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Big Data Truths and Myths in B2B Financial Industry

We encourage you to complete the following True or False exercise in deciphering true values of Big Data. Please answer True (T) / False (F) / Unknown (?) for the following statements in the context of today's B2B firms in financial industry or financial technologies arena.

1. Big data initiatives are for the primary benefit of improving client experience.

2. B2B firms are innovators in adopting the latest and most advanced technologies. 

3. Transparency is much improved because B2B firms have lots of data.

4. Veracity refers to perfect exactitude of data in one centralized data warehouse.

5. Enterprise architecture projects are prioritized because IT knows the big data's ROI.

6. Tech vendors join forces with each other to seek higher values through big data.

7. Big data emphasis on standardization and a single golden copy of security master.

8. Data is counter intuitive, conventional wisdom always prevail in product development.

9. There are well kept rosters for the # of data sources & how data is used and reused.

10. Big Data supports tightening of risk appetite and favor more frequent risk reporting.

Scoring Method 
• Add 3 points for every correct answer
• Subtract 2 points for every incorrect answer
• Add 1 point for every question you answered ‘unknown' (It is for your honesty and cautiousness. Keep in mind there are high sunk fixed costs in technologies investment. Hence, we help you discern truths and myths for your big data bet.)


Everybody talks about Big Data, but not just anyone can come up with a patent pending invention and be nominated for FinTech Person of the Year!

Simulation / Data Science

Push corporate innovation with advanced analytics,

Big data game plan, evaluate and implement new

technology, pattern recognition, predictive

algorithms, machine learning, quantitative risk

modeling and more.  

Datafication / Text Mining

Pluck diamonds from data and meta-data for value-

added opportunities. Navigate across non-

standardize info source(s) to pull the right data

quickly, natural language processing, ontology,

taxonomy and more.

Infomediary / Data Aggregation

Build portal/ venue/ network by crossover and

cross-tabulation of data. Build utility model or

transaction gateway for syndicate participants in the

network to benefit from the collective intelligence

and more.

Workplace Organization

Information management assessment; SEC 17a-4,

FINRA 3110, SAS70, ISO-27001 compliance

preparation; recommendations to prevent defects,

exceptions, rework, system/ product failure, time

lost, unnecessary procedures, and save costs.

 

Value Chain Management

Vendors/ distributors management; rationalize

integration/ restructuring projects, resources

deployment/ capacity planning, M&A research and

values creation, due diligent to support buy/ build

decisions and more.

I.T. Agility Optimization

Optimize what ought to be fast, resilient, scalable

and flexible, assess efficiency and effectiveness in

project execution, avoid unnecessary delivery of

low-value apps, performance tuning, project

portfolio planning and more.

Big Data, Let’s charge forward ... move?! Big Data privacy and security control?? Big Data, did you get the sight? Check Answers Check Answers Check Answers Check Answers
Our recommeded reading - The Usefulness of Useless Knowlege
Big Data