In recently concluded Gartner MDM, Andrew White in his keynote said – “Only 40-50% companies are methodical in their approach to success with their information management”.  Andrew emphasized on importance of a systematic approach to MDM initiative for it to be effective.

Big Data Data QualitySimilar to Analysts, we in master data management profession have campaigned for a disciplined approach to MDM for a really long time. Our community continuously talks about importance of taking holistic approach to mastering data in the form of blogs, webinars and tweets. I believe, our efforts have not gone wasted. While we have seen MDM projects getting initiated without much consideration other than, “It seems like a great idea”, that notion has changed for the better.

Some of the customers I work with are taking an agile approach with clearly defined goals for their MDM program. They have realized that being nimble is an important aspect of this challenging journey. Taking an iterative approach with small implementation cycles is helping them build an incremental solution that provides success in every step of the way. These companies realize that master data, even though “small”, is a backbone that ensures efficient execution of every business process and functions in the organization.

So what is next for these organizations?

MDM delivers clean, consistent and quality data to your organization. The next step is to use it as a foundation on which you can build analytical capabilities that leverage big data. Big data analytics has a lot of potential and helps organizations in their digital transformation. However, this once in a generation shift we are seeing is hindered by same data quality issues that have bothered us over the years, now in a larger scale.

To gain most value out of big data initiatives, let’s ensure data quality is at the center

Poor quality and lack of trust are two main reasons that will hamper our ability to deal with big data effectively. The good news however is, we can address these two big data challenge effectively with same holistic approach we took to master data. To make sure big data projects offer us the right insights, be methodical and make sure reliable and trustworthy information fuels analytics.

Data quality is more important in big data world than it was ever before. The quantity is meaningless if it doesn’t drive actionable value and the phrase “garbage in, garbage out” remains valid in this new era. Winning with big data on our side requires data quality at the center.

Do you monitor data quality proactively across your MDM and big data projects? Please drop your comments below.

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