Big data projects will fail less quickly if you can follow success models. More information about three data analysis implementations that have positive results.
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Research in 2019 into the success of analysis and artificial intelligence (AI) projects does not produce the kind of results that we hope to see. Gartner reports that 80% of the analytical insights until 2022 will not yield business results. VentureBeat AI says that 87% of analysis projects do not go into production, and New Vantage reports that 77% of companies acknowledge the acceptance of big data and AI initiatives are a major challenge.
Although this is not good news for analytics champions and project leaders, there is good news that there are business applications where analyzes that work on both big data and fixed data produce results. Here are three usage scenarios for analysis that are really on the rise.
SEE: Data analysis: a guide for managers (free PDF) (TechRepublic)
1. Analysis of customer relationships. In e-commerce, website trackers can collect data about which products users buy and browse, and this data can be aggregated with demographic data from buyers such as age, gender, location, other products that the buyer buys, etc. when this information is linked to other systems such as customer service, organizations can also see how purchased products perform for customers and whether customers have had to call for repairs or service – and how they thought about the experience.
End-to-end, these analyzes bring companies closer to the 360-degree view of the customer. Companies are already seeing results, such as less customer turnover, higher income and more products that were purchased per customer.
2. Follow logistics. The combination of IoT and analysis enables logistics companies to track trucks with freight on the road and redirect trucks to alternative routes in the event of accidents, bad weather or other factors that affect driving time and deliveries. The analyzes work on real-time IoT information and can optimize routes for the fastest, safest and most economical delivery.
3. Environmental monitoring. Goods such as computer equipment and perishable foods require the right combination of temperature and humidity to protect against damage or deterioration during delivery. Today, IoT sensors monitor pallets with goods and in the containers that they continuously monitor for temperature and humidity, and provide real-time alerts to logistics managers when an environmental error occurs so that they can intervene. The Food and Agriculture Organization of the United Nations estimates that more than a third of the food produced, or around 1.3 billion tons, is lost or wasted each year.
Losses due to spoilage and other factors contribute to world hunger and they affect revenues for food and beverage companies, food retailers and producers and food transport companies. Analytics and real-time reporting make the difference because these losses are reduced.
SEE: AI and machine learning: Top 6 business use applications (Tech Republic)
It is important to define and visualize business cases
One of the reasons why customer analysis, logistics tracking and environmental tracking worked so well as analysis projects was that it was easy to find a business need that affected business finances (more sales or less spending); it was also easy for C-level executives without IT backgrounds to visualize how the technology would be used.
For example, customer analysis can show the company the products that a customer would probably buy the following; or a warning about an overheating of a truck turn to Atlanta could allow a logistics manager to divert the load to a smaller market so that the products do not spoil.
Although there are no guarantees for the success of analysis projects, there are guidelines that increase the proven opportunities and that work. It is always important to get your supervisor, user and IT teams behind every project and its potential to deliver value to the company, as well as a strong partnership with your supplier. Viewing the successes and modeling your subsequent efforts is a great way to start the process and stay on track.
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