What if you are your project’s biggest risk?

Post written by Jason Z., Project Manager at Ideaca. Read more about project management on his blog: Unnatural Leadership.

“A little knowledge is a dangerous thing” – Alexander Pope

While I was studying to become a project manager, I believed what A Guide to the Project Management Body of Knowledge (known as the PMBOK) and my professors had to say as the gospel truth: a project is a project is a project. It didn’t matter that I had no experience building a bridge, planning a wedding, or configuring a database server…I was going to be a professional project manager, and that meant that I could manage anything (so long as I followed the 5 phases of the PMBOK and did everything that the 11 knowledge areas told me to do)!

For a while, that was the case. I made sure that all of my projects had strong technical people that were good communicators so as to provide good estimates, identify risks early and often, and manage the details of the deliverables. I was able to focus on managing at the executive level, facilitating problem resolution, and provide project administration support.

But then it happened – I was assigned a project where I had a little bit of technical knowledge, but not much, and was paired with some intermediate resources. They were technically strong, but were relatively inexperienced in working in a large project setting. At the time, though, I did not know this and just assumed that they were as skilled as every other project team I had worked with in the past. When we sat down to plan, I used the same process as with my other project teams; when we ran status meetings, I used the same process as with other project teams; and when we identified risks, I used the same process as with other project teams. However, development activities continued to miss dates, and my inquiry into what went wrong with the team yielded answers like “we don’t know.”

As a result, I used my fairly limited knowledge of the subject area to help plug the gaps that I saw. When asked questions by the sponsor and subject matter experts, I gave them answers that I believed to be true without consulting the team. When asked questions about the technology by the IT operations team, I gave answers that I had heard given in the past without consulting the team. And then things started to go really wrong. The client kept asking the team about things that I had said, and were told opposite things, the project team kept freezing me out of discussions, and my Program Manager came back to me with feedback that I was about to be fired from my project.

At that point, having a team that was not as strong as my previous teams was not the issue. My assumptions, silo’d decision making due to frustrations, and unfair expectations of the team introduced a myriad of risks, which of course I didn’t capture in the risk register, to the deliverables. These risks almost immediately became issues when I communicated out without consulting the team. I was the issue. I was the project’s biggest risk to scope, schedule, and budget.

So what should I have done?

1. Don’t assume you know everything

Even though I had some experience with the technical area, and was a well seasoned project manager, I should not have assumed that I knew better than the team. It’s ok to say “I don’t know”, so long as you promise to get the answers and follow up.

2. Consider your team’s requirements

Instead of forcing the team through processes that worked well for other teams, I should have considered their requirements in the locus of their experience level. During the “forming” and “storming” phases of team development, I should have been asking questions rather than imposing processes. When I saw a process that did not work, I should not have knee-jerked into command and control mode; rather I should have worked with the team to re-assess.

3. Recognize that you cannot push a rope up a hill

As a project manager, it is your job to facilitate successful project outcomes. Unless you are explicitly performing a specific role on a project team, your only true deliverables are status reports, communications, and facilitated sessions. It is up to your team to deliver the technical content. If there are performance issues, talk to the team members (and then their managers if required). If there are scope concerns, talk to your sponsor. If there are resourcing concerns, talk to your project management office. Do not try to own the issue; try to facilitate resolution.

In the end, I focused heavily on #2 and #3 and struggled with #1 through to project closure. After giving answers for so long, it was hard not to. As far as the project was concerned, after a re-baseline, it was delivered to scope, schedule, and budget constraints.

What type of learning opportunities have you had through projects? How else could a Project Manager be the project’s biggest risk?

The Internet of Things: Our Bright Future or Inevitable Downfall

Post written by Blake W., Management Consultant at Ideaca. Read more on his blog: Blake Watson.

The Internet of Things (IoT), essentially a future-focused concept where everyday devices connect and communicate data in an intelligent fashion is a highly contested topic. Will it mark the beginning of a new era in our civilization or a catastrophic detriment to the world as we know it? Skipping over the possibility of our technology becoming self-aware and “terminating” us, these two polar opposites are often portrayed in the discussion of this topic. This post will broadly summarize the IoT and discuss the positives and negatives in relation to our daily lives.

