Bulk Data Management

Going Beyond Tables to Deliver the Irreplaceable

When working with big datasets, long tables and wide grids are staples. But leaning too much on these can lead to generic tools that are overlooked and easily replaced.

Create the ideal workspace for each collection of tasks. How you present large datasets, and what you spotlight, will have an outsized impact on how well you mirror the mindset and expectations of your customer.

The Reality

A Bold Vision. Feels Like a Spreadsheet.

Data-heavy

Your app revolves around big datasets, and the management of and interaction with it.

A New Category

Your solution solves a common problem in a simpler way, for an under-served industry or segment.

Stuck on Sheets

Your customers, unaware of how your POV intersects with their goals, keep requesting spreadsheet stuff.

Common UX Issues

Without Emphasis,
All Roads Lead to Excel

Too Generic

In filling too many low-order requests, products creep away from their speciality

Smooshed

All actions are crammed into one monolithic table, making nothing clear or easy

Lacks Opinion

Individual views provide no clues at to what the user should be looking at

No Guidance

Uniform UI solutions leave no sense of what outcomes are expected or desired

Data Management Design Principles

Use Constraints to Create Meaning

One big dataset. Several ways of looking at it. Rather than put everything in "a single pane of glass", parcel the data out into discrete, purpose-built workspaces, like the one above. Here we are opinionatedly hiding all but a few key metrics in order to help make the next decision.

Contrast is Interest

A visual pecking-order of objects and their properties clarifies a view's intent

Think in Workspaces

Group overlapping tasks to bundle shared actions into distinctive workspaces

Each View Solves a Need

Arrange nav by user need to convey mindset, and dedicate space to key data

Frame Every Decision

What are users looking for? Provide a spotlight to help them make decisions

Discovery Questions

How Might Architecture Drive Outcomes?

Customer POV

What are they asking for? Why? How are they trying to interact with data?

Business POV

What unique value does your concept create? How does that overlap with requests?

Marketing

Where are you deviating from your marketing in order to meet customer requests?

Industry

Why does this particular vertical or segment value your perspective?

Related Use Cases

A Differentiated Solution in Two Weeks

Showcase what your approach to data does better by underlining what makes it different.

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Concise, achievable objectives. No creep. No drift. No surprises.

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