things to do with data
PERSPECTIVES

Over 1001 “Free” Things You Can Do with Your Data – Outcomes-as-a-Service

Written by Dr. Kirk Borne

When I was a child, I saw an ad in a magazine for a list of 1001 things you can get for free. The list cost very little (less than one dollar, I believe) plus the cost of a postage stamp. The list was totally legit. My parents provided the financial capital, and so I ordered the list. A few days later, the list arrived. It had quite an impact on me – a whole new world opened to my young curious mind. 

For the cost of another stamp and envelope for each item, I was able to acquire government reports, maps, compilations of sports statistics, small starter stamp collections, puzzles, games, booklets on all sorts of topics, and so much more. It was perhaps my first real exposure to information-rich content and to the thrill of discovery – in this case, the discovery of knowledge resources. I was hooked. Maybe that was my first significant (and impactful) exposure to data, and it didn’t frighten me whatsoever:

Maps, reports, numbers, and visualizations, oh my!

Today’s Data Sources

Fast forward to today, and we are faced with much more than 1001 sources of data that we can access for free. Open Data repositories include government data, economic data, research data, social network data, internet accessible repositories, etc. Add to that all the data sources that our organizations own internally. The former provides outside insight (context) and the latter provides inside insight into the people, processes, purposes, and products that define our organization’s mission.

We may be frightened by such a firehose of data. Or we may be excited by the prospects of discovery in the content-rich signals emanating from all types of sources. I strongly encourage the second response. Think of it as an invaluable analogue to that list of 1001 things that you can get for free and explore.

External open data sources are effectively free for anyone to access and use. But why do I say “free” for our internal data sources? Of course, they are not free in the financial sense. But they are free in the sense of being a reusable (even sustainable) source of value creation and knowledge discovery. To emphasize this point, some people say that “data is the new solar power”. That makes much more sense than saying “data is the new oil”, since oil is not reusable nor sustainable nor renewable. But your data can be used repeatedly, for many different applications and emergent needs beyond the initial needs for which it was collected, even for new projects not previously conceived. In that sense, your internal data sets are “free” to be used in countless additional use cases.

Data as Inspiration

Those who are already invested or otherwise involved in the data revolution have certainly found many uses for their data collections. At the risk of repeating some that you already know, I offer below some free ideas that I have compiled, with the hope that some of these are novel, interesting, inspirational, and valuable for your projects.

The list is fewer than 1001, but our collective imaginations have already found many thousands of data use cases, data-inspired innovations, and data-driven outcomes across countless application domains.

Included at the end of the article is my Big List of Things to do and Outcomes to Achieve with Your Data. It is divided into two groups (General and Specific), though the classification is somewhat ambiguous in some cases. Also, the list is much less than 1001, but if we identify specific instances of these things across multiple individual industries, disciplines, and domains, then that would probably cause a multiplication of the list by at least a factor of ten:

 

For illustration, here is an example of one category of use cases: predictive analytics on real-time (perhaps streaming) business data. Fifteen such use cases include:

  1. Real-time credit risk prediction.
  2. Real-time fraud risk prediction.
  3. Real-time personalized customer interactions.
  4. Real-time context-specific and location-sensitive product and/or content marketing to consumers.
  5. “Do Not Pay” classification on fraudulent insurance claims prior to payment.
  6. Real-time determination of benefits eligibility to mitigate underwriting fraud.
  7. Detection of insurance rate evasion tactics within the policy quote process.
  8. Optimal actuarial price determination at the point of policy-quote decision-making.
  9. Health risk prediction at the point of healthcare decision-making.
  10. Real-time detection of anomalous and adversarial cyber network behaviors.
  11. Stop data breaches before they happen.
  12. Stop illegal funds transfers before they happen.
  13. Stop non-compliant business transactions before they happen.
  14. Optimize supply chain and warehouse product flows: position the right products in the right quantities at the right locations just-in-time.
  15. Predict product demand and pricing by finer levels of product subcategories.
things to do with data

Signals (data) from ubiquitous digital sources within our enterprise systems carry transactional information (what happened to what?), as well as contextual metadata (where and when is it happening?), and analytics information (what insights do the patterns in the data encode?).  The analytics can be descriptive/diagnostic (describing what has happened or what is happening within a given transaction), predictive (providing behavioral insights into the interests, intentions, and preferences of the actors within that transaction), and prescriptive (providing actionable insights into the right decisions and actions to take in those circumstances). Those analytics alone can expand into a vast number of unique use cases across hundreds of different disciplines and industries.

Finally, when you count your blessings and remember your favorite things, remember to include all your sources of high-variety data and their corresponding high-variety of uses and re-uses for value creation. Then you won’t be frightened by the volume of the data but instead be excited for its discovery potential. Then you will encounter one more item to add to that big list of outcomes: Data Impact!