So what is the IoT? It is a term that has been vaguely used since the 1990’s and has gained traction since its initial public proposal by Kevin Ashton in 1999. It is a term that suggests a heavy increase in device-to-device and device-to-Internet connectivity. By equipping these devices into a worldwide network of miniscule identification devices the IoT could transform our daily lives.

Is this interconnectivity even possible? Simply put, yes. Technology is growing at a rapid pace, confirming Moore’s Law, wherein Gordon E. Moore’s observed that the transistors, and thus the processing power of our devices, double approximately every two years. Although many have debated the staying power of this observation, the exponential potential of this theory is astounding. If the past few decades are any indication, the IoT is a highly probable (and sometimes frightening) reality.

As mentioned before, the IoT could transform life as we know it. A simple scenario: You get home from a busy day at work. Monitors in your home identify you and let you in. Immediately, the room taps into a wealth of your personal information and preferences…climate control, music, lighting, and digital décor. These sensors may even be able to determine what you want for dinner and start preparing it for you based off of what is in your refrigerator. Some of these devices are already available through “smart” technology. Within the next 20, the possibilities are endless.

THE GOOD:

For the individual, the IoT integration arguably increases our standards of living. No longer are we plagued by menial tasks that take up our days. The IoT essentially frees up time and energy that could be better spent productively or recreationally. It doesn’t necessarily mean that as a collective we will be healthier, happier individuals. However, we will have more opportunity to achieve this lifestyle.

From the business perspective, greater analytic capabilities are accessible to management and supervisors. Asset tracking, inventory controls, and financial drilldowns are far more accurate. Location services, automation, and device interconnectivity eliminate a great deal of the “guess-timation” involved in these operations. Sectors such as consulting, financial services, and even health could benefit greatly from these advancements.

Businesses also have access to a huge amount of data. Big Data will be a simple task compared to the vast amount of information that corporations will be able to collect from client usage and habits. We will have to start considering XXXL Data as opposed to Big Data. Billions and even trillions of source data will give business owners the tools to minutely tailor their products and marketing to individuals in the most literal sense of the word.

THE BAD:

The IoT has a dark side to it. Many people feel a sense of unease when they consider the privacy concerns the IoT imposes. If you are the slightest bit afraid of “Big Brother”, then the IoT is not for you. The amount of information that can be collected by governments and corporations through the billions of personal, business, and home devices is astounding. These devices sometimes know more about you than you do.

Another hot topic at the moment is job security; for low income earners, the IoT could make things especially tough. A number of unskilled tasks (and even some higher level analytics) might easily be replaced by a network of devices connected to the IoT.

Another concern is the effect that this new world order may have on our physical health. When all of our devices are communicating, making decisions, and essentially managing our lives for us, the opportunity to become complacent with that level of comfort is tempting. The World Health Organization estimates that over 65% of the world’s population currently lives in countries where obesity kills more people than being underweight. This upward trend isn’t expected to slow down anytime soon, especially with the continued introduction of technology that makes our lives even easier.

OVERALL:

Although there are many negatives that could affect reception of the IoT, it is my belief that reactions will be mostly positive. Although some aspects of these new technologies are to our detriment, there is a great deal of benefit that can come from an increased awareness of the IoT. As younger generations are brought up with modern day technologies, we may begin to see society move away from a privacy-centric culture. This shift would effectively reduce public outcry for greater privacy rights in this changing environment.

Although malicious Internet hackers and identity thieves may pry on the wealth of information available, we are facing no greater threat in the future than we are now. Security safeguards are in place and continue to develop. At the same time, data thieves are growing their methods for subverting such safeguards. This struggle for access and security will continue with no real definitive end in sight. Therefore, data security should not be considered within scope of this discussion.

The main problem moving towards our ideal vision of the IoT is that it will depend heavily upon data sharing and corporate cooperation. Think of all of the different products in your home… appliances, personal devices, clothing, and climate control. Seamless integration is necessary to ensure the IoT is able to function effectively in your daily life. If your devices cannot access the personal information it needs it will not function properly. It is hard to imagine companies (For example, Apple and Samsung) sharing customer data and integrating their products out of the box. Cooperation will be mandatory and it is something that companies will have to be overcome as we move forward with the IoT.

As briefly mentioned above, consulting firms such as Ideaca Knowledge Services will benefit greatly from the wealth of information available to them. The greater availability of information resources will allow their consultants to better assess client needs. Having clear needs from both the client and end consumer is essential. Better data means better solutions and ultimately better deliverables.

