The Future (IS) Worker

Post written by Chris S., Project Manager at Ideaca. Read more about project management on his blog: The Outspoken Data Guy.

If the lines have not already been blurred, they will be…Over the next 10 years business and IS work will undergo a major transformation, largely driven by the Cloud and Data Analytics.

In the next 10 years, internal IS staff will act solely as advisers and managers of cloud services.

As more and more businesses embrace cloud services, IS will be called upon to act as advisers to ensure that these services are managed as efficiently as possible. As a consequence, this will push IS Governance further into the limelight. For years IS has had the notion of charge-back to the business to help manage costs and allocate them to those that use services. This approach has been mired in political push back and logistical challenges around how this would be done in a fair and equitable manner. As we move towards a “Pay for Usage” model in the cloud, these costs will be far easier to allocate back to those that use and hence IS will get a more accurate picture of costs of services and a far better allocation model.

This likely will not sit well with legacy users but the notion of “pay for usage” is so common place with Generation Z that this will be a virtual non issue. With this political hurdle out of the way, the focus can shift to more efficient use of IS resources and to ensure that businesses are getting value.

It is hard to argue with the value of using cloud services. At present there are the usual security and performance questions but over the next few years these concerns will be addresses and we will all have our heads in the clouds.

The new beast - hybrid IS and Business Person

Who is the future (IS) worker? And what skills will they need to bring to the table?

In my opinion the niche where people will have the most success will be with a hybrid of IS and business skills. There is no real debate that the world is increasingly becoming more data driven and the ability to turn data into actionable insights will become more in demand. So what does that mean? It means that workers will need to have 2 very key kills:

a) A deep understanding of the business and b) the ability to analyze data and derive insights.

This phenomenon, coupled with the cloud will allow Business Intelligence services to move closer to the business with IS once again acting as advisers, which is where BI needs to be currently in organizations. Unfortunately it gets stuck into an unnecessary tug of war between IS and the Business.

Bottom line: Business users will have to become more technically savvy as is articulated in Thomas Davenports “Keeping up with the Quants.”

Business Intelligence is weaving its way into our daily lives - it is the age of data.

Building on the above, on a daily basis we are increasingly faced with data that we use to guide our actions, personal or otherwise. Real time traffic signs that tell us how long it takes to get somewhere, integrated budgeting software in our banking site that monitor our daily spending and alert us to certain conditions that we are interested in and feedback about restaurants that we may want to have lunch at. These are just a few examples of where data is used daily to guide our decisions.

 Bottom line: Data and analysis are becoming a way of life and will continue to forge its way into the mainstream.

Big Data: A Mysterious Giant IT Buzzword

Post written by Niaz T., Senior Solution Architect/SAP BW Consultant at Ideaca. Read more about SAP HANA on her blog: Discover In-memory Technology.

In the world of technology there are a hundred definitions for “Big Data.” It seems confusing to come up with a single definition when there is a lack of standard definition. Like many other terms in technology, Big Data has evolved and matured and so has its definition. Depending on who we ask and what industry/business field they’re in, we will get different definitions. Timo Elliott summarized some of the more popular definitions of Big Data in “7 Definitions of Big Data You Should Know About.”

You may be familiar with three “V’s” or the classic 3V model. However, this original definition does not fully describe the benefits of Big Data. Recently, it has been suggested to add 2 more V’s to the list such as Value and Verification or Veracity which are resulted from “Data Management Practices.” As a BI expert who is been involved in Big Data, my approach is to have a practical definition for my clients by emphasizing the main characteristics of data and purpose of Big Data related to each specific area. I like Gartner’s concise definition. Gartner defined Volume, Velocity and Variety characteristics of information assets as not 3 parts but one part of Big Data definition.

Big data is high-volume, high-velocity and high-variety information asset that demands cost-effective, innovative forms of information processing for enhanced insight and decision making. (Gartner’s definition of big data)

The second part of the definition addresses the challenges we face to take the best of infrastructure and technology capabilities. Usually these types of solutions are expensive and clients expect to have cost effective and appropriate solution to answer their requirement. In my opinion this covers the other V which is related to how we implement Data Management Practices in Big Data Architecture Framework and its Lifecycle Model.

The third part covers the most important part and ultimate goal which is Value. Business value is in the insight to their data and to react to this insight to make better decisions. To have a right vision, it’s important to understand, identify and formulate business problems and objectives knowing practical Big Data solutions are feasible but not easy. So when I define Big Data for my clients, I use Gartner’s definition and explain the journey we need to take together to achieve their goal.

