Why it's important to apply big data to everyday activities
Big data analytics can do more than just deliver reports to decision makers. It can also help with the day to day running of a company.
Big data analysis is no longer a nice thing for enterprises to do: It is now essential in mission.
In 2022, Veritas said, "In just a few years, big data has evolved from distributed experimental projects to achieve critical-mission status in digital enterprises, and its importance is growing. IDC, by 2022, organizations will be able to analyze all relevant data, data and the delivery of workable information will earn $ 430 billion more than their less analytical peers Big data analytics, once performed from time to time, is now performed daily by many enterprises, including Amazon, Walmart, and UPS. "
SEE: Inside UPS: Digital Logistics Company at the Logistics Company (Free PDF) (TechRepublic)
But organizations still have a problem trying to implement it.
Gartner defines big data operations as, "implementing and maintaining predictable and ordered models. Both clients and vendors emphasize the importance of data science transfer. out of the prototype environment and into a state of continuous production and development. "
In other words, to operate big data, you need to move it out of the test sandbox and into an active career in the industry.
The most active roles for big data in the industry to date have been in decision support.
- Consumer buying patterns from web-based data inform sellers about the fastest-moving products, who is buying them, and where they are buying them.
- Diagnostic analysis systems enhanced by machine learning inform medical physicians about the most likely diagnoses and treatments for specific conditions.
- Sensors installed on tramways and major pieces of equipment notify cities of areas in their physical tram systems that require immediate or near-term repairs to prevent system failure.
All of these examples illustrate the first set of big data analytics use because they use unstructured big data and have a mandated role to provide consistent reports to managers that can be applied. action.
Using analytics in daily workflows
However, when you are fully working on analytics, the second level of active engagement is the level at which companies integrate big data analytics directly into the day-to-day workflow of their activity. . In these cases, the analytical tools inform decisions but also operate automatically in company workflows based on the information they receive from data.
A good example of system automation is in decision-making activity in bank loans. For many years, software programs evaluated the creditworthiness of a loan applicant and determined a “loan” or “no loan” decision and loan rate that took into account the credit status of the loan applicant, the size of the loan, and the level of risk.
The loan manager still has the final say, but of course it was the lending software that made the decision.
We can extend this model to the maintenance area of a city tram system.
Internet of Things (IoT) sensors are connected to key pieces of track and equipment. The sensors can detect signs of failure in these physical components before failure occurs. Data is collected, and reports are issued to supervisors, who then organize security maintenance tasks and pathways.
SEE: An increase in real-time big data and IoT analytics is changing physical thinking (TechRepublic)
Now, what if these analyzes could be worked out even further? For example, an analytics system picks up big data in real time from IoT sensors scattered throughout the city's mobile system. The system analyzes this data and makes maintenance reports to management - but also interacts with a work order planning system that organizes location work by location and deployments. work orders arranged for teams.
These work orders could be sent directly to maintenance teams or the organization could choose to review a human supervisor and then approve the work orders before the work orders are submitted. take out.
By integrating big data analytics into day-to-day workloads that go beyond just reporting (i.e., phase two operation), organizations can get a greater return from their analytics and big data investments.
This is more important than ever because just last year Venturebeat announced that 87% of data science projects are still never being produced.
Going forward, we cannot afford this failure rate for big data and analytics. It is more important to implement it in business workflows as well as in static reports.