Monday, December 31, 2012

Business Analytics: Analytics Predictions 2013 by Alberto Roldan

Business Analytics: Analytics Predictions 2013 by Alberto Roldan

Analytics Predictions 2013 by Alberto Roldan



This year I would like to do something different and address trends in the business analytics industry that, in my opinion, will affect all industries in the next 1-3 years.  The purpose of these predictions is to provide some guidance so that the companies we represent today make informed decisions in the area of analytics that could affect their profitability and revenues in the future:

1.     Electric power transmitted through the air. Could you imagine the implications of this technology on all industries?  This will eliminate the need to plug in to restore electric power to any device.  This would include cars, computers, laptops, and mobile devices.  The opportunity for smart-energy solutions is going to transform business analytics with a potential of a ten-fold increase in the amount of business opportunities.  Key skills will be experience in smart-grid analytics for short-term forecasting of electricity utilization, big data, and machine learning algorithms like principal component analysis (PCA).

2.     Embedded semiconductor analytics. This technology is currently used in automobiles as sensors (e.g., check oil, gas gauge).  Could you imagine the implications in the business-to-business (B2B) market for this technology?  Right now alerts that are generated (e.g., retailer fraud, institutional compliance for anti-money laundering, or pharmaceutical utilization) need to be programmed and maintained at a substantial operational cost.  This embedded technology can produce some measurable costs improvement in most industries.

3.     Executives will become more conversant in analytics methodologies and technologies. The larger the investment in analytics, whether technologies or solutions, the more important it will become for executives to have a deeper understanding in this area of investment.  This type of conservation will facilitate moving analytics from the realm of the data scientist to permeate the operational side of any company.

4.     Reliance in machine-learning algorithms for big data analytics. The concept that analytics methodologies that work in small data sets but not in big data analytics will come to the forefront.  Utilization of machine-learning algorithms to provide business insights and forecast future trends that have an impact on revenues or cost levers will become make significant inroads in the next 36 months. 

5.     Operational analytics. The term operational analytics will be used to have a good or bad implication depending on the experience of each person.  It will be bad for those that have spent millions of dollars in data warehousing and technology tools but have not obtained a measurable cost savings as a consequence of their investment.  It will be good for those that have made a prior investment that has had a measurable impact in revenues or costs.  The ability of consulting and analytics companies to explain the measurable value of operational analytics will become the cornerstone of whether a company perceives analytics as a good or bad investment.

6.     Analytics reporting will undergo a transformation to visualizations that can accommodate multiple dimensions involved in big data. Reporting will undergo a transformation that allows end users to simultaneously visualize multiple dimensions in real-time big data situations.  The advent of big data, machine-learning algorithms, and the need to prove measurable business value will require 3D visualizations for reporting purposes so that companies have a 360-degree view that will capture business insights as well as the impact on profitability of multiple what-if scenarios.  

Thursday, April 26, 2012

Sunday, April 22, 2012

Big Data Innovation Conference - April 25 & 26, San Francisco

Big Data Innovation Conference - April 25 & 26, San Francisco

I am the co-chair for the first day. This is an exciting conference for those of us that develop analytics methodologies to get business insights to provide measurable business lift. If you are attending contact me at alberto.roldan@cognizant.com so that we could meet during the conference

Tuesday, January 10, 2012

Big Data and Analytics: Game Theory in Practice

This new year I have decided to concentrate in big data and advanced analytics.  Specifically, I am emphasizing the area of game theory and big data to give business insights in the area of "what if" scenarios.  This article inThe Economist on Modelling Behaviour is a good starting point. 

The objective is how to provide business insights in a rapidly changing world with big data challenges.  One of my proposed solutions is to adapt or use game theory in business analytics.  I had the fortune and privilege to be trained in game theory by Dr. Allen Whiting in the mid-1970s.  Game theory has been around since the 1940s (although there are references for as long as 1713!) and is used in 2007 Hurwiczs, Maskin and Myerson were awarded awarded the Nobel Prize in Economics "for having laid the foundations of mechanism design theory."

I will always be in debt to Dr. Whiting (former Undersecretary of State in the Kennedy and Johnson administrations) for teaching me how to do analysis in a way that has become my livelihood since the 1976. Friendship like imagination has no limits!

Link: http://www.economist.com/node/21527025

Enjoy!

