How unstructured data analytics transforms enterprise decision making
The information enterprises need to grow and transform their business is within reach, but they don’t leverage most of it.
Enterprises possess a wealth of data. Structured data is typically easy to access and analyze. It’s well-organized and is stored in tools like databases and spreadsheets that organizations control. As a result, it’s comparatively straightforward to consolidate and use.
, on the other hand, presents data teams with a number of challenges as they seek to aggregate, clean and operationalize it. That’s because this content is everywhere in dozens of formats, is often incomplete or has unnecessary information, and is growing incredibly fast. Bernard Marr, a futurist, estimates that up to 90 percent of data is unstructured and that it is growing at a rate of 55 to 65 percent a year.
What is unstructured data? And why is it so important to enterprises today?
Unstructured data usually comes in the form of text in documents, publication, and digital communication. It often provides important context that can sharpen decision making. For example, enterprises possess structured data on their clients and partners, such as lead and sales data in customer relationship management (CRM) systems; procurement, inventory and other data in enterprise resource planning (ERP) systems; and planning, material sourcing, manufacturing and logistics and other information in supply chain management (SCM) systems.
However, unstructured data paints a fuller picture of these relationships. This data includes:
- Emails, which provide a treasure trove of insight into why and how decisions are made
- Mobile and communications data, including texts, chats, and instant messages
- Social media sentiment, including likes, emojis, and comments, which can provide insights into customer needs and behavior
- Digital media, including pictures, audio recordings, and videos
- Text files, such as Word documents, presentations, and log files
And the list goes on and on.
Thus, an organization that only looks at static data from structured sources likely has a fragmented, out-of-date view of its customers. An enterprise that can capture customer behavior and feedback across all its channels has a much better understanding of what that customer thinks.
As an example, multiple calls to customer service representatives after a large purchase could indicate that a customer has growing dissatisfaction with a new product, indicating that the relationship is in jeopardy. Similarly, extensive positive social media chatter about a new offering in different regions could indicate that it’s time to accelerate production and make that product more available globally.
First, a cognitive search platform connects to desired data sources. The Sinequa Insight Engine provides more than 200 ready-to-use connectors and converters that link to more than 350 document formats. As a result, organizations can combine both unstructured and structured data and analyze them in concert.
Next, the AI-driven search platform uses automation and natural language processing to remove “noise,” such as errors, redundant or unnecessary content. That means analytics can be developed on the content that matters most.
Then, Sinequa’s Insight Engine uses machine learning to transform content into insights and presents it in an easy-to-use interface. Now, users can capitalize on search, text mining and deep content analytics to dig deeper into issues. They also can easily integrate this clean, categorized information with other analytics tools, such as business intelligence platforms.
That means sales teams can drive quickly to insight on market trends and customer demand triggers. Call center teams have an integrated view of the customer relationship. It includes product usage and service requests, when customers call, chat, or email. Researchers can access and validate prior studies, to avoid repeating research and build on these findings. Truly, the applications are as varied as a company’s needs.
A biopharma company put Sinequa Insight Engine to work to speed up drug discovery and development. The firm’s clinical scientists were spending too much time searching for relevant research across numerous data repositories. After deploying the Sinequa platform, teams were able to easily access the data they needed. As a result, the firm anticipates that it will save two years of time developing new drugs, while increasing clinical scientist efficiency by €5.46M.
An IT manager at this biopharma company quickly realized that the AI-driven search platform could be extended to other unstructured data types, such as call center logs, regulatory compliance data, email searches and social media. The company plans to connect to these content sources and use them in creating unstructured data analytics.
How will your organization capitalize on the unstructured data you possess? What problems can you solve with broader-ranging, real-time insights?
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