Innovative technologies such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are transforming the way we approach data analytics.
AI, ML and NLP are categorized under the umbrella term of “cognitive analytics,” which is an approach that leverages human-like computer intelligence to identify hidden patterns and correlations in data.
With a Research and Markets study forecasting that the industry will expand from $20.5 billion in 2020 to $77.5 billion in 2025, cognitive computing is set to grow substantially.
This poses several questions: Is cognitive analytics right for my business? How do we use the technology in practice? What kind of competitive advantage will we gain? What KPIs can we drive?
Let us explore the what, why, and how of cognitive analytics.
As mentioned, cognitive analytics applies human-like processing to datasets. This helps businesses uncover hidden patterns and correlations in their data that would otherwise be missed. Use natural language processing (NLP) as just one example:
Legacy language processing analyzes content at the surface-level. From the text string, “Hello, I am interested in your product” legacy processing would identify the language (English), but it cannot determine context.
Under the hood, this means that legacy processing can match words and phrases and follow an input/output (I/O) process to generate a response — though it is unable to identify language patterns to determine context. It only knows that someone is interested in a product, but not who the person is or what product they are looking for.
NLP is different as it can link words and phrases with other external data points to determine context. From the text string, “Hello, I am interested in your product,” the NLP software will discard Hello as a redundant language formality, unimportant to the context.
“I am,” provides the context for who, “interested in,” shows the intent, and “your product,” shows what the subject intends to do — in this case, purchase your product. An NLP solution follows this workflow under the hood. When integrated with other business tools, NLP can use context clues such as stored contact details, email trails, past user browsing habits, and any recent products they have accessed on your website. As a result, the NLP will determine the most relevant response and compile a message using these data points, sending a human-like response.
Digital Champions use inputs from NLP interactions in their analytical engines, creating a more robust 360-degree view of the customer that is leveraged in service, sales, and marketing actions that further differentiate the brand from its competitors.
NLP requires vast amounts of data to work effectively. This means a scalable data warehouse will function as a prerequisite to maximize the potential of an NLP solution. You can learn more about data warehousing in this Trianz blog: Data Lake vs Data Warehouse.
With cognitive analytics, the impact is akin to receiving an autoreply in an email instead of a human response. A non-NLP response will look manufactured and automatic even to the untrained eye. In contrast, applying NLP can give the illusion of a human response, without the monetary addition of a human resource.
More generally, cognitive analytics is applicable across many low-level workflows involving language, images, video, and business data processing:
A good example of cognitive analytics is the Google Photos service. The service applies Google’s Cloud Vision API to analyze and categorize photos or videos for users. This partially relies on user input, labelling each file with names and descriptions. Otherwise, the Cloud Vision API is trained on billions of photos from Google Images.
Pro Tip: most CAPTCHAs you complete on the internet contribute to training visual processing AI, as discussed by TechRadar.
For businesses, cognitive analytics can be applied to structured or unstructured datasets. One example on the market comes from our partner Microsoft and their Cognitive Services. This service runs on the Azure cloud, offering domain-specific AI capabilities coupled with development APIs to promote cognitive analytics adoption.
For example, with decision-making, the anomaly detector processes system data to proactively identify problems in deployed infrastructure and applications.
Content moderator can detect and censor offensive language in social or communication channels — something which Trianz has leveraged in its proprietary Pulse digital workplace platform.
Personalizer tracks advertising IDs and customer interaction data to improve the customer experience (CX) through targeted personalization. This offloads decision-making for low-level workflows to the cognitive AI, freeing up workloads to prioritize resources.
Before you adopt cognitive analytics, you will need to plan and create a roadmap for adoption. To assist, Trianz offers consulting support with planning and road mapping for analytics and business intelligence (BI).
Some areas to consider:
Cognitive analytics requires constant, real-time access to datasets. This requires a scalable data lake or warehouse with support for high throughput. A single source of the truth (SSOT), is desirable as the cognitive analytics tool will have more centralized data available, leading to more context and insight.
After identifying existing data sources, data generation should follow. Ecosystems like enterprise resource planning (ERP), customer relationship management (CRM) and Internet of Things (IoT) all generate actionable datasets ideal for cognitive analytics. This data should then be ingested into a centralized data warehouse to increase the scope of analytics potential.
To measure the effect of cognitive analytics on your business, you should establish KPIs for each integrated system or software service. By knowing what the business wants to achieve, it can identify relevant datasets for analysis by the cognitive analytics solution. This helps to target the technology, rather than a one-size-fits-all approach that may be inefficient for some organizations.
To adopt cognitive analytics, a high-level of IT maturity is essential. The Digital Enterprise Evolution Model (DEEM™) is designed to help enterprises assess their maturity through comparison against leaders in each technology field.
DEEM™ uses data from 5000+ companies across 18 different industries to assess IT maturity — courtesy of our sister data company Trasers — enabling accurate benchmarking of your IT capabilities.
Competitive Benchmarking of Analytics With DEEM™ can help you assess whether your business is technologically prepared for cognitive analytics, guiding your next steps towards AI-driven digital transformation.
Copyright © 2021 Trianz
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