Data Management Services - Types and Benefits

Imagine doing business without tracking your resources. Or tracking your accounts. Or revenue!  

But why stop there? 

Why assume that only those parameters are critical to the growth of your business? Imagine not keeping a pulse on employee and customer satisfaction. Or process management. Or the market and your competition.  

One thing is for sure.  

Modern businesses are complex.  

And their complexity will only increase as we identify more factors that impact their growth.  

How does a business, then, take control? 

By understanding those very factors. In other words, by collecting and analyzing data.  

A world-class data management and analytics firm like SG Analytics understands that data should be at the heart of your strategy, and that data is your most valuable asset. 

And hence, we offer data management services that provide your business with an all-encompassing framework to use that asset to its full potential.  

Such a framework enables your business to collect, store, organize, and access data, across all its domains, with minimum friction and error.  

That said, there is more than meets the eye. There is more depth to data management solutions than simply collecting, storing, and using data.  

This is what businesses ought to get right first.  

Understanding Data Management Services  

Data management is the process of collecting, organizing, and accessing data to support efficiency, productivity, and business decision-making. Today data plays a pivotal role in business, and a solid data management strategy along with a modern data management system is critical for every organization. The data management process includes the following: 

• Collecting, processing, and storing data 

• Integrating data types from different sources 

• Ensuring high data availability and recovery 

• Governing data that is used and accessed by people as well as apps 

• Protecting and securing data to ensure data privacy 

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Data Management: Consulting and Strategy  

If data management dictates what data, and when, ought to be collected, stored, and used, data governance dictates how that data ought to be collected, stored, and used.  

Data governance forms the scope. Data management forms the solution.  

Data strategy consulting, therefore, is twofold.  

Yes, we devise data management solutions that 

• Make onboarding and transferring data easy. 

• Enable business intelligence solutions like Robotic Process Automation (RPA).  
• Enable product, sales, marketing, customer, and competitor analysis.  
• Enable data visualization, among other things.  

But here’s what we do first.  

We create a strategy or blueprint tailored to how you operate.  

This involves — 

• Identifying the various sources of data: to ensure it is high-quality.  
• Identifying data-critical parts of your business: these are parts or sectors that need access to data most quickly and readily. 
• Identifying data bottlenecks: so that data can be accessed with maximum efficiency. 
• Thorough quality control: so that data can be accessed with minimum error, duplication, or redundancies. 
• Identifying security threats: establishing security or privacy policies like encryption to safeguard valuable data. 

A strategy ensures that data is always structured. It ensures consistency. It ensures that data is error-free and never incomplete. This is the goal of scoping — part one of data management consulting. 

What does that mean for you? 

Since it is this very data that drives decision-making, such a strategy also ensures that decision-making is ordered, consistent, and accurate. This is the goal of data solutions — Part Two. 

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Types of Data Management Services  

Remarkable customer relationships can only be built from a consistent process of developing, understanding, and getting to know the consumers. Organizations today are following a data-first marketing policy as the guiding mechanism to foster growth. Different data services that can be integrated into the organizational framework include: 

1. Master Data Management (MDM): Centralizing Core Information  

A technology-enabled discipline, master data management helps in creating a single master record for each individual or place in a business. This further helps in ensuring the uniformity, accuracy, stewardship, and accountability of the official shared data assets. Once created, the master data serves as a trusted viewpoint for business-critical data that can be further shared to promote accurate reporting and reduce data errors.  

2. Data Warehousing: Storing and Organizing Data for Analysis 

Product satisfaction and data insights are stored in data warehouse systems. They serve as the primary customer data repository for businesses. These customer data sources help with different data requirements for various systems, including operations platforms, financial applications, and marketing and sales systems. Data warehouses benefit marketers in identifying how customers are purchasing and what products or services they are using products or services, along with the satisfaction levels or issues they face. 

3. Data Analytics Services: Extracting Insights from Raw Data 

Businesses today have access to a gamut of data analytics tools that can be used by marketers and sales professionals. Some of the major types of data analytics tools are reporting, data visualization, and business intelligence. While data warehouse systems are critical for storing customer data obtained from diverse data sources, data analytics can be used to process the accumulated data, visualize and format it to generate insights. These analytics tools are used for identifying generic customer trends and getting data presented in visual-rich graphs to measure and extract actionable intelligence.  

4. Data Migration Services: Smooth Transitioning Between Systems 

The data migration process involves moving data from one location to another or from one format to another. The process is a result of introducing new systems for the data. The business driver is an application migration where legacy systems are often replaced by new applications that share the same dataset. The data migration process starts when a firm moves from on-premises infrastructure to cloud-based storage. 

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