Data Governance Services - Artha Solutions

Navigating the Cloud: Unravelling the Power of Cloud MDM in Modern Data Management

Master Data Management (MDM), according to Gartner, is a “technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of the enterprise’s official shared master data assets. Master data is the consistent and uniform set of identifiers and extended attributes that describe the core entities of the enterprise, including customers, prospects, citizens, suppliers, sites, hierarchies, and chart of accounts.”

Traditionally, organizations deployed MDM solutions on-premises i.e. installing, and maintaining them on their own servers and infrastructure. However, with the advent of cloud computing, a new option emerged: Cloud MDM.

This blog unravels the ‘What, Why, and How’ of Cloud MDM, emphasizing its advantages over conventional approaches.

What is Cloud MDM?
Cloud MDM solutions host and deliver services over the internet instead of on-premises. The design of cloud master data management aims to establish a centralized platform for data management, empowering organizations to attain heightened levels of consistency, accuracy, and completeness in their data. Cloud MDM is among the top 5 MDM trends in today’s digital realm.

Cloud MDM offers several benefits over traditional on-premises MDM, such as:
Lower cost: Cloud MDM eliminates the need for upfront capital expenditure on hardware, software, and maintenance. Cloud MDM also offers flexible pricing models, such as pay-as-you-go or pay-per-use, which can reduce the total cost of ownership.
Faster deployment: It can be deployed faster than traditional on-premises. They have prebuilt templates, connectors, and integrations, which can speed up the implementation process.
Easier management: It simplifies administration and maintenance, with cloud providers handling updates, patches, backups, and security. It also offers self-service capabilities, which can empower business users to access and manage their data.
Greater agility: Enabling faster changes and enhancements without downtime, Cloud MDM supports scalability and elasticity, adapting to changing data volumes and organizational demands.
How does Cloud MDM differ from Traditional On-Premises MDM?
While Cloud MDM and traditional on-premises MDM share the same goal of delivering high-quality and consistent data, they differ in several aspects, such as:

Architecture: Cloud MDM uses a multi-tenant architecture, while on-premises MDM relies on a single-tenant architecture, increasing costs.
Data storage: It stores data in the cloud, making it accessible from anywhere, whereas on-premises MDM restricts data access to the organization’s network.
Data integration: Supports integration from various sources, including cloud applications, web services, social media, and mobile devices. Traditional MDM primarily integrates data from internal sources such as databases, ERP, CRM, and BI systems.
Data security: Relies on the cloud provider’s security measures, while on-premises MDM depends on the organization’s security measures.
Key Features of Cloud MDM
Cloud MDM solutions offer a range of features and functionalities to enable effective and efficient MDM, such as:

Data Centralization: Serves as a unified hub for housing all master data, consolidating details related to customers, products, suppliers, and various other entities into a singular repository. This system eradicates data silos and provides universal access to consistent and current data across the organization.
Data merging: Allows for the consolidation and reconciliation of data records from different sources into a single, golden form, which represents the most accurate and complete version of the entity.
Integration Capabilities: The seamless integration with various cloud-based services and enterprise systems. Ensuring accessibility wherever it is required, this interoperability elevates the overall utility of master data.
Data governance: Allows defining and enforcing the policies, roles, and workflows that govern the data lifecycle, such as creation, modification, deletion, and distribution.
Cloud-Based Security: Incorporate stringent security protocols, including encryption, data backup procedures, and adherence to industry standards and regulations. This safeguards data against potential threats and breaches.
Conclusion
As we conclude our exploration, it becomes evident that Cloud MDM is not just a modern approach to data management; it’s a strategic advantage. The advantages it offers, coupled with its distinct features, position Cloud MDM as a transformative force in the dynamic landscape of Master Data Management.

Artha Solutions  is a Trusted Cloud MDM Implementation Service Provider

With a decade of expertise, Artha Solutions is a pioneering provider of tailored cloud Master Data Management (MDM) solutions. Our client-centric approach, coupled with a diverse team of certified professionals, ensures precision in addressing unique organizational goals. Artha Solutions goes beyond delivering solutions; we forge transformative partnerships for optimal cloud-based MDM success.

Future of Data Governance Services: Top Trends For 2022 and Beyond

There was a time in the early 2000s when data governance was not really a thing. Surely, there were pioneers back then who laid down the groundwork for data governance, but it wasn’t still taken seriously. Cut to the present time and Data Governance Services are in high demand.

