Data quality is a major concern for businesses and has to be dealt with effectively to promote decision-making based on a data-driven model. Here, we’ll discuss how to clean datasets and make them more usable to derive actionable data analytics insights.
Data is the core of every business in today’s world. With about 402.74 million terabytes of data being created each day, you cannot ignore the importance of identifying useful insights by collecting and analyzing relevant parts of this data.
From social media posts to generative AI tools, business transactions, consumer searches, promotions, and just about everything else, a business has multiple data sources to track and connect with its systems. Additionally, the ERP, HRM, CRM, and other business management software also have vital data about markets, customers, products, services, competitors, and more.
However, to set up high-quality data analytics in your organization, you need more than data and tools. You need clean and usable data that can provide reliable insights and help in decision-making. The data collected from sources is not clean. It is raw data in multiple formats and has duplicates, missing information, incorrect tags, etc.
So, a successful business doesn’t just require data. It should have clean, refined, and enriched data to give accurate insights and promote data-driven decision-making. How do you achieve this? How to determine if your business data is of good quality? How to enrich data and why?
Let’s find out in this blog.
Do you know that poor data quality costs $12.9 million every year on average? According to Salesforce, poor data quality can cost a business 30% of its average revenue. This is a high number to ignore. Yet, some businesses don’t implement data cleaning and refinement processes due to the costs and struggle with low-quality and incorrect insights.
But what are the risks of using unclean data? Why should you invest in data cleaning techniques to improve the quality of your business datasets?
Historical business data is analyzed to identify hidden trends and patterns and provide predictions for future planning. Sales forecasting is useful to measure the possible interest in a product or service among various markets. It helps identify the demand vs. supply ratio and determine the production capacity, promotional campaigns, sales targets, etc. If poor-quality data is used for forecasting, you will end up with incorrect insights and wrong planning. This could literally benefit your competitors as you struggle to make last-minute changes.
Customer segmentation is necessary for personalized marketing. You should know where your customers are from, their purchase habits, behavior patterns, preferences, etc., to target them with tailored ads and promotional offers. With missing or outdated customer data, your marketing campaigns will not give the expected results. Imagine spending thousands of dollars on ads only to get the bare minimum returns. Such data analytics errors can be avoided if your business datasets are clean.
Apart from financial issues, poor data quality also results in compliance risk. Industries like insurance have to follow stringent data policies for greater transparency and accountability. Moreover, depending on the geographical locations, you have to adhere to different data security and privacy laws when using customer data for analytics. A misstep at any point can lead to lawsuits and other complications. It could affect the brand name and push customers away from the business.
No enterprise has unlimited resources. You should allocate resources carefully based on the requirements of each department or process. Wrong insights due to unclean datasets can negatively affect resource allocation. This could result in wastage of precious resources or bottlenecks due to a lack of sufficient resources for critical processes. The money spent on the entire process can end up as a loss in either instance. High-quality datasets mitigate such risks and play a role in optimizing operations for greater efficiency.
In short, we can summarize the risks using a popular statement, ‘garbage in = garbage out’. If you use poor-quality data, the outcome will be equally poor and lead to a multitude of losses for the business. The sooner you fix the issue, the less the risk of affecting your organization in the long run. That’s why end-to-end data engineering services include data cleaning and refinement using different techniques.
Every business that uses data for analytics needs professional data cleaning and enrichment services. Here are a few ways to assess the business datasets to hire a reputed data engineering company for streamlining the entire process.
Data auditing is the process of carefully and thoroughly reviewing the datasets to identify inconsistencies, missing values, duplication, etc. The audit report provides insights into how much effort is required for data refinement.
Data profiling is the process of analyzing data to examine its quality, understand the structure and the content, identify anomalies, etc. It helps highlight inconsistencies and errors that result in low-quality data.
Data validation is the process of ensuring that the business data is clean, accurate, and reliable to derive meaningful insights. It helps in preventing invalid data from being used for analytics and promotes data enrichment to improve the overall data quality.
While these processes require resources like time and money, they are necessary to get a clear picture of where things stand in your business. You can partner with data analytics or data engineering companies to perform these assessments and provide recommendations for data cleaning. Typically, this is the first step to implementing the data-driven model in an organization.
Data cleaning is a part of data refinement, which can ensure high-quality datasets for analytical insights. Simply put, data refinement is the process of transforming raw data into usable and quality datasets to support data-driven decision-making. It involves multiple processes, such as the following:
Data enrichment can further be divided into three stages:
But how can these steps improve decision-making in an enterprise? Here’s how:
When raw data is transformed into clean and structured data, it has little to no errors, typos, or missing details. In short, the datasets contain complete, clear, and correct data. This naturally results in accurate insights that the top management, as well as the other employees, can rely upon to make instant decisions. When the input quality is good, the output quality will also be similar.
The purpose of using data analytics is to derive insights that promote decision-making. An actionable insight is one you can act upon. It is measurable, tangible, and relevant to the query you entered into the interface. So, by using insights from clean data, you can implement your decisions.
