Advisory Services

For today’s businesses, data is an invaluable resource. Although the value of information is well-understood, unlocking that value is often a challenge due to the high volume of data and the challenges associated with collecting, organizing and activating it. Developing a data strategy can help businesses overcome these challenges and access the value of their data while efficiently using their resources. Data Zen will help you build and execute a data strategy for your organization.

Master data management (MDM) is the core process used to manage, centralize, organize, categorize, localize, synchronize and enrich master data according to the business rules of the sales, marketing and operational strategies of your company. 

Master data can take the form of product, customer, supplier, location and asset information, in addition to any information sources that drive your business.

The efficient management of master data in a central repository gives you a single authoritative view of information and eliminates costly inefficiencies caused by data silos.

It supports your business initiatives through identification, linking and syndication of information across products, customers, stores/locations, employees, suppliers, digital assets and more.

Data Zen has tons of experience in building MDM solutions. We can help you in these solutions.

Data is a cornerstone of successful application deployments, analytics workflows, and machine learning innovations. When moving data to the cloud, you need to understand where you are moving it for different use cases, the types data you are moving, and the network resources available among other considerations.

Data Zen offers a wide variety of services (using AWS, Microsoft Azure & Google Cloud Platform) and partner tools to help you migrate your data sets, whether they are files, databases, machine images, block volumes or even tape backups.

Data Governance is a collection of practices and processes which help to ensure the formal management of data assets within an organization. Data Governance often includes other concepts such as Data Stewardship, Data Quality, and others to help an enterprise gain better control over its data assets, including methods, technologies, and behaviors around the proper management of data. It also deals with security and privacy, integrity, usability, integration, compliance, availability, roles and responsibilities, and overall management of the internal and external data flows within an organization.

Data Zen can help you build and execute Data Governance Policies & Standards, relevant to your domain and organization.

Data Zen provides advisory services for data governance, data stewardship, data privacy, and sensitive data management. Our Solutions are designed to align data governance and enterprise management with compliance for regulations such as the European Union GDPR and CCPA. 

Data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning".Moreover, data is deemed of high quality if it correctly represents the real-world construct to which it refers. 

Our data quality tools offer a series of tools for improving data, which may include some or all of the following:

1. Data profiling - initially assessing the data to understand its current state, often including value distributions
2. Data standardization - a business rules engine that ensures that data conforms to standards
3. Geocoding - for name and address data. Corrects data to U.S. and Worldwide geographic standards
4. Matching or Linking - a way to compare data so that similar, but slightly different records can be aligned. Matching may use "fuzzy logic" to find duplicates in the data. It often recognizes that "Bob" and "Bbo" may be the same individual. It might be able to manage "householding", or finding links between spouses at the same address, for example. Finally, it often can build a "best of breed" record, taking the best components from multiple data sources and building a single super-record.
5. Monitoring - keeping track of data quality over time and reporting variations in the quality of data. Software can also auto-correct the variations based on pre-defined business rules.
6. Batch and Real time - Once the data is initially cleansed (batch), companies often want to build the processes into enterprise applications to keep it clean.

Data management is an administrative process that includes acquiring, validating, storing, protecting, and processing required data to ensure the accessibility, reliability, and timeliness of the data for its users. Organizations and enterprises are making use of Big Data more than ever before to inform business decisions and gain deep insights into customer behavior, trends, and opportunities for creating extraordinary customer experiences.

Data Zen also advises on enterprise data management tool selection and integrations to accomplish your goals within your ecosystem.