Understanding data quality and master data management: The need for systematic, centralized data (part 1 of 3)

Biggest data challenge faced by most companies

Having delivered data solutions to Fortune 500 clients for over a decade, we have encountered various types of data issues. For most companies, the biggest and the most common data challenge is the same:

Building a unified view of core data assets.

Although there is a single way of resolving this challenge – which involves a step-by-step process of record linkage and consolidation – still, people get confused about the technology required to achieve this goal. Some think they require a data quality management tool, while others worry if they need to implement an end-to-end master data management solution; and not to mention the biggest concern that they have: what will be the overall impact of this solution implementation on business operations?

Answering all questions: DQM, MDM, and which one do you need?

To help you answer these questions, we have written a three-part blog series on this topic. This will be an in-depth guide that will help you to understand master data management in good detail, as well as understand its relation to data quality management, and when to use which one?

The series includes following three parts:

  1. Building a case for systematic data management
  2. Master data management: Definition, components, and process
  3. Data quality management versus master data management: Which one do you need?

This blog covers the first part of the series, but do keep an eye out for part 2 and 3, which will be published within this week as well.

Let’s get started!

Need for systematic data management

With 2.5 quintillion bytes of data being created roughly every day, we definitely need a systemized way of capturing, storing, sharing, and synchronizing data. One of the most common challenges associated with data is maintaining one definition about the same ‘thing’ across all nodes or data sources.

For example, if a company uses a CRM and a separate billing application, a customer’s record will end up in the databases of both applications. The task of maintaining a consistent – or simply, the same – view of customer information across all databases over time is difficult.

Avoid complex data topologies

Such consistency requirements can lead us to create connections between siloed applications where every update is synchronized throughout the system. This architecture gives birth to a complex topology where an exponential number of interactions happen between nodes every day, like this:

Centralized data management

This clearly highlights the need for a central, intelligent hub that models and preserves data objects, as well as serves data retrieve and update requests linearly, hence, easing data management.

Responsibilities of an intelligent, central hub

The responsibilities of such a system include:

  • Data object modeling – especially for the main data assets.
  • Maintaining data hierarchies or relationships between data objects.
  • Connecting to all data sources or applications.
  • Tracking changes made to any connected database.
  • Processing changes made to connected databases and intelligently synchronizing updates.
  • Enabling the implementation of governance rules for automated alerts, moderation workflows, etc.

This is where master data management comes in. (More on this in our upcoming blogpost in the series.)

Business benefits of centralized data quality management

Before we move on to the conceptual and implementation details of centralized data quality or master data management, it is first necessary to make a case for it. Meaning, it is important to know that this initiative requires company-wide buy-in, and in some cases, a huge investment – in terms of time, cost, and other resources. That’s why it is necessary to first onboard important stakeholders to this initiative by mentioning the impact it will have on business.

Let’s cover some of these points below:

1. Comprehensive view of main data assets

By far, the greatest benefit of centralized data management is: the ability to access a comprehensive and complete view of any data asset at any given time. This can be a complete view of: customer profiles, product lists, employee information, location details, or any other data asset critical to your business.

2. Efficient business operations planning

When information is scattered across multiple sources, it gets almost impossible to predict and forecast future business needs; especially if your business has data assets such as vendors and suppliers. If important data assets become centralized and aggregated, you can plan business operations effectively and efficiently with the help of a single data store.

3. Increased business agility

Data has always played a key role in finding new growth and expansion opportunities for a business. But if main data assets are not centralized, it can be quite impossible to uncover hidden market opportunities. Contrarily, if you centrally manage data, it gets easier for your team to improve competitiveness and business agility through quick and timely data analysis.

4. Improved operational efficiency and business productivity

Oftentimes when same data resides at separate locations, team members are required to fetch and gather data from all sources before they can start working on their tasks. At other times, different members end up working on the same task, not knowing that it is already being handled by someone else on the team. Both these problems reduce operational efficiency and business productivity, and central data management is something that can help resolve such issues.

5. Effective decision-making and reporting

When a company’s business intelligence tool outputs inaccurate or biased results, it is usually an issue with data replication or decentralization. To attain clearer and faster results for accurate and timely decision-making, it is quite necessary to input high quality and centralized dataset into your business intelligence system.

6. Timely data compliance and governance

Data quality, governance, and compliance are tightly integrated with each other. You cannot comply to federal or organizational data standards (such as GDPR, HIPAA, or CCPA) if your business does not possess well-governed and high-quality data – something that is made possible through central data management.

I’m in! What’s next?

The points stated above clearly highlight the need for investing in data quality as well as centralized data management tools. The next step is to understand the meaning and working of such a tool better, so that you can exactly scope out your business needs and see which solution satisfies them best.

Look out for our next blog in the series that covers the definition, components, and process of master data management in greater detail, and how it relates to data quality.

In this blog, you will find:

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Merging Data from Multiple Sources – Challenges and Solutions

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