Published on 18 Dec 2017
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Some would say that the days of data chaos are over. With rapid digitization, modern businesses can take command over the quantity and quality of information rather than robotically process it from databases. They can also analyze streams of unstructured information on a real-time basis to derive strategic insights. Sometimes, this might involve processing 12 terabytes of tweets every day to ascertain product sentiment or 350 billion meter readings to predict power consumption patterns. And in others, this could go up to terabytes or even petabytes of information.
In the oil and gas (O&G) sector, a typical offshore oil rig is embedded with more than 30,000 sophisticated sensors that generate data masses at scale. But, only one percent of this data is leveraged for decision-making, leaving the rest in data graveyards-unanalyzed and dormant. While this is common in other process industries too, it is poised to change due to the growing availability of unified information management systems. By establishing an intrinsic linkage between core processes, these solutions are all set to transform communication, collaboration, innovation and deliver superior experiences across the entire value chain.
Information management requirements tend to vary according to the industry under consideration. In the pharma sector, management information systems combine and analyze data from drug development and production, allocate resources across factories, ascertain cause of action, estimate supply and demand, maintain security and regulatory protocols, and monitor and evaluate company management.
For heavy industries like manufacturing and O&G, business units dealing with generation, transmission and distribution, energy trading and risk management, and cybersecurity traditionally had disparate data management systems. Now, however, these industries have begun to leverage machine learning, artificial intelligence (AI) and other technologies to harness and make sense of the data from multiple touch-points like manufacturing, production, supply chain and procurement.
For many, the Internet of Energy is the next frontier in the global energy management landscape. Electricity producers are using the model to automate and upgrade their infrastructure with sensors, which are connected to a central data management system. The collated data is then thoroughly analyzed to obtain valuable insights that help enhance production and reduce waste.
There are similar applications in the construction industry, where building information modeling (BIM) is enabling an overarching information management plan. This is helping organizations lower the initial cost of construction and the whole life cost of built assets by 33 percent while reducing overall completion time by as much as 50 percent.
The implementation of such integrated information management (IIM) systems is crucial for business sustainability. Whether data streams emanate from sales demand analysis, internal control deviation reports or capital expenditure projections, it can only be transformed into usable and actionable information with these tools. For agricultural, mining and shipping organization, this could mean CAPEX optimization through the careful analysis of long-term future climate trends incorporated in supply chain plans.
Even though 80 percent of senior executives accept the importance of accessing the right information at the right time for success, they might not necessarily be doing so—like in the oil rig example we cited earlier. Many businesses struggle with the lack of data consolidation across siloed processes and often end up with an information overload.
Data consolidation for a single source of truth is an ideal way around this familiar problem where complete control over enterprise data is the objective. Analytics helps process unstructured data pools to reduce downtime and optimize asset utilization. In fact, analyzing control and maintenance system data together enables 10 to 15 percent reduction of downtime in an oil refinery.
For business functions like marketing and sales, however, data consolidation could inhibit flexibility for certain processes such as extracting data to maintain client relationships. For instance, a company producing steel for different industries like consumer electronics and automotive will need to provide each customer with specific information. Multiple versions of truth (MVOT), an alternative to the single source of truth approach, can be the key here. Not only do MVOTs support the required data flexibility, they also allow businesses to select the most relevant insights for decision-making.
As the need to segment data into actionable information grows, the focus is now on companies that can craft a viable information protection and disaster recovery plan, to ensure business continuity. Overall, this means a dramatic improvement in information storage frameworks that are Cloud-friendly, secure, and sustainable enough to support future data proliferation.
With time, information channels will become well-defined, quadrupling the amount of data generated every second. As overwhelming as this might seem, the forthcoming data flood will evolve into a strategic resource that distinguishes high-performing businesses from the crowd.
