According to a report by Forbes, Data Scientists spend 80% of their time on data preparation and management — cleaning, labeling and annotating — instead of focusing on core analytical tasks. This means there's much less time for doing the core data science and visualization work that could help their business.
By consolidating tools and automating repetitive tasks, you can focus on refining algorithms and uncovering data insights that drive better business decisions.
Here, we offer 4 tips to reduce time spent on time-consuming data preparation, and free up time to uncover insights, refine algorithms, and mine data for patterns.
One of the reasons cleaning, labeling, and annotating data is so time consuming is because of the number of tools being used. It’s common to start with SQL or a Jupyter notebook, and then create charts in Excel, Tableau, or Power BI, then take screenshots of them to embed in presentation slides. Hopping between three, four, five, or more tools to stitch together an end-to-end data science workflow — exploration, analysis, modeling, and communication of findings — adds complexity, room for misinterpretation, and manual errors. It certainly doesn’t help streamline the workflow.
Our 2021 research report — The State of DataViz — found that the team members involved in the above workflow use two, three, four, and sometimes even more tools to stitch together an end-to-end flow to analyze data and communicate insights. In fact, there were more than 180 unique tools named by respondents, optimized for different roles with different skills (see color-coded diagram below), that are involved in the data workflows.
Tool-hopping also forces us to switch context. While it may be subtle to those experienced and accustomed to stitching together custom data workflow solutions, moving between tools requires us to stop working with the muscle memory of one tool and pick up another. According to a joint report by Qatalog and Cornell University’s Idea Lab, it takes people nine and a half minutes, on average, to get back into a productive workflow after switching between digital apps. And half of the knowledge workers reported a decrease in productivity and an increase in fatigue from constantly switching between tools. Consolidating your data science processes into a seamless, integrated environment not only simplifies your workflow but also reduces mental strain, enabling you to work more efficiently and make better data-driven decisions.
Observable provides a unified platform that takes you from raw data cleaning to in-depth exploratory analysis without the need to jump between disparate tools. With Observable, you can:
Import data seamlessly: Pull data from inline sources, local files, public or private web APIs, or live databases.
Manipulate data visually: Utilize intuitive visual tools and real-time collaboration features to explore data and share insights with your team.
Quickly generate interactive visualizations: Create charts and graphs in seconds with Observable Plot’s concise API and extensive examples.
Combine code and text: Document your workflow and insights side-by-side with code, ensuring clarity and reproducibility.
This integrated approach helps streamline your data science workflow while reducing errors and saving time.
Cleaning and preparing data can be both time consuming and frustrating, especially when data is collected from different sources. Dealing with format mismatches, null or missing data, and unruly joins can eat up time that is better spent on mentally challenging work that still must get done, no matter how long data-cleaning takes.
Using SQL natively with Observable minimizes the need for external tools, enabling you to seamlessly transform, join, and clean data, securely connected to your data.
Data scientists may be collecting data from qualitative surveys, fetching product data from databases, or creating new datasets from various sources. Wherever your data is coming from, collecting and integrating data from multiple sources can be challenging when using a patchwork of tools. The disparate tools used by most data scientists to collect data for analysis range from a menagerie of free, open-source tools to pricey options that do one thing really, really well. Rather than using several different tools, Observable allows data teams to manage their data workflow (from import to analysis), and present findings in the same place, with no screenshots or exports required. When it comes to data collection, Observable offers several ways to streamline the process.
Observable’s powerful visualization tools let you munge and group text data with ease. Generate word clouds, analyze sentiment with Tensorflow, or find surveys that most frequently express dissatisfaction with TF-IDF.
Most product data lives in a cloud-based source. However, the security of these connections is always a concern. Often, it's hard to see exactly what database tables are used to generate a chart or dashboard. With the minimap in Observable Notebooks you can clearly identify dependencies and the cells that rely on them. No more trudging through every cell to find a connection error or all the charts that rely on that connection. See the video below to learn how minimaps increase transparency and security.
Clear database management in settings makes it easy to always stay connected. On Observable, you can connect private Observable notebooks directly to PostgreSQL, MySQL and BigQuery databases. Use the DatabaseClient() to plug live data into reactive visualizations.
Plus, sharing settings allows each team member to be able to interact with your analysis. This way, they see the source of truth the way you can-without having to download, setup, or manage anything new.
Sometimes the relevant data lives in two wildly different formats, and in different places. Pull together and combine those datasets easily with Data Wrangler, a UI tool that lets you join and select the data you want with just a point and a click.
Data visualization is often discussed in terms of what to show stakeholders and clients. However, visualizations are also an important part of data science workflows, helping data teams quickly identify trends and patterns. Data table cells in Observable Notebooks offer a seamless way to include data in table form, enabling data teams to quickly scan and explore their data, and automatically generate top-of-the-column summary charts. From there, data teams can quickly build prototype visualizations to share with their stakeholders.
Leveraging these strategies not only improves your data workflows, but also enhances your overall data management and analysis capabilities. Start streamlining your data science workflows and take your analytical skills to the next level with Observable.