It’s no secret that data fuels decision-making, drives innovation, and empowers organizations to stay competitive. As the volume, variety, and velocity of data continue to grow, mastering the art of data management becomes more important and more challenging. In this blog post, we'll explore the significance of a robust Master Data Management (MDM) strategy and delve into some best practices for implementing MDM in the age of artificial intelligence (AI).

AI applications are trained on data, and the quality of the data has a direct impact on the performance of the AI. MDM can help organizations to ensure that their data is of the highest quality, so that their AI applications can produce the best possible results.

The Role of Master Data Management

MDM is a discipline that involves the processes, governance, policies, standards, and tools that consistently define and manage critical data to provide, with data integration, a single point of reference across an organization. This critical data, often referred to as "master data," includes information about customers, products, employees, and more.

In the age of AI, MDM takes on a pivotal role as the backbone of data-driven decision-making. Here's why it's essential:

1. Quality Data Feeds AI Algorithms

AI algorithms thrive on quality data. Accurate, clean, and well-managed master data is the foundation upon which AI models are built. Without this strong foundation, AI predictions and recommendations may be unreliable, leading to suboptimal decisions.

2. MDM Ensures Data Consistency

MDM ensures that data is consistent across the organization. This consistency is crucial for AI models that rely on data from various sources. Inconsistent data can lead to errors, bias, and misinterpretation, compromising the integrity of AI-driven insights.

3. Compliance and Data Governance

In an era of increasing data regulations, having a solid MDM strategy in place is vital for compliance. It helps organizations adhere to data privacy laws, security standards, and auditing requirements, reducing the risk of legal issues and fines.

Master Data Management Strategy

To harness the power of MDM in the age of AI, consider these best practices to craft a Master Data Management strategy:

1. Start Small and Scale Up 

Don't try to manage all of your data at once. Start with a small subset of your data and then scale up your MDM program over time.

2. Get Buy-in from all Stakeholders 

MDM is only successful if everyone is on board. Get buy-in from all stakeholders, including business users, IT staff, and data owners.

3. Define Clear Data Governance Policies

Establish data governance policies that outline data ownership, access controls, and data quality standards. Clearly define roles and responsibilities to make sure everyone in the organization understands their part in maintaining data integrity.

4. Invest in Data Quality Tools

Utilize advanced data quality tools and solutions to identify and rectify data errors and inconsistencies automatically. Machine learning algorithms can assist in cleansing and enriching master data, making it AI-ready.

5. Choose the Right MDM Solution

There are a number of different MDM solutions available, so it is important to choose one that is right for your organization's needs. Consider factors such as the size and complexity of your data, your budget, and your IT resources. Exela creates tailored Master Data Management solutions that fit your company's needs. We’ll cover that in more detail below. 

6. Leverage AI for Data Matching and Deduplication

AI can play a significant role in data matching and deduplication, a critical aspect of MDM. AI algorithms can identify duplicate records and merge them intelligently, reducing data redundancy.

7. Embrace Cloud-Based MDM Solutions

Cloud-based MDM solutions offer scalability, flexibility, and cost-effectiveness. They allow organizations to adapt quickly to changing data needs and accommodate AI applications seamlessly.

8. Foster a Data-Driven Culture

Promote a culture of data awareness and data-driven decision-making within your organization. Encourage employees to understand the value of master data and the role it plays in AI initiatives.

9. Monitor and Measure MDM Success

MDM is an ongoing process, so it is important to monitor and improve your MDM program over time. This includes tracking the quality of your data, including the impact of MDM on AI-powered initiatives, identifying areas for improvement, and making necessary adjustments to your MDM solution and data governance framework.

Excel in Your MDM Strategy

At Exela, we understand the critical role that Master Data Management plays in today's data-driven landscape. Our MDM services are designed to empower your organization with the tools and expertise needed to harness the full potential of your data. This includes advanced technology such as AI. With a focus on data migration and consolidation, we help you bridge the gap between disparate systems, ensuring seamless data integration, superior data quality, and the creation of a single source of truth for robust analytics.

Our solution consists of four major components:

Rapid Integration Hub - A modular Rapid Integration Hub simplifies data unification by seamlessly integrating data sources into a centralized platform, all using a single data language. 

Federated Search - We offer a federated search capability, enabling easy retrieval of information from various sources through a single search query. 

Data Unification - We specialize in transforming disparate data into a harmonious single source of truth that can be accessed conveniently from a single hub.

Data Governance - Our MDM specialists work diligently to establish and uphold data governance rules, procedures, and information technologies. 

With Exela's Master Data Management services, you can confidently navigate the complexities of data management, ensuring that your organization has the solid foundation it needs to thrive in the age of AI and data-driven decision-making. 

Empowering Your Data-Driven Future with Master Data Management and AI

In the age of AI, a robust Master Data Management strategy is the linchpin for success. It ensures that your AI models have a solid foundation of high-quality data, enabling them to deliver accurate insights and drive informed decision-making. By adhering to best practices in MDM, organizations can unlock the full potential of AI and stay ahead in the competitive business landscape. Master your data with Exela, and you'll master the future. Contact us today to learn more!

