Data Governance is different from Data Management but it technically falls under this category. Our Data Governance strategy has both pre & post process that will be implemented and the pre process starts at the top of the business by sanctioning data and making policies and decisions around data and that includes how to form data and who can use the data. The post process is creating day to day tactics that support the formation of data policies and decisions.

Typically Data Quality comes into existence because of issues around the quality, integrity, or usability of data. DataFactZ has expertise in creating a program that will address different types of data such as sets of Master Data, Sensitive Data, and Acquired Data etc. Further our program sets a direction of data quality, collect data quality rules across the enterprise, reconcile gaps, monitors data quality and also has the expertise to deploy various Data quality software tools.

In today's world Data integration is most challenging and it often comes into existence with acquisitions, development effort, roll out of new application system, data sources that are required to integrate for decision making purposes. DataFactZ has vast amount of experience in this area and has assisted several enterprise customers by creating initiatives such as Metadata Management, Master Data Management aligning with the Service Oriented Architecture (SOA) and standardizing on platforms with consistent data definitions across the enterprise.

Traditionally Business intelligence systems have been built to handle "structured data" while "unstructured data" data from such sources as forms, e-mail, social media, documents contains a great deal of information that can be usefully employed in a business intelligence system and some of this information is vitally important. According to Sarbanes-Oxley (SOX) it requires corporations to be able retrieve any data (especially documents and e-mail) that pertain to the reliability of a corporation's financial statements. However emerging technologies in Business Intelligence now have the ability to combine both structured and unstructured data and DataFactZ has the depth of experience assisting its customers in building a strategy to integrate unstructured data for advanced analytics purposes.

In many organizations Data Security & Privacy is always a major concern due to several factors such as security controls, regulatory compliances or internal data compliances etc. DataFactZ has the experience working the senior management to under the data regulations and formulates data security policies across the enterprise and also during this stage we will assist with locating the most sensitive data and align with appropriate compliances, identify and define data related controls to manage risk.

The simplest definition of Metadata is "data about data" but in reality it is more than just the definition and it plays a significant role in an organization describing business processes and align with both business/IT. DataFactZ has the experience to fully assist its customers and build an effective metadata management strategy within the organization. Our approach begins with analysis of the current environment, applications and business needs surrounding Metadata followed by defining high-level business, technical and metadata solution requirements. We will also provide recommendations on approach, architecture, governance, risk assessment, etc., along with a roadmap and high-level project plan. In addition to leveraging our experience, our clients are able to take advantage of our proven methodologies and templates.

Data Management in large organizations is very complex due to the fact that the business entities such as Customer, Product etc. exist in many fashions and keeping the data accurate and consistent for immediate use across an enterprise has become a performance and productivity issue for every large business. However Master Data Management can successfully address this challenge and leveraging it into organizational value is a process that can be difficult and expensive if right strategies are not employed. DataFactZ can help you define and understand MDM, and the extraordinary value it provides. We offer an overview of the different architecture types within MDM, and can help you determine and implement the services that best suit your organizational needs.

With in the life cycle of a Business Intelligence project building the appropriate data architecture that supports the decision making process is vitally important. At DataFactZ, we understand how to design conceptual, logical and physical data models for traditional OLTP systems and dimensional modeling for data mining and data warehousing projects. We also review and reengineer existing data models to ensure that they conform to business rules and naming conventions, ensure that the end results will provide a scalable model which is optimized.