So here is a list of a couple of hundred 'problems' you could go out there and solve and have immediate business customers. Some of these will make the engineers brains out there whirring!
Unstructured data challenges – customer insight. Unstructured data – how to
deal with it? How to structure it?
2. Integration of different input sources.
3. Abstraction from different industry verticals.
4. Standards for data exchange.
5. Vertical aligned challenges.
6. Standards for data exchange.
7. How to deal with different languages.
8. Machine translation: handling data in different languages.
9. Algorithms for unstructured data.
10. Semantic search vs tagged search.
11. Structured vs unstructured.
12. Interaction between public domain & internal data.
13. Standards around migration.
14. Data harmonisation.
15. Social data mining. E.g. Social network adaptors (can plug into Facebook,
GMark etc).
16. Architecture that handles data protection control cross-boarders.
17. Import/export data standards for bulk data down/uploads.
18. Integration of Data from different system e.g. mainframes to mobiles.
19. Infrastructure around homogenous (Americas) & non-homogenous mkts for
data access.
20. Uniform extraction.
21. Cross industry ‘fertilisation’ maps to financial ‘non spatial’ data.
22. Real-time vs structured + unstructured data.
23. Migrating data from multiple systems.
24. System integration (diff. systems). SOA of data availability for consumption.
25. Translate data into your own model.
26. Data relationship between public & private data so they can be used
interchangeably.
27. To guarantee the integrity and availability of content/data from different data
source that may have different format and size.
28. Integration of multi-sources and different sources.
29. Compliance on local/international regulations/rules re data storage/privacy/
security.
30. Sovereignty of data – can we move data across jurisdictions.
31. Legal/ regulatory restrictions / boundaries.
32. Regional & national boundaries.
33. Cultural influences on data structures and meaning.
34. synchronisation of data sources
35. Impact of data ownership.
36. Geographical context.
37. Data Storage not analysis friendly
38. “Frictionless” from data storage to analysis
Privacy/ Data Privacy Laws
Online data security/ Cloud data security
Security
Data Audit Trails
Customisation of Data views based on user security policies
Anonomisation of data
Validation of data, is it correct? Can I trust?
How do you know the data is still relevant (e.g. is the e-mail still up-to-date)
/ how to maintain the currency of the data or at least have a measure of its
currency
9. Data Accuracy.
10. Data verification (Duplication, integrity) Determining the risk in the source or
a measure of confidence in the data.
11. Noise reduction.
12. Trust in transition and collection of data or at least have a measure of risk/
trustworthiness.
13. Certification standard to guarantee or give a measure of data security in the
pipeline.
14. Identification or profiling of the consumers of data so that what they can see
can be determined by policy management.
15. A process to get rid of/ minimise data redundancy.
16. Risk profiling of data sources.
17. How to maintain and know the currency of the data.
18. How to maintain confidence on data sources – trustworthiness of sources.
19. Manage the duplication of data in cloud.
20. How does one know the correctness/integrity of data from social media/data
sources?
21. Media assets tracking.
22. The ability to track the use of data/content and maintaining its integrity.
23. Noise reduction in data.
24. Verified data – determining the risk in source.
25. Determining ‘good’ vs ‘bad’ data.
26. Contingency planning.
27. Data modelling take into account of its context – so the relevancy can be
established.
28. Defining ‘data relevancy’ standard or parameters in order to find the relevant
data.
29. Innovative way to establish relevancy of data – without creating more data.
30. Learning the relevancy of data on the fly.
31. Suitability of content – social aspect.
32. Trusted partner networks.
33. Content targeting.
34. Contextualise different solutions.
35. How to structure the data to maintain relevancy – control should be closer to source.
1. Automate data via visualisation.
2. Incorporate scenario planning info data visualisation.
3. Visualise ‘what if’ scenarios.
4. Analysis capability within visualisation.
level.
5. Tools for making data ongoing at
6. Play with data in visual way.
7. Unstructured (Game like?) visualisation vs. structured (BI) visualisation.
8. Adapt data for mobility world.
9. Tools usable for non technical people.
10. One interface – from null???? Tools.
11. Tools to make data simple for board level.
12. Visual interactive data that builds scenario planning.
13. Adaptive visualisation using data/lol user prefs.
14. Correct info delivery every time.
15. Data should be relevant and understood to all stake holders.
16. Analytics by example.
17. Data delivery through adaptive visualisation.
18. Interaction between visualisation and analysis.
19. Contextualise accurate real-time for customer use.
20. Bench mark against best practice business systems (reporting).
21. Making data accessible via different methods – mobile, special room, apps etc.
22. Expertise in correct visualisation to answer a question.
23. Prizes for visualisation problem solving.
24. Preset board level data via interactive visualisation tools.
25. Visual data that allows predictive modelling.
26. Human interface i.e. Kinnect/SIRI for data streams.
27. Information overload solutions.
28. Expertise in dashboard/design/viz.
29. Application of gaming tech to other industries.
30. Reports downloadable to mobile.
31. Dashboard optimisation.
32. Presentation widgets.
33. Cross referenced metrics.
34. Real-time alerting ‘anticipation’.
35. Making outputs relevant.
36. Real time aspects.
37. Data trends over short periods.
38. Formatting.
39. Not knowing what info is useful to me.
40. Bespoke requirements.
41. Presentation device detection.
42. Quality of results found.
43. Summary tasks.
44. Immediacy of information/ I want it now!
45. Insight generation link to the business.
46. Real Time.
1. Stream computing – real time.
2. Arch. Selection framework.
3. Hybrid dafta – private …..Meta/data – cloud.
4. Power of cloud.
5. Verification of backup.
6. Architecture optimization.
7. Hybrid (unstructured SQL for report).
8. Optimise the data loading.
9. Data storage for less and faster access.
10. Velocity of data proliferation.
11. Access to data real time – cloud + virtual.
12. Event detection (real time?).
13. Integrating ext data links at analysis time.
14. Flexibility to adapt to different operating models.
15. Real-time event driven analysis architecture.
16. High performance computing applied to machine learning data mining.
17. Data curation.
18. Latency/currency.
19. Network/device latency affecting the real-time.
20. Scaling analysis to cope with data scale.
21. Information creation – information curation.
22. Open source solutions to improve efficiency/ Scalability of open source
23. Ability to move providers of data storage processing
24. Event detection in Real time
1. Understanding of free tools that could be downloaded into corporate
environment.
