Data Management Systems: Catalyst to a Better Customer Experience

A business is only as good as its data. Data is a catalyst for business improvement, growth, efficiency, productivity, innovation, and survival. In fact, structured and validated data is the irrefutable driver of engaging, personalized, timely, and relevant customer experiences. For example, financial services, one of the earliest adopters of digitalization, that used data management and analytics to transform massive volumes of available data into value generation. The finance industry today uses data to introduce new service models and applications and spends hundreds of millions of dollars on data solutions annually.

Structured data is the key that unlocks smarter business decisions, and to this end, data management systems are essential to managing the diverse, complex, and voluminous quantities of data from various sources.

Top Five Data Management Systems to Choose From

Before selecting a data management system, businesses need to evaluate the kind of data they have and define their objectives. Since a significant portion of generated data is useless, it’s important for companies to segregate their data in order to create a meaningful data pipeline across data collection, data management, and eventually, advanced analytics that produces revenue-driving insights.

Let’s take a deep dive into five great data management systems to choose from.

Technologies Shaping up Data Management Systems

Revolutionary technologies like big data, blockchain, and IoT impact how data is stored, monitored, and accessed for visualization and decision making: 

• Role of Big Data Analytics in Data Structuring and Access Datasets that are too massive for traditional data management, big data analytics is the solution. The use of big data-enabled applications has increased recently due to factors such as the Internet of Things (IoT), which created a surge in data generation. Because of the data structure which enables in accessing data mush quicker and from huge data sets, data lakes are a popular solution for storing big data and helps data scientists access, analyze, and process data more quickly and precisely.
• Blockchain and Data Management It boasts secure, tamper-resistant storage and cryptographic certainty that helps minimize fraud and improve cybersecurity and accountability. Blockchain and data management are the most efficient method for online data storage. Moreover, blockchain SaaS technologies enable companies to visualize data trends, issues, and events. 
• Machine Learning (ML) Contributing to Data Management A sub-sector of AI, this technology reveals patterns within data to generate accurate predictions and insights. Python is a common tool for data visualization, requiring specialized data science skills. To use machine learning to its fullest, apart from high computing power, companies have to get storage with a robust data architecture given the complexity of ML models and workloads.
• Log Analytics and Data Management Log messages and files produced by different components within a technology stack that are saved for analysis, used to mitigate website problems, or improve web server performance. It requires robust data management to correlate data from fragmented sources, along with intelligent data visualization dashboards, to accurately troubleshoot or uncover data anomalies.

Manual Data Entry and Data Validation to Support Data Management Systems

Regardless of the technology driver, organizational infrastructure, and data requirements, data management systems are dependent upon clean, current, and accurate data in order to function and produce desired outcomes. 

Manual data entry and validation: the unsung heroes of data management and the pillars that support requirements for automated databases that store validated data and produce accurate, dependable results.

• CRM data entry Organizations that want to scale customer personalization utilize artificial intelligence (AI). AI enables data management systems in providing personalized information without much human intervention. Manual CRM data entry ensures adds value to this exercise by supporting B2B sales and marketing departments in developing strong data management systems with high-quality data.
• eCommerce data entry The copious quantities of data in this industry must be managed for an eCommerce site to run efficiently. Product data management, for example, is an area of eCommerce that is built upon manual data entry and validation to ensure consistency and accuracy across product images, web copies, features, and more. 
• ERP data entry In the form of production data management, advanced ERP systems accelerate business productivity via a centralized shared database that provides a broad perspective of the organization as a whole. It integrates numerous working cogs within the enterprise, such as finances, HR, customer databases and marketing, and requires optimization to support separate departmental tasks. If an ERP system is collecting sales order data to send to the company’s financial department, for example, it can benefit from manual data entry and validation to ensure data has been classified according to unique sales parameters.

Data Management Is the Key to Improved Customer Experiences

The though is plain and simple – you not only need to know everything about your customers but should be able to predict their behavior to improve their level of satisfaction. How do you do it? – the availability of data at the right time and form is the key. In today’s intelligent era, businesses must get their data houses in order if they’re going to compete, thrive and deliver an exceptional customer experience.

Data Entry Outsourced (DEO) applies globally recognized industry innovation across professional data entry services, techniques, and practices to help businesses with all of their varied data management, data extraction, and data analytics needs. Our data entry, data conversion, and data processing services are backed by years of experience skill. Learn what our skilled team and extensive service model has to offer and start turning your data aspirations into realized customer experience results.

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