Now, onward to the list…

 

General Things to Do with Data:

 
 
  • Analytics product development
  • Anomaly/Outlier/Novelty discovery 
  • AI (= the ultimate data consumer!)
  • Association discovery 
  • Augmented Reality (AR)
  • Autonomous application systems 
  • Bias detection in algorithms
  • Blind source separation
  • Causal (Explanatory) factor analysis 
  • Classification 
  • Computer Vision (CV)
  • Content generation 
  • Correlation discovery 
  • Correlation discovery 
  • Data-driven decision support
  • Deep Learning (DL)
  • Diagnoses
  • Document labeling
  • Edge analytics
  • Embedded analytics
  • Fraud detection
  • Image classification
  • Immersive Reality experiences
  • Independent Component Analysis (ICA)
  • Industry 4.0
  • Intelligent edge
  • Link discovery
 
  • Machine intelligence
  • Machine Learning (ML)
  • Mixed Reality (MR)
  • Natural Language Generation (NLG)
  • Natural Language Understanding (NLU)
  • Network (Link) analysis
  • Optimization 
  • Pattern discovery and recognition 
  • Personalization
  • Predictive analytics (forecasting)
  • Predictive maintenance 
  • Prescriptive analytics (optimization)
  • Prescriptive maintenance
  • Principal Component Analysis (PCA)
  • Rare event detection
  • Recommendations (recommender systems) 
  • Regression analysis
  • Reinforcement learning (gamification)
  • Robotic Process Automation (RPA)
  • Segmentation 
  • Streaming analytics
  • Supervised learning (classification, diagnosis)
  • Text summarization
  • Time series anomaly detection
  • Topic modeling
  • Unsupervised learning (pattern discovery)
  • Virtual Reality (VR)
 

Specific Things to Do with Data:

 
 
  • 4-D Printing
  • A/B testing
  • Alert fatigue mitigation (fewer false positives)
  • Anti-money laundering
  • Application-specific dataset recommendation
  • Automated data labeling and tagging
  • Automated feature importance ranking
  • Automated image/video captioning
  • Automated question answering
  • Automated question generation
  • Automatic data integration
  • Automatic data quality assessment
  • Autonomous disaster response planning
  • Call center automation
  • Change-point detection in streaming data
  • Chronic illness prediction
  • Compliance verification
  • Connected products/vehicles
  • Content labeling
  • Conversational AI (chatbots)
  • Cross-sell/Up-sell opportunities discovery
  • Customer behavior analytics
  • Customer experience optimization
  • Customer journey modeling and analytics
  • Customer service automated text response
  • Customer/Employee churn prediction
  • Customization of portals and experiences
  • Cyber behavior analytics
  • Cyber threat detection
  • Data (metadata) enrichment
  • Data loss detection
  • Data Product Development
  • Data security verification
  • Data usage tracking
  • Data use case recommendation
  • Dataset recommendation
  • Digital Twins
  • Drug discovery
  • Drug interaction discovery
  • Email filtering, ranking, sorting automation
  • Email phishing detection
  • Email spam detection
  • Entity cross-identification
  • Entity disambiguation
  • Facial recognition
  • Fake content detection (news, images, videos)
  • Food security tracking
  • Healthcare patient behavior analytics
  • Hierarchical segmentation
  • Home price prediction
  • Human Resources (HR, People) analytics
  • Humanoid robots
  • Hyper-dimensional data indexing
  • Hyper-personalization
  • Illicit trafficking detection
  • Image generation from captions 
  • In-chip sensor analytics
 
  • Intelligent data management
  • Intelligent search
  • Intent detection
  • Inventory optimization and prediction
  • IoT (Internet of Things) sensor fusion
  • IoT edge & streaming analytics
  • Legal e-discovery
  • Legislation & Policy analytics
  • Legislation loophole detection
  • Literature-based discovery
  • Location intelligence
  • Machine translation
  • Map routing
  • Market discovery
  • Market trends detection and prediction
  • Medical diagnosis
  • Missing alerts recovery (false negatives)
  • Missing value imputation
  • Mortgage approval acceleration
  • Mortgage risk analysis
  • Multichannel customer analytics
  • Music classification
  • Music generation
  • New materials discovery
  • Object detection and classification
  • Omnichannel customer engagement
  • Omnichannel marketing attribution
  • Operational efficiencies discovery
  • Pandemic spread prediction
  • Personalized training recommendations
  • Precursor event analytics
  • Predictive crime trends
  • Predictive customer choice selections
  • Predictive pricing
  • Predictive product innovation
  • Procurement/invoice/contract auditing
  • Product demand prediction
  • Risk analysis (detection and prediction)
  • Self-driving vehicles
  • Sentiment analysis in text
  • Shipping optimization and prediction
  • Smart cities
  • Smart energy grid
  • Smart farms
  • Smart homes
  • Smart manufacturing
  • Smart transportation
  • Speech (Speaker) recognition
  • Sports analytics
  • Supply chain prediction & optimization
  • Sustainability analytics
  • Time series change-point detection
  • Traffic congestion mitigation
  • User experience optimization
  • Virtual digital assistants
  • Voice analytics (content, sentiment)
  • Voice Assistants
  • Voice-based search

Now, take the conversation to Twitter! Agree or disagree with this perspective on data and outcomes? Want to ask Kirk a question? Tweet @KirkDBorne using the hashtag #datamakespossible right now!

Leave a Response