Whatever your take is on the IoT, there are a lot of variables to take into account. The changes that it will bring to our society are truly hard to imagine 20 years out. For good or for bad, the world is growing and developing towards the IoT. Will we try to hold on to our present state of technology or embrace these changes when they come?

Advice For Junior PMs – Do Not Be Afraid To Communicate Risks That Have Become Issues

Post written by Jason Z., Project Manager at Ideaca. Read more about project management on his blog: Unnatural Leadership.

You saw it coming. You captured it in the risk register, reviewed the mitigation plan with your team and had them alter some of the response strategy. It’s even part of your status report. And then the risk event occurred, but you didn’t know how to have the conversation with your project sponsor.

I understand. I’ve had some awkward conversations myself. It can be intimidating to walk into your sponsor’s office for a status update and having to try to (not so subtly) clearly say that you will need more money, time, or resources to properly respond to the risk event and keep the project on track.

So how should you handle it? What should you have done?

Before the project begins, provide your sponsor some context of the situation. Not all status meetings will be positive progress updates, but not all status meetings will require intervention. You are there to be honest and to steward the process, not sugar coat things. Besides, when it comes time for the risk event to occur, you have identified it and have a response plan.

If you are stuck, and feel like you need to save your skin during the project – don’t panic. If your sponsor has even one more grey hair then you, this is not the first time they have had to have this type of conversation. Be honest, be confident, and have your facts in order. You have identified the risk, and you have a response plan.

For both circumstances – ensure that at subsequent status meetings, you are reviewing risks that are relevant for your current project phase.

Have you ever had a really awkward conversation about risks with your sponsor? How did you handle it?

Visibility in EHS

Post written by Peter T., a Management Consultant at Ideaca. Read more on his blog: Visibility.

You might ask yourself, why the focus on Environment, Health & Safety (EHS)? Well, besides the fact that there is a lot of attention on this specific operational area, I feel the industry as a whole has created such a buzz about the benefits of an effective EHS Management System (EHSMS), that organizations are looking at implementing an EHSMS without clearly thinking about the overall value drivers for doing this.

While operationally most organizations have varying EHS needs, the following requirements are often the same: ensuring that they minimize operational risks, sustain and improve the safety record of the workplace, maintain and advance environmental management efficiency, and comply with regulatory mandates. Meeting these requirements however involves designing cohesive EHS processes, a systems integration approach, cooperation across the enterprise, the ability to consolidate information, and a supportive management team that will ensure roadblocks are eliminated or minimized. In essence, a true EHS Management System is beneficial.

However, there are significant challenges that exist when it comes to designing an effective and complete EHS solution. These core challenges are related to data and the specific industry sector. The way data is being managed, collected and utilized is an important component. A common complaint is that there is too much data and not enough information. Individuals seem to be spending more time organizing and finding data than analyzing it. In most cases data management techniques within organizations are not integrated, coming in various forms such as paper files, countless reports, and spreadsheets.

By the time data comes into the operation it is already out of date and not current. It often takes more time to reinterpret and merge current data into existing reports than to redo all the reports over again. Also, tedious ways of doing things and the lack of resources needed to truly re-engineer business processes leaves an operational gap with no big picture of organizational conditions. Departments often work in silos, data and knowledge is not shared across the organization, which leads to inconsistent EHS event handling. These business process, technology and data-specific challenges are further magnified by additional industry related conditions that usually prevent already resource drained organizations from engaging in optimization and improvement initiatives.

So with all these challenges, how do you setup an effective EHS Management System? The key is to identify the core business value drivers of the organization, and ensure that all your EHS initiatives drive to meet these values. These tangible business values can include: increase revenue, decrease costs, operational efficiencies, increase capacity, etc. Intangible value drivers however are significantly harder to prove and require a larger effort to gauge their business value.  These include: expertise, company reputation, employee morale, compliance risk, etc. It is not hard to address a tangible profitability business value.  The value gained from the purchase of a new piece of equipment that will improve operations can easily be determined. Reputation or compliance risks, however, are important non-tangible value drivers. A non-compliance incident can easily push profitability value initiatives to the bottom of the list.

By implementing an effective EHSMS, we can easily monitor and measure all intangible value drivers identified.

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.

Project Management isn’t just for IT or Engineering anymore

Post written by Jason Z., Project Manager at Ideaca. Read more about project management on his blog: Unnatural Leadership.

