Sustainability is a term we hear often in today’s economic system. The need for greater sustainability across business processes trickles down to every corner of our world. 

Our world and its business systems are wasteful, in ways that some have found difficult to even quantify. To offset that excessive waste, modern teams turn to data to help sort and process extensive portions of the economic landscape. 

What happens when the data we use for sustainability is subject to waste by itself? How will teams overcome the unique challenges of data waste?

First off, what is data waste? 

Data waste happens when data gets used without taking on significant meaning. 

This can happen in the following situations: 

• When data is not being processed. 

• Data collection is ongoing, but value is not being extracted from data.

• Collected data gets stored so disorganized that findings are not visible. 

Viable data is visible data. When a system’s structure does not make an effort to make data visible and meaningful, it is wasting data. 

Costs of Data Waste

Data waste costs add up. Telecom bills offer clear insights into the billable hours of data used versus the trackable data made ready for the business management process. 

CIO magazine called these data highlights failures “poor choices” of both data habits and management.  

This data waste has a significant impact on wasted billable hours and makes for poor management of time. It also translates into dollar costs. IBM estimates the cost of “bad data” or data that has lost its usefulness or was incomplete or unuseful at the time it was gathered.

Over the years, that number has been as high as $3 trillion per year in the United States alone.

This has been an ongoing problem for years, for example, in 2017, IBM estimated that the average cost of bad data gathering in that year was 9.7 million dollars. Experts have placed that cost estimate for 2023 at $12.9 million per year, citing a report by Gartner.

Comparing the data from year to year, companies can see that the problem won’t go away by itself. As more data is processed, there is a greater risk of waste, and an even greater need to make that data sustainable. For that, companies look to the business intelligence toolkit for guidance. 

Data Waste and Business Intelligence

At a high level, data needs a venue to be usable. IBM explained that business intelligence tools “ingest” data and then digest it with various usable tools. These tools can then be used to diagnose needs and problems within a business’ systems and help formulate tactics for addressing them.  

Microsoft highlighted business intelligence as having three core processes it takes:

1. Collecting and transforming data 

2. Uncover trends and inconsistencies
   
   
3. Use data visualization to prevent findings

Powerful systems architecture drives the first two actions. However, once they are executed, that data needs somewhere useful to go, and that’s why the third step gels the whole together to power the purpose of business intelligence. 

How Data Visualization Brings It All Together

To solve this problem, teams need to make their data work smarter instead of just adding work hours and collecting more ticket items. This is where data visualization comes in as a powerhouse tool. 

Data visualization is an interface tool that makes data visible and easily navigable. 

Data visualization strategizes a clean view of insights, and then, through tools such as dashboards, hones focus on key areas. 

Business Intelligence and Physical World Sustainability 

The efficiency of well-maintained business intelligence has its proof in the physical world. More often than ever before, companies are using data to drive logistics and maintain supply chains more sustainably. 

Recycling Today explained that companies using data to target physical world waste are called waste analytics companies. These examples give us an understanding of how business intelligence congeals the whole of business systems to one unified purpose of making tech work better for the world around us. 

Data visualization has shown it has what it takes to empower future-forward sustainability efforts. Project experts apply data visualization directly in logistics settings, using the visuals to identify problem areas and clean up waste in supply chains. 

A World of Possible Use Cases 

While we’ve been looking at the direct impact data has had on sustainability, and how data itself is subject to waste, it’s important to take a moment to acknowledge the wide scope of uses business intelligence has. 

From physical supply chains to tracking marketing engagements, to understanding and categorizing favorite flavors of soda pop in a specific region, data intelligence tools have a plethora of use cases. 

To review, the goal of strong business intelligence systems, and purposeful data visualization is to generate imagery and action points that can be viewed from the context-specific lens of the business needs. In that light, the more customizable the business intelligence interface is, the more useful it will be to the unique needs of a business and its growth strategy. 

Experts have agreed that this is an important distinction to make as business intelligence is not a single-sized solution that every business can swap out. This has driven a lot of competition in business intelligence software and has made selecting the right tools a bit of a hassle for companies. 

How Wyn Enterprise Can Help 

The role business intelligence plays in a future-forward company is paramount, so having the right interface is vital. However, many companies run into hidden fees with business intelligence software that puts nuanced and powerful tools outside of the immediate reach of their budgets. 

With Wyn Enterprise, these hidden costs evaporate, and business intelligence users pay for the tools that most underscore their deliverable needs. In this way, Wyn Enterprise empowers not only the highest value of data usage but also the highest value of cost.