Big data projects are less likely to fail if you can follow models of success. Learn about three data analytics implementations that are having positive results.
Research in 2019 into the success of analytics and artificial intelligence (AI) projects is not yielding the kind of results we’d hope to see. Gartner reports that 80% of analytics insights will not deliver business outcomes through 2022. VentureBeat AI says that 87% of analytics projects are not making it into production, and New Vantage reports that 77% of businesses acknowledge that adoption of big data and AI initiatives are a major challenge.
While this isn’t good news for analytics champions and project leaders, there is good news in that there are business use cases where analytics operating on both big data and fixed data is producing results. Here are three analytics use cases that are really taking hold.
SEE: Data analytics: A guide for business leaders (free PDF) (TechRepublic)
1. Customer relationship analytics. In e-commerce, website trackers can collect data on which products users buy and browse, and this data can be aggregated with buyer demographics such as age, gender, location, other products the buyer purchases, etc. When this information is linked into other systems such as customer service, organizations can also see how purchased products have been performing for customers and whether customers have had to call in for repairs or service—and how they felt about the experience.
End to end, these analytics get companies closer to the 360-degree view of the customer. Companies are already seeing results, such as reduced customer churn, increased revenues, and more products purchased per customer.
2. Logistics tracking. The combination of IoT and analytics is enabling logistics companies to track trucks carrying cargo on the road and redirect trucks to alternate routes in the event of accidents, bad weather, or other factors affecting drive time and deliveries. The analytics operate on real-time IoT information and can optimize routes for the fastest, safest, and most economical delivery.
3. Environmental monitoring. Goods like computer equipment and perishable foods require the right combination of temperature and humidity to guard against damage or spoilage during delivery. Today, IoT sensors placed on pallets of goods and within the containers that carry them continuously monitor for temperature and humidity, and they issue real-time alerts to logistics managers when an environmental failure occurs so they can intervene. The United Nations Food and Agriculture Organization estimates that more than one third of the food produced in the world, or around 1.3 billion tons, is lost or wasted annually.
Losses from spoilage and other factors contribute to world hunger, and they affect revenues for food and beverage companies, food retailers and producers, and food transporters. Analytics and real-time reporting is making a difference because it reduces these losses.
SEE: AI and machine learning: Top 6 business use cases (Tech Republic)
It’s important to define and visualize business cases
One of the reasons customer analytics, logistics tracking, and environmental tracking have worked so well as analytics projects is that it was easy to find a business need for them that affected corporate financials (more revenue or fewer expenses); it was also easy for C-level executives without IT backgrounds to visualize how the technology would be used.
For example, customer analytics could show the company the products that a customer was most likely to buy next; or an overheating alert for a truckload of lettuce to Atlanta could enable a logistics manager to reroute the cargo to a closer market so the produce would not spoil.
While there are no guarantees for analytics project success, there are guidelines that increase the odds that are proven and that work. Getting your executive, user, and IT teams behind each project and its potential to deliver value to the business is always important, as is a strong partnership with your vendor. Looking at the successes and modeling your efforts after them is a great way to begin the process and stay on track.
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Source: TechRepublic