As part of this month’s Ideaca blogging network challenge, we were tasked with discussing our thoughts on Emerging Practices.

One of my favorite quotes to reference from the The Project Management Body of Knowledge (PMBOK, pronounced pemmmmbock) is “As project management is a critical strategic discipline, the project manager becomes the link between the strategy and the team. Projects are essential to the growth and survival of organizations.” So, while operational duties are of very high importance to maintaining the forward momentum and revenue generation for a company, projects are strategic and help organizations react to changes in the external environment that may slow forward momentum and/or impair revenue generation.

Taking this as rote, one Emerging Practice that I am pleased to see is that more industries and functions – outside of Engineering and IT – are recognizing the need for project management:

• In the legal sector, project management has been a fairly hot topic since approximately 2010. It’s also getting more attention in law schools.
• Digital Ad Agencies position project managers to help their customers realize a marketing vision.
• The Human Resources designation – Certified Human Resources Professional (CHRP) - now has a focus on Project Management.
• Investment Banks have started hiring project managers to help manage deals.

So what does this mean for Project Management as a career? It means that effective Project Management is not just for IT and Engineering anymore. In fact, the rest of the organization is going to have to contend with:

• Increased workloads for Subject Matter Experts. If you know the organizational area, you must know how to manage the project to do something in this organizational area.
• Gone are the days of black box projects – clients are demanding more visibility into what is being delivered, how it’s being delivered, and how delivery is progressing.
• Organizations are demanding value from their staff’s time - projects are going to have to deliver more than a “thing.”
• Successfully implementing changes in an organization can no longer be ad-hoc, and to a lesser extent grassroots. Rather, efforts must be controlled activities.

This is both amazing, and troubling at the same time. It’s amazing because having proper control, visibility, and communication for organizations can return recognizable and material value. It’s troubling though, as many organizations may start expecting their people to be expert project managers without any proper training or experience (this link is a great discussion on LinkedIn, by the way).

If your organization is transitioning to more of a project focus, and you don’t have the time or desire to become a fully trained PMP, there are a number of ways to get up to speed on how to be effective:

• Hire a dedicated (or shared) Project Manager – This person should be able to apply project management best practices while you are focused on the subject matter at hand. If your department doesn't have the budget or enough work for a full time Project Manager, share the PM (both cost and time) with a different department.
• Mentoring – Junior PMs will often work with Senior PMs for mentoring, so why not do the same? Your company should have a PM for you to reach out to, or you can contact someone in your local PMI chapter.
• Training – Most colleges offer introductory PM training. In exchange for some of your time over a couple of weeks, you can get trained up on how to run a small project effectively.
• Reading – There are many great books available. One that I recommend is Project Management Lite: Just Enough to get the Job Done…Nothing more. Another, more detailed, is the big bible - Rita Mulcahy’s guide to passing the PMP on your first try. You don’t have to attempt the PMP, you just need to read this book.

Has your organization made the transition to more project-based initiatives?  How has it impacted you?  What have you learned?

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.

Web Analytics supplants Business Intelligence?

Post written by Wade W., BI Consultant at Ideaca. Read more about BI on his blog: Pragmatic Business Intelligence. 

In reading industry material, I recently came across a statement that can only be, in my opinion, the product of tunnel vision. It was one of the most short-sighted and fundamentally erroneous statements I have seen in some time. Analytics

“At the 2005 Emetrics Summit in London, Bob Chatham from Forrester Research described what it means to be the key. He told the assemblage that we are the leaders of tomorrow – and he wasn’t just preaching to the choir to curry favor – he made sense. Chatham told us that “web analytics” would eventually be subsumed into business intelligence, thereby changing the game. Instead of giant data warehouses being sifted in hopes of finding patterns, it would be the likes of us web analysts in charge.” (Jim Sterne, Target Marketing of Santa Barbara, edited by Erika Lindroth, The Weather Channel Interactive, Inc.)

I agree that web analytics will be (and is starting to be) subsumed into BI. However, I question the sentiment that “giant data warehouses [are] being sifted in hopes of finding patterns” and that Web Analytics would “change the game.” Is Web Analytics really going to revolutionize the art of Business Intelligence so significantly? The implication in this quote is that somehow traditional Business Intelligence is somehow inferior to Web Analytics.

