It's more important than ever to be able to make informed choices in your company. Data can help with that.
Among the reasons for poor decision making in business are lack of strategy, a poor decision-making process, lack of communication, and information loss. I'd add poor data quality and business data practices.
As we navigate through the COVID-19 crisis, data quality and sound business practices for using data are more important than ever. They're critical in the trials of drugs and vaccines; in predicting where and when the next disease outbreak will be; in assessing the availability of resources and personnel; and in evaluating economic impact.
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In all of these endeavors, analytics, artificial intelligence, and play key roles— but data quality and sound business data practices are equally important.
On the IT side, companies achieve quality data by using a master data management (MDM) methodology that defines how data is collected, aggregated, matched, consolidated, checked for quality, and distributed.
There are numerous tools and automation solutions available for master data management. They reduce the tedium of manually culling through data, normalizing it, deciding what should go with what, and who gets which data. The tougher part of the job is enacting sound business data practices that lay the groundwork for the work of data preparation that IT does with MDM.
Here are four business data practices that are vital for any organization striving to deliver the highest quality data for decision making.
Digitize dark data
Dark data is data that is available but never used. It can exist in the form of a paper-based document created by the business years ago, or as sensor-generated IoT output that isn't used.
IBM estimates that 90% of the data companies possess never gets used, and is dark data.
While much of this dark data may turn out to be superfluous, some of it isn't. Organizations need to review this data, determine what they want to eliminate, and then digitize the data that they want to keep. This digitized data can then be used in decision making so it doesn't remain as a non-digitized wasted data asset.
Determine data value in decision making
Even though data might be digitized, it still might not be relevant for decision making. If the data isn't valuable, it should also be considered for elimination.
Deciding which data to keep is a balancing act. There is data that isn't important today, but could become valuable at a later date. However, there is other data (e.g., Internet of Things (IoT) "jitter," or memos about a company holiday party 20 years ago) that likely isn't ever going to be relevant to decision making and that should be eliminated.
Be able to aggregate different data types
Master data management frequently focuses on normalizing or consolidating disparate data fields from different systems that refer to the same piece of information. However, there is also the need to aggregate unlike types of data, such as aggregating a weather report with photos or videos of a storm system.
Data aggregation is most successful when its business use cases are clearly identified up front, along with all of the data and data combinations that are needed for decision making.
Data digitization and preparation projects don't mean much to anyone outside of IT. This is because there are preparatory IT projects that don't produce deliverables that business decision-makers see.
The question that IT must answer early on is whether management is willing to fund data preparation processes that don't yield tangible business results.
Educating (and reminding) upper management of the importance of high-quality data for high-quality business decisions is an ongoing process. IT should take every opportunity to do this and to ensure that the necessary executive sponsorship for data preparation is in place.
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