Thursday, January 05, 2012

What's your Algortihm?

This is a good article that describe what do I do for a living and different business analytics applications.

Wednesday, December 28, 2011

Alberto’s Business Analytics Predictions for 2012

Alberto’s Business Analytics Predictions for 2012

2011 was a great year for business analytics across all industries. As business analytics projects proved their measurable value within companies, I believe that in 2012 we will continue to see a mathematical increase in the number of companies that use business analytics. My predictions for 2012 are not in order of importance.

1. Increased utilization of advanced analytics in retail, CPG, healthcare, energy, banking, and healthcare – These industries will continue to lead in incorporating advanced analytics in their day-to-day operations using real-time or near real-time systems. Education and understanding of how advanced analytics can help companies will increase within companies in strategic and tactical areas.

2. 3D visualization techniques used in gaming will become more prevalent in business analytics – Visual analytics will enhance dashboarding and reporting techniques currently used by companies due to its measurable lift in providing better business insights.

3. Decrease in the amount of time that it takes to successfully design, test and implement an advanced analytics project to no more than three months – As companies become more educated in the benefits of advanced analytics, tools, and resources, they will start demanding the streamlining of analytics projects so that they can “turn around in a quarter.”™

4. Enhanced integration of IT and business stakeholders – Business analytics will bring the long- sought goal of better integration between IT and business stakeholders within companies. Business analytics will become the bridge between IT and business stakeholders since it answers specific business questions that are implemented through the enterprise by the IT organization.

5. Resources with analytic skills will continue to be a hot commodity – Companies will seek these resources to incorporate them into their infrastructure. As the demand for these finite resources increases, the marketplace price will increase.

6. Outsourcing and Offshoring of analytic projects and resources – Companies will seek analytic business models that can turn projects around quickly and seamlessly within their infrastructure. They will discover that it takes a combination of analytics, IT, and project management skills to successfully implement an analytics project with measurable business lift. Outsourcing and offshoring models will become a more attractive alternative in terms of pricing and delivery.

7. IT organizations will continue to struggle with analytics projects – The learning curve of the differences and similarities between analytics and IT projects will continue to plague IT organizations, and outsourcing and offshoring delivery models will become more attractive methods to deal with these issues.

8. Analytics tools will continue to thrive in the marketplace – Companies will continue to purchase these tools to give them the ability to predict and segment their big data. Analytics appliances that are industry- and problem-specific will proliferate in the next 2-3 years.

9. Social media integration with transaction data will become a priority for business stakeholders and the IT organizations – Companies have a gut feeling that this data is important and that it will help them to better target their customers and decrease costs. Although companies will struggle with this integration issue, they will ultimately turn to using advanced analytic techniques for successful and measurable business lift integration.

10. Voice recognition and natural language software will become a major data integration issue – As companies increase the use of voice recognition software, integration of these massive amounts of data will become a challenging issue and they will turn to advanced analytic techniques to solve this issue.

11. Big data analytics will become a priority for companies – As companies acquire more and more data, the issue of how to get value of this data will become a priority for many companies. The difference between having big data and getting the most value of this data will become part of the strategic goals for many companies. Big data with actionable and measurable business insights will go hand-in-hand.

12. Continuous improvement and refreshing of predictive models and business segments – As companies implement predictive models and statistical valid segments within their organizations, the ability to improve and the need to know when and how to automatically refresh these models will become an issue for many IT organizations. Initially, companies will move to offshoring and outsourcing these tasks. In the longer term, IT organizations will look to automate these tasks, and they will incorporate techniques such as embedded analytics.
Innovation is alive and thriving in the area of analytics.  Applications in different industries, cross-utilization of techniques in different domains, and new optimization techniques are always improving.  The future belongs to the young generation and the role of my generation is to provide guidance so their dreams are realized.  Let us roll up our sleeves and work for a better and brighter future.  We are unleashing the power of the mind!

Sunday, February 27, 2011

HR Analytics Conference in San Francisco, March 7-8

I will be the feature speaker on HR Predictive Analytics at the HR Planning and Analysis conference in San Francisco in March 7-8. I will cover predictive analytics in HR, a new management asset metric (the Workforce Turnover Efficiency ratio), an Employee Lifetime Cycle Measurement, and an organizational structure model to adapt HR metrics at the enterprise level.




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