Changes in consumer demand have made managing each aspect of the supply chain increasingly complex. As a result of the Amazon Effect, the supply chain industry is being pressured to become faster, more efficient, and cheaper, while minimizing human errors. With so many interdependent processes and little margin for error, now more than ever it is critical for companies to take a proactive approach to overseeing their operation, fixing problems before they arise. Despite this problem, supply chain management has been slow to take up AI. According to a MHI Annual Industry Report survey of supply chain professionals, only 12% say their organization is currently using data science to inform decisions. Many companies cite a lack of workforce skills necessary for AI or a lack of IT infrastructure.
Even companies that actively employ data science and collect data run into issues regarding the quality of the material collected. The common process of manually entering data can lead to irregularities that not only create inaccurate models, but also poor decisions based off of these models. Furthermore, the process of cleaning and sifting through unorganized data is one that can be an impediment to company productivity. As estimated by the IDC in 2018, wasting time looking for data can cost a company 2.5 hours a day, and given the rapidly increasing volume and velocity of data received, it is increasingly critical that companies clean up their data.
For a company to be “AI ready” it must first establish an accurate and easily searchable set of data, which analytics can be run off of and accurate decisions can be made. We at Cognistx have experience helping companies get AI ready, cleaning up erroneous and disorganized data using our Data Quality Engine (DQE). Using the power of AI and machine learning, the DQE is a tool not only locates and eliminates costly data errors, but helps in structuring and cleaning your data so that your company has a platform from which it can make data based decisions. For organizations with a lot of data, this is a process that could take months and perhaps years to go through manually with basic data display tools. This is an issue Armada was faced with when trying to clean their data for AI. With over a million records of data received a day and no way to sift through it all, Armada employed Cognistx’s DQE to analyze these high velocity datasets for errors. Still in use, the DQE identifies over one million anomalies and processes 40gb of data per year. Correcting these errors allowed Armada to make more informed decisions and helped correct operational inefficiencies, saving the company over $10 million per year.
Just like Armada, companies that have taken a proactive approach to managing data science problems have reaped the benefits. Whether having more time to devote to other tasks, saving money, or being able to make smarter and more informed decisions, the effects of AI are vast and can be felt throughout your organization. If you feel your company is ready to take this step, reach out to Cognistx today.
You do what you do best and let us handle the data.