Data Quality Management: Everything you need to know

December 6, 2022
Written by Barnana Sarkar

Data Quality Management is one of the highest priorities for any organisational structure.

Source: Unsplash.

Have you ever tried to make a decision using a spreadsheet filled with incomplete or incorrect data? It can be frustrating and time-consuming to try to make sense of it all. Now imagine trying to run a business using faulty data – the consequences could be disastrous. In 2022 alone, it is estimated that 2.5 million terabytes of data will be used daily – that’s the equivalent of 250 million human brains working together. With such a vast amount of data being generated and used, it’s important to ensure that it is accurate, consistent, and reliable. That’s where data quality management comes in. It helps businesses make sure their data is in good shape, so they can make better decisions and avoid costly mistakes.

What is Data Quality Management (DQM)?

For data experts, like Echo Analytics, data quality management entails consistency and accuracy of the information that ultimately generates good data. It is the process of maintaining high-quality information right from the data acquisition stage to the implementation stage. Utilising good data involves managerial oversight and putting in place an automated evaluation process.

Why is Data Quality Management important?

It is a fact that you don’t get good data overnight. You have to filter out the bad-quality data in order to keep the data that is useful to you. It is almost like winnowing undesirable substances like stones from grains of rice.

Here are some reasons why data quality management should be a business priority:

1. Effective decision-making process

Data quality management can have a ripple effect on your business decision-making process – right from stocking your inventory to planning your marketing strategies to generating revenue. Good data can help you create data banks that will come in handy when you want to analyse trends, observe customer patterns, and establish long-term propositions.

2. Cost-effective functioning

Inefficient and bad-quality data can lead to a high loss of money and resources. When you are faced with bad-quality data, you have to invest a huge amount of money in setting up the right tools and the right people to ensure that you can derive the information that you need. But having good data at your disposal means that you can allocate all the money and resource in the right place, thus saving unnecessary investments.

3. Competitive Advantage

Good data quality means that you will have a clear picture of the market by providing you with valuable information on your competitors – via different datasets which can be on pricing, location, or customer trends. When you have the upper hand over your competitors, and you know how they are performing, you can set yourself apart through unique strategies. Good data quality can also help you understand pricing trends. But it is only through a proper management system can you retrieve these desired information from the datasets.

To ensure good data through and through, organisations like Echo Analytics, set strong standards for data quality control. As Europe’s most reliable provider of geospatial datasets, we aim to provide the most precise picture of the real world and its movement.

Here’s how we put together our Data Quality Management model at Echo Analytics:

1. People and Responsibilities

We cannot achieve what we have set out to do without putting together a strong, responsible team. Whether it is a project manager, data analyst, data engineer, or data ingestion engineer, each role plays an important part in quality control. Our data experts have put strict rules in place where we have automatic monitoring evaluation.

2. Data Profiling

Ingesting data from a public source can be a tricky one since here data content can be added and edited by anyone. Our years of experience in ingesting data from various public sources help us identify the trusted ones. As for private sources, we always examine them multiple times to check for missing information. All the datasets acquired from these various platforms are mixed and matched, which gives us a starting point to extract the best attributes address, brand name, building type, and building ID and do away with the duplicate ones.

3. Data Consistency

We take utmost care to ensure that the datasets our clients receive from us are consistent. Our data transformation process rigorously checks for dissimilarities in schemas or structures and arranges them into one uniform structure. For example, two sources might provide two types of descriptions for the same building information, like Building_Name or No._Building. Through the data transformation process, we put them into one structure which can be Building_Name_No.

Once we have that uniform structure in place, we begin the data normalisation process where all the information – that is available in scattered formats in different sources – is arranged by our algorithm in a single format.

4. Multiple Evaluations

Our datasets are of high quality because everything we do is reviewed multiple times before it gets sent out to the client. Once we have created the datasets, we evaluate them both from a machine-learning perspective and a human perspective. This way we can ensure that our datasets have all the required information and are accurate with all the mandatory attributes.

Our Key Takeaway

In conclusion, data quality management ensures that clients receive datasets that are clean, up-to-date, easy to understand and maintain high quality. To do so, we have ensured that our team takes accountability for their work and our methodology provides our clients with best-in-class datasets that help them make informed decisions.

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