All business processes benefit from becoming data-driven as your organization’s data collection and storage grow. As your organization’s data collection and storage grow, so does its decision-making. Of course, the increase in data volume brings with it new issues. The more data assets your organization accumulates, the more data difficulties your firm will encounter.
As more data items come in from dozens or hundreds of different sources, companies often lose control over the data lifecycle process. It becomes increasingly difficult to scale to meet the expectations of data end users, assuming you can even satisfy them at all. Before you can put a data management framework for your company, you must first understand what should get included in one.
This article will explain what you need to know about Data Management Framework:
What is a Data Management Framework?
A data management framework is a model of the people, procedures, and rules that you will need to be successful in managing large amounts of information.
Various business drivers, which are essential aspects or procedures that are critical to a company’s continued success, guide the operations of every firm. Your organization’s specific business drivers determine how closely you must regulate data and to what extent as part of your data management plan.
For example, one of the business motivations of a healthcare organization may be to protect the privacy of patient-related data assets. This necessitates the secure management of sensitive data as it travels through the organization to maintain compliance with relevant government and industry standards. However, patients’ healthcare professionals must have easy access to their medical records simultaneously. These requirements serve as the basis for the provider’s data management strategy, which serves as the foundation for its data management system.
A well-planned data management framework encompasses roles and responsibilities at the strategic, tactical, and operational levels. You may rest assured knowing that your data is trusted, well-documented, and easy to discover within your business. It is also maintained secure, compliant with regulations, and protected from disclosure.
Examples Of Data Management Frameworks
The traditional ways of developing a data management framework get divided into top-down and bottom-up. These two approaches derive from diametrically opposed philosophical perspectives. One prioritizes data control to maximize the quality of the data. On the other hand, ready access to data gets prioritized to optimize data access by end-users across all business units.
For Data Control: Top-Down Method
This method centralizes data management. It is based on a small group of data experts who use well-defined procedures and well-known best practices to accomplish their goals. This method implies that data modeling and governance receive top priority. Subsequently, the data gets made more broadly available to the rest of the organization to do analytics.
Although this strategy is effective, it has a significant scaling problem. In this concept, there is a clear distinction between data producers (usually IT) and data consumers (often business users). Only data suppliers have the authority to exercise any control over the information they collect. Before the advent of cloud computing, this was less of a concern because there was less data to regulate, and fewer teams required access to that information.
However, these small teams of data producers are now unable to keep up with the demand from data consumers. It is currently considered a corporate requirement to have clean, full, and uncompromised data available to everyone who requires it. For these teams to continue operating as gatekeepers, there are too many business users making too many requests on their behalf.
For Data Accessibility: Bottom-Up Method
The bottom-up approach provides significantly greater flexibility when it comes to data management. Unlike the top-down strategy, which begins with data modeling and governance, the bottom-up approach starts with unstructured data. After the raw data has gotten ingested, it is possible to build structures on top of the data (this gets referred to as “schema on reading”) and add data quality controls, security rules, and policy guidelines.
This structure, which gained popularity with big data, is more scalable than the centralized method. It raises a whole new set of data challenges. As a result of data management not being applied until later in the process and that anyone can submit data, it is more difficult to maintain control. Also, as previously noted, a lack of data management can result in increased regulatory risk, a loss of stakeholder faith in the organization’s data, and a higher cost of data management due to a jumbled-up collection of data assets that you must manage.
We require a modern approach to data management frameworks that strikes a healthy balance between access and control to achieve this. We must establish control early in the process without compromising the ability of users and subject matter experts to take on the roles of data owners and curators.
Data Management Frameworks Best Practices
Here are some of the best data management methods that you should incorporate into your framework:
Ensuring Convenient Access to Data
Data scientists and business analysts that have access to a large amount of information can make better predictions. Thus, the more data they have at their disposal, the better. It becomes easier to quickly discover which data points are most predictive of a particular outcome with greater access to data. As a result, a comprehensive data management framework can assist by providing an abundance of native data access capabilities that make it simple to interact with data from various sources, formats, and structures that are constantly expanding in number.
The ideal data management framework includes advanced statistical analysis capabilities integrated into the ETL process. The frequency analysis of data, for example, can be used to discover outliers and missing values that can distort other metrics such as the mean, average, and median.
Data is not always regularly distributed, as many statistical approaches assume. Therefore, summary statistics can aid analysts in understanding the distribution and variance of a data set. Correlation reveals which variables or combinations of factors will be most useful based on predictive capability strength, in light of which variables may impact one another and to what extent, and in light of which variables may influence one another and to what extent.
Reduce Irrelevant Data
Up to 40% of all strategic procedures fail due to insufficient information. It is possible to incorporate data cleansing straight into your data integration flow when using a data quality platform built on optimal data management standards. Performance gets improved by moving the process down to the database level. It also removes erroneous data based on the analytic method you’re using, and it enhances data by binning it together with relevant information.
Organizations may be more agile and competitive in the global business environment thanks to the capacity to harness, store, and analyze data. Although this ability comes with immense power, it also comes with great duty: the task of maintaining the safety of stored data.
Data breaches continue to generate headlines, even though laws and regulations have improved overall data security and compliance. It’s also prompted businesses to step up their data security efforts. Companies can enhance security while also reaping numerous advantages from using data management strategies.