There is a prediction that by 2017, 33% of Fortune 100 companies will experience information crises due to lack of adequate testing measures. To prevent this, the robust testing approach must be used. Developing a well-planned data warehousing, testing process will help in minimizing such risks. And while moving to agile, we can do more testing in comparison to traditional projects. The testing being done during development in parallel aids to agile development, because development and testing can be completed in a single sprint. The roadmap for building agile data warehouse will consist of following steps-

·         Build business conceptual model

·         Grass roots Data governance

·         High-level architecture with repeatable design patterns

·         Strong testing tools

·         Robust data quality program

·          Self-managing teams.

Read the full article here http://www.cio.com/article/3012097/data-warehousing/take-testing-seriously.html

With the analytics boom, businesses are treading the data-driven path. But organizations should realize that not every data is useful- one needs to sift through a huge amount of data to find out the useful ones. Often having a large database doesn't indicate a successful data program, instead it implies a program requiring considerable amount of work. The problem with a large database is that the data does not possess a reasonable degree of accuracy hence making it difficult to make accurate business analysis. Knowing more about the data- what it is and what it represents- will enable companies to filter, categorize and analyze data. Cleaning up data isn't something that can be easily achieved through technology. Data gathering system should be developed keeping in mind what data is being collected and why it is being collected. Read more at: https://channels.theinnovationenterprise.com/articles/your-data-is-garbage

Financial managers find it very difficult to reconcile goods received against invoices not received. This issues called 'GR-NI' is time consuming to manage. The problem hence gets reduced to lowest of the priorities and leads to increase of financial liabilities and risk for the business. Without proper automation to monitor the discrepancies, paperwork gets piled up which leads to supplier connections suffering and credits being misused. However, with software based supplier reconciliation process in place, the organization can get rid of this huge task and also improve its audit processes solving several risk issues as they arise. A simple overview of the new 'GR-NI' process says that creation of a database with financial and orders data is to be integrated followed by different other steps. The key elements of this solution are methods to get statements directly into the system regardless of format and source and many others. To maximize the value, operational areas should be looked into that can benefit from better processes. Read more at: http://www.smartdatacollective.com/keith-peterson/329860/using-procurement-analytics-simplify-your-supplier-reconciliation

Up to 16 Exabytes of RAM can be supported by a 64-bit system. Machines with 128GB RAM or more are becoming common with this era of Cloud Computing and Big Data. The data sets for Big Data are getting too large for even heavily loaded machines with memory despite the best efforts, don't fit into the RAM even after clustering in some cases. Researchers at MIT created a cluster called BlueDBM using Solid-State Drives (SSDs) to get rid of the memory problem. They also moved some of the computational power off the servers and onto chips. By pre-processing known parts of the data onto the flash drives prior to passing it back to the servers, the chips made distributed computation much more efficient than before. They thus got rid of the overhead of running an operating system. Read more at: http://www.itworld.com/article/2947839/big-data/mit-comes-up-with-a-no-memory-solution-for-big-data.html

A specific kind of data may be important for a few seconds or sometimes even a few years. Changing times catch up with all kinds of data. So what to do when data reaches its golden age? Archive! But it isn't enough to dump old data in a traditional data warehouse. We must think not only of data's immediate analytical value, but also at how to retain the value of data as it ages. This requires a new approach to archiving. We must ensure regulatory compliance and follow policies of data governance. We must provide access to data while preserving immutability of it or its metadata. Data should query enabled when needed, to generate new insights. Archives must be cost-efficient, enabling you take advantage of advances in data compression. Read more at : http://www.forbes.com/sites/teradata/2015/05/22/archived-but-accessible-retirement-planning-for-your-big-data/

Don't ignore your supply chain risks if you love your profits, that's the advice Keith Peterson, president and CEO of Halo, is giving in his article. He talks about:

• Various supply chain risks
• The department they affect
• Its financial impacts
• Finally! Solution. 

Solution to all the problems these days is in analytics. Right software and analytics programme will increase your profit generating abilities to multiple folds. Intrigued? Follow the link at:http://smartdatacollective.com/keith-peterson/322401/solving-supply-chain-risks-infographic

Today companies have vast amounts of informations available to carry business moves. The General Mills and IBMs of the world now are using complicated-and expensive-Business Intelligence (BI) systems that can combine data together from a multiple operating systems and generate best reports that highlight not only what has occured and what is happening, but also what will probably happen. Can Small Business Benefit From Business Intelligence Software?

• It can help a small business compete with larger competitors or enhance market share.

• Vendors are getting experienced at making software that’s affordable.

• BI vendors are starting to educate a younger crowd. 

• The cloud now puts non-IT users in the driver’s seat. 

• Before investing in BI tools, one must know what answers they are looking for.

• Forget about using BI software if one don’t have access to best operational data.

• BI is increasingly going mobile. 

 Read more at:

http://www.inc.com/articles/201109/business-intelligence-software-for-small-business.html

Healthcare is altering rapidly and so is the industry's need for analytics and business intelligence. Sometimes business intelligence refers to a large group of analytics, data warehousing and revelation tools. Sometimes, business intelligence tools are associated to the visualization coating only - tools that take the data and gives a visual illustration of it. The core of data warehousing is a dimension to assist the understanding, approaching and exploiting data in terms of BI. In general, a data warehouse is a centrally managed and easily available copy of data gathered from the transactional information systems of a health system. Such research and analysis enable measurement, which helps in accepting and the most informed business and clinical decisions. Sources can be internal , such as electronics health records (EHR) systems, patient happiness systems, prices or financial systems; or exterior, such as systems linked with a state or central administration . Read more at:

http://www.healthcatalyst.com/healthcare-business-intelligence-data-warehouse