Data Science

Data Challenges for all organizations

There are many market vendor system, platforms and application options an organization can choose to help solve common business challenges that rely on data.

How does a company without data science expertise make these choices? or recognize a bespoke coded solution would be more advantageous and achieve the greatest return on data?

A small selection of functional challenges data science can uniquely solve.

Data is agnostic and can be used to solve business challenges regardless of industry.

Sales Teams Challenges

  • Lead generation and sales pressure
  • Longer, more complex sales cycles
  • Aggressive cost targets and demanding SLAs


  • Longer than needed sales cycle
  • Client dissatisfaction leads to higher churn
  • Lost opportunities due to missing insights


Marketing Team challenges

  • How to stay onto of market events and trends
  • How to stay ahead of the competition
  • How to manage masses of increasing data

2.3 Zettabytes data to sift through annually- Only occasionally identifying relevant insights


  • Missed market and product opportunities
  • Missed moves of the competition
  • Longer than needed sales cycles


Risk challenges for Organizations

  • How to identify risk events impacting you company
  • Detect and erase blind spots
  • Improve monitoring while not slowing business

>70% est. increase in client risk cost margins during crisis years - Example: Up to >96% false positives  anti-money laundering-case detection


  • Know immediately when risk levels change
  • More accurate risk assessments by client or transaction
  • 360º view client risk dashboard


  • Risk Insights

Asset Managers Information Overload  Challenges

  • AM managers receive 150+ sell side research reports per day
  • Provide specific research recommendations


  • Spotify-cation for Asset Management
  • Personalized Insights App with relevant research –Categorization


  • Specific recommendations tailored to portfolio covered, strategy(ies), coverage area, etc.


Service and Support team Challenges

  • Increasing load of service tickets
  • More complex cases
  • Aggressive cost targets and demanding SLAs


  • Service gaps lead to upset customers
  • Skill gaps lead to SLAs not met and extra cost
  • Lack of automation increases workload

Unplanned downtime costs the US $14.3 B annually. Poor customers service equates to 25% > drop in customer loyalty


•               Service Insights.

Customer feedback analysis challenges

  • Efficiently deal with large number of client chats
  • Manual and time-consuming process


  • Realtime categorization and analysis of chats
  • Automated routing to support expert


  • Better customer insights, better client service
  • Sales increase because of better service levels


Slow Incident Response time challenges

  • Siloed data: 8 incident and support systems
  • > 6'000 applications; > 22'000 tickets / day
  • Reactive servicing, not pooling case categorization into a single case view


  • Unified views across all systems
  • Seamless integration into ServiceNow


  • Automated root cause identification and solution recommendation; automated expert sourcing
  • 30% reduction in average-time-to-resolution


Unified data challenges for any organization

  • Increasing data generation and availability
  • Siloed, unstructured and fast changing
  • Incomplete data restricts businesses


  • Up to 30% of an employee’s workday can be spent searching and gathering data - 2 1/2hours a day
  • Productivity loss and likely error prone duplication
  • High costs related to inability to find relevant data


Solution - Cognitive Search

  • Automatic search across multiple data silos for information with key words
  • Integration of data and consolidated overview of all relevant data sources


  • Easy, fast search across all relevant data and speed to act through contextual recommendations


Human Resource challenges

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