Clean and refined data shows exactly where you should spend, where to allocate more resources, how to shorten the production cycle, and so on. This allows you to eliminate unwanted costs and divert money to processes that are beneficial to the business. You can also take corrective steps in real-time to prevent losses.
When you use clean data to derive insights, you can get a comprehensive idea about the various processes in your organization. Are all the steps necessary to get the output? Can you enhance the steps without increasing costs? High-quality data makes it easy to find reliable answers to these questions.
When your data and insights have fewer errors, your processes will be more effective and free of bottlenecks. From automating recurring processes to increasing employee performance, clean data can promote greater efficiency across the departments and verticals in an enterprise. Moreover, neatly formatted and structured data takes less time and computational resources to provide insights. That means you can get insights in real-time without consuming too many resources.
Another reason to invest in data preprocessing is to ensure regulatory compliance. Every business has to adhere to various industry, national, regional, and other regulations set by registered authorities. When you use clean and enriched data to derive insights, it will be easier to ensure adherence as the process increases transparency and accountability. This will also reduce the risk of legal complications.
Data governance is the collection of policies, standards, and processes that define the data architecture in an organization and ensure compliance. Data cleaning is a part of data governance as it results in reliable and consistent output that aligns with business and industry standards. It promotes better data management.
Customers are the most crucial part of a business. If they aren’t happy with your offerings, it’s hard to survive long in the market, isn’t it? Using clean and processed data for analytics can directly increase customer engagement and experience with your brand. That’s because the insights derived will point out exactly how to approach the customers, what products and services to promote, how to improve your offerings, and so on.
You will find competitors in every industry and sector. While some markets have more competition, some have fewer competitors. Either way, you should stay one step ahead of others to proactively grab market opportunities and avoid risks. With clean data, predictive analytics will give more accurate insights about future trends, allowing you to make timely decisions.
Data normalization is the process of structuring and organizing your data in a database to improve its integrity and efficiency by eliminating redundancy. It also brings consistency and accuracy across the database. Here are a few data normalization best practices and tools recommended by experts.
A Master Data Management Platform (MDM) is a solution to create a single master record for each entity, person, or thing in a business. It collects data from multiple sources to create this central repository. SAP, IBM InfoSphere Master Data Management, and Oracle Enterprise Data Management are some examples.
A more advanced solution for a central data repository is a cloud-based data warehouse or a data lake. Tech giants like Microsoft, Google, and Amazon have powerful built-in data warehouses that can be connected to several third-party data sources, analytical tools, and data visualization dashboards. These are more scalable, flexible, and cost-effective in the long run.
ETL (extract, transform, load) and ELT (extract, load, transform) are two commonly used data cleaning techniques in most enterprises. These solutions extract data from sources and transform it before loading it into the central repository, or load it first and transform it next, depending on how you set up the connections. Both methods have advantages.
Data quality and management tools are used to identify, understand, and correct errors and inconsistencies across datasets. These tools can be integrated with your data architecture to streamline and automate the process. Informatica Data Quality, DQLabs, IBM InfoSphere DataStage, and SAP Data Quality are some examples of these tools.
Enterprises will need powerful and scalable data normalization tools to manage large datasets quickly and automate the steps to save resources. In such instances, it is more effective to use artificial intelligence and machine learning tools for the purpose. They also have more features like bulk processing and validation, an intuitive interface, industry-specific support, collaboration support, etc.
Yes, data analytics companies can help set up a data refinement pipeline to convert your raw business data into high-quality datasets for supporting data-driven decision-making.
The process involves the following steps:
Define the goal for building the data refinement pipeline and setting up the data analytics connections. This helps to understand the scope of the project.
Make a list of various internal and external sources that should be integrated with the pipeline or the central repository. Use as many relevant data sources as possible.
The actual data collection process is called ingestion, while data validation refers to ensuring the quality and accuracy of the collected data.
These are the key steps in the data refinement pipeline, as the magic happens here. The raw data is refined and transformed to make it ready for analytics.
Data storage is the process of retaining or holding a copy of the datasets in a secure and safe location/ database. Data consumption is where the stored data is used for various purposes, like deriving insights, generating reports, etc.
The last step in the data refinement pipeline is a continuous process. You should monitor the architecture to ensure every stage is running seamlessly with or without human intervention. The systems should be upgraded as and when necessary to give better results. Companies can provide long-term maintenance and support for data engineering services. Such partnerships are highly beneficial for businesses.
Data refinement and enrichment, as well as data normalization, are necessary to build high-quality datasets in your organization. Investing in these processes will give long-term returns by improving customer experience, reducing risks, and enhancing employee performance. Talk to a certified and experienced data engineering company to clean your business data and unlock its full potential. Occupy busy markets using accurate and actionable data analytics insights for proactive decision-making.
Data analytics companies help businesses convert their raw data into meaningful insights to enable improved decisions. It includes an array of solutions and analytics to provide a comprehensive view of the organization. Data analytics can mitigate risks, increase operational and employee efficiency, increase customer satisfaction, and generate higher revenue.
Read the following links to learn about how data analytics can help your business.
Fact checked by –
Akansha Rani ~ Content Creator & Copy Writer
