Master Data Management (MDM) is a comprehensive method of enabling an enterprise to link all of its critical data to a common point of reference. When properly done, MDM streamlines data sharing among personnel and departments. In addition, MDM can facilitate computing in multiple system architectures, platforms and applications. The need for this method arises from the fact that organizations, over time, end up having the same data replicated in multiple places, which can lead to inconsistencies and confusion.
On the other hand, Data Analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today’s business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.
Understanding Master Data Management
Master Data Management (MDM) is the core process used to manage, centralize, organize, categorize, localize, synchronize and enrich master data according to the business rules of the sales, marketing and operational strategies of your company. MDM is typically a step in the larger data management process and considered a key pillar of an effective data strategy.
Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts. The goal of MDM is to provide processes for collecting, aggregating, matching, consolidation, quality-assuring, persisting and distributing such data throughout an organization to ensure consistency and control in the ongoing maintenance and application use of this information.
Components of Master Data Management
MDM systems need to manage four key types of information: reference data, master data, metadata and hierarchical data. Reference data is the set of permissible values that may be used by other data fields. Master data represents the business objects that contain the most valuable, agreed upon information shared across an organization. Metadata provides the context, definitions and lineage of master data and reference data. Hierarchical data stores the relationships between master data and other data.
MDM, when well implemented, helps with the management of data consistency within the enterprise. This can enhance the process of decision making, as data will be reliable and give a true picture of the information it represents. It can also help to reduce redundancy: MDM systems are designed to decrease the amount of duplicity for a given piece of information.
Benefits of Master Data Management
MDM provides a unified view of all critical data of an organization, creating a single, reliable, “source of truth”. This can lead to significant benefits, including improved decision-making capabilities, increased operational efficiency, improved compliance, and an enhanced customer experience.
MDM can also provide a roadmap for integrating data from different platforms, including merging systems from different business units and consolidating applications to reduce IT costs and improve end-user productivity. The end result is that MDM can help to create an IT environment that is simpler and less costly to manage and change, enabling business agility.
Understanding Data Analysis
Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. According to Shamoo and Resnik (2003) various analytic procedures “provide a way of drawing inductive inferences from data and distinguishing the signal (the phenomenon of interest) from the noise (statistical fluctuations) present in the data”.
Data Analysis is a process, within which several phases can be distinguished. Data cleaning is a preliminary phase of data analysis where the analyst checks for errors in the data and rectifies them if necessary. Data integration is a precursor to analysis, where multiple data sources may be combined in order to enrich the data and make it more useful for analysis.
Types of Data Analysis
Data analysis can be classified into several types, depending on the methodology used. There is qualitative data analysis, quantitative data analysis, business intelligence data analysis, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on what the data suggests, while CDA focuses on confirming or falsifying existing hypotheses.
Furthermore, data analysis can be classified into descriptive, exploratory, inferential, predictive, causal, and mechanistic data analysis. Descriptive analysis looks at the past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. The other types of analysis are all about using historical data to predict future outcomes.
Steps in Data Analysis
The process of data analysis is not a single, straightforward process. It involves multiple stages and steps in order to reach a valuable conclusion. These steps include data cleaning, initial data analysis, main data analysis, final data analysis, and interpreting the data.
Data cleaning is the process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted. This data is usually not necessary or helpful when it comes to analyzing data because it can distort the interpretation of the data.
Combining Master Data Management and Data Analysis
Master Data Management and Data Analysis are two crucial pillars of any data-driven organization. While MDM provides a unified, single version of truth, Data Analysis helps in making sense of this data by applying statistical and logical techniques. When combined, they can provide a powerful tool for decision making and strategic planning.
MDM ensures that the organization has high-quality, reliable, and consistent data at all times. This data is then used by Data Analysis to generate insights, identify trends, and make predictions. Therefore, the success of Data Analysis largely depends on the quality of data, which is ensured by MDM.
Role of MDM in Data Analysis
MDM plays a crucial role in Data Analysis. It ensures that the data being analyzed is accurate, consistent, and reliable. Without MDM, the data may be inconsistent, which can lead to inaccurate analysis and misleading results. Therefore, MDM is an essential prerequisite for effective Data Analysis.
Moreover, MDM also helps in eliminating data silos in an organization. Data silos occur when different departments or units within an organization have their own set of data and do not share it with others. This can lead to a fragmented view of the organization’s data and can hinder effective Data Analysis. MDM helps in breaking down these silos and ensuring that a unified view of the data is available for analysis.
Role of Data Analysis in MDM
Data Analysis, on the other hand, helps in identifying the quality of master data. By analyzing the master data, organizations can identify any inconsistencies, errors, or gaps in the data. This can help in improving the quality of master data and making MDM more effective.
Furthermore, Data Analysis can also help in identifying trends and patterns in the master data. This can provide valuable insights to the organization and can help in strategic planning and decision making. Therefore, Data Analysis not only benefits from MDM but also contributes to making MDM more effective.
Conclusion
In conclusion, Master Data Management and Data Analysis are two interrelated disciplines that can greatly benefit from each other. MDM ensures the quality and consistency of data, which is a prerequisite for effective Data Analysis. On the other hand, Data Analysis can help in improving the quality of master data, making MDM more effective.
Therefore, for any organization aiming to become data-driven, it is essential to have a strong MDM strategy in place along with robust Data Analysis capabilities. This will ensure that the organization has reliable, high-quality data at all times and can generate meaningful insights from this data to drive decision making and strategic planning.
