Finger pointing to table with charts on

Data management roles: Which can add the most business value?

Written on December 23, 2019 by Jonathon Webley

Data management is a multi-disciplinary term for how an organisation or individual essentially looks after their data. It is THE most crucial aspect to an organisations data activity, and it underpins all other data related activities like data science, data analytics, business intelligence etc.

​Businesses always jump into doing the sexy stuff, things like analytics and data science. The thought of analysing data to get quick ROI and a shiny new technology they’ve heard so much about is often just too tempting. This is without thought to Data Management and the data’s consistency, accurateness, or completeness. On top of this, whether the data is stored appropriately or securely, whether all of the data sources have been identified and are accessible, whether data items are relatable to the business and whether there are multiple version of the same data and so on. Thus, I give you Data Management. Broken down into roles of Data Governance, Data Quality and Master Data Management.

Data Governance

Ungoverned data is like a society with rules and laws. Data Governance is the controls and processes that are put in place to ensure the use of data is in line with business requirements and remains relevant and fit for purpose. It’s not just an administrative process, though, data governance is the driving force for frontline business engagement. It also allows for traceability of data through lineage for things regulatory or audit purposes and is often high on the agenda of regulators cross-industry.

Data governance is all about getting the business bought into data, what data actually means and its uses/usage within the organisation. It allows businesses to understand how organisation data moves around, what systems its used by, and importantly, governance removes ambiguity between different departments in relation to what a particular data item means. In today’s big data world, the volume of data is vast and different business units may have to use the same data in different ways, and data governance is there to control how and why and ensures the data remains fit for purpose for all.

Data Quality

Now, we could talk about data quality until the cows come home. Data quality is all about applying the rules of data governance within data warehouses or management systems as well as ensuring data is profiled, cleansed and duplicates removed. Data quality is more than just checking the data and acting, it’s a proactive role to ensure various different ‘dimensions of data quality’. Things like its robustness, accuracy, completeness, uniqueness, its integrity and validity. All of these things ensure the data is fit for purpose no matter where, when, or how it is used. Data quality is known to have saved organisation thousands if not millions of pounds as well as increasing ROI from data analytics and data science.

Data quality is vital in any organisation, ensuring data quality issues are often tackled at source, effectively where business users input the data into the system. Rules and validation are established as is best practice business processes for capturing data. Data Quality can be measured in the form of scorecards or dashboards, which is an effective way to communicate to business leaders, data owners and others the importance and impact of data quality within the company​

Master Data Management (MDM)

Master data is effectively a way of managing an organisations data; it includes defining ‘areas’ of data to be managed and ensures data remains consistent throughout the company. Essentially, if you have multiple systems where customer data is inputted or stored, you could have multiple versions of the same customer with different details like address, phone number or email id. Mastering this data would provide a single point of reference for a single customer; essentially, this integrated data allows for improved data quality and accuracy.

There are different ways of mastering data, all of which contribute to its role within the company to provide a holistic view of all the data within the defined areas such as customer data, product data, vendor data amongst others. Master Data provides a foundation for data analysis, data science and things like machine learning. This is because the data is more accurate, more complete and ready for the magic of data analytics.​

Start managing your data now…

​So, we know data management is crucial to any organisation, whether currently leveraging data for analytics, data science or Business Intelligence or simply running some form of MI. Data Management will increase your efficiency, productivity and ROI from data analysis; potentially, the impact could be HUGE. The different roles we’ve discussed today in Data Governance, Data Quality and Master Data Management are intertwined disciplines that interact and combine with one another to provide robustness in the data
management framework. Just having control and processes isn’t enough without data quality validation, profiling and cleansing, for example. Likewise, MDM is vital for managing your data as a single view or point of reference and ensures minimal data leakage whilst improving costs and efficiencies on a pretty large scale!

Here at Agile Recruit we specialise in a variety of data management roles, so please get in touch with the team in our Manchester or Milton Keynes office today to find out more.