In any Big Data project, I start with BDAF or Big Data Architecture Framework which consists of Data Models, Data Lifecycle, Infrastructure, Analytic tools, Application, Management Operation and Security. One of the key components is having high performance computing storage. Since Big Data technologies are evolving and there more options to be considered, I’m focusing on SAP HANA capabilities which enable us to design practical and more cost effective solutions. HANA could be one part of overall Big Data Architecture Framework but it’s the most essential part. The beauty behind SAP HANA is that it is not just a powerhouse Database but it is a development platform to provide real time platform for both analytics and the transactional systems. It enables us to move beyond traditional data warehousing and spending significant time on data extraction and loading. In addition we’re able to take advantage of hybrid processing to design more advance modeling. Another big advantage of HANA is the capability of integrate it with SAP and non-SAP tools.

So, why am I so excited about it? Looking around I see tons of opportunities and brilliant ideas which could get off the ground with some funding. So far, HANA has been more successful in large enterprises with big budgets and larger IT staff. However I’m also interested to encourage medium size enterprises to see the potential of HANA to provide a solution for their problems. The majority of businesses don’t spend their budget to develop a solution. They are eager to pay to solve a particular problem. Now, our challenge as SAP consultants is to help businesses see this potential and how HANA can address their challenges. The good news is SAP supports by providing test environment and development licenses for promising startups.

Got your attention? Well, just to give you a glimpse, take a look at some of the success stories. In addition there are many many other cases if we look around. For instance, these days many applications capture Geo-location data like trucking company, transportation, etc. it means capturing data every 10 seconds or so from every section, every piece of equipment, every location. This could add up to a Petabyte of data! This is an excellent way to bring insight into data and drive intelligence out of it and have it circulated back to scheduling and movement processes. Another example could be companies needing to mine information from social media regarding to their products and connecting this intelligence back to their back end processes to increase customer engagement and satisfaction.

So, do you have any Big Data Challenge? With some funding, we’re able to provide cost effective and practical solution for your challenge to add value to your business.

The data has the answers

Post written by Evan Hu, Co-founder of Ideaca. View his blog here: evanhu.wordpress.com

In a 2001 research report by META Group, Doug Laney laid the seeds of Big Data and defined data growth challenges and opportunities in a “3Vs” model. The elements of this 3Vs model include volume (the sheer, massive amount of data or the “Big” in Big Data), velocity (speed of data processed) and variety (breadth of data types and sources). Roger Magoulas of O’Reilly media popularized the term “Big Data” in 2005 by describing these challenges and opportunities. Presently Gartner defines Big Data as “high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.” Most recently IBM has added a fourth “V,” Veracity, as an “indication of data integrity and the ability for an organization to trust the data and be able to confidently use it to make crucial decisions.”

The volume of data being created in our world today is exploding exponentially. McKinsey’s 2012 paper “Big data: The next frontier for innovation, competition, and productivity” noted that:

• to buy a disk drive that can store all of the world’s music costs $600
• there were 5 billion mobile phones in use in 2010
• over 30 billion pieces of content shared on Facebook every month
• the projected growth in global data generated per year is 40% vs. a 5% growth in global IT spending
• 235 terabytes data was collected by the US Library of Congress by April 2011
• 15 out of 17 sectors in the United States have more data stored per company than the US Library of Congress

IBM has estimated that “Every day, we create 2.5 quintillion bytes (5 Exabyte) of data — so much that 90% of the data in the world today has been created in the last two years alone." In their book “Big Data, A Revolution That Will Transform How We Live, Work, And Think,” Viktor Mayer-Schonberger and Kenneth Cukier state that “In 2013 the amount of stored information in the world is estimated to be around 1,200 Exabytes, of which less than 2 percent is non-digital.” They describe an Exabyte of data if placed on CD-ROMs and stacked up, they would stretch to the moon in five separate piles.

This sheer volume of data presents huge challenges. For time-sensitive processes such as fraud detection, a quick response is critical. How does one find the signal in all that noise? The variety of both structured and unstructured data is ever expanding in forms: numeric file, text documents, audio, video, etc. And last, in a world where 1 in 3 business leaders lack trust in the information they use to make decisions, data veracity is a barrier to taking action.

The solution lays ever more inexpensive and accessible processing power and the nascent science of machine learning. While Abraham Kaplan (1964) principle of the drunkard’s search holds true: “There is the story of a drunkard, searching under a lamp for his house key, which he dropped some distance away. Asked why he didn’t look where he dropped it, he replied ‘It’s lighter here!’” A massive dataset that all has the same bias as a small dataset will only give you a more precise validate of a flawed answer, we are still in early days. Big Data is the opportunity to unlock answers to previously unanswerable questions and to uncover insights unseen. With it are new dangers as the NSA warrantless surveillance controversy clearly exposes.

I have had the privilege of listening to Clayton Christensen speak several times. In particular he has one common through line that stuck with me and forever embedded itself in my consciousness. “I don’t have an opinion. But I have a theory, and I think my theory has an opinion.” I believe the same for Big Data. The data has an opinion, the data has the answers.