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 Impact
Longer than needed sales cycle Client dissatisfaction leads to higher churn Lost opportunities due to missing insights Solution
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
Impact
Missed market and product opportunities Missed moves of the competition Longer than needed sales cycles Solution
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
Impact
Know immediately when risk levels change More accurate risk assessments by client or transaction 360º view client risk dashboard Solution
Asset Managers Information Overload Challenges
AM managers receive 150+ sell side research reports per day Provide specific research recommendations Solution
Spotify-cation for Asset Management Personalized Insights App with relevant research –Categorization Benefits
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 Impact
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
Solution
• Service Insights.
Customer feedback analysis challenges
Efficiently deal with large number of client chats Manual and time-consuming process Solution
Realtime categorization and analysis of chats Automated routing to support expert Benefits
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 Solution
Unified views across all systems Seamless integration into ServiceNow Benefits
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 Impact
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 Benefits
Easy, fast search across all relevant data and speed to act through contextual recommendations
Human Resource challenges
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