Companies today, especially those who are striving to be more data-driven, are investing in data management. But when it comes to finding the right solutions for your data management stack, sometimes the most difficult question to answer is “what functions does my company really need?”
Data management solutions are made up of a number of components, each with its own purpose and function. But at Tamr, we believe there are seven data management functions that every data-driven company should have as part of their data management practice.
Seven Critical Data Management Functions
Connectivity: every company is managing a myriad of different data sources, each with its own set of data. Connectivity is an important function because it allows you to extract data into a data warehouse, clean and enrich it, and then publish it back to the source system for consumption by the data end users.
Schema mapping: by integrating different schema from different sources into one, companies can then master around the data. And while this function isn’t fancy, it’s practical and necessary, enabling you to map multiple schemas into one in order to match column names together.
Data mastering with machine learning: the best data mastering solutions integrate machine learning and enrichment, while keeping humans in the loop, in order to break down data silos and deliver clean, accurate data for use in BI and analytics. Think of it as the next generation of master data management (MDM), critical for businesses who want to scale and become truly data-driven.
Data enrichment: it’s unlikely that the best version of your data lives within your internal systems. That’s why you need data enrichment. Using third party data, you can help to ensure that your data is complete by integrating relevant information from external sources.
Data stewardship with lineage: machine learning gets your 90% of the way there when it comes to data mastering. But keep in mind that humans still play a role, too. Data owners know valuable information about the data and their role is to override or make changes in the data to improve it. Good data stewardship also preserves the lineage so that you know who modified what in the data.
Data transformation: an integral part of the data management workflow, data transformation includes basic manipulation of the data to address data quality issues. It also allows you to standardize formatting (such as using consistent codes for states or countries) and validate syntax of the data.
Golden records: using golden records, companies can maintain one single view – and a single source of truth – of data from multiple sources. Using various rules and mechanisms, you can determine which version of the attributes to manifest into the golden record.
One Key Data Management Philosophy
When considering providers for these data management functions, we believe that best-of-breed is the best approach. The days of companies going “all-in” with a single vendor are a thing of the past. Instead, a modern data management ecosystem includes solutions from multiple vendors so that you can get the best solution for the function you need to fill.
Tamr Mastering’s cloud-based, machine learning-driven approach delivers all seven of the data management functions that we believe are important for your business. Implementing these capabilities takes work, including development of a strategy and leadership buy-in. It also requires you to adopt a set of MDM best practices to ensure you are getting the most out of your data.
By investing in these data management functions and best practices, you’ll be on your way to becoming a more efficient, data-driven organization.