Big Data Governance for the Manufacturing Sector

Is there ever such a thing as too much data?

For the manufacturing sector, the answer to that question is complicated. Big Data provides unprecedented insights into inventory management, supply chain optimization, demand forecasting, logistics, quality improvement and countless other important metrics. But governing this avalanche of data can also be a heavy burden to bear. Managing, integrating and making sense of the data — which is increasingly derived from diverse functional groups, applications and other sources — can leave manufacturing CXOs feeling frustrated at best and completely overwhelmed at worst.

An agile data governance strategy — one that takes into consideration both process and software design — is essential for converting raw data into fodder for good decision-making. And yet according to a study by the Economist Intelligence Unit, only 42 percent of manufacturers have a well-defined data-management strategy in place. To navigate the relentless changes associated with the manufacturing sector while also supporting strategic development, successful firms employ Master Data Management techniques — including governance — to manage information and related processes

The benefits of implementing an innovative data governance and master data management system are many. Inventory management, for example, is a natural fit for governance, given the potential for both direct and indirect cost savings when data is tracked and shared among stakeholders. Identifying duplications among inventory (or common spare parts), sharing inventory and purchasing internally are all made possible by careful governance, as is more efficient time management — employees will spend less time trying to locate items and unplanned equipment downtimes will be less frequent when pertinent information is made available to those who need it. Other governance strategies — such as logging parts shortages — can also improve inventory management by ensuring that inventory stocks are maintained at an optimal level.

Companies can also achieve more precise demand forecasting with data governance policies that are structured intelligently to enhance Product Information Management (PIM) and Customer Relationship Management (CRM). CRM applications typically support a company’s interactions with employees, clients, customers and supply base for marketing, customer services and technical support. A data governance policy for such data would look very different from one regulating PIM data, which can include equipment, spare parts and commodities such as office supplies or hardware. But when considered in tandem, information from both datasets can prove to be a real operational asset for corporations seeking to deliver the right products in less time, with less waste.

Supply chain optimization is another continuous concern for manufacturing firms, particularly international firms with suppliers and vendors scattered across the globe. Supply chain planning is driven by forecasting, and forecasting is driven by data. Of course, supply chains have long been informed by statistics and quantifiable performance indicators, but understanding underlying demand requires robust governance and real-time analysis of often large, rapidly changing and unstructured datasets (such as demand fluctuations reflected in social media posts), as well as traditional structured enterprise data such as that from point of sales systems, order books and shipping records.

In many manufacturing plants, for example, data is typically only comprised of the bill of materials necessary to complete a task on the production line, and such data is lost when the product is shipped from the factory. A related data challenge arises from the difficulty of obtaining vital information from people on the shop floor, such as information about defects, casting problems or machine down-time and availability. Legacy Manufacturing Execution Monitoring systems don’t always provide the most optimal solution, as they’re commonly decades old and not always capable of capturing complete or relevant datasets.

A data-driven supply chain — which makes quality data accessible to users across the supply chain, in a role-based fashion — can also help prevent and mitigate errors in legal and regulatory compliance, avoid or reduce interruptions to business processes and generally help companies become more agile. By implementing standardization informed by a better understanding of data, companies can lower costs, improve productivity and generate a significant ROI.

Likewise, integrating manufacturing workers’ workflows and capturing production data via governance strategies can vastly improve the quality and efficiency of manufacturing processes. Instead of relying on industrial engineers to use their own judgement to improve productivity, a systematic approach to data governance can assure that production bottlenecks are identified and operatives are provided with adequate time to complete each manufacturing task.

Systems for managing logistics, transportation and sourcing can also benefit from big data governance, which can track everything from weather to the condition of vehicles to breaking financial or political news, allowing for more effective rapid decision-making. The emerging Internet of Things — where real-world environments and processes are embedded with intelligent sensors — provides an additional element of predictive modeling within the manufacturing sector. Digital cameras placed in warehouses, for example, can monitor stock levels and provide alerts when restocking is required or even predict when restocking will be needed, and sensors can detect which brands and logos are visible on shelves, allowing manufacturers to monitor and measure how their products are being allocated shelf space at retail outlets. But the among of data being generated by the Internet of Things is vast — so vast that governance is of fundamental importance if companies are to optimize that data for profitability.

product-overview-slide“To succeed in the data-driven economy, those in the manufacturing sector must look toward data as a both a predictive and a prescriptive force for decision-making,” says Ajay Sarkar, CEO of RoundWorld Solutions. “Our Big Data 360-degree tool is designed to help CXOs evaluate data in a meaningful way while also streamlining production, ensuring quality control and optimizing supply chain.

“Our template-driven tool,” continues Sarkar, “provides checkpoints that make for more intelligent, trusted and timely data collection while also providing strategic access to the data in a way that makes sense from a governance perspective. Spearheading big data projects needn’t be messy or overwhelming if proper data governance principles are incorporated. Our tools can do the hard work for you by creating data usage policies and implementing controls to ensure business decisions are informed by data that is accurate, consistent and accessible. It’s what can set your company apart from manufacturers that lack a data governance strategy — manufacturers that will find themselves less and less competitive in this rapidly changing marketplace.”

For more information on our unique 360 Degree Tool, please contact:
Emi Hara, MBA
Phone 866-868-5130

Learn More About Our 2 Week Assessment

If you would like to discuss the Roundworld Solutions Big Data 360 View Tool, or this article please contact Emi Hara – Vice President, Solutions and Delivery at
or contact us directly today.
This article was provided by:
Tiffany Fox
Public Information Officer
RoundWorld Solutions