SigmaWay Blog

SigmaWay Blog tries to aggregate original and third party content for the site users. It caters to articles on Process Improvement, Lean Six Sigma, Analytics, Market Intelligence, Training ,IT Services and industries which SigmaWay caters to

Rules to Follow in Data Analytics

Analytics is one of the major jobs performed in companies these days. Daily operations are carried out involving data that presents us with results which helps an organization to carry out further processes and helps in decision making. Effective business intelligence is the product of data processed. This data is raw and can be either structured or unstructured. 

Firstly, one needs to manage data before processing it. Rules are to be set for the analytics process which can offer better insight and an easy processing. Below are the five rules that can help in managing your data more effectively:

  1. Establish Clear Analytics Goals Before Getting Started
  2. Simplify and Centralize Your Data Streams
  3. Scrub Your Data Before Warehousing
  4. Establish Clear Data Governance Protocols
  5. Create Dynamic Data Structures

The field of data analytics is always evolving and thus it is important to create a proper structure that can help in future. By establishing them we can enhance the quality of data processing.

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Data Quality and Governance determines Self Service BI Success.

Implementation of self-service Business Intelligence (BI) can help you reap many benefits. At times non-technical professionals can make better, faster and efficient decisions by generating their own reports and conducting analyses without any sort of assistance from IT staffs. However, these self-service BI environments must be very user friendly in order to be effective. Agility, bandwidth and personnel are some of the factors that should be taken into considerations by the company before implementing self-service BI. Such self-service BI brings along independence and autonomy from IT with it. Few capabilities that these BI tools must possess are anti-hacking facilities and non-stop operation. This technological tool depends on the Enterprise Data Quality. Data Governance on the other hand helps to provide accurate information using the self-service BI tools. Read more at:


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How BI is playing an important part in ERP

It is known that ERP centralizes data assets and improve data governance. But, as companies are shifting their focus towards big-data initiatives, they are using ERP as a basis for business intelligence (BI) operations. Nowadays, Analytics is a standard feature in ERP, and using built-in tools and easy-to-integrate add-ons help organizations to make an immediate shift towards real-time measurement, increased oversight and predictive analytics. According to Tom Davenport of Harvard Business School, the three primary benefits of analytics include: cost reduction, faster decision making, and improved product development. Read more at:



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Self service analytics

Self-service analytics softwares are being used by businesses now and it has given rise to a new need, data governance plans. These new softwares provide a host of benefits. These have made accessing and analyzing data a lot easier coupled with the ability to feed bad data back into enterprises. A competition has been brewing between software vendors regarding improved data governance. Companies are launching business intelligence products that stress data governance. Such softwares can be built around the company's pre-existing enterprise data infrastructure bringing multiple data sources into a common data warehouse accessible by the entire organization. Read more at:

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Metadata fabric in data governance

It depends on the enterprises' ability to find value in IOT generated data and come up with metadata fabric that enables efficient data analysis. Metadata fabric works in sync with IOT to benefit the enterprise. It can be viewed as the storehouse of business intellectual property. It makes data driven decisions a lot easier. Metadata fabric transforms unified interpretation of data and analytics into layman format and interface easily understood by businesses. IOT ecosystem often throws challenges as the number of unique data sets collected is smaller than the number of applications that need it. Introduction of an intermediate curation layer can solve this governance problem. The aim of the governance system should be to reduce onboarding cost with increasing chances of success. Optimization should also check for homogeneity in the collected data. To know more, please follow:

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Customer Data Governance : An Insight

In modern world, there has been an increase in communication channels and hence this customer-centric era presents both challenges and opportunities for businesses. Companies must have the skill to connect to the data sources relating to customer experience. Hence, nowadays the big data challenge has gained more importance. In case of customer experience management, the data needs to be combined with unstructured customer feedback data and this is important in order to have a complete picture of customer experience. Data governance plays a crucial role here. One big challenge of customer data is that they don't know which data is more relevant in the first place. Data governance creates the base for the common understanding of the customer across the business.

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Hadoop Adoption Ahead

The mission of Matt Morgan, the vice-president of global product marketing of Hortonworks is to establish Hadoop as the foundational technology of modern enterprise data architecture. Hortonworks Data Platform (HDP 2.3) is the only enterprise Hadoop-based platform that is made up of 100% Apache open source components. Enhanced security and data governance have been added to HDP 2.3 including new encryption of data, and the extension of the data governance initiative with Apache Atlas. But many doubt that skill shortage is one of the barrier to Hadoop adoption. Read more about this article at:


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