Technology

Operational Data Store and Machine Learning

When a company is receiving data from multiple sources, it becomes hard to merge it into a single format that the company can easily access and use in its decision-making process. The Operational Data Store (ODS) helps solve this problem by acting as the central data repository for the business.

It collects data from various sources that are connected to the specific information warehouse system. On the other hand, machine learning helps improve the stored data accuracy using algorithms the way Artificial Intelligence would do.

An overview of the Operational Data Store (ODS)

For companies collecting data from various sources connected to their warehouse system, the operational data store merges all that data and acts as the central repository. It helps make the company’s reporting and analysis process easy by providing the view of its current data and all related deals in a specialized way.

ODS also cleans all its collected data to eliminate replication and junk. It validates redundancy, ensuring the data is following the company’s systematic rules. ODS stores the current data held in its system for a short period before it’s finally moved to the warehouse’s storage or its archives.

Deployment of Operational Data Stores

When creating an ODS store, the company must integrate its data from various sources into a single system. Even after achieving this goal, the data cannot qualify as ODS unless it meets the following criteria.

It must be integrated: Before completing the data integration process from multiple sources, it is first taken through another process. It’s extracted, transformed, and loaded through a process called ETL (Extract, Transform, Load) flow.

The ETS process ensures the integration process is smooth and the data is converted into a single format that meets the company’s needs. It achieves this goal through various tools. This process is what is called cleaning and is what makes data qualify as ODS.

The data must be current: The purpose of the ODS system is to display the current status of all company functions from the apps that are connected to its information warehouse. For it to qualify as ODS, it must be current because old data should not be in ODS.

It must be subject-oriented: ODS works on the current data in the specific department or area, and its infrastructure is designed to meet the functional requirements of that area.

It must be granular in details: ODS is built to give support to a company’s business activities and the ODS must therefore follow a set of rules for a sustainable and comprehensive level of details the business requires to execute its functions.

Application of Machine Learning on Operational Data Store

The future of business is bright because of fast innovation and highly advanced technological growth. Already, pioneer companies in the manufacturing sector and other major players in the technology and entertainment sector are using AI, ODS, and machine learning.

These technologies are helping major companies make accurate decisions, streamline their processes, and get deeper insights to help enhance their operations. Even with all these technologies, the current processes in the manufacturing sector and other major players still rely on repetitive tasks combined with manual processes when harvesting and processing data.

The sectors are faced with a combination of challenges like a lack of optimized and effective equipment. These are qualities that traditional tools cannot meet and companies have no option but to look for more dependable alternatives. As a result, experts in various fields like blockchain professionals, data scientists, programmers, and software developers are on their heels 24/7, working on systems that will eliminate every challenge faced in the path of data production, distribution, storage, retrieval, and usage.

One of the solutions already released by these experts is machine learning models to help address every challenge not only in the manufacturing sector but also in every other business field. Machine learning helps solve these complex challenges in various ways. It helps increase visibility in an entire business environment. Through machine learning, product rework is reduced and production downtime is eliminated. Companies enjoy greater levels of benefits from machine learning like adopting predicting applications like anomaly detectors, apps for optimizing the supply chain, and monitoring of quality in real-time to help the business gain momentum fast.

How machine learning supports ODS

Machine learning supports operational data store in three main areas –

Error detection: the error detection component of machine learning does not detect errors after they have happened, although this can be applicable. If a company, especially the manufacturing sector, waits until an error occurs to rectify it, it can translate to huge losses, production of substandard or poisonous goods, and long court battles from customers and governing bodies. The application helps predict if an error is likely to occur because it is configured to understand the operations of the entire system. It, therefore, compares the functionality of each manufacturing component to measure its accuracy. If the levels are not at par, it sends warning data for investigation.

Decision-making process: For machine learning to process and do predictive data classification, it must be fed with the right data. The data fed can either be labeled or unlabeled. Machine learning will use the data algorithms to process and classify it into predictive segments.

The model optimization process: If there is a chance for the model to fit better within the training set data points, the weights are changed to lessen the difference between the known value and model estimation. The entire procedure will be repeated by the algorithm after undergoing evaluation and optimization. It will, in turn, update the weights on its own until a certain level of accuracy is reached.

Categories of machine learning that can be applied to ODS

Three categories are specifically common in machine learning models

Supervised machine learning is fed with labeled data which it uses to train algorithms and to adjust weights until the model fits correctly.

Unsupervised machine learning is fed with unlabeled data and using its algorithms. It discovers any hidden patterns/groupings without requiring any human support.

Semi-supervised machine learning acts as an in-between application. It is fed with smaller portions of labeled and unlabeled data to produce a guiding data classification and help in training the supervised learning algorithm.

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