Data Management Services
How Our Data Management Services Help:
We’ll identify, govern, clean, and store your data in a technology solution you’ll actually use and help you:
Improve Data Quality: A uniform data set helps ensure the data you collect meets certain standards.
Limit Duplicate Data: Data management systems eliminate duplicate data, meaning fewer resources are wasted collecting and storing the same information more than once.
Make Data-Driven Decisions: Creating a data management strategy allows you to better utilize your data to make more informed business decisions.
Cut Costs: By combining your data sources, storage, and analytics tools, you reduce costs associated with running multiple programs or spending time reformatting data.
Why 5P is the Right Partner?
As a team of experienced data professionals, we understand how to get the most out of an organization’s data.
We will help you standardize your data and maximize its effectiveness by looking at your existing processes and technology systems to find areas that need improvement.
Then we’ll get to work and create a custom management solution that addresses your data needs.
Our team will keep your budget in mind and look for scalable solutions that allow your management plan to grow with your business.
To make data-driven decisions, you need data that is secure, accurate, and accessible. The only way to achieve this is through data governance.
Check Out Our Client Case Studies
Read more about our successes, and find out how we’ve helped other customers with real problems, real strategies, real results.
Data architecture is the governance of your organization’s data. This includes the guidelines, rules, and procedures to collect, store, and use data within your organization.
Data management involves the processes, technology, and organization involved in putting your organization’s big data to use. The goal is to organize and govern your data so you can remove data quality issues and make better operational decisions. Good data management provides opportunities to measure how you operate visually and make decisions quickly based on trends
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Data Management FAQs
What is Data Management and why is it important?
Data management is the practice of collecting, organizing, and accessing data to support productivity, efficiency, and decision-making. This system is critical because every application, analytics solution, and algorithm (the rules and associated process that allow computers to solve problems and complete tasks) used in an organization depends on seamless access to data. At its core, a data management system helps ensure data is secure, available, and accurate.
What is a centralized data management strategy and how can I achieve it?
A centralized data strategy outlines the people, processes, and technology involved in managing your organization’s data. Designate a leader to manage the strategy, along with a team of stakeholders to oversee day-to-day data practices. Define your processes for collecting, storing, and using information, and determine what tools you have, or need, to execute the strategy.
How can improved data quality enable me to make better decisions?
A strong data quality program leads to accurate, standardized data that is ready for analysis. When you know you have high-quality information, you can derive greater insight and make decisions with confidence. Trusted data helps expedite processes, improve decision-making, and increase overall productivity with the valuable time you save on manually resolving data errors.
What’s the difference between Data Management and Data Governance?
Data Governance deals broadly with organizational strategies, policies, and procedures. It provides executive oversight and dictates how data should be handled to advance business objectives. By contrast, Data Management deals with the tools and practices used to handle data and implement the policies outlined by Data Governance.
What is Big Data Management?
Big data is the data you collect in large volumes. It can also refer to data that’s incredibly complex or that changes rapidly. There’s no specific amount or type of information that makes data “big.” Instead, big data is data that can’t be handled using traditional, often manual processes.
Big data management refers to the processes, technology, and organization needed to put this complex data to use. To effectively use big data for actionable insights, you need to be able to access, organize, and analyze it easily. Traditional data analytics and management processes can’t process data of this volume or complexity.
Traditional methods also tend to take longer, meaning fast-changing data could be irrelevant by the time you make an insight. The goal of big data management is to improve data quality, access, and analytics for complex data so you can use it to drive decisions.
What is Master Data Management?
Master data is uniform data shared across an organization. Data is often spread across an organization, but master data serves as a standardized reference on how to define certain information.
By adopting a master data plan, employees from different teams and departments can easily send and receive data and analysis without the hassle of converting or reformatting data each time.
A common type of master data is standard customer data. Let’s say, the marketing department wants to display customer data using name and location first, while the sales team prefers displaying name and total purchases first. Each time the teams send information to one another, the receiving team must translate the data into their preferred format. Your organization decides to create master data for customer information that specifies how customer information should be displayed. Now that data is standardized, the teams can easily send data to one another without reformatting before looking at the data.
Master data management (MDM) is a technology-focused process of defining master data sets for consistency across the organization. Like other types of data management, MDM creates a strategy and plan to organize, store, and use your master data.