Amurta

Data Mining is the practice of using a wide range of algorithms to search and analyze large stores of data to predict trends and patterns that can influence  the outcomes. You can use this data to mitigate risks, increase revenue, capitalize on new opportunities and improve customer relationships by using a broad range of techniques. To know more please visit our website https://www.amurta.com/blogs/the-art-of-data-mining-for-turning-data-into-insights/.

High-quality data is one of the greatest drivers of revenue for a modern business in this era of digital disruption. Data-driven companies are highly dependent on the latest technologies and artificial intelligence to be able to process and get the best out of their data assets. To know more please visit our website https://www.amurta.com/blogs/top-5-most-common-data-quality-issues/.

Data Lake is a system where we can store unstructured and structured data and also a method helpful in organizing huge volumes of diverse data from multiple sources. Data lakes are vital for people in technology and business who want to discover and explore data by bringing all the data together in a single place. To know more please visit our website https://www.amurta.com/blogs/data-lake-what-is-it-the-need-its-architecture-and-the-benefits/.

Reference data is the subset of the master data which is used for data classification and is the most shared class of data in an entire organization. Reference data may originate either externally (from 3rd party data sources) or internally (within an organization) and is described in one-to-one and one-to-many relationships or depending on hierarchies. To know more please visit our website https://www.amurta.com/blogs/reference-data-management-its-features-and-benefits/.

In the era of the enterprise data-driven world, it is vital for organizations to clearly define and understand the business terms across business to achieve success. An incorrect interpretation that might be caused by polysemy or clusters contributes to misunderstandings and increases the risk of having poor information and which in turn may lead to poor decisions. Business Glossary is used to avoid such risks. To know more please visit our website https://www.amurta.com/blogs/how-to-create-a-business-glossary-software/.

GRC (Governance, Risk Management and Compliance) is the term used to describe the company’s approach in addressing risks, staying compliant and managing the company’s direction and GRC strategy helps to streamline business risks accordingly. GRC is a set of practices and processes that runs across multiple departments and functions. Many companies have a dedicated GRC team to manage the GRC tools and platforms. GRC helps to avoid the consequences of silos in the governance, management and assurance of the business attributes. For more information please visit our website https://www.amurta.com/blogs/what-is-governance-risk-management-and-compliance-why-do-we-need-it/.

Data becomes an asset when it is harnessed and managed effectively. Enterprises face numerous challenges regarding data such as data duplication and multiple references that decrease the data value. The banking and financial sector enterprises generate a plethora of data regarding the customers buying history, financial transactions, information of the product and services, and other data which may be sourced from smartphone applications. To know more please visit our website https://www.amurta.com/blogs/master-data-management-software-for-banks/.

5 quintillion bytes of data is generated every day globally, which is available in different forms. It is a surprising fact that nearly 97 % of the data is unused by the enterprises, as per the Gartner report. Data is considered as an asset in any enterprise and the data which is collected is a source of information that is used for not only improving the decision making but also for predicting the future trends and behavior of the customers. For more information please visit our website https://www.amurta.com/blogs/data-governance-key-to-data-driven-business/.

Data lineage is about the origin of the data and where it moves over time. It is related to what happens to the data when it goes through diverse phases and processes.  Data lineage is used to analyze the way the information is used and to track the elements of the information which serves a particular purpose. It can be defined as the process of documenting, understanding, and visualizing the data from its origin to the utilization. For more information please visit our website https://www.amurta.com/blogs/what-is-data-lineage-top-5-benefits-of-data-lineage/.

In this era of digital disruption, data is of utmost importance for enterprises to gain a competitive edge. High-quality data enables more efficiency in driving enterprise success because of the dependence on empirically-based decisions instead of human or habitual intuition. Data will be highly useful and will serve its purpose only when it is of high quality. Enterprises that constantly focus on improving their data quality grow faster. To know more please visit our website https://www.amurta.com/blogs/why-is-data-quality-important-what-are-its-characteristics/.

Globally, enterprises are managing more data than ever before and according to IDC, by 2025, the global datasphere is projected to reach 175 zettabytes. Data is of utmost importance for enterprises to access and use the data to manage day-to-day business operations and improve efficiency. For more information please visit our website https://www.amurta.com/blogs/the-art-of-protecting-sensitive-data-with-data-governance/.

Tons of data are generated every day and the enterprises are focusing to identify what information should be considered for value. The enormous reliance on data for achieving enterprise objectives is a challenge in terms of achieving value and managing data. Data may include in the form of documents, catalogs, transactions domains, metrics, etc. To know more please visit our website https://www.amurta.com/blogs/treating-data-as-an-enterprise-asset-in-industry-4-0/.

Master Data Management (MDM) is all about creating a single source of valuable data which is used by the different departments across the enterprise. This type of data helps in addressing the data challenges and provides your business with a plethora of business benefits and competitive advantage. To know more please visit our website https://www.amurta.com/blogs/master-data-management-challenges-and-solutions-in-2021-and-beyond/.

The definition of Metadata Management includes the setting up of rules and processes to enable the ability to access, integrate, maintain, share, link, and analyze metadata across the enterprise. it helps the users focus on how much they can trust the data, when the data has entered into the system and what type of business rules were applied. For more information please visit our website https://www.amurta.com/blogs/the-critical-importance-of-metadata-management-in-data-governance/.

In the Era of Digital Transformation, manufacturing business systems, production units, supply chains, the Internet of Things, and Robotics to just name a few tend to generate real-time data of high importance. To address and enhance this type of overwhelming data and make sound decisions the process of Data Governance is initiated. For more information please visit our website https://www.amurta.com/blogs/roles-and-responsibilities-of-data-governance-manager/.

Henry is an Investment Banker in Canada for a decade. All his present & future decisions on the investments are now based on the data which might be collected from many sources, following many processes and procedures. As an Investment Banker, he might be using past, present data to ascertain his future investments. During Covid-19, it was a challenge for Henry to assess the impact and design a strategic response accordingly as he used to receive datasets from various sources globally. To know more, please visit our website https://www.amurta.com/blogs/characteristics-of-dataquality-and-how-to-improve-it/.

Master Data Management by Amurta