As the rules and trends of data governance keep evolving every year, let’s look at the following trends that are going to stand out in 2022 and beyond:

1. Operational Data Modelling

One of the most meaningful operational actions to be derived from data governance this year comes from data modelling. Interchanging data between different systems as part of one collective data fabric remains more indispensable now than ever as more companies keep adopting this approach for data management.

Expressive data models that have clear taxonomies and semantics can use machine intelligence to figure out how different schemas of various data systems are blended for frictionless integration. So, you get similar details in various systems and the governance has the maps regarding how it is expressed in these systems. Governance solutions can be involved in real-time in this case.

This particular approach spares time and cost by bypassing the need to write special programs to make the most of what happens in the data governance arena.

2. Metadata Insights

In 2022 and beyond, inferences regarding metadata in the data models will streamline the taxonomies for entertainment and media content engines, for instance, across local and global sources to gain real-time results. There will be quicker automation of metadata inputs thanks to cognitive computing methods. Otherwise, all metadata descriptions are going to be manual.

So, in other words, detailed visibility of metadata might presage events or offer a complete roadmap of previous events to make sure data quality and lineage remains intact. Thus, one can expect the following positive changes related to metadata this year:

Traceability of metadata: The traceability of metadata is crucial for trusting and understanding the details presented in analytics.

Root Cause Analysis: All aberrations and outliers in procedures related to analytics can be easily illustrated through metadata analysis. When someone notes an error or something appears as an anomaly on the dashboard, there will be a graph to show what went amiss.

Impact Analysis: Metadata will be scrutinized for each phase of SQL to extract information through rows, columns, and tables of data. The graph that will accompany the process will outline the exact changes.

3. Activation of Data Stewardship

The fact that data stewards are empowered is a direct repercussion of changing data governance from passive employment to an active one. Modern innovations regarding controlled data access (focusing on data stewards) are essential for speeding up the time necessary to utilize data. At the same time, it is necessary for conforming to the governance standards regarding which users can see which data.

Such shared data governance approaches issue the automated approval and centralized governance regulations in infrastructural setups. For instance, the owner of sales data can decide what part of the data he wants to allow John Doe to access.

Automating this distribution of the centralized governance regulations into decentralized sources can remove the IT bottleneck for data access. It will facilitate low-latent data sharing. Thus, data stewards from Talend Data Management Services – the people who understand the data best – remain at the forefront of delegating and deciding data access.

4. Data Quality

Data quality, along with the attendant features of data reliability and data validation, happens to be the substratum on which all forms of data governance, specifically in an operational setting, depends.

You will not be able to augment or automate processes when your data is not high quality or healthy to start with. Thus, the trend would be to embed a data governance staple like superior metadata management into the operational systems to generate metadata specifics in real-time. It will need proper data validation measures to make sure that it is sensible and adherent to the best practices.

The organization will use different means of ensuring data security and quality at a level that is reliable for operations and traditional decision-making. As the nuances of data governance are changed, the organizations will derive greater profits from their IT initiatives.

About Artha Solutions:

Artha Solutions is a premier business and technology consulting firm providing insights and expertise in both business strategy and technical implementations. Artha brings forward thinking and innovation to a new level with years of technical and industry expertise and complete transparency. Artha has a proven track record working with SMB (small to medium businesses) to Fortune 500 enterprises turning their business and technology challenges into business value.

Unmask the 3 Levels of Holistic Data Governance Strategy

Gathering quality data is the first step towards business success. However, the growth of the same business relies on the usage of given data. The trick to any successful business nowadays is defined not by the data collected, but by the best use of data.

As important as data is to a successful business, it is not any good on its own. It is only as good as its usage, and that is governed by Data Governance.

It is not easy to decide the best use of collected data, keeping in touch with every single department of the organization, taking the needs into account, and ensuring that they are all met is confusing, and challenging.

However, organizations that cannot spare their resources on setting great data governance strategies, seek help from the experts who are behind the most successful businesses. Here are the three things that you will find common inside every single strategy.

A data governance strategy is like the foundation of the process that allows a company to base its operations on. Understanding the strategy truly allows the organization and the individuals to carry the business towards a successful outcome.

Data governance strategies are unique to every business model. Like every new idea for a business is unique, the strategy to make it work is unique too. However, these 3 strategies are the common denominator everywhere.

Framework

Taking into account the different departments of an organization and their needs, building a framework that accommodates the growth of every individual department, and building a framework that syncs up each department with the other while making use of the data that is collected is the goal.

