Julia Montgomery October 10 2018 3 min read What's in this article? 4 Main Components Influencing Big Data Big Data and Master Data Management 4 Ways Big Data is Changing Inventory Management *This post was originally published September 2015; it was updated October 2018* Big data is a term used to describe a massive volume of both structured and unstructured data that’s too large to be processed using traditional database and software techniques. In most enterprise scenarios, the volume of data is too big, moves too fast and exceeds processing capacity of existing applications. Despite these challenges and limitations, big data has the potential to help companies improve operations, increase profitability and make faster and more intelligent business decisions. Processing a large volume of information the main benefit of big data analytics. However, having high data volumes without using the proper technology infrastructure can create more problems than value. High data volumes present the biggest challenge to conventional IT structures since on-premise solutions typically do not have the capacity to manage large datasets. 4 Main Components Influencing Big Data Big data is heavily influenced by 4 main components including: volume, velocity, variety and veracity. Volume– The size of the data captured Velocity– The speed at which data changes Variety– The different forms of data sources Veracity– The uncertainty of data accuracy The real value businesses receive from big data is the ability to analyze data trends in real-time and accordingly extract critical data for management and operations. The analyzed data is gathered from a variety of sources, including both software and hardware, and is collected from devices all over the world, ranging from jet engines and heavy equipment to solar powered trashcans and electric toothbrushes. Companies are using big data to change the way they monitor product performance, research, develop and innovate. Big Data and Master Data Management It is tempting to draw a comparison between big data and master data management (MDM) as the two terms are interconnected, but they are different. Despite their differences, both big data and MDM benefit each other. Big data feeds insights to MDM, and MDM feeds big data with master data definitions. Big data exists in real-time and involves prompt access to data, alerts and inventory management and control metrics through cloud computing systems. The value of big data is how quickly patterns and trends can be identified. However, the velocity and volume of big data hinders companies in making accurate and timely decisions without the use of data management solutions. To overcome these challenges, the use of MDM can create a logical starting point for big data analysis. Master data manages core information that is critical for business processes. While MDM involves combining internal information from different company departments and bringing it together on one file, this is still considered basic information that involves smaller data sets. This information may include inventory levels, customer information, sales and other basic data that can be moved onto one file. In contrast, big data collects information from a variety of sources that extend to external sources, such as scanners, CRM, sales systems, social media, web-based data, enterprise resource planning tools, and accounting systems, and puts them together into a high-velocity and real-time overview of operations. The speed and size of this type of data would overwhelm conventional processing and storage systems. 4 Ways Big Data is Changing Inventory Management In the wholesale distribution of non-perishable goods, big data helps to integrate business systems to improve enterprise-wide operational efficiency and deliver higher profits more than ever before. Innovative leaders in the supply chain industry enjoy these 4 main benefits resulting from using big data analytics across supply chains. Below are 4 ways big data is changing the way companies manage inventory: Improved Operational Efficiency: With big data, operations managers may have an overview of real-time operations and better access to metrics, which helps to remove bottlenecks and improve efficiency. Big data enables supply chains to proactively enhance performance compared to traditional models. Maximized Sales & Profits: In the wholesale distribution industry, access to real-time data helps finance directors to manage tight profit margins with greater insights. This ensures that maximum profits can be achieved from investments in. Increased Customer Service Levels: Having access to real-time customer demand pattern data helps service managers match inventory and inventory levels with customer orders accurately, which will contribute to increased customer service levels. Data can be analyzed to predict seasonal trends, spikes or depressions in customer demand to ensure the right levels of inventory are maintained at all times. Reduced Costs By Migrating to the Cloud: A Software-as-a-Service (SaaS) approach for IT management means that the cloud-based nature of big data reduces hardware and maintenance costs. It can also be seamlessly integrated into existing systems at low costs. Before looking for a specific solution that supports big data operations, it is highly recommended to have a general checkup of the systems already in place as well as of the implemented processes. In the area of inventory, we identified 12 questions one has to examine and answer to determine how well your business manages inventory. Have a look at this guidebook which contains the questions in detail: Share Julia Montgomery October 10 2018 3 min read Sign up for the EazyStock Newsletter Stay on Top of the Latest News, Trends, Tips, and Best Practices for Supply Chain Management, Inventory Optimization, Replenishment & Purchasing, and Demand Forecasting with Our EazyStock Newsletter.