Data collection and analytics are tightly coupled. The mistake we see made over and over again is that companies tend to focus their customer data collection efforts with a single objective (or a single program) in mind. This treats the data collected as a short-term objective, not as a long-term asset. Over time, this results in data islands that eventually “go dark” given that no one is managing customer data as part of an explicit long-term effort.
Have A Long-Term Data Strategy
When it comes to customer data, a long-term data collection strategy almost always proves critical for any advanced analytical work that leads to meaningful business outcomes that can optimize (i.e., simulation management, condition-based maintenance, predictive maintenance and digital twins). Trending analysis, predicting behavior and customer profiling all benefit from long-term data collection strategies. Companies that understand customers’ buying patterns over longer time frames stand to win key insights versus their competitors.
Customer data deserves a data-access-centric strategy to ensure that the data is treated as a reusable asset. This implies that the data should be available to the right people in the company when they need to repurpose it or mine it months or years later. If the data is not findable, threadable (tied to other data sets) or readily accessible, then it’s effectively dark, and its chances of being repurposed are low.
If you are storing your customer data like you store everything else, chances are much of the data you’ve collected from customers has already gone dark. The tendency is to focus on analytical outcomes without preparing the precondition required for the analytics to occur over a longer period of time. If a data strategy for customer information isn’t well-executed, then customer data will reflect the problem you already have in your data center — lots and lots of data sets that represent difficult-to-access data islands.
Thread Your Data
Sophisticated analytical efforts require advanced techniques such as data threading. Threading data across many silos of data is a challenging undertaking.
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