Big Data For Organizations

Big Data for Organizations

Finding the appropriate use for never-ending generated data has to be one of the most challenging problems organizations face. With how essential information is in decision making, knowing that the answers you seek can be in a bottomless pit of data can be less than exciting on a good day. Nonetheless, this article isn’t meant to frighten you about the challenges of mining big data, it is meant to show you how advantageous starting your journey with big data can be.

Big data is a combination of structured, semi-structured, and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modelling and other advanced analytics applications.

- Bridget Botelho & Stephen J. Bigelow

Analysis is an act that can be said to be common in organizations, whether big or small, and ultimately, the point of this act is to assess environments and determine what decisions or actions would be most suitable for progress. With the variety big data comes in, every industry (from fashion to agriculture, to aeronautics, to consulting) has a use case for it.

In this article, we explain the most common applications of big data in education, climate change, healthcare service delivery, and waste management/recycling- because of our interest in these industries. Some examples of companies that have successfully incorporated the use of big data in their daily activities in the aforementioned industries will also be highlighted.

Applications of Big Data

To ensure big data is used efficiently, having a comprehensive understanding of what you are trying to achieve helps accentuate the kind of data you mine for. This provides the criteria for profiling, cleansing, validation, and transformation of data sets (data preparation) and ultimately feeds the analysis. Once that has been done, analysis can be carried out using analytical processes such as machine learning, deep learning, predictive modelling, data mining, statistical analysis, streaming analytics, and text mining.

Education

In the education industry, the ultimate goal is ensuring knowledge delivery is optimised. Big data helps in understanding what methods are most efficient for achieving that. With how useful technology has been in the education industry, especially with EdTech products emerging at an exponential rate, more people are taking advantage of the availability and this sets the basis for the need to understand what deliveries are most appreciated and efficient. Big data is utilised in the following areas:

  1. Knowledge assimilation: The way new information is added to already existing knowledge in our minds differs for each person and that ultimately affects the rate at which learning occurs in groups. With big data, educational institutions can understand what factors affect a student’s interest and commitment to learning.
  2. Learning approach: With an understanding of the student’s interests and commitment to learning, educational institutions can identify what approaches would be most suitable for their students. They can look at either a student-centered, teacher-centered, or hybrid approach to learning and also identify what learning supports would be most effective for each student. They can also make suggestions to parents about how they can help their children understand subjects better (perhaps with home tutoring) for those in basic educational institutions.
  3. Grading structures: Grading can be focused on assessing how much progress has been made through the learning approaches initiated, highlighting strengths and weaknesses, and helping students understand what deliveries helped them better.
  4. Feedback: The data gathered during an academic year (which has been analysed and insights have been extracted from) can assist students in understanding what areas to direct their efforts to in the coming year. Students can receive data-backed feedback that can help them with critical decisions like determining what careers are more aligned with the skills they have and for those whose skills are lacking, to help them determine the steps they need to take to excel at their dreams.
  5. Career progression: Big data helps here by highlighting the experiences and requirements necessary for progression in a particular career. It helps students understand the environments in which these careers thrive and the resources that might be needed to keep things going. Asides that, big data can give updates on careers that are in demand or may have become irrelevant as eras pass.

Solutions that have made an impact in the education industry using big data include edX, 12twenty, Civitas learning, CogniFit & GradeBook Pro amongst others.

Climate Change

With climate change, the main goals here are understanding what factors are the main drivers of climate change and consequently highlighting what remedial measures are necessary for a greener environment. Big data assists in the following areas:

  1. Awareness: With how advanced the level of analytics required for this industry is, it is easy for the public to forget how necessary their involvement is. With big data, experts can identify focus areas for change and break them into actionable steps that are necessary for people to take towards creating a greener environment. The solutions derived can be specific for different locations. Big data also helps identify areas that are vulnerable and might need immediate assistance.
  2. Monitoring & Evaluation: Majority of the work done with climate change adaptation is with observing how environments react to certain stimuli. Big data helps with weather forecasts by researchers comparing conditions from the past with current environments to determine what steps are necessary to mitigate certain effects, especially with natural disasters, and also with determining the best course of action for areas that are vulnerable against disasters with predictive analysis.
  3. Early warning: Big data helps in reducing the delay with emergency response systems. First responders get relevant information quickly and are able to take swift countermeasures.

