Extracting more value from big data
STEPHEN BAKER, ATTIVIO, NEWTOWN, MA
THE PROMISE of Big Data to harness a wealth of information and turn it into business insights looms large in all industries. Now more than ever, data and content are strategic assets that can provide competitive advantage. Nowhere is this more evident than in the oil and gas sector.
An agile business intelligence and analytics program is the key to delivering on the promise of Big Data. The challenges with establishing an effective analytics program are twofold:
- Big Data is hard to wrangle. With the proliferation of data from the Internet of Things, and the agile storage of Hadoop, a wealth of information is available. As more sensor-driven devices are incorporated into the oil and gas segment, the amount of data will grow exponentially. The challenge as the industry attempts to leverage all this information is the actual provisioning of data into business intelligence applications. As we've seen across industries, in a typical project, it takes longer to profile and identify the data sources to feed into the business intelligence tool than to run the analysis and develop insights.
- Unstructured information is often left behind. The untapped potential of qualitative information buried in unstructured and complex content is the next frontier for organizations that compete on analytics.
These are big challenges, but the benefits are game-changing: transformational productivity, the ability for executives to confidently seize opportunities and act with certainty, and achieving global impact on revenue generation.
A few areas where agile business intelligence can make a big impact for oil and gas companies include drilling nonproductive time (NPT), health, safety & environment risk management, and procurement and contract analysis.
MINIMIZE NONPRODUCTIVE TIME
In general, there's a significant opportunity to save billions of dollars of NPT by applying advanced analytics techniques to data and content captured in Big Data and enterprise content repositories. This requires more than leveraging existing business intelligence (BI) platforms to understand historical production trends. That explains what has already happened. The goal should be to understand why it happened, and then ultimately to predict when it will happen again.
Tapping into unstructured content, such as maintenance notes, drilling reports, and other sources, yields the qualitative insights associated with NPT. With sophisticated content analytics techniques to turn qualitative operations reports into data - essentially structuring the unstructured - this contextual information can be analyzed using traditional business intelligence methods.
Understanding why this occurs provides the raw inputs for reducing NPT in the future. For example, this could be the basis for building a predictive model based on weather, seismic patterns, and other environmental models to plan preventative well maintenance to minimize NPT.
REDUCE HEALTH, SAFETY, AND ENVIRONMENTAL RISK
A comprehensive analytics strategy can significantly reduce the risk of HS&E issues. Across the industry, firms have between 15 and 25 zero-margin days due to on-field incidents. From a cost perspective, the accumulation of these days is enormous. The data related to these issues, such as weather and numbers of workers on site, can surface trends and patterns.
Firms can correlate health and safety data with unstructured content to understand the likelihood of injuries or environmental damage given the presence of specific attributes. They can build predictive analytic models based on this information that goes beyond the what to explain why an incident occurred and when it is likely to happen again. This powerful insight enables them to programmatically set up controls to minimize the risk of HS&E incidents.
CREATE EFFICIENCIES WITH PROCUREMENT AND CONTRACT ANALYSIS
Another area that is ripe for reaping benefits from data analytics is contract analysis. Creating procurement efficiencies typically requires analyzing contracts and data from numerous procurement, enterprise resource planning (ERP), and customer relationship management (CRM) platforms. This analysis can reap significant bottom line benefits in discovering savings, managing vendors, and identifying opportunities for supplier consolidation. However, just finding and cataloging data sources from across the oil and gas supply chain requires manual effort and resources to consolidate data silos. That effort can account for 65% of the project - in other words, it requires more time and resources than the actual analysis.
Rather than spending significant time and money to consolidate supply chain information systems, data source discovery solutions should be used to quickly identify and unify information from the data lake and data repositories for streamlined analysis. This enables comprehensive and faster decision making, in order to find significant cost savings with less investment.
-Stephen Baker, Attivio
IDENTIFY DATA-DRIVEN SYNERGIES POST-M&A
All of these use cases offer compelling reasons for faster, more comprehensive analytics. When you take them into an M&A scenario, the level of complexity multiplies. The problem of data silos is exacerbated when data is spread across numerous stacks of legacy IT.
Wall Street expects publicly held companies to show results quickly. With an acquisition, rationalizing data sources could lead to critical business insights that have a global impact. If only. Re-architecting the IT infrastructure to consolidate CRMs, ERPs, customer support systems and so forth would be a multi-year, costly project, with a lot of risk.
By crawling information sources where they live and building a universal data source index, data source discovery solutions automate a manual, costly process and eliminate the bottleneck between the wealth of information available and the business intelligence program that can develop business insights and surface opportunities.
HARVEST BIG DATA FOR BIG OPPORTUNITIES
In a business climate that rewards speed and agility, data is more strategic than ever. Companies will increasingly compete on advantages that can be derived from BI and advanced analytics. All organizations are dealing with heterogeneous content sources involving complex, unstructured data which, if analyzed properly, can yield true transformational insight.
Furthermore, information and data silos are not going away. Even with a hub-and-spoke model, data profiling and cataloging remains a challenge. Analyzing data across silos is necessary in order to make better decisions.
Oil and gas firms have well understood use cases that can benefit from technologies that help them supercharge their BI initiatives.
ABOUT THE AUTHOR
Stephen Baker is CEO of Attivio, the Data Dexterity Company. In leading Attivio, Baker brings more than 15 years of experience as a top executive within the enterprise software industry. Baker holds an MBA from the University of Pennsylvania - The Wharton School as well as a BS in Music and Marketing from Hofstra University.