Back in 2006, the mathematician, Clive Humby, made the observation, “Data is the new oil[1],” thus spawning a seemingly endless debate, which should have been considered settled when the Economist observed in 2017 that data has overtaken oil as the world’s most valuable resource.[2] But what Humby said next has never been disputed. Like oil, he pointed out, data must be refined in order to be made useful. Specifically, Humby was referring to data “analysis,” although it seems unlikely he meant to exclude the other forms of refinement that together comprise what we now know as “Enterprise Information Management” (EIM).

Here’s a round-up of some of those “refinements” that can help turn your data into the Digital Age-equivalent of “black gold”:

Data unification

Data comes in many forms, but most falls into the categories of structured versus unstructured data. Structured data is highly organized into discrete fields and is easily recognized, digested, searched, and otherwise utilized by machines. Unstructured data, which refers to information that doesn’t exist in machine-ready form, is pretty much everything else (think: photos, handwriting, social media, the contents of emails). Unifying data that currently exists in multiple forms is critical to EIM. One way we accomplish this at Exela is through cognitive automation, including RPA (robotic process automation), which helps reduce error and otherwise streamlines the process, and which we invite you to read about here in the context of billing and receivables and here in the context of legal discovery

Data centralization

Data silos are separate sets of data that aren’t integrated enterprise-wide. Sometimes the result of legacy IT systems or pre-existing corporate culture predating the adoption of EIM, silos are a common obstacle to effective data management, resulting in internal inconsistencies, redundancies, and other inefficiencies. Although limited segmentation of information may be called for in certain circumstances, enabling a common information source for the enterprise is a far more useful starting point. For example, a “single source of truth” can be critical to accurate and effective financial reporting. In sales and marketing, that “single source” can mean the difference between your customers receiving the same communication once versus multiple times.

Federated Search

In those cases where a central data hub is either not possible, not desirable, or not required, it may nevertheless make good sense to adopt federated search capabilities for easy information retrieval. Federated search allows for information in disparate systems to be pulled via a single query. Here’s an example of how and why federated search can streamline your company’s compliance with KYC (Know Your Customer) regulations.

Data analytics

Analytics and modeling engines can extract deep insights from your data sets, enabling you to discover patterns, identify correlating factors, and utilize predictive modeling to anticipate future trends. Imagine, for example, a hospital emergency room that used patient-traffic analytics to predict which times of the week, and which times of day, require heavier staffing. Or what if your information were “assetized” to the point where you received automated triggers on an integrated dashboard to notify you that it’s time to make an equipment lease payment or time to file a required regulatory disclosure.

Data visualization

Even the most insightful data analysis loses its worth if the critical information uncovered can’t be displayed in a way that people can understand. Integrated visualization tools are an aspect of EIM that permit users to easily create intuitive charts, reports, and other comparative visualizations.

Quality control

What’s the value of data that isn’t accurate? Rhetorical question, of course. A comprehensive EIM program requires quality controls to sustain data fidelity and usability. As with data unification and other aspects of EIM, the use of cognitive automation can reduce error and streamline the process.

Ultimately, there’s a harsh truth at work here, which is that just like digital transformation, implementing EIM isn’t really an option so much as a mandate. But like digital transformation, implementing EIM need not overwhelm your business’s resources, particularly if you enlist the help of the right technology partner—one who’s experienced at “refining” data for enterprises of your size in your industry and is committed to guiding through the process of defining and executing your overall data strategy.

Stay tuned for future installments in our EIM series, including Best Practices for Implementing EIM.

[1]https://enterprisersproject.com/article/2019/7/data-science-data-can-be-toxic

[2]https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data

If you have any association with the tech industry, then you know that data rules the world. It has become the digital currency and businesses that learn to harness the power of data are the ones who manage to be the leaders. Dig deeper and you’ll find that businesses keen on growth always ensure that enterprise data management is their key strength. Call it information management, database management systems or enterprise information management, data-driven digital strategies have become part of boardroom discussions in enterprise organizations.

What is Enterprise Data Management?

Enterprise Data Management (EDM) is an organization’s capacity to integrate, govern, secure, and distribute data from multiple data streams, online and offline. This includes the ability to accurately and safely transfer data across processes, applications, subsidiaries, and/or partners. Effective information management is no easy feat and can only be accomplished by fully understanding your data and implementing an intelligent EDM strategy.

Why is Data Management Important?

Enterprise Information Management (EIM) is about the overall management of information assets. Data generated by different departments live in siloed systems and can be used to create some of the most innovative solutions for your customers. The trick is to create a data lake and interconnect systems breaking the silo organizational culture. Organizations create their own source of superpower when an additional layer of automation intelligence is added to the data lake creating information flows that can transform business processes.

How Does it Benefit Businesses?

If data drives the world, the ones who can control their data can truly benefit from it. The advantages of having an efficient enterprise data management system are numerous. Some of the important ones are as below.

• - Drive digital transformation: A strong enterprise data management strategy is the first step.
• - Fully leverage data: Control and analyze structured and unstructured data for efficient business processes.
• - Enhance customer experience: Deliver highly targeted and personalized experience to your customers.
• - Improve operational efficiency: Speed up business processes by connecting to the correct data streams.
• - Increase business agility: The right data at your fingertips allows you to respond faster and keep up in the digital age.
• - Improve decision-making: Analysis of the data can potentially turn into the actionable insights needed to improve decision-making.

If data management and information systems interest you, you’d also like our eBook. It’s an insightful report based on interactions with various Fortune 100 companies that happen to be our customers.