Sustainability and Data Analytics: Missed Opportunities Due to Integration Failures
Data scientists only witness the green power of data analytics with smart integrations. There is publicly known potential, with more strategies yet to be discovered for how data can decarbonize and optimize for net-zero and circular targets. How do these integration failures manifest, and how can academics, researchers, and industry experts avoid mistakes to achieve sustainability goals?
Incomplete Understanding of Resource Usage
Data analytics requires intelligent integration to provide a holistic viewpoint of a company’s resource usage. Failure to incorporate reliable technologies, repair troubled sensors, or keep up with maintenance delivers misguided, incomplete, or false numbers. However, operators must review the sources and their infrastructure attentively as the assets gather the information.
Smart tech attaches to countless utility reference points, including water and energy infrastructure. Imagine if reliable patterns feed into data wells and scientists realize the energy consumption information was unusable because HVAC systems weren’t operating normally. Concerns like this arise from negligent integration, yielding poor information availability and quality.
Failure to Identify Waste Reduction Opportunities
Misinformed tech installation also leads to troubled sustainability data analytics. An enterprise may place data-gathering assets incorrectly because it failed to determine its most prominent waste sources. Discovering what origins are responsible for the most significant output may reveal more lucrative opportunities for clarified information.
Integrating data analytics to highlight waste reduction requires curiosity and experimentation. For example, corporations may think in-house toilets and sinks are the largest source of water waste, collecting data about flushes and gallons of water lost per hour. What about the water wasted cleaning production lines or failed lab tests? Thinking outside the box to locate every possible awareness gap will employ data analytics more resourcefully.
Insufficient Emission Source Identification
Determining where waste originates from is one of many searches. Discovering where emissions are greatest is another elusive trial. Putting IoT tech and sensors in improper locations can’t promise accurate emissions calculations or depictions of pollutants in the air. Missing critical sources misconstrues progress toward mitigation targets. Easily traceable, industry-agnostic sources of greenhouse gases include:
- Transportation
- Energy production and use
- Waste generation
- Land use
- Chemical reactions
It’s critical to acknowledge that specific industries have unique emissions concentrations. They should prioritize identifying the most prominent internal sources instead of defaulting to blanket trends.
Limited Ability to Optimize Supply Chains
Inadequate integration also provides poor visibility over supply chains. Data analytics helps join disparate assets, like international suppliers, into centralized online hubs where information and communications pool. Constructing a sustainable future happens by knowing the equity, sustainability, and investment strategy of all parties.
However, if companies don’t discuss how they and their partners must integrate information-gathering tools for success, then the results will muddle. How else can influential green data, like packaging waste, raw material sourcing emissions, and transportation routes, become more eco-friendly?
Poor Energy Management and Oversight
Superb integration also implies data alignment. This means all resources funnel into the same digital space in compatible formats for interoperability. Sloppy analytics will craft fragmented data sets with countless variations and anomalies simply because of wrong formatting, leading to import mistakes. The amount of human error and inconsistencies present during integration and digital transformation is why only 30% of these efforts succeed.
Some information might duplicate, while some might get lost in digital ethers because it can’t function with connected systems. How can energy-saving opportunities reveal themselves without data curation, oversight, and management?
Lack of Predictive Maintenance
The predictive potential of data analytics leads to lean maintenance behaviors. It is one of the most prominent advantages of data mining for sustainability because it improves machine efficiency by up to 25% and reduces waste. Historical and incoming information determines the best time to operate on tech to avoid catastrophes. However, inaccurate integration could cause operators to miss repair opportunities or overmaintain equipment.
Missing Stakeholder Transparency and Engagement
Stakeholders want to know the nitty-gritty details of eco-friendly operations because nothing is trendier than putting financial resources into green operations. Quality data analytics is essential for conveying the correct information to investors and the public.
Presenting data clearly and in a suitable format makes the numbers accessible and effective at telling the corporation’s story toward sustainability. Discovering falsehoods could lead to greenwashing claims or loss of customer trust.
Inadequate Tracking and Reporting Measures
Just as companies must report accurate findings to their stakeholders, they should have clarified records to maintain green momentum. How can a business know if it met eco-conscious metrics if tracking protocols aren’t followed or enforced?
Additionally, businesses with robust data analytics integrations may easily receive stamps of approval for energy-efficient buildings from green compliance frameworks, such as LEED. A well-thought-out strategy lets managers and data scientists take advantage of these opportunities for increased competitive advantage.
Dismissing Data Mistakes for the Planet
Making data analytics a pillar of any business will lead it to greener horizons. However, lazy integrations could cause sustainability initiatives to take a wrong turn, leading to increased waste and clouded visibility. Knowing these consequences must motivate those undergoing digital transformation to adopt it carefully because a company’s carbon footprint is at stake. They should perform multiple tests and seek external advice to ensure a positive social and environmental impact.
Originally posted on OpenDataScience.com
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