Using Visual Data Discovery to Analyze Data and Create BI Reports

Using Visual Data Discovery to Analyze Data and Create BI Reports

Data tables and spreadsheets full of numbers are hard to digest. If data is hard to understand, it’s even harder to apply it to business scenarios.

To make data more useful, organizations turn to data discovery. When data discovery is translated in a visual context, it is known as data visualization. Data discovery leverages the brain’s inherent pattern recognition capabilities to spot patterns and outliers quickly.

In a traditional business, decisions tend to be made top-down. Today, in high performing organizations, decisions are being pushed down to the frontline. Support staff are being empowered to amplify the voice of the customer.

Sales reps are making suggestions that feed the product roadmap. Every employee, at every stage, makes decisions big and small that cumulatively impact the progression and growth of a company.

These decisions need a common basis. Data is that common denominator that underpins every function, and makes this democratization of business possible.

Visual data discovery is the most important BI trend in 2017


In earlier times, visualization data discovery was only reserved for experts in the academic or high performance computing space. Today, it is common for any enterprise leader to utilize visual dashboards to make decisions on a daily basis.

According to the BARC BI trends monitor 2017, data discovery is the top BI trend for 2017. It even trumps other powerful BI trends like self-service BI, and master data management.

Importance of BI Trends chart

Source: BARC

Data is the new economy of the digital age. Today, every employee is a data worker. Whether you respond to customer queries, build applications, lead a team, or manage a supply chain, data is the lifeblood of every business function.

One of the ways companies differentiate themselves from their competition is in the way they capitalize on the data in their systems. One of the most powerful ways to make data usable is to visualize it and draw insights from it.

Competitive advantages of visual data discovery


Data discovery is a key differentiator in the way businesses compete today. Organizations that make the most of their data are ahead of their competition. Let’s look at some ways data discovery impacts a business’ performance.

Big Data Needs Visualization

With the explosion of data volume, variety, and velocity, organizations can no longer use manual methods of data exploration. Only data visualization can make sense of big data.

Facilitate Collaboration

The data that resides in systems across teams needs to be integrated for it to be useful. Once integrated, it needs to be acted on by each team. The complexity of integrated data requires data visualization for it to be actionable.

Drive Growth At Scale

The amount of data a company works with scales exponentially as a company grows. Accordingly, the way organizations consume this data should mature and scale. Using data visualization, you can make sense of data whether it’s stored in a few spreadsheets, or in data storage spanning multiple terabytes.

Identify Threats & Opportunities Faster

How you identify and respond to threats and opportunities facing your business will decide the fate of your business. Whether it’s a breach of security, a sudden drop in sales volume, or a new technology trend sweeping your industry, data can tell you the true story behind every incident, and help you make an educated decision on how to respond.

Identify Threats & Opportunities Faster

As customer experiences are augmented with data in applications, or as customer support teams use data to resolve queries faster, user experience is enhanced. An organization that’s well-connected with data, and is able to visualize the data it has access to, is able to deliver the best user experience. It’s the companies that deliver the best user experience that will stand the test of time.

How companies use data for visual storytelling


Uber is likely one of the hottest startups in the world today. Uber didn’t get this big without exceptional use of data. In a recent blog post, Uber revealed how it uses data, and particularly data visualization, to power its cab-hailing service.

Uber set up a data visualization team that built tools and methods for teams to consume and utilize the live GPS data that Uber cabs generate.

Uber Network Data Visualization

Image: via Uber Engineering blog - reveals distributions of Uber dropoffs (eng.uber.com)

This data helps Uber’s City Ops teams and their managers to gain real-time visibility into driver drop offs across cities in real-time. This gives them insight into supply and demand, and informs their marketing campaigns.

They even take this a step further, by using the data to empower their products. UberPOOL, for example, is a cab-sharing service that’s based on real-time user data. Uber is able to measure and notice the impact of UberPOOL on a city’s traffic.

