Data Quality and Master Data Management: A brief guide to improving data quality

In the modern data-driven world, the importance of data quality and master data management (MDM) is indisputable. In its pure, chaotic form data is useless, but if it’s of high quality, it can become a tremendous advantage for business leaders. Unfortunately, as the company collects more and more data, the risk of data becoming ‘dirty’ increases. Around 27% of business leaders can’t vouch for the accuracy of their data. Dirty data is the product of human error, duplicate data, the passage of time and other factors. It can undermine the efficiency of analytics and machine learning and cost the company 12% of its revenue.

According to The BI Survey, data quality is one of the biggest problems for BI users since 2002. In this article, we’ll explain what Data Quality and Master Data Management (MDM) is and how to improve it.

Defining Data Quality and Master Data Management

There is no single definition of data quality. Rather, data quality is considered good if it can be used for a certain purpose. It also has a few characteristics. Good quality data is consistent, up-to-date, accurate, complete, valid, and precise. However, a set of data can be good in one context and useless in the other. Knowing how many items the store has sold may be enough to place an order for the next month, but this data doesn’t show whether there was a profit.

This is why we need Master Data Management (MDM). It helps collect data from different sources and coalesce it into a substantive whole. Among other situations, MDM comes in handy when:

…aside from an ERP system, your company works with other SCM or CRM systems and needs consistency across these platforms

…you need to ensure effective cooperation with business partners and fabulous customer experience

…your company needs to merge on-premise and cloud-based systems

Many respondents to the BARC Trend Monitor surveys consider data quality and MDM as one of the most important trends. BI specialists hold the same opinion because they know the popular self-service BI technologies and data discovery tools are valuable only when they’re fed good-quality data.

Steps to improve Data Quality

To enhance data quality and MDM, you must adopt a holistic approach that would address your company’s modus operandi, data quality assurance processes, and technologies. The company ought to define clear responsibilities for data domains (e.g., customer, product, financial figures) and roles. Establishing processes to assure data quality will be easier if you adopt great practices like the Data Quality Cycle. Apt technology is important too, but it’s crucial to focus on the organization and its processes first since they are pertinent to your company’s strategy.

Now let’s look at some concrete steps to improve Data Quality.

1. Assign clear-cut roles

You cannot improve data quality without fostering a culture within your company that recognizes the significance of data for generating insights. This culture includes defining clear roles that will ensure the data is gathered and treated responsibly. Roles help with assigning tasks to certain employees based on their capabilities. The typical roles are:

  • Data Owner: a person responsible for ensuring data quality, defining data requirements, giving others access to data, and authorizing Data Stewards to manage data. Data Owner is the contact point for data domains.
  • Data Steward: a person who coordinates data delivery and specifies the requirements and rules for handling data. They deal with tasks concerning operational data quality (e.g. checking for duplicate entries).
  • Data Manager: a person who implements the requirements of the Data Owner, manages IT infrastructure, and protects access to data.
  • Data Users: business people or IT specialists that can access reliable and accurate data.

2. Adopt the Data Quality Cycle  

You cannot check data quality once and then forget about it. This is an ongoing project. That’s why it’s best to do it using an iterative cycle of analyzing, cleansing and monitoring of data. You can break down the cycle into the following phases:

  • Establishing Goals

Data Quality goals are defined according to your company’s needs. It will give you a clear understanding of what data you should focus on. To outline these goals, you can start by answering questions like “How can we define the data domain?” or “How can we identify that data is complete?”

  • Analyzing

After establishing the metrics, you need to use them to analyze data. Here some essential questions are “Is the data valid?”, “Is the data accurate?”, and “How can we measure data values?”

  • Cleansing

To reach the data quality goals, you need to clean and standardize your data. There is no universal rule on how to do it because every organization has its own standards and regulations.

  • Enriching

You can enrich your data using other data such as socio-demographic or geographic information. This way, you’ll develop a comprehensive and more valuable dataset.

  • Monitoring

As we mentioned earlier, it’s crucial to constantly check and monitor your data since it can quickly become irrelevant or erroneous. Thankfully, there is software that allows you to automatically monitor data according to the pre-defined rules.

3. Use the Right Tools

Most technologies support Data Quality Cycle and offer extensive functionality to assist different user roles. To use such technology to the fullest, you need to integrate the phases of the data quality cycle into the operational processes and match them with a specific role. Carefully chosen software can aid in:

  • Data profiling
  • Data quality operations like cleansing, standardizing, parsing, etc.
  • Data enrichment
  • Data distribution and synchronization with data stores
  • Defining metrics and monitoring components
  • Managing Data Lifecycle and more.

These are just a few examples of modern data management tools’ functions. The full list is quite impressive and should encourage you to prioritize the functions relevant to your business needs.

Better late than never

The complexity of the issue may be intimidating but in the era of digitization, maintaining a high quality of data is a must. Accurate and reliable data can guarantee excellent customer service, intelligent business decisions, and economic prosperity for your company. Like all good things, it requires some effort, but, ultimately, data quality management will pay off.

 

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