they really want a sustainable piece of software.
26. The data science team competes with other departments(rather than working together)
For some reason or another, office politics exist. Data scientists can often accidently walk over every other department because they are placed in position to help develop strategies and dashboards for the entire company. This might take away jobs from other analysts completely. In turn, this might start fights. So make sure the data science team shares and shows how their projects are helping rather than hurting!
27. Allowing company bias to form conclusions before the data scientists start
Data bias does exist! As a data scientist you can make algorithms and data say whatever you want them to sometimes. However, that doesn’t make it true. Make sure you don’t go into the project with a biased hypothesis that will push you towards early conclusions that might be incorrect.
28. Try to take on to large of a first project
Reading the news about what Google and Facebook are doing with their algorithms may tempt the data science team to take on too large of a project for their first projects. This will not lead to success. You might be lucky and succeed. However, you are taking a huge risk!
29. Manually classifying data
One part of data science that not everyone talks about is data classification. Not just using SVM and KNN algorithms. Nope, we mean actually labeling what the data represents. Someone human has to do that first. Otherwise, the computer will never know how to. If you don’t have a plan on how to classify the data before it gets to the data science team, then someone will have to manually do that. That is one quick way to lose data scientists and have projects fail.
30. Failing to understand what went wrong
Data science projects don’t always succeed. The data science team needs to be able to explain why. As long as it wasn’t a huge drop in the capital budget executives should understand. After all, projects do fail, it is natural. That doesn’t give you an excuse to not know why.
31. Wait to seek out outside help until it is too late
Sometimes the data science team is short on staff, other times you just need new insight. Whatever it might be. The data science team needs to make sure it seeks outside help sooner rather than later. Putting off for help when you know you need it will just lead to awkward conversation with management. They might not want to spend the money, but they also want a project to succeed.
32. Fail to provide actionable insights and opinions
Finally, the data science teams data science project needs to provide actual insight, something actionable. Simply providing a correlation, doesn’t do any good. Executives need decisions, or data to make decisions. If you don’t give them that, you might as well not have a data science team.
If you have any questions, please feel free to comment below! Let us know how we can help!
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