Unlocking the potential of data in the oil and gas industry
The oil and natural gas industry is heavily driven by data. Everything from the drilling rigs to the pipelines to the refineries and beyond has to be closely monitored. This is after all dealing with the most precious of natural resources. Companies in the oil and gas sector are constantly trying to find new ways to better their performance through more updated systems and modern methods. There’s a lot of logistics and process control involved which employs sensors, gauges and other infrastructure to collect the data across the system. Data can be collected in a variety of formats, including structured, unstructured and semi-structured data. However, data is not of much value unless it’s broken down and examined. The oil and gas industry uses large amounts of continuous data for various purposes. Real-life use cases of data analytics in the oil and gas industry Data analytics is a major skill set in the oil and gas sector, whether it’s for the improvement of ROI or for health, safety and environmental measures. Processes in the oil industry depend on the ability to understand and predict future supply, demand and production challenges. This is why many oil companies have found it beneficial to invest in advanced analytics and forecasting. Due to the industry’s increasing dependence on data and the need for new frontiers in research and production, oil and gas have realized the importance of state-of-the-art analytics. Reduce production costs Many factors have an impact on the overall finances when it comes to oil and gas industry production costs. The production costs of oil and gas companies are affected by logistics, drilling wells, and laying pipelines. Data analytics for oil and gas increase production efficiency. This is used to lower or stabilize production costs. Companies use rock analysis techniques to locate reservoirs. Predictive analytics tools are used to process data from nearby oil wells. This allows oil production data to be paired with a downhole to adjust the boiling strategy. Increase equipment life span with predictive analytics Shell collects tons of sensor data and performs advanced analysis on the machinery at drilling sites to improve performance and determine what equipment needs maintenance. This results in a longer drilling duration and fewer stops. Shell is the only company to have saved over $1,000,000 using sensor analytics. Reduce net carbon footprint According to Shell’s most recent sustainability report, the company supports the vision of a net zero emissions energy system. The company intends to reduce emissions by using carbon capture and storage technology powered by big data software. Ensuring worker safety One of the most important concerns in the oil and gas industry is the safety of workers and the environment during drilling. There is always the risk that employees may be permanently or fatally harmed by hazardous fumes when they are being extracted. Oil and gas companies use Big Data and predictive analytics to find new sources of oil or gas. This is without the need to undergo potentially dangerous procedures in order to reduce this risk. Oil and gas data analytics for upstream, midstream and downstream optimization: Sector upstream Manage seismic data. Upstream analytics starts with the acquisition of seismic data (collected using sensors) over a potential area for searching for petroleum sources. After the data has been collected, it is processed to identify a site for drilling. You can combine seismic data with other data sets, such as historical data from a company on past drilling operations, research data, and so forth to determine the oil and gas content of oil reservoirs. Optimize drilling processes. To optimize drilling operations, you can customize predictive models to predict potential equipment failures. The equipment is equipped with sensors that collect data during drilling operations. These data are combined with metadata about the equipment (model, operational settings etc.). This data is then run through machine learning algorithms to determine usage patterns most likely to lead to breakdowns. Want information about our data analytics solutions? Click here. Improve reservoir engineering. There are many downhole sensors available (temperature sensors and acoustic sensors, among others). Companies can collect the data they need to increase reservoir production. Companies can use data analytics solutions to develop reservoir management apps to gain timely and actionable information on changes in reservoir pressure, temperature and flow. This will allow them to improve their reservoir performance and profitability. Sector midstream The logistics of the petroleum industry are extremely complex. It is important to minimize risk and ensure that oil and gas are transported safely. To ensure safe logistics, companies use sensor analytics. Predictive maintenance software analyses sensor data from tankers and pipelines to identify abnormalities such as fatigue cracks, stress corrosion, seismic ground movement, etc. This allows for the prevention of accidents. Downstream The downtime of machinery in industries is an unplanned event that interrupts production for a period. This could happen for any reason, including malfunction, repair or changeover of equipment or tools. Oil and gas industries use predictive analytics to forecast downtime. They do this by using simulation data that builds prediction data. Predictive maintenance techniques are used by oil and gas companies to reduce the cost of unexpected reactive maintenance. These forecasts give updates about optimizing downtimes for large-scale maintenance operations well before the downtime event occurs. This could help protect machinery and reduce production losses. Unlock big data potential to leverage data better Data analytics allows companies to transform huge datasets into sound oil-and-gas exploration decisions. This results in lower operational costs, longer equipment life, and a lower environmental impact. Advaiya’s data analytics consulting team can help you secure the benefits mentioned above. For more information about our Oil and Gas data analytics solutions, schedule a free consultation. Chiranjibi Kunda Chiranjibi Kunda is an Associate in BI & Analytics team at Advaiya. He is a Microsoft certified data analyst specialized in analytics, reporting and analytical tools that work seamlessly with business intelligence, data warehousing, architecture, data modelling, and cloud solutions to create effective solution models and optimize the operations.
