What is Networked BI? Let’s discuss. As an example businesses today do not operate as a collection of disconnected silos. Above all, your BI and analytics solution shouldn’t either. Frequently this is what happens with expanding data ecosystems and desktop-based data discovery tools that can’t support enterprise-wide analytics governance. For Instance, Business people are forcing themselves to make decisions in a vacuum and work with conflicting and unreliable interpretations of the data. Furthermore, As these analytical silos proliferate, companies suffer from what experts call a “spreadmart effect”, which undermines trust in the data and leads to poor decision-making. Beyond this point, common terminology is a must have.
In conclusion, Networked BI is a breakthrough approach to analytics that connects every part of your organization via a shared analytical fabric that every person can easily access and extend. It eliminates analytical silos once and for all, empowering everyone with self-service BI capabilities that enable you to leverage the collective intelligence of your organization. For this reason, Networked BI is quickly becoming the new IT standard.
THE CENTRALIZED AND DECENTRALIZED DIVIDE- BUSINESS INTELLIGENCE TODAY
Overall, much has been written about the evolution of business intelligence (BI) and analytics space. However, opinions differ about where the market is moving, there is no debate about how substantially it has been transformed. In essence of this transformation has been mounting in recent years, with the emergence of data discovery tools aimed at business users frustrated with long wait times and lack of access to data. The next step has resulted in declining market share for vendors of traditional or “legacy” enterprise BI platforms, which dominated the industry throughout much of the late ’90s and 2000s but have failed to keep up with growing business requirements for ease of use, speed, and agility. In addition, the amount of data has also increased and more silos.
With that in mind, the evolution of the BI market is attributed to doing a fundamental shift from the traditional model of owned, centrally managed analytics (what technology research firm Gartner calls “Mode 1” analytics)1 to a more decentralized, user-oriented style of delivering BI (“Mode 2” analytics) 1. Legacy BI platforms that support the traditional centralized model are generally known for delivering sophisticated analytical capabilities, high scalability, robust security, and strong governance management mechanisms. These legacy tools, however, require extensive BI expertise and have a reputation for a high cost of ownership, long development cycles, and limited self-service capabilities that hinder users’ ability to work with data on their own. Just as interesting, self-service Networked BI is fast to deploy and lessens the burden on IT.
The decentralized model, on the other hand, supports desktop-based data discovery tools designed for ease of use and speed. These products make it possible for a business person without broad BI experience to access and analyze data independently. But despite their benefits, decentralized tools are not without problems. Among them, data discovery products generally lack the underlying technology architecture necessary for data governance and high scale. As analyst Wayne Eckerson points out, desktop discovery tools, left unchecked, result in “ungoverned spreadmarts that increase your support costs, undermine data consistency and waste your staff’s time reconciling reports.” For this reason business’s can operate faster and with the assurance of good data.
Networked BI: Effectively Moving Beyond Centralized and Decentralized Analytics.
By and large Networked BI is a new approach to analytics based on the idea that trusted and well-governed data is not at odds with speed and ease of use. In essence, it leverages new capabilities made available by modern technologies like cloud computing – multi-tenancy, virtualization, and web-scale architectures – to truly combine the centralized and decentralized models of BI, delivering the best aspects of both: end-user self-service without analytical silos.
In other words in a Networked BI model, analytics mirror how companies operate in the real world: by empowering business units and individuals to act independently, establishing mechanisms by which they can leverage and extend the work produced by other teams, and defining a common set of business rules that govern how everyone interacts. Consequently, the same data is leveraged with security across the enterprise.
With this in mind, at the core of Networked BI is the concept of a “shared analytical fabric”. Firstly, the analytical fabric is a living network of data and insights that connect every part of an organization. Similarly, every person plugged into the network can benefit from data produced by other people, as well as extend it with their own data. With this in mind, because all data added to the fabric becomes governed under the same business logic, analytical silos are never created. Moreover, there is no ambiguity around what a particular dimension or KPI means. Contrary to other solutions, Birst developed and mastered this architecture. Restating the obvious, self-service BI accelerates business decisions.
With that in mind, there is no doubt the shift from centralized to decentralized analytics has addressed some of the problems with traditional BI approaches, rather created or exacerbated others. Thus it has become clear that neither a centralized or decentralized approach by itself is sufficient. Similarly, successful companies understand that solving modern BI problems requires a new approach that combines decentralized self-service with centralized governance.
Simultaneaously, the evolution of the BI space, along with the emergence and large-scale adoption of technologies like cloud computing, enable modern alternatives to traditional analytics that present exciting opportunities. So moreover Networked BI will reshape how we think about enterprise analytics. Also, the next step will enable IT, leaders, to extend the adoption of BI across the enterprise with confidence. In brief, by building networks of virtual instances, businesses can deliver governance that moves at business speed, especially eliminating data silos once and for all and giving people the freedom to work with data on their own terms. With this in mind, read the detailed whitepaper to provide more in-depth analysis.
Above all Performance and Scalability in Numbers
- 1.5 Petabytes of data stored
- 420,000 queries per hour
- 10,500,000 dashboards
- 125,000 dashboard views per day
- 50,000 data sources
- 1 multi-tenant architecture
User Administration: Networked BI Demo Part 4
- Privilege based
- Data connections
- Semantic layer
Graphical Modelling in Birst
- Model source relationships
- Write ETL scripts
- Visualize star schema warehouse
Beginning with Security and reliability in Birst
Simultaneously to protect the privacy of its customers and the safety of their information, Birst, an Infor® company, maintains high standards of data security. Firstly Birst relies upon state-of-the-art and secure data centers, enforces strict internal product controls, and regularly audits its policies and procedures using third-party auditors. Specifically, the following sections of this white paper cover the key areas of Birst security in detail, including physical security, system security, operational security, reliability, and application and data security. In other words, iron-clad Networked BI.