Despite the idea that business intelligence is a crucial tool for getting and keeping customers, adequately measuring company performance, and delivering flexibility, challenges remain. One of the most important: data governance.

Although data governance is crucial to successful BI and data warehouse efforts, it isn't easy. To the rescue: five dirty data practices you may be guilty of, and five ways to clean them up.

Dirty Data Practice No. 1

You think buying the coolest business intelligence tool is all you need.

It may be a truism that your BI reporting tools are only as good as the information you feed them (that is, "garbage in, garbage out"), but that doesn't mean that the right actions are a given. Since most organizations still take an isolated view of data, data governance remains a difficulty, says Ian Charlesworth, principal analyst with IT consultancy Ovum. Data is all too often siloed in different business units and is entered, treated and viewed differently, making "one version of the truth" impossible.

Clean It Up

Know your data.

The first step of data governance is to establish a clear view of your data; find out what you have, how reliable the information is, what data is beneficial but previously unused, which data is corrupted and which IT projects are duplicating information. And be sure to communicate to stakeholders the cost of not having data governance and the value of creating it.

Dirty Data Practice No. 2

You procrastinate until you can do a complete overhaul.

An all-or-nothing approach is almost guaranteed to fail. For starters, bringing all data under control in one fell swoop is not realistic given time and money constraints, and in organizations where such an overhaul is possible, user resistance is almost a given.

Clean It Up

Start small, think big.

Instead of all or nothing, prioritize the most crucial aspects of data governance, in keeping with your overarching vision. For example, Charlesworth recommends focusing on four key areas.

Create data quality processes and procedures, and where possible embed these at the point of data creation or capture. For example, create a data validation routine in an order entry system or establish a corporate standard for name and address nomenclature.