International Institute of Analytics (IIA) 5 Predictions and 5 Priorities for 2016

On Dec 10, 2015 I attended the 2015 Analytics Predictions Webinar by International Institute of Analytics (IIA), with Thomas H. Davenport - Analytics Thought Leader and IIA Co-Founder, IIA Director of Research Dan Magestro, and IIA Lead Faculty Robert Morison.

Here are predictions and priorities presented in IIA webinar - watch replay here.

The first 5 predictions were presented by Tom Davenport.

1. Cognitive technology becomes the follow-on to automated analytics

See also IIA report on Early adopter of IBM Watson in healthcare: challenges and progress.

2. Analytical Microservices facilitate embedded analytics

(example: Watson Q&A functionality became just one of 32 diff APIs )

3. Data Science and predictive/prescriptive analytics become one and the same

see IIA report on Chief Analytics Officer vs Chief Data Officer.

The distinction in current usage of Analytics and Data Science can be summed up as

• Data Science is more about technology, how-to do
• Analytics is more about business, what and why

 
Tom also commented that there is a danger that analytical managers can be too removed from technology - one can graduate from some business schools and not take any analytics courses or hear the word regression.

4. The analytics talent crunch eases as many university crunch programs come online

However crunch still there at regional level - it is harder to get Data Scientist in some midwest cities than in Silicon Valley or New York City.

5. Analytics are focused on data curation and management

Examples companies providing analytics/data science-based data curation platforms: Tamr, Paxata, and Trifacta.

Next Dan Magestro presented 5 Analytics Priorities

Priority 1: Align analytics and Business strategies, and use analytics in strategy development.

Priority 2: Leverage existing analytics strength broadly across the enterprise

It was noted that cybersecurity frequently has the biggest data lake

Priority 3: Improve discipline in analytics project intake, definition, and prioritization

Priority 4: Retain talent with career and leadership development programs specific to Data Scientists.

This is a big problem, since analytics skills are more cross-industry mobile.

To retain analytics talent, recognize leadership potential, train for business skills , and provide extra support in management roles

Priority 5: Measure the comprehensive value of analytics to establish undeniable relevancy

One can measure Direct ROI of analytics which

• increase value
• reduce costs
• better user-satisfaction

 
But also long-term organizational analytics "muscle" that

• makes the company smarter
• helps avoid cost of external consultants

 
Bonus Priority: who owns digital business in your enterprise? Analytics must be in a mix



Related:

• Vincent Granville Predictions for Data Science in 2016 and Beyond
• 22 Big Data & Data Science experts predictions for 2016 - Dec 11, 2015.
• IIA 2015 Analytics Predictions.