Analysts: Use Dataiku to Minimize Friction in Your Work

Dataiku Product, Scaling AI Catie Grasso

At Dataiku, we talk a lot about how analytics and IT leaders can remove friction from their MLOps processes in order to save ML engineers’ time, increase efficiency, and avoid duplicate work. But it’s not just these stakeholders that have friction points that lead to lost time, lower levels of efficiency, or a lack of reusability. 

Analysts play a critical role in an organization’s overall mission to draw insights from data to make informed decisions. However, their effectiveness depends on the tools and platforms at their disposal. So, if you’re an analyst, discover how Dataiku’s analytics and AI platform minimizes friction across various points of your work and empowers you to excel, saving time and energy.

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1. Make Data Access Less of a Hassle

Data analysts often find themselves dealing with data silos and fragmented systems, which can hinder productivity. Dataiku offers a unified data ecosystem that brings all data sources together, making it easier for analysts to access and work with the data they need. With dozens of connectors to cloud storage technologies, on-premises databases, and enterprise applications, Dataiku enables analysts to connect to data regardless of its size, shape, or location. 

Plus, if analysts are accustomed to working in spreadsheets (more on this later!), Dataiku uniformly presents data in a familiar tabular view so that working with disparate source formats is less of a hassle.

2. Remove the Pain of Data Prep

Dataiku’s user-friendly interface is designed with analysts in mind. It doesn’t require coding or technical expertise, which lowers the barrier to entry, and enables them to perform data exploration, transformation, and modeling using a drag-and-drop interface, reducing the friction often associated with complex data tasks.

With 100+ native data transformators, analysts can handle and automate the tedium of data preparation and enrichment in order to quickly cleanse, join, aggregate, reshape, filter, geocode, and more. Plus, advanced analysts can go further by writing custom formulas and code for bespoke transformation. All code, some code, or no code, the choice is yours. 

Dataiku prepare recipes

While exploring data, analysts can investigate potential quality issues and instantly receive insights into every column with built-in charts and summary information at their fingertips. They can also leverage visual statistics worksheets to interactively discover patterns and statistical relationships between columns. 

Plus, Dataiku’s AI Prepare allows analysts to build production-ready data transformations, simply by typing what they want done to their data. This breaks down the last barriers between knowing what needs to be done and making it happen in enterprise databases and cloud environments. AI Prepare is now available in limited preview. 

3. Don't Skip Knowledge- & Insights-Sharing (Anymore!)

Now there’s no excuse not to deliver and share results. Analysts can convey insights with 25+ standard chart and map types, plus integrations with popular visualization solutions like Tableau, Power BI, and Qlik enable fast and efficient knowledge- and insights-sharing. Analysts can also build easy-to-use application interfaces on top of their analyses — once again without code — that others can use to generate and deliver custom insights that matter to them. Analysts can also save time by creating a recurring report once and letting Dataiku automate the repetitive heavy lifting thereafter, notifying stakeholders of updates via email or messaging apps like Microsoft Teams or Slack. 

data visualization in Dataiku

Plus, it’s never been easier to reusing or sharing project assets. Analysts can copy/paste recipes, package workflows as reusable visual components, and publish curated reference data to a central feature store. This ability to seamlessly share work, collaborate on projects, and get input from colleagues not only enhances the quality of analysts’ work, but ensures that data scientists and data engineers don’t reinvent the wheel if something from an analyst’s project can be reused and scaled. 

4. Go Beyond the Spreadsheet

We know that many analysts work in spreadsheets or spreadsheet-like tools. The good news is that Dataiku is as intuitive and robust as a spreadsheet, and then some. It allows analysts to do complex things to your data, without needing to be an expert coder to do them. 

When analysts are ready to make the leap from descriptive to predictive analytics, though, Dataiku’s guided visual interface and built-in helpers enable them to quickly build, evaluate, and interpret a wide variety of predictive models — you guessed it, without code. They can also use the default AutoML settings or configure every aspect of a model, from feature selection to algorithms to hyperparameters.

AutoML in Dataiku

Make Data Friction a Thing of the Past

As organizations grow, the volume and complexity of data also increases. Dataiku is designed to scale with an organization’s needs, ensuring that analysts can continue to work with data efficiently, regardless of the size of the data environment. By empowering analysts with the right tools, Dataiku enables them to focus on what they do best: turning data into actionable insights that drive business outcomes. 

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