The Customer Approach: Data for Decisions Not Data for Itself
Data, Data, Data. It’s hard to hear about anything else. When concepts take flight in the business of technology, they soar to amazing heights. This is a great thing about our industry but also creates confusion. People rightfully ask, “If we are truly in the age of Data and Data is indeed so great, why is my business not flourishing as it should?” In some sense, this is the eternal question. For 3 decades, Technologists have been promising miracles with Business Intelligence; many companies have realized significant benefits, but many others have come to conceive of Data as an “end in itself” and not as the fuel to turbocharge their organizations. Where BI has failed, it has failed spectacularly. To understand this, we must be fair in assessing blame. No doubt, many vendors offered hyperbolic rhetoric in place of great technology and many implementation partners believed that technology would be a silver bullet. However, there is more context to be had. Some pundits discussed the lack of a “Data Culture,” and while they were perfectly right, they too missed part of the story. In reality, the reason that “Data” is often seen as a false prophet, is that organizations forgot two basic things: 1. Customers must be the real beneficiaries of a data strategy 2. Data is only useful if it supports Decisions In the absence of true customer focus, Data gathering becomes a psychosis. In the absence of decision-based Data gathering, this psychosis becomes endemic. When we bring the data conversation back to the customer and back to the specific decisions the customer needs to make, we rediscover its beauty, elegance, and practical value. Guest Author: Samir Saluja, DeriveOne Joint Author: Dharmesh Godha, Advaiya Samir Saluja is the Co-Founder of DeriveOne, a firm specializing in decision based research and analytics. Samir’s business leadership experience spans multiple industries including software and cloud services, manufacturing, consumer goods, financial services, and consulting. Prior to DeriveOne, Samir led the Microsoft Professional Program business for Microsoft. Dharmesh is the President and CTO at Advaiya Solutions. He has 20+ years of experience in various technology platforms, solution design, and project implementations.
Are you unable to make data-driven decisions?
Regardless the industry and the size of your company, when it comes to making strategic decisions, we often rely on gut feeling, ad-hoc reports or other traditional approaches. A recent study found that 58% of respondents said their companies base at least half of their regular business decisions on gut feel or experiences, rather than on data and information. And, some of the most crucial reasons for not using data for decision making are the lack of data availability and data of poor quality. With the increasing emphasis on data-driven decisions, we can no longer depend on emotions and intuitions. Because, a good decision always need insights from the past, present, and the future. To gain better visibility and control over the data, you need a data-driven culture across the organization. So that everyone can recognize hidden patterns, can find the solution to their unanswered business problems and can make smarter business decisions. For instance, in project portfolio management – from executives to project managers to team members – everyone needs insights at various levels. A team member wants to know the list of tasks, the priorities, and the timeline for these tasks, etc. A project manager wants to know which project is performing well, who is available to work for the project and so on. Executives need to know the overall company portfolio to check the overall business progress and plan investments. They all need data that they can trust to facilitate decisions making and take actions. Advaiya AdValue offers a set of pre-built templates and click-to-deploy BI dashboards available for different roles in the organization to access all relevant project information quickly and make informed decisions on-the-fly. If we talk about marketing, the digital world has changed the way marketing happens. Unlike in the past, marketers today need varied insights before planning their strategies. They want to know the performance of the campaigns executed in the past, platforms that worked well for them, tactics that have given good results and other crucial numbers. Getting an in-depth and unified view of all the relevant information is easier said than done. Because, data is coming from a variety of sources, both internally i.e. within the organization, or externally from social media and other channels. Data-driven marketing help marketers reach the right people with the right message at the right time and make marketing relevant. A study by Forbes states that two-thirds of engaged data-driven marketers are seeing new customers because of data-driven initiatives. Advaiya Adaptive BI services help you monitor and analyze the business health data the way you want so you can take actions right away. From improving the customer experience to increasing efficiency and reducing costs, organizations of all sizes are using business intelligence and data analytics to make smarter and data-driven business decisions to plan better for the future. Do you want to transform data into insight and action? Contact us at connect@advaiya.com or give us call +1-425-256-3123 and let’s get started!
