This article was written by a guest author, Bunmi Akinremi. Bunmi is a data scientist and Android developer. She's passionate about using AI to build apps with better user experiences. Bunmi is also interested in leveraging AI to solve environmental problems, such as plastic pollution in the oceans.
Data is like crude oil: It isn’t especially useful until it undergoes some degree of refinement. Data needs to be processed and refined to inspire meaningful action and create valuable insights. Getting insights from data is the most crucial job that data scientists do, and yet it isn’t what data scientists spend most of their time on. Instead, data scientists spend most of their time simply preparing data to be analyzed.
Data processing can be labor-intensive, time-consuming, and somewhat convoluted. You’re likely to get bogged down performing repetitive, experimentation-driven tasks such as data sourcing, cleaning and enrichment, visualization, and model experimentation and evaluation. These iterative processes are often inefficient and demand more of your time than reasonable.
These inefficiencies are compounded when trying to perform data preparation, analysis, and visualization across different platforms. For example, you might begin by combining different data sources and transforming, cleaning, and modeling the data in a locally-hosted Jupyter notebook, deploying and monitoring the model in production using cloud services, and then using a separate tool for visualizing performance and insights.
Dataiku provides a unified platform where you can efficiently perform all these processes in one place. Dataiku systemizes and centralizes the use of data, automating repetitive processes and making it simpler to complete your data analysis from data collection to visualization.
This article explores five of the biggest time sinks that data scientists experience and highlights how Dataiku frees up your time to focus on solving business problems with your data.
1. Cleaning and Organizing Data
Cleaning and preparing data is typically the least straightforward and most time-consuming process in an analytic workflow. One survey shows that cleaning and organizing data consumes 60% of a data scientist’s time and that 57% of data scientists find it to be the least enjoyable part of their work.
During this stage of a project, you need to blend data sources, convert data from one format to another, handle duplicate, missed, or invalid values, and complete other tasks that ensure high quality and the proper format for the final data that will be used for modeling or analysis.
Additionally, if the challenges of having large quantities of data to sort through weren’t enough, the risk for error when repetitively cleaning and organizing data presents a significant pain point. A minor formatting or copy-paste error could cause major quality issues and problems downstream.
Fortunately, Dataiku offers a way to clean and organize your data more efficiently. In addition to offering tips and tricks for preparing and cleaning your data, Dataiku provides more than 100 data transformers designed to help you handle the tedium of data wrangling and enrichment. Some of its features include:
- Connecting to data sources both on-premises and in the cloud.
- A visual interface that facilitates building pipelines and common tasks, such as merging datasets, aggregations, filtering and splitting, deduplication, and more.
- Automation features so you can build a data pipeline once and automatically refresh pieces of it as often as needed after that.
Additionally, you can extend the built-in tools to write custom formulas and code for bespoke transformations that don’t exist in the Dataiku library. Use visual tools for maximum speed or write custom code for maximum flexibility. At every step, the choice is up to you.
2. Performance Comparisons
Choosing the best model to deploy is an iterative task that requires you to perform multiple experiments to determine which combination of data, hyperparameters, and algorithms produces the best outcome while still staying aligned with business and technical objectives. However, while logging model hyperparameters and the settings that lead to specific model outputs is key to model reproducibility, it’s an easy way to waste time.
A common way to log model parameters is to programmatically store them in dictionaries, running different model experiments using the same code framework but systematically applying different hyperparameters to determine which model works best. But these seemingly endless comparison processes lead to a significant time loss. Moreover, logging and evaluation in this way is a very tedious, time-eating process.
Instead of manually comparing model performance, you can turn to Dataiku to help capture all the relevant artifacts and visually compare your model evaluations. It automatically logs model details like the time, the algorithm used, which dataset you use, and the evaluation name. It also tracks which features were used (and which were rejected) and compares the performance metrics of the different models to help you evaluate models more easily and efficiently.
Plus, you can even track custom model experiments performed programmatically outside of Dataiku and import these external models to evaluate them against existing model versions either under development or in production.
3. Explaining Models
Ensuring that your model is learning correctly and generalizing well — and not just that it’s giving high accuracy scores — is crucial. Determining partial feature dependence, spotting potential bias, and explaining row-level predictions can be tricky. And generating and storing these types of explainability and interpretation metrics manually for each experiment is time-consuming and tasking.
Dataiku’s model explainability features help you with this process. Dataiku provides advanced, explainable AI capabilities that help you interpret your models and create reports on feature importance and partial dependence plots, individual prediction explanations, subpopulation and model fairness analyses, and even interactive what-if analysis. With this information, you and your business stakeholders can better understand how your model behaves and what factors influence your model predictions.
Moreover, advanced features like outcome optimization allow you to go beyond predictive toward prescriptive, enabling users to prescribe what specific input changes would lead to the desired outcome.
4. Documentation
Every data scientist loves when a project is properly documented, but no data scientist enjoys creating that documentation. Creating proper documentation for a complex data pipeline, or for your model settings is a challenge. It’s not a technical challenge, more of a time management challenge. Not only is there a lot to cover to achieve proper documentation, but it is constantly in flux, meaning documentation has to be updated regularly. The result is that many pipelines go undocumented or under-documented, hurting future data scientists working on the project and potentially leading to operational or regulatory risk.
With Dataiku there is an easier way. They have created an automated export function that fully documents both the flow of the pipeline and the model settings, meaning you have fully updated and accurate documentation at all without having to write it yourself. This approach saves time on both ends. Not only do you save time by not having to spend hours meticulously documenting your pipeline and models, but whoever inherits the system from you has everything they need to understand it quickly and completely.
5. Explaining Results
Explaining your technical findings and interpretations to a non-technical business audience is challenging and requires exceptional presentation and communication skills. Images can often communicate faster and more effectively than words, so visualization is an invaluable communication method when you’re explaining your results.
However, exporting your findings to a different platform from the one you’ve used to complete your analysis can be stressful. You have to work within the confines of how this secondary platform can save, represent, and visualize the data, and you also need to worry about adjusting to the secondary platform’s data standards and security practices. Instead of using multiple platforms just to explain your results, you can use Dataiku’s data visualization capabilities to create your data visualizations in the same place you analyze your data.
Additionally, you might want to tailor the presentation of your findings differently for different audiences. For example, if you’re sharing the results of your analyses with a highly technical audience — like your lead data scientists — you’ll want to present your results in a way that emphasizes and clearly articulates feature importance, feature dependency, and what model and selection of data you used during your analysis. Conversely, if you’re sharing findings with the line of business or executive audiences, the methodology is less important than the projected business impact, and technical details should be abstracted away.
Dataiku empowers you to quickly visualize results in dashboards and easy-to-use web applications that include a wide variety of charts and graph types, geospatial maps, and plenty of interactive elements. With these different presentation options, you can select a visualization format that aids stakeholders, colleagues, and business leaders in understanding the information you share easily. Plus, Dataiku integrates with dashboarding tools like Power BI, Tableau, and Qlik for extra visualization capabilities.
Spend More Time Creating Data Solutions
You’ve likely experienced some of the major time wasters we’ve explored here and felt how significantly they impact your overall productivity — not to mention your job enjoyment. Therefore, it is quite easy to imagine the benefits of using a single, unified platform for your data analysis process — from start to finish.
Dataiku helps you automate processes like accessing and preparing data, logging model settings and performance metrics for comparison, interpreting and explaining model results, documenting the process, and visualizing key insights — all in one place.