Industries that are historically asset-heavy, such as utilities, face an additional challenge: constantly undertaking efficient asset performance control to ensure that those assets produce revenue, not costing money because of underperformance or an unforeseen stoppage. Although data analytics is doing a lot these days, maybe one of its most important strengths is the ability to keep the current assets running at the most profitable level possible.
Assets owned by a corporation are, by their design, not passive. It may seem like such assets exist to serve the needs of internal operations, such as buildings and installed equipment, but a deeper investigation shows the interconnectedness of all your assets. You will soon understand, along this line of reasoning, that no asset is independent of the rest of your activities. Downtime for one piece of equipment, whether it is instantly viewed or not, also causes a negative ripple effect across the entire operation.
These facts point to the need at any stage of the operation for proper and effective asset performance management. Fortunately, for asset-heavy and asset-dependent companies, the advent of the Industrial Internet of Things ( IIoT) comes at the most important moment. In ways you didn’t think existed, combining IIoT with data analytics will empower your business. You will also begin to see and appreciate how all your assets are interrelated and interdependent, with the opportunity to obtain a comprehensive and clear overview of your asset operations. This allows you to predict interferences and interruptions better, which in turn gives you the ability to intercept and prevent unwanted downtimes and crashes of equipment.
#data-visualization #technology #machine-learning #data-science #artificial-intelligence
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
What exactly is Big Data? Big Data is nothing but large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and Big Data Tools created to manage this large amount of information.
To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.
The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:
#big data engineering #top 10 big data tools for data management and analytics #big data tools for data management and analytics #tools for data management #analytics #top big data tools for data management and analytics
For Big Data Analytics, the challenges faced by businesses are unique and so will be the solution required to help access the full potential of Big Data.
Let’s take a look at the Top Big Data Analytics Challenges faced by Businesses and their Solutions.
#big data analytics challenges #big data analytics #data management #data analytics strategy #business solutions by big data #top big data analytics companies
Disclaimer: Many points made in this post have been derived from discussions with various parties, but do not represent any individuals or organisations.
Defining clear roles, responsibilities and ways of working is very important. Although my other post has already described the Engine and the Driver, it is interesting to understand what capabilities should remain centralised and what should be decentralised for an organisation to become more effective in their data analytics journey.
Let’s start by looking at the essential functions required to facilitate a data-driven organisation.
Before considering what capabilities should be decentralised or remain centralised, it is worth to understand what can happen under a different context.
#data #data-analytics #data-strategy #data-asset #agile-teams #business #data-pipeline #analytics