Data Analytics Project Failures and How To Improve Efficiency

According to Dataiku, about 85% of big data projects fail. Though such a large percentage of failure can be discouraging, it is important to embrace it. Data science is complicated and risky – this understanding allows for using failures as stepping stones on the journey of a successful data project. Fail fast, fail often, iterate.

Data is a valuable resource for every enterprise. It is an engine of innovation if used correctly. Enterprises and their partners should focus on data as critical in their business plan. Thus, they should brace for a lot of data analytics stepping stones on their journey.

Big data projects are challenging. An easy first step to making them less challenging is to focus on making sure your teams are fully aware, organized and informed of the end benefits of a model. In other words, you need great collaboration. With modern analytics and data science tools, like Dataiku, all this information is visible throughout teams, accessible to the business and easily deployable for immediate benefit. Check out our blog on collaboration, including 4 must learn tips and tricks here.

Beyond collaboration, another core principle of a successful data and AI project is to use all the existing data on the project to create simple, fast, and lean models (in Dataiku we like to use AutoML for experimental models). These models allow the experimentation of a successful prediction system, simply by trial and error. These simple models are expected to fail, though that shouldn’t be the main intention. The process of model creation to failure stage should be as streamlined as possible. Each team or member failing, learning, and improving each iteration.

Start from Nothing

Starting from zero is a risk and long times to deploy a model can be an intensive hurdle. A collaborative model is a way to overcome this. An example of a collaborative model can be seen in team practice. To correctly structure a big data project, the project task and team separation is the critical initial step.

In a traditional project, you'll have to organize your task into split teams. This ensures collaboration. This workflow will help to prevent many conflicts in a project as it allows you to focus on one work area rather than a group.

With an effective collaborative workflow, you can focus on what is useful to your team, what is less risky and to avoid potential conflicts.

The main take-away is to reduce risk of project time-loss by splitting tasks and model components. The resulting benefit comes in the form of data discovery trends from the team tasks as well as early discovery of road-blocks.

 

Setting Realistic Goals

Using the S.M.A.R.T methodology of goal setting, we should expect to achieve maximum collaboration through team members by making their piece of the model available throughout the development cycle. Each piece is tested and only the highest performing are integrated into the final solution. This allows the development of predictive model solutions which are Specific,  Measurable, Achievable, Realistic, and Timely.

It’s very important to frame the ultimate data-driven goal in specific questions. Answering these key management questions will enable you to make smarter, more effective decisions to keep your company successful and profitable so long as it provides business value.

With today's data science efforts being made both easy to execute and complex, it takes time to build and expand your data analytics efforts. Smaller models, safer, and more natural for data scientists because of their simplicity, scalability, and flexibility.

 

Efficient Failure Recovery

You must try to understand your data. If your data project is based around multiple components, the splitting of these components allows for more streamlined development. As soon as the conceptual hurdles are overcome, you should know that you can build bigger, more streamlined data projects. Don't panic about the technical obstacles you create for yourself and others. Using the ‘smart’ goal system previously discussed, will allow for a fast iteration of issue recovery.

It’s important to cycle through several simpler, smaller projects at a time to guarantee maximum efficiency. The next step is to document failures and in record the parameters used in the projects to tune and optimize in subsequent iterations. 

It takes hard work, not effort in a few clicks over a thousand lines of code. It takes smart people to make you use and protect your data. But in the end, the hard work of a prepared team is what pays off.

In considering all the data projects you create, failure is unavoidable. Though the ‘smart’ project methodology is a good base for starting, it’s important to know it’s not fail-proof. Understanding failure and iterating through failure quickly using the steps highlighted above provides the ultimate way to achieve a business impacting data model project.

 

Simple Steps to Data Driven Business Growth

The following are a few key things to consider when building your team or going through the initial steps of a data analytics or AI project. 

  • Step 1 - Obtain and process data,
  • Step 2 - Segment analytics team with individual tasks,
  • Step 3 - Sharing and simplifying a large project/model into smaller steps using the S.M.A.R.T. methodology of goal setting. 
  • Step 4 - The hardest work comes before a data project becomes a reality. Small, less risky models are the way to go and Dataiku’s interface makes this process simpler.

For more help please follow our other articles and guides. Here at Excelion we strive to provide top analytics services. We work with cutting edge technologies like Dataiku to implement big data analytics and result in business growth from big data. Here is one of our success stories as proof.