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One way of measuring the effectiveness of your data governance program is to assess it against an existing maturity model. This can help by indicating: where you are with the data governance program how you are progressing or not; and what are some of the steps you need to take in order to evolve your
In  “The trifecta of the best data quality management” article, I’ve addressed why a data quality program is needed and what the recurring steps you should always go through are in order to carry on continuous and sustainable efforts to improve and/ or maintain the quality of your data. When it comes to working with the data itself,
Managing reference data poorly can have a profound impact on your operations as well as business intelligence and analytical outcomes. Here are my 5 best practices for managing reference data: 1. Formalize reference data management (RDM) Most often than not, reference data is not maintained if there is no accountability and ownership determined. Usually the
I get these questions a lot from organizations wanting to start a data governance program or who are in the early stages of planning. A data governance council is a governing body responsible for the strategic guidance of the data governance program, prioritization for the data governance projects and initiatives, approval of organization-wide data policies and standards,
A data sources inventory is meant to record basic information about the different systems across and/or outside your ecosystem from which you are capturing, producing, acquiring or procuring data. The data sources tend to be well known to their end users and administrators, but not so much to the organization as a whole. I believe
Data is only meaningful if it serves a purpose. This purpose is to have it transformed into meaningful insight, hindsight, and foresight for an organization to take advantage of. Similarly to how raw materials go through a complex conveyor belt system in a factory before the end product is created, so does the data before
According to the IBM Big Data & Analytics Hub, poor data costs the US economy $3.1 trillion every year. Ovum Research reported that poor data can cost businesses at least 30% of revenues. These numbers are staggering and in my opinion, they should be enough to motivate any organization to start investing in data governance,
It’s been long proven that the human process of comprehension and insight is faster with visualizations than with plain numbers or tabular data. Since the 14th century, when Nicole Oresme used a bar chart in “The Latitude of Forms” to plot velocity of a constantly accelerating object against time, charts and graphs have been used
Data stewardship roles can be segmented and categorized in multiple ways, depending on their responsibilities and required skills, as well as the organization’s structure, industry, goals and objectives and its data management needs. There is also an argument to be made that everyone is a data steward, but for the scope of this article I
It’s generally accepted that any organization working with data needs to adopt data governance. Readily available frameworks, tools, and services can be adapted to the requirements and environment of your organization, yet when it comes to big data governance, the options are a bit more complex. Organizations must adapt to big data in four important
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