data management Archives

Yashica Chopra
May 26, 2025
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How to Achieve Clean, Usable Datasets with Data Analytics?

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. What are the Business Risks of Using Unclean or Raw Data? 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? Inaccurate Forecasting 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. Incorrect Customer Segmentation 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. Compliance and Legal Concerns 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. Mismatch in Resource Allocation 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. How can the organization assess if it needs professional data analytics and enrichment services? 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 Audit 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 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 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. How Can Data Cleaning Improve Decision-Making 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

Yashica Chopra
December 27, 2024
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A Modern Approach to Scalable Data Management Pipeline

A streamlined and automated data pipeline is the core of a well-built IT infrastructure and results in proactive decision-making. Here, we’ll discuss the detailed guide into a modern approach to data management pipeline and how to build a robust data system in your enterprise. Data is the core of every business in today’s world. You can no longer ignore the importance of data and its role in running an establishment. Whether a startup or a large enterprise with a presence in multiple countries, data holds the key to insights that help make better decisions. It doesn’t matter which industry you belong to. Business and third-party data are necessary to make informed choices in all verticals. As per Statista, the total amount of data created and consumed globally was 149 zettabytes in 2024 and is expected to be over 394 zettabytes by 2028. But how will you manage large amounts of data in your enterprise? How will you store it when more data is added every day? How will you clean and organize the datasets? How will you convert raw data into actionable insights? That’s where data management and data engineering help. Data management is the process of collecting, ingesting, preparing, organizing, storing, maintaining, and securing vast datasets throughout the organization. It is a continuous and multi-stage process that requires domain expertise and knowledge. Luckily, you can hire a data engineering company to provide end-to-end services for data management. In this blog, we’ll learn more about data management’s process, tools, and pipeline and how it can benefit your business in the long run. How the Data Management Process Works? According to a report by IOT Analytics, the global data management and analytics market is predicted to grow at a CAGR (compound annual growth rate) of 16% to reach $513.3 billion by 2030. The modern data management workflow relies on various tools and applications. For example, you need a repository to store the data, APIs to connect data sources to the database, analytical tools to process the data, etc. Instead of leaving the data in individual departmental silos, the experts will collect the data and store it in a central repository. This can be a data warehouse or a data lake. Typically, these can be on-premises in physical units or cloud servers in remote locations (data centers). The necessary connections are set up for data to be sent from one source to another. These are called data pipelines. The data management process broadly includes seven stages, which are listed below. Data architecture is the IT framework designed to plan the entire data flow and management strategy in your business. The data engineer will create a blueprint and list the necessary tools, technologies, etc., to initiate the process. It provides the standards for how data is managed throughout the lifecycle to provide high-quality and reliable outcomes. Data modeling is the visual representation of how large datasets will be managed in your enterprise. It defines the relationships and connections between different applications and charts the flowchart of data movement from one department to another or within the departments. Data pipelines are workflows that are automated using advanced tools to ensure data seamlessly moves from one location to another. The pipelines include the ETL (extract, transform, load) and ELT (extract, load, transform) processes. These can be on-premises or on cloud servers. For example, you can completely build and automate the data management system on Microsoft Azure or AWS cloud. Data cataloging is the process of creating a highly detailed and comprehensive inventory of the various data assets owned by an enterprise. This includes metadata like definitions, access controls, usage, tags, lineage, etc. Data catalogs are used to optimize data use in a business and define how the datasets can be utilized for various types of analytics. Data governance is a set of frameworks and guidelines established to ensure the data used in your business is secure and adheres to global compliance regulations. This documentation has to be followed by everyone to prevent unlawful usage of data. The policies ensure proper procedures for data monitoring, data stewardship, etc. Data integration is where different software applications and systems are connected to collect data from several sources. Businesses need accurate and complete data to derive meaningful analytical reports and insights. This is possible by integrating different third-party systems into the central repository. Data integration also helps in building better collaborations between teams, departments, and businesses. Data security is a vital part of the data management pipeline and a crucial element in data engineering services. It prevents unauthorized users and outsiders from accessing confidential data in your systems. It reduces the risk of cyberattacks through well-defined policies. Data engineers recommend installing multiple security layers to prevent breaches. Data masking, encryption, redaction, etc., are some procedures to ensure data security. A Guide to Scalable Data Management Pipeline The data management pipeline is a series of steps and processes required to prepare data for analysis and share data visualizations with end users (employees) through the dashboards. It automates the data flow, increases system flexibility and scalability, improves data quality, and helps in delivering real-time insights. Steps to Building a Data Management Pipeline Define Objectives and Requirements The first step in building a data management pipeline is to know what you want to achieve. Focus on the short-term and long-term goals to build a solution that can be scaled as necessary. Discuss the details with department heads and mid-level employees to consider their input. Make a list of challenges you want to resolve by streamlining the data systems. Once done, consult a service provider to understand the requirements and timeline of the project. Aspects like metrics, budget, service provider’s expertise, etc., should be considered. Identify and List the Data Sources The next step is to identify the sources to collect the required data. These will be internal and external. Determine what type of data you want (unstructured, semi-structured, or structured), how frequently new data should be uploaded to the repository, how