I think this is an excellent example of what happens when someone seen as a leader in a field becomes too engrossed in what he is evangelizing…he becomes blind to the bigger picture.

The fact is that Web Analytics, though impressive in its power to aggregate user behaviour and use this to optimize website profitability, it is by nature a limited field. You are able to track user behaviour – generally anonymous at that – through a single customer-facing channel. Web Analytics is Business Intelligence, that only leverages a single source.

“Giant Data Warehouses,” however, are repositories of cross-organizational data, in most cases that extracted from up to hundreds of disparate data sources – Legacy systems, ERPs, CRM systems, finance, operations, HR, desktop apps, web services, external sources – and loaded into a database of a very specific architectural design optimized to return query results on the huge amounts of data very quickly.

Further, this data will certainly have different meanings across and organization – what does “Customer” mean? How do we define this? Part of the process is to work closely with the business to define common business definitions of business entities…so all that data of all that depth and breadth and richness is (should be….) based on common meanings that have been agreed to by key stakeholders. We can mine the data to identify unknown customer segments. We can do Predictive Modeling. Starting with a business mentality, there is the potential to leverage some powerful Business Intelligence.

But I do agree that Web-sourced data represents a substantial opportunity. We can take those Web-specific data sources that power our Web Analytics Apps, and add that to the existing Data Warehouse, passing through the same business rules to ensure heterogeneous data has a single meaning. Now we are talking organization wide, multi-source Business Intellligence.  Plug BI’s powerful analytical tools into our database, and with some targeted, business-driven KPI’s, and we have another, very powerful means of driving profitability

Web Analytics could be said to be proportionally less expensive than traditional BI – same basic cost range for the analytics tool, but less demand for investment in multiple software licenses from different vendors (possibly), less complex data massage (or not…) and shorter time to implement.  And that in itself is a strong argument in favour of Web Analytics – reduced time to market.  However, you won’t have the spectrum of information you have in a well-implemented Data Warehouse.

I believe that Web Analytics is a complement to BI. It can be integrated into a dashboard, or can stand alone to guide developers and webmasters to optimize content. It does have an effect on our database architecture – we must adapt the design of the database to integrate web data. But does it “change the game”? No – it  makes it more interesting. And as a Business Intelligence professional, I welcome another tool that will add value to my service offering and to my clients.

Wade Walker

Ideaca To Become Hitachi Solutions Canada!

Same people, same values, different name

On December 2, 2013, Ideaca was officially acquired by Hitachi Solutions and will become “Hitachi Solutions Canada.” As Hitachi Solutions Canada, we look forward to providing our customers with a wider array of proven industry solutions and access to global resources.

"Ideaca is extremely pleased to join a global brand with the outstanding caliber of Hitachi Solutions,” said Muneer Hirji, newly appointed president of Hitachi Solutions Canada.

“We look forward to integrating our experience and strengthening our synergies to bring great industry-focused solutions to both regionally-focused and multinational companies throughout Canada. With its long history of technology excellence, industry leadership and employee-driven culture, Hitachi Solutions will make a great home for our employees.”

Our name may be changing, but our people and our values will stay the same!

Click here to read more.

How Technology Changes Us: Canada In 10 Years

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.

The theme of the Ideaca Blogging Network for the month of August is a very interesting subject. Certainly, technology changes the way we do things on a daily basis. Not only in Canada, but also globally. There could be some specific cases in Canada, such as Green technology to combat climate change. However, most of the technology changes impact us globally, specially in more advanced countries.

The first thing that it comes to my mind is technology will enable us to convert Zettaflood (10 to the 21st bits, or a thousand exabytes) of data in to meaningful information which we are very dependent. Like it or not, business intelligence already has an increasingly important part of our life. The challenge will be how to deal with the explosion of data coming from all types of gadgets and smart technologies because value-based intelligent information helps us to get things faster, better and easier. The speed of rising adoption of cloud, mobile, real-time applications and social technologies and exponential data growth is a big challenge of staying current.