Big Data: A Golden Shot to Cyber Security?
Cyber-attacks are getting worse with every passing day and only 20% of the organizations are able to effectively curb them. Can Big Data Analytics make organizations secure by recognizing the patterns that represent network threats? Industries, today, are more effectively mining intelligence buried in sheer volume of data available to them and Big Data is transforming the way data is analysed. Huge volume of diverse and fast changing data helps organizations to gain new insights by analysing them to run their business in a prominent way and gain competitive advantage. The way Big Data has been transforming businesses in each and every domain, the same competitive transformation is expected for Information Security sector as well. Organizations are now dissolving their network boundaries by opening and extending their data networks to all the stakeholders of data to access corporate information. This openness leads to more vulnerability to data theft and misuse. The applications used by organizations, access data through cloud and mobile devices, thus increasing new information risks. At the same time, Cyber attackers are using and developing new technologies and complex attacks to breach any secured system and sometimes these attacks are not even spotted until the entire damage has been done. How Big Data can solve the problem of Security? Advaiya’s Big Data research and development team conducted the study on ‘Role of Big Data in Cyber Security’ to find out its impact in recognizing cyber threats to make organizations more secure. Big Data can be introduced into security programs or tools introduced under the security model of an organization. Organizations can gauge the security related risks using multiple data sources by incorporating Big Data into their security model. Furthermore, Big Data can magnify the capability of finding abnormal activities and behaviours which can cause serious damage to an organisation. It can emerge as a separate intelligence security model for threat detection and prevention. This Big Data driven intelligence security model will use automated tools which will capture real time internal and external data and make it useful for analytical engines. An advanced monitoring system will be setup which will monitor information systems and network resources to provide risk assessment based on the dynamic risk models generated on the basis of behaviours and activities within a network. An N-Tier infrastructure with centralized data warehouse will be setup to provide all security related information for helping the security analysts to process complex dynamic security related queries and searches. Advanced active controls along with integrated security based tools will trigger the automatic defensive measure. This will facilitate the security analysts in taking immediate decisions when any high risk security breach is detected. Advaiya believes that Big Data Analytics in Cyber Security will bring a vast impact in areas of identity management, risk compliance, authentication, threat identification, data loss, network monitoring, authorization, etc. It would change the way people look into the IT world from suspicion to a trust based environment
Big data impact and concerns
Businesses are overloaded with the sheer volume of data, churned out daily by operational/ transactional systems, web logs, industrial sensors, customer data, social media information etc. The information from these data is critical to businesses and future businesses decisions as it can uncover hidden patterns, unknown correlations and other useful information. Businesses can make sense of these data as a whole (by applying aggregation) and can also comprehend individual constitutes. How ? Please read my article below that was published in Asian Management Review magazine (APRIL – JUNE 2014) Big data impact and concerns from Advaiya
Big Data Insights
Big Data will be in mainstream use in all major organization in coming years. So NO organization, big or small, can afford to miss this bus.
