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Accessing data in analytics works best when companies understand the roles that structured and unstructured data play.
What makes data analytics valuable is its ability to combine data from variegated sources that are compiled into a single data repository. Both structured systems of record data and unstructured data from photos, videos, social media and websites can flow into this data repository. This data combination enriches data analytics and enhances the ability to deliver invaluable and unusual insights into business problems that have long eluded solutions.
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However, there is also a second key to getting the most out of analytics: Companies have to know how to access analytics data for best results. By better understanding the roles that both structured and unstructured data play in data access, companies can design the best access paths into their data.
The role of structured data in analytics access
Transactional, structured data plays a pivotal role in analytics access because it already has database keys for the data that enables easy access. The data access keys in transactional systems are organized around the key informational constructs — such as customers, orders and products — that a company wants to know about, so you don’t have to start from scratch as you define your data access paths for an analytics data repository.
The role of unstructured data in analytics access
To unlock the additional value that unstructured data brings to analytics, you have to build access paths into this unstructured data that are able to link it with the data you already have from your transactional data.
To do this, you can use the names from your transactional data access keys and assign them as labels for unstructured big data. This enables your analytics software to identify both types of data and link them.
For example, if “customer” is the key into your transactional data records on your order system, and you also label your customer-related unstructured data this way, your analytics software will find every transaction on your order system that a customer ever did with your company and also what the customer said about your product and your company in an unstructured data social media post. This can produce a meaningful insight into what the customer is likely to buy next and what they think about your company — an insight that you couldn’t have derived just by looking at the transactions a customer did on your systems.
Analytics software: The third key to unlocking data
There is also a third element for unlocking data value in analytics. This is the machine learning that is built into your analytics software and that references your data access keys and labels. The machine language can rapidly process data and seek out repetitive patterns based on a keyword like “customer” that might also lead to new insights into what a customer wants and how he thinks of your company.
Analytics vendors are highly experienced with how their clients prefer to access data and what the most efficient ways of accessing data are — but their approach is generic. It might not be the best fit for your company.
You can assure that you are maximizing your ability to access all data in your analytics by first taking the recommendations from your analytics vendor, which are generic best practices for data access, then additionally assessing your transactional data access keys, ensuring that they are aligned with how you are labeling your unstructured data, and then finally working with your analytics vendor to ensure that the machine learning models within the analytics are calibrated to work on all access paths.
This can be time-consuming work, but it is well worth it if you can derive every ounce of value from your data and deliver remarkable business insights.
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