Data analytics is no longer a trend – it is a necessity for organizations to stay relevant in their fields. Big data has been sweeping across every industry, from healthcare to the food and beverage industries, and it has been making analytics skills more and more necessary in every industry it touches.
Data analytics is a segment sure to showcase many fascinating developments in the new year. As businesses harness the power of data analytics, 2020 use cases are both varied and compelling.
Here are five outstanding examples:
1. Depending on data analytics to fight the opioid crisis
It’s impossible to solve the opioid crisis in 2020, but data analytics may play a key role in reducing its effects. Officials in Pennsylvania said an 18% drop in opioid-related fatalities was aided by data analysis. The state created a dashboard that brings various data sources together to make them more accessible.
Data analytics also lets physicians be more aware of opioid prescribing patterns. For example, at EvergreenHealth in suburban Seattle, once physicians saw hard data about their prescribing behaviors, there was a decrease in the overall number of opioids prescribed, as well as the number of pills per prescription.
With the opioid epidemic continuing to be a major crisis nationwide, expect data analytics to increasingly be a part of efforts to find a solution in 2020.
2. Putting a bigger focus on POS system data during inventory management
Point-of-service (POS) data includes information about cash sales and credit card transactions. It can tell a business how much foot traffic it receives on a given day or how many people buy items on sale versus full price. In 2020, people should expect more companies to use POS systems data to improve inventory management.
One of the most significant advantages of this method is that it allows organizations to see what is or is not selling well. Then, users can know when stock levels are getting low and if it is time to order more. Connecting information from a POS system could mean product outages happen less often. Plus, when some items don’t sell as swiftly, merchants can make smarter decisions about when to make them available at lower prices.
3. Tapping into data analytics to reduce chronic absenteeism in schools
The education industry considers a K-12 student who is absent 15 or more days per year to be a chronically absent learner. This can lead to issues where students fail to master critical skills, or they drop out of the education system before graduating.
Schools can use data analytics to address this problem. For example, systems can screen for warning signs associated with an above-average rate of absenteeism. They can then alert teachers and other administrators to reach out to those students and their parents and intervene before it’s too late.
In one successful case of applying data analytics to combat absenteeism, at-risk students were paired with mentors and received other proactive measures. These helpers translated to an additional 7,000 days spent in school across all the participants. Due to results like these, education professionals should feel motivated to apply data analytics in 2020 to cut down on chronic absenteeism.
4. Reducing supply chain-related waste
In 2019, many companies used data analysis to pinpoint the abuse of programs where stores allowed lenient merchandise returns. How might they also use data analytics in 2020 concerning returns? One example is to minimize waste.
IKEA recently started working with a company that uses data analytics to send returned and excess inventory to the best places within the retailer’s network. Such a targeted approach should reduce carbon emissions, plus give a comprehensive view of how to cut down on items sent to landfills.
Another source of waste in the supply chain happens when brands manufacture too many clothes. However, efforts to apply data analytics in the fashion world aim to fix that problem. Some of the approaches could help other industries cut down on waste, too.
5. Analyzing health data to detect disease
Scientists are working on new ways to screen data for signs of disease. For example, a team at the University of Oxford found biomarkers related to blood vessel issues that could relate to a future heart attack. Another application detected which veterans did or did not have post-traumatic stress disorder (PTSD) with 89% accuracy.
Doctors already have a range of ways to diagnose illnesses, but they increasingly use data analytics to help. Results like these suggest why they should.
The examples cited above show why it continues to be an exciting time in the world of big data. Organizations have not just used data to find solutions to problems they currently face, they have set an example for themselves or others to build on in the future. That means there are more exciting breakthroughs still to come in 2020 and beyond that we haven’t yet begun to think about.
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