Harnessing Big Data
We are living in the era of information surplus where information and data is omniscient and omnipresent. Our lives have become digitized today. We are generating and managing volume of data of various variety – streaming data, mobile data, social media content, day to day communications, banking transactions, weather logs, etc. Making sense of these available data has become imperative. In this context, Big Data analysis has become the talk of town. People mostly talks about Big Data Analysis at different levels of abstraction and understanding. They talk about real time and advanced analytics, frameworks and products, which is typically not a good idea. So let’s try to step back and look at what Big Data means? Big Data is described by three characteristics of data: volume, variety and velocity. Volume means making sense from terabytes—even petabytes—of data. Variety refers to diverse set of data being created from sources like social networking feeds, videos and audio files, emails, sensor data and other raw data. Velocity means bringing up data from real time data sources like websites, ATMs, point-of-sale devices, and other sources. Thus, Big Data analysis can be define as the process of analyzing large volume, variety and velocity of data to discover buried patterns, hidden correlation and other useful information. Now the next question that comes to our mind is, how Big Data creates value for my business? Big Data enable business value in four broad ways. First, it helps making data more apparent and workable for business. Second, it enables better decision making through richer and broader data sets. Third, it helps to do narrower segmentation of customer for targeted marketing campaigns and sales pitch. Finally, opens new business opportunities by developing new and innovative products and services. Business that have begun to embrace Big Data approaches are now realizing that the possibilities for new innovation, improved agility, optimizing productivity and increased profitability are nearly endless. There are several use cases associate with the Big Data. Let us take an example how supply chain process can be optimized using Big Data. In supply chain system, enormous amount of data is collected from a variety of sources ranging from ERP systems, supplier’s details of order and shipment, web log files of customer shopping patterns, sensors data like RFID, electronic meter readings, data generated from mobile devices data and social channels, etc. Big Data in real time can help supply chain process for example by tracking minute by minute shipment details, thus providing deep visibility and control on the process, By tapping data from real time web logs and social channels, industry can predict market trends and shift to a demand–driven production model. This can help them to tune their production schedule, procurement plan, distribution plan, pricing model and much more. Let us take another example of how Big Data can create real impact. The telecom industry is already generating large volumes of data including call detail records (CDR), network data, customer data from calls and purchases to downloads and updates. The data volume is so high that manual analysis is unmanageable and impossible. This is because legacy software systems cannot handle this data load, resulting in valuable data being ignored and untapped. The telecom industry with the help of Big Data can store and analyze these data patterns and usage. Combined with the customer demographic data, industry can get a good idea of what is important to their customers and become more responsive in product offerings and marketing initiatives. Big Data analysis can also help in predicating customer churn by identifying the customers that are most at risk of leaving, and tailor special offers or programs to retain them. By examining the user behavior, they can offer personalized recommendations for services and add-ons. Also using real time network information including call duration, answer/seizure ratio and post-dial delay, the network administrator can quickly see issues as they arise and proactively take corrective actions to resolve them even before customers start complaining about the same. Now you can imagine, Big Data is different from traditional relational database systems as it entails more advance mechanism to understand, process, store and analyze data. Big Data ecosystem constitutes of data storage and management, Big Data analytics tools and technologies, and associated IT services. To develop and manage this, Big Data requires two kinds of skillset – First, the technical skills that are required to store, process, discover and analyze the massive and data sets and the knowledge of Big Data architectures (i.e.Hadoop, MapReduce, Hive , Key Value stores etc.). And second – the next-generation business analyst with strong statistical skills who are able to extract information from large data sets and then present it to businesses that should be understandable and valuable. But in current scenario, there is still a significant shortage of skilled professionals who can evaluate business needs and impact, and apply the Big Data concepts and technology/tools to it. This poses biggest impediment to adoption. There are many other important issues like data security, policies and governance, organizational change and talent, industry structure considerations that will have to be addressed in order to adopt and realize the full potential of Big Data. To overcome the aforesaid issues, enterprise can take help from technology vendors that can help with strategy, consulting, training, technical and professional services. A right technology vendor can help in identifying and prioritizing the most valuable Big Data opportunities for their customers – the ones that will generate the highest ROI using Big Data and will create the most effective roadmap, architecture and plan. You can imagine all the possibilities that Big Data can bring to your business, but you should find answers to few important questions. Do you envision that Big Data can create new opportunities for your business in areas such as Product/service development, design, marketing, sale and support? What are the possible Big Data use cases for your business? Can your current environment handle it? What are the organizational readiness and areas that require investment? This