Most business intelligence is derived from structured data. It’s easy to work with, already populating your data storage, and fits nicely with traditional analytical platforms. Unstructured data, on the other hand, isn’t easy to work with. It requires new data storage solutions, new skill sets, and completely different analytical platforms.
Yet leveraging “big data” requires taking on unstructured data. All of the valuable data streaming in from the IoT, mobile devices, sensors, social media, clickstreams, email, website logs, call centers, etc. is unstructured. According to a study by IDG last year, 83 percent of all IT pros listed structured data initiatives as their highest priority, contrasted with just 43 percent who saw unstructured as their greatest priority. This means a whopping chunk of businesses are missing out on the opportunities inherent in those unstructured data streams. Fortunately, some of these are your competitors. With a strategy for deriving business intelligence from those unstructured data sets, you can easily gain an edge before they see what’s coming.
Why Bother? We Have Plenty of Structured Data for Business Intelligence
Unstructured data holds secrets that your structured data never even heard of. It provides insight into your customers, how the market feels about your products, how well your customer service is doing (far beyond what your customer surveys say), and how your brand and company is perceived within the marketplace and across the industry. For instance, the Twitter firehose holds valuable insight about customer sentiment, both toward your own products and your competitors’ products. Social media data delivers critical business intelligence like a potential problem with your product’s quality, long before the issue is made known to your customer service or tech support teams.
These kinds of data also reveal parts of the customer ecosystem your business couldn’t see otherwise. Unstructured data can be used to boost a business’ revenue, find new revenue streams, lower costs, improve pricing structures, respond more quickly to changing conditions in the market, and stay ahead of customer sentiment, according to IDC.
Conquering the Challenges to Derive Valuable BI From Unstructured Data
So, how does your organization begin deriving all that valuable business intelligence from the unstructured data you’ve got squirreled away? Begin by putting a strategy in place for collecting and preparing the data for analysis. This means choosing whether to establish a data lake, and whether to take on a product or tool that can help you get a handle on your unstructured data.
The second step is more managerial than technical. You need to develop a corporate culture that values data in all its forms and treats it as the critical asset that it is. This means saving, collecting, and analyzing unstructured data that might otherwise be tossed — such as old marketing materials, documents related to projects that were scrapped, and even company memos. Determine what unstructured data to collect and develop a data repository that’s capable of managing the scale of your data sets.
Don’t overlook external sources of unstructured data that can add to your business intelligence. That includes Twitter and other social firehose data, data from IoT devices, data from your or others’ mobile apps, and public data repositories. Big data analytics isn’t powerful because there is lots of data, or even because the analytical tools are so powerful. In fact, it’s very difficult to use these tools, and getting the data to reveal the answers you seek is not a trivial task. Big data is a potent business intelligence tool because of the varied data sets it can examine for patterns and correlations. More data isn’t necessarily better data, but more kinds of data definitely makes for better business intelligence.
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