This blog was first published by same authors on Amazon APN Blogs. 

As mentioned in the first post in our series, Seagate Technology asked Mactores Cognition to evaluate and deliver a data platform to process petabytes of data with consistent performance, lower query processing time, lower total cost of ownership (TCO), and the scalability required to support about 2,000 daily users.

Seagate Technology is a United States-based data storage company with worldwide manufacturing facilities that generate huge amounts of manufacturing and testing data.

When businesses collect data, they do so with the ultimate goal of making better decisions. The data collected typically undergoes multiple levels of analysis, including analyzing historical information and making predictions of future outcomes.

Today’s competitive business environment is driven in large part by data analytics. By collecting and analyzing historical data, organizations can estimate what’s likely to happen in the future. This approach is the basis of predictive analytics. With a predictive analytics strategy in place, information such as previous sales, inventory levels, and customer behavior can be used to uncover insightful patterns and estimate future outcomes.

Many businesses use descriptive analytics without even realizing it. From preparing sales reports for a particular period to calculating revenue, companies use descriptive analytics to understand what happened during their daily operations.
It would be difficult to operate any business without using descriptive analytics. This type of analysis involves collecting essential data and drawing conclusions from it. For example, reports about inventory levels, sales, pricing changes, and customer acquisition all form a part of descriptive analytics. This approach is the foundation of your entire data analytics strategy.

It’s important for your business to be data-driven in today’s economic environment. However, a data-driven approach involves much more than just collecting and organizing data. You also need to analyze this data, uncover meaningful insights, and make relevant conclusions. Intelligent analytics is the primary process through which your business can derive value from collected data.

Data and analytics have become key components of how businesses operate. More than ever before, businesses rely on analytics to understand customer preferences, cut down on costs, and identify opportunities for growth.

Just a few years ago, Big Data revolutionized how businesses make decisions. With the ability to collect and analyze data from diverse sources (such as IoT sensors, click streams, and application logs), businesses were able to uncover more valuable insights regarding their daily operations. Most companies now use Big Data to learn more about customer behavior, market trends, and production processes.

Artificial Intelligence (AI) is a hot topic right now. This digital technology is top of mind, especially for those in the manufacturing industry. From automating basic decisions to streamlining processes, AI has become a valuable asset in the manufacturing ecosystem. AI is now such a large component of manufacturing businesses that many are asking the big question, “If factories can rely so heavily on machines, won’t they begin to eliminate human jobs altogether?”