2. Guidance on governance of architecture + best practice tools for filtering.
3. Benchmarking how they deployed the data in different ways – app ???? ck.
4. High volume data – data standards.
5. Principles & guidance for managing business intelligence.
6. Understanding of best practice. Tools to filer and govern.
7. Standards – based web services (rest/wms/future).
8. Harmonise to industry standards (storage – analysis).
9. Industry models – starting point.
10. Get to an industry model to adopt & adapt.
11. Best practice tools repairing – insight.
12. Benchmarking on what is a good no. of reports/dashboards in a high
performance company.
13. Data Governance.
14. Methodology from reporting to insight.
15. Teaching corporates what tools can be deployed and latest thinking.
16. Standard data transfer Including security.
17. Quality benchmarking for analytics outcomes
18. More standardised profiles/ models
19. Filtering disruptive analysis techniques.
20. Compliance on local/international regulations/rules re data storage/privacy/
security.
21. Sovereignty of data – can we move data across jurisdictions.
22. Legal restrictions.
23. Regional & national.
24. Geographical context.
25. Legal boundaries.
1. Normalising data.
2. Domain knowledge in a feasible format.
3. Industry specific & domain customisation.
4. Predictive analytics.
5. Industry standard needed.
6. Sentiment analysis.
7. Customer control of presentation.
8. Client focus.
9. Profiling segmentation.
10. Target audience for output.
11. Quantitative to qualitative translation.
12. Expectation gaps.
13. Model validation.
14. Unstructured data mining.
15. Tools to consume Symantec data web.
16. Combine structured and unstructured data.
17. API’s.
18. Perception of scalability.
19. Data analytics as a service.
20. Making data mining mainstream for the smaller guys.
21. Abstraction of implementation from consumption.
22. Hardware acceleration e.g. AMD & INVIDIA.
23. Vertical scaling w/flash memory
24. Data Visualisation.
25. Video Analytics.
26. Crowd sourcing.
27. Location & context.
28. Trend spotting.
29. Fraud preventing.
30. Churn prevention.
31. Customisation schemas
32. Alerting protocols (escalation etc).
33. Best practices on different algorithms on preparing/processing data.
34. Turning data to information.
35. Predictive alerting.
36. Informed prediction based on historic events.
37. Migration problems: conversion, time, expertise, structured – no SQL
database.
38. Middleware tools to filter large legacy systems and filtering.
39. Tools for Interoperability without deep integration.
40. Organise work volumes.
41. Smart Data Filters Loc/Volume/Contact.
42. More tools for rapid metadata creation.
43. Tools for private cloud/infst. Not dep. on public services.
44. Data archiving & data subsetting.
45. Synchronisation tools inadequate.
46. Huge task integration.
47. Automating subsetting of data (dynamic).
48. Relationship Mining.
49. Automation of Data Extraction.
50. Real-time access to quality data
Make analytics relevant to bottom lines.
Cost saving for SMEs.
Scaling & cost effectiveness – ROI.
Putting value into information.
Business needs rules – data analysis. Review your architecture all the time.
Data analysis gets detached.
6. Push analytics CS out to the business.
7. Data relationship mining e.g. on-line
8. Automated feature.
9. Analytics tools.
10. Analytics skills.
11. Development of more complex algorithms to data analyse unstructured.
12. Which algorithms are worst appropriate?
13. Human understanding of data – get the message across.
14. Accessible tools.
15. Data accuracy (+ tolerances).
16. Analytics tools + real-time (expense).
17. Multidimensional vs. map reduce.
18. Pattern analysis.
19. Making complex analysis simpler for user ‘analytic workbench’.
20. Analysis to insight.
21. Business strategy – then data from there.
22. The problem of ‘small data’ analysis.
23. Reporting – insight – actionable.
24. Skill set – hard to find and train.
25. Six sigma for analytics.
26. Predictive modelling difficult in longer term.
27. Methodology on how to look at data – six sigma.
28. Real time access to quality data.
29. Can Google analytics methodology be applied to structured business?
30. No time for analytics reporting-storage.
31. Narrow data to ‘so what’ factor – How?
32. Data reporting – business implications.
33. Collecting right data – align with business.
34. Better triggers for alerts for selected topics.
35. Real-time – millisecond DJ vs. 30 mins.
36. Simplify data analysis via graphical interface.
37. Identify specific use cases.
38. Artificial intelligent machine decisions.
39. Customer behaviour – narrow to relevant data.
40. Data Mountain – issue/insight.
41. Analysis of outliers.
42. Important to automate reporting to free resources for analytics.
7. Energy – demand of data storage.
8. Ability to move providers for data storage processing.
9. Tradition VS cloud in data storage.
10. Reselling information with added value
11. ROI on different data storage strategies.
12. KPIs
13. Ensuring that the right question is answered
14. modelling the problem in advance
15. 3 levels of data analytics problem understanding 1.Data 2.Information 3.impact to bottom line
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