Similarly, at Boeing, engineers use data to make their aircraft, Osprey, faster and more efficient.

Boing Employees in front of a chart

Photo: courtesy of Kevin Vasko/Boeing (testing a prototype of a visualization at the Center for Applied Simulation and Analytics in Huntsville, Alabama.)

Each takeoff and landing of an aircraft produces terabytes of data. Analyzing this data is not feasible without visualization. As they test products earlier in the lifecycle, they are able to refine their products which saves them from costly errors.

Data visualizations are key to democratizing data. While advanced visualizations like the examples above can be created by data scientists, data visualization equally empowers business users who don’t know how to query data, or fine tune visualizations to meet their needs. This is made possible by mature self-service BI tools.

These tools are source-agnostic, and can pull data from a variety of segments. They provide end users with powerful tools for interacting with the data to derive insight from it.

Best practices when visualizing big data


Data visualization is part art, and part science. There are rules to follow to craft great visualizations, and to draw unique insights from visualizations you interact with. Let’s discuss a few of them.

1

Use The Right Chart Type

There are many chart types - bar, pie, line, donut, stacked, radar, spider, bubble, scatter plot, pareto, marimekko, and the list can go on. However, you need to consider which chart types suits the kind of data you’re working with.

The most common charts - the bar, and line - are great for a majority of use cases. Pie charts are only good when you have a few data points, and aren’t good at comparing many data points. Similarly, bubble charts can plot data that has four dimensions, and is a great option for more complex data. For large volumes of data, a scatter plot can be useful. A BI tool that gives you an option to visualize the same data using various chart types is a powerful ally.

2

Pack More Data Per Pixel

Data visualization expert Edward Tufte coined the term ‘chart junk’ to define the percentage of a visualization that doesn’t actually communicate, but distracts from the real data.

Ideally, you want to reduce any gimmicky distractions from your visualization, and keep the focus on the data. Strive to pack as much data as possible in a single chart, so long as it’s organized well, and doesn’t conflict internally within the chart area. Visualizations are great at making sense of large volumes of data, and they should be used for precisely this purpose.

3

Leverage Interactive Features

Charts are often not static, but dynamic. It could be data that gets updated in real time, or data that has many dimensions and layers that need to be explored.

This kind of data needs interactive features like scroll, zoom, labeling, selection, and custom axes. These interactive features can make the difference between insight and confusion. BI tools that give you these advanced interactive features are ideally suited for advanced data discovery.

4

Use Colors With Care

Colors are a powerful, yet loud, way to call attention to something in your visualization. Typically, the best visualizations are monochrome, and the worst ones are bizarre in their use of colors. You could use colors to call out a single data point among thousands, and that is valid. However, over-using color to mark off axes, chart area, and labels, for example, could end up looking very confusing and yield very little insight.

Conclusion


Data discovery, or data visualization, can be a powerful force for transformation in your organization. It can be the difference between you or your competitor winning. It's a powerful tool to have in your arsenal and that’s why data discovery is the top BI trend of 2017.

Companies like Uber and Boeing use data visualization to build the most revolutionary products and empower teams across their organizations.

Yet, it’s not just the uber-smart who use data visualization. Self-service BI tools are empowering data visualization for any and every employee. As you work with data visualizations it’s great to educate yourself on the basics so you can get to the insights faster with little pain.

If you’re in search of a self-service BI tool that can power your data visualization drive, look no further than Allocable. The cloud based predictive BI software comes with advanced visualizations that can be customized by each team and each user. It can pull data from various sources, and make them available in a single view. It has advanced interactive features and multiple chart types to help you go from data to insight in the quickest time.

View a demo of Allocable today, and change the way you see data in your enterprise going forward.

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Allocable is a cloud-based automated time tracking and business intelligence (BI) software platform that provides  a complete visualization of your workforce and project productivity data empowering you to turn information into actionable insight to optimize and forecast performance with more certainty.

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