Since the inception of writing, recording events and facts has been indispensable to the progression of human evolution. Although, technology has revolutionized the way in which data is generated and captured, the impulse to derive logic out of unstructured data to envisage future needs remains the same. Advancement in industrial motors, hydraulic systems and sensing technology, in addition to simultaneous progress in computing technology, has disrupted industries with the complex numerical control systems (CNC systems) that led to an exponential increase of data generated in various industries.
Big Data is the umbrella term defining these large chunks of data found in industrial machines, government filings, medical records, and other such sources. Factors such as computing power, storage space and the use of analog analytic tools have suppressed the power of Big Data to transform computing and analytics until now.
The advent of advanced communication networks has increased the volume of industrial data generated every second. This, however, can be utilized only if the data is acquired, stored/managed, and analyzed in real time. The technology involved in several stages of Big Data, such as sensing, communicating, analysis and predicting, has undergone rapid progress to facilitate the same. Big Data is now the fundamental requisite for the successful realization of “Industrial Internet of Things”. Apart from prospective applications in various sectors such as health, consumer behavior, defense and government, Big Data poses huge benefits in terms of increased operational efficiency, lesser costs and diminished risk to the industrial world. This Frost and Sullivan perspective aims to discuss the latest trends in Big Data and their effect on factory operations.
Banking Data in Lakes
The buzz around Big Data has faded with companies increasingly accepting Big Data as a necessity in the industrial world. Organizations have invested in creating the necessary infrastructure to capitalize on Big Data. Unsure of the most suitable modeling and analysis techniques to be adopted, many manufacturers are opting to store their voluminous data in parent form, creating data lakes rather than storing them in data warehouses. Data warehouses are storage repositories that store data in large files and folders; however, in data lakes, it is stored as raw data, ready to be used whenever needed. Optimized neural chipsets, high-performance computing architecture, advancement in real-time data streaming and complex data pipelines will also help in refining data into separate information assets like actionable data and fast data.
In asset-heavy industries such as pharmaceuticals, electrical and electronics and aero parts manufacturing, superior asset management is critical for sustained profitable operation. In such a scenario, adopting the approach of data lakes for historic asset maintenance, supply chain and front-end sales data is more beneficial than data warehousing. The data can always be, in the future, retrieved by an analysis model.
On the other hand, there is a central need to simultaneously track, monitor and expend dynamic data generated by processes and quality checks. These might demand a data warehouse model as immediate response and action boost operational excellence. By examining this kind of data strategy, companies can leverage data analytics efficiently. As data volume is anticipated to only grow in the coming years, choosing the right mix of data models is imperative. This convergence of operational and business intelligence will maximize competitiveness and boost adoption amongst various industry verticals.
The competitive landscape of Big Data analytics is diverse, encompassing top-notch tools like Hadoop and Apache Spark and also small players specializing in individual analytics segments, such as machine learning and data clustering. The speed of information delivery, ease of use and power of processing engine are some of the key criteria used in assessing an analytic tool. Exploiting memory computing for faster large-scale data processing is emerging as a key differentiator. Considering the large datasets in place, easy-to-use application interfaces to sort and manipulate unstructured data to semi-structured data is evolving as an added advantage. In addition, add-ons for incorporating live streaming, visualization and graph processing create seamless workflows, enhancing developer productivity.
Manufacturing companies are increasingly leaning towards embedded technology for enhanced efficiency. In the current model, data analysts are responsible for feeding the data lake to stand-alone cloud service and for verifying the contextual correctness of the result. With embedded analytics, decisions are no longer dependent on the data analyst, influencing the number of decisions driven directly by data (without the intervention of a data analyst). This would in turn result in the transformation of the role of a chief information officer into a strategic one.
In effect, Big Data has aided organizations in streamlining their processes to align to Six-Sigma standards and lean methodology. Numerous use cases of Big Data can be found in shop floor applications such as improved production, quality and asset maintenance. Such lucrative outcomes have led to their incorporation in other support functions such as product design, research and development, customer service and support. Though the extended effect of Big Data is uncertain now, these trends strongly point to the fact that it has carved a niche for itself in the factory of the future.