Bring in the framework that supports a greater ROI. Changes will be common, the change in the collected data will change too. The framework should allow changes in the collection of data and the steps that will be taken.

Understanding the efforts that will be put in extracting the best out collected data and how individual teams are to meet their set goals is what building a framework is all about.

After the framework, comes the planning.

Planning

Setting expectations and requirements is tough, sure but drawing a route map of execution is rough too. Knowing what we want from a company from the beginning and understanding where to take it in the next time frame is the agenda of data governance strategy.

Drawing up a route map is however a step towards achieving the said ROI. A process on how each individual in the organization and each team will lead the company towards the desired goal of success.

Fixing how the individual teams will work and the operations being carried out every day, and on a bigger annual basis is what efficient planning looks like. Keeping in mind that there will be some unexpected circumstances and preparing the company as a whole for them includes the best use of resources.

This also means drawing up an execution strategy that supports the data growth and methods to imbibe the best data usage policies keeping in mind to adhere to the requirements of the organization.

Adherence

Building a strategy that is easy to execute is one of the most important aspects that help in adhering to it. Knowing that a strategy will be possible and is in fact a scalable target will help in adhering to it.

Keeping in mind that data governance strategies are the center of a company’s operations, it is important to notice that it is also just a plan that is a well thought out idea for a company’s growth.

These are the three levels of data governance strategies that decide the growth of a company. Now, there are many different approaches to understanding each of these levels and attempting to personalize the strategies at each level to suit the business model, but the intention of each level is to meet the company growth targets in the swiftest, most economical and efficient way possible.

What’s The Foundation of Hybrid Cloud Self-Service Automation?

In the last one decade, cloud application delivery has become extremely important but undeniably complex, sometimes getting out of direct control. This has become a roadblock towards achieving total self-service automation within budget control principles.

According to a report by the IDX, 69% of enterprises believe that they are overspending on the cloud and the lack of automation is the number one reason they cite. It all boils down to data governance because that’s what essentially, well, governs who can access what, where, and for how long.

This makes data governance not just essential but crucial for self-service automation. Naturally, the question arises as to what is the foundation upon which it rests.

Hybrid Cloud Self-service Automation

Since the cloud is not a singular destination, it must be adaptable to change and not averse to evolution. Self-service automation provides the necessary agility which enables the end users to provision their applications into the right cloud based on their needs, whether they want a public cloud or a private cloud – a truly hybrid synergy becomes the need of the hour.

However, governance becomes even more important with such cross-cloud environments where the control needs to be more poignant and strong.

Data Governance: The Foundation of Hybrid Cloud Self-service Automation

As mentioned above, data governance is key to better comprehend the value that a cloud provides which makes it the most important, foundational need of the cloud environment, especially a hybrid cloud or multi-cloud.

Having said that, developing and implementing a common governance model that’s adaptable to the various requirements and complexities in such environments is a challenge in itself. Therefore, there has to be a shared control plane that enables centralized governance across clouds and other associated technologies.

Most companies fall short with data governance when they treat it as just another tool in their cloud arsenal – it’s so much more than that. It includes all the required integrations into the existing technologies that organizations have deployed over the years along with any operational links that enable collaboration among them, across the lifecycle of an application.

With a foundational data governance framework in place, businesses can assign and manage the applicable multitenancy, role-based access controls, and policies.

Principles of Good Governance

Data governance isn’t confined to data. In fact, it blankets the people, the processes, and the technology that surrounds the data. As such, there’s a need for auditable compliance for these three areas that are well-defined and agreed-upon. When done correctly, this could help organizations make data work for them.

Moreover, organizations need to think macro, not micro. They must consider the entire data governance lifecycle instead of monitoring in siloes. Although this could prove to be overwhelming, especially for the small and medium enterprises, it’s also extremely important and worthy of detailed attention.

Some of the key areas organizations must focus on includes:

Data Discovery & Assessment
Classification of Sensitive Data
Data Catalog Maintenance
Data Sensitivity Level Assessment
Documentation of Data Quality
Defining & Assignment of Access Rights
Regular Audits for Evaluation of Security Health
Enabling Encryption & Other Additional Data Protection Methods

With these guiding principles, organizations are able to create a highly effective data governance strategy that enables them to achieve control over their data assets and maintain total visibility. This translates into a culture that’s data-driven, helping organizations make better decisions, improve risk management, and most importantly, maintain regulatory compliance as per industry standards.