Solutions that have made an impact in climate change adaptation using big data include Dynamhex, RisQ, Tomorrow, FoldAI & Blue Sky Analytics amongst others.

Healthcare Service Delivery

For everyone who has had the “opportunity” to wait 4 hours for a doctor’s appointment, understanding the benefits of big data in this industry isn’t something that is particularly difficult. Big data can improve living conditions through increased accuracy in healthcare. This is true especially in the following areas:

  1. Strategic planning: The velocity at which data is generated in the health industry is overwhelming and with this overflow of sometimes repetitive information, actionable steps are seldom taken because of unprovable conditions. With big data, large volumes of medical data can be analyzed to provide insights that can rapidly improve service delivery .
  2. Inventory management: Medical institutions can get insights into patient behaviour, product performance, and channel performance when analyzing supply chain performance metrics. With this knowledge, institutions can minimize waste by focusing on restocking medication in-demand and also highlight patients who are likely abusing or not adhering to prescriptions, for assistance.
  3. Disease control and prevention: Early detection is key in the provision of quality treatments. Data generated from smart devices and hospital visits can be fed into predictive analytics tools to give doctors the information they need to make data-driven decisions and improve patient treatment with accurate preventive care. In situations of epidemics, it reduces the delay for emergency response systems.
  4. Research: The insights generated from researchers feed into every area of healthcare. With big data, researchers can better understand how patients react to treatments and observe success rates of drugs in the market, they can use time-series analysis to predict admission rates and also drive innovation in the industry.

Solutions that have made an impact in healthcare service delivery using big data include Tempus, Oncora Medical, Pieces, Linguamatics, Cloudera, Human API & Apixio

Waste Management/Recycling

The integration of technology into the waste management industry is one that is crucial. Between the dangers of improper waste management (such as disease outbreaks) and the economic advantages of proper management, finding innovative solutions for the industry is one that is necessary for community development. Big data can assist in the following areas:

  1. Education: Handling waste properly is still misunderstood by a lot of people. Most people are so quick to get rid of waste that they don’t think about whether or not it’s been done the right way. The way biodegradable and non-biodegradable waste is disposed of differs and instead of having waste sorted through after compilation, it is better each home, organisation, or business separates waste generated in-house before disposal. Big data assists in this area by highlighting the best ways for waste to be disposed of while also helping the government determine what locations generate the most waste for focused sensitization.
  2. Sorting: On this end, big data feeds the tech that is used to make sorting run more efficiently and safely. Machines have been programmed to identify different types of waste and with big data, their efforts can be optimised.
  3. Logistics: To reduce the carbon footprint of trash hauling trucks, big data generated from sensors placed in waste disposal systems can inform operators when trash bins are full and also let them know what times would be suitable for pickup in different areas.
  4. Solutions: This focuses on the third R of waste management, recycling. At least 50% of waste generated can be recycled but data shows that less than 30% of waste is being recycled even in developed nations like the United States. Big data feeds into the research done to determine the best ways for waste to be recycled and highlights the volumes of different types of wastes generated which can serve as input for recycling companies.

Solutions that have made an impact in waste management using big data include Bin-e, AMP Robotics, Ecube labs & ecoATM amongst others.

Integrating big data into an organisation’s daily activities requires an understanding of the data available and how it can enhance productivity while remaining aligned with business goals. Because of the uniqueness of each company, it is paramount that an organisation highlights how big data will be used by developing use cases to identify what resources would be necessary for operations. With key resources highlighted, the organisation can evaluate what skills are required to handle the aforementioned resources and determine if its staff has the necessary skills, and if not, determine the best course of action for acquiring them.

It is also important for organisations to put safety measures in place in order to prevent the loss or misuse of data in the company. Laws like the EU’s General Data Protection Regulation (GDPR) limit the type of data that organisations can collect and requires opt-in consent from individuals or compliance with other specified reasons for collecting personal data. It also offers a right-to-be-forgotten provision, which lets individuals ask companies to delete their data.

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