How technology solves this challenge? Some of the biggest improvements have been around networking. We will be able to move more data faster from many sources and applications to where is needed. We won’t have any restriction in terms of capacity, scalability and processing speed. Organizations will be able to leverage the three “V’s”, volume, variety and velocity, of data to augment the value of data for their decision making. Powerful in-memory technology such as SAP HANA enable us to design complex predicative and preventative models for all type of data from structured and unstructured like audio and video files. Next generation of data visualization and intelligent reporting tools empower users to slice and dice information any way it is demanded. We will be able to tell stories with data by connecting millions of data points to get a bigger picture. Big data will change our world and it will blow our mind by providing us tons of opportunities. It will make our word smaller and we will be all connected.

I believe in the next 10 years, another significant change will be human and machines interaction. It seems that human interaction, communication and relationships will be more efficient, faster and stronger through smart technologies. Also, we will be able to have better understanding of machine behaviors and machines will have a better understanding of ours. Ideally humans and machines will work alongside each other and hopefully not replacing human with machines. Although there are ongoing developments and opportunities to replace human with machines, it’s required to consider all potentials dangers and associated risks.

Personally, I’m very excited to see how technology will enable us to access information easily, increase our potential and creativity, improve our lifestyle and promise of longevity, and improve communication and social networking. On the other hand, I believe we need to keep things in balance with respect to human identity and our social behavior. For example, neuroscientists are concerned about how modern technology is making us not use our brains to their full potentials. Based on the evidence, loneliness and depression is increasing and people are less happy in modern society. It’s been observed that the newer generation—equipped with all kinds of smart technology—is less effective in terms of communication skills and human interaction.

The bottom line is we use technology to change the world to suit us better. The important thing is to control it so it doesn’t destroy human intelligence and social interaction. For instance, it would be great to get a relaxing massage after a long day by a smart robot that has already taken care of the house chores. However, nothing will replace a nice face to face conversation with your favorite person or a warm friendly hug to someone you care about. I don’t think we could ever replace our human connection with human-robot connection.

Is Big Data Only About…Big Data?

Post written by Wade W., BI Consultant at Ideaca. Read more about BI on his blog: Pragmatic Business Intelligence.  

If nothing else, IT is all about buzzwords, and “Big Data” is one of the new arrivals to the party.
It is, however, a descriptive one. “Big Data” evokes images of enormous relational databases, providing analytical (or operational) reporting.

Big Data is not only about size however. Rather, it refers to attributes of the data that together challenge the constraints of a business need or system to respond to it. Those attributes can include any or all of attributes such as size/volume (of data), speed (of generation), and number and variety of systems or applications that simultaneously generate data. Another thing that is unique about Big Data is how it varies in structure. Elements of “structure” would include the diversity of its generation (eg. Social media, video, images, manual text, automatically generated data, such as a weather forecast, etc), information interconnectedness and interactivity.

I heard somewhere a thumbnail statistic that 80% of data in companies is unstructured or semi-structured. Just to clarify the meanings of those terms, an unstructured data artifact would be a document, an email, a video or audio clip. A semi-structured data artifact would include data that does not conform to the norms of structured data but contains markers or tags that enforce some kind of loose (or not so loose) structure. XML documents would be an example of semi-structured data.  Tagged documents in a Knowledge Management system would also fit into this definition.

Structured data is what we would find in any database – a Data Model has been defined and the data is physically arranged within this model into tables. The data in these tables is described with metadata (i.e. data types (such as “character”) and the maximum length of that data (number of bytes)).

The methods of data creation are multiplying and the velocity of its creation are increasing. And that, in itself is a complicating factor. Some analysts (IDC, for example), predict that the Digital Universe -  that is, the world’s data – will increase by 50x by 2020. There will be in the same period, a growing shortage of storage, which will drive investment in the cloud as both individuals and corporations look for scalable, ubiquitously accessible, lower-cost and environmental data storage options. In addition, the same study predicts that of all that data, unstructured data, especially video, will account for 90% of that data.

There is also an important historical dimension to Big Data. For decades, companies have been hoarding structured, semi-structured and unstructured data in hopes of one day being able to extract value from it at some point in the future.

A large percentage of all this data will come with a wrapper of automatically generated Metadata – that is, (as indicated above), data about (or that describes) that data. A practical example could be the generation of a data artifact coming wrapped with metadata from those GPS enabled, media rich, socially linked mobile devices we all carry with us that transparently capture location, GPS coordinates, time, weather conditions and a plethora of other data elements when you click that holiday photo with your mobile phone. IDC predicts that such metadata is growing twice as fast as data.

It is clear from the last three paragraphs that Big Data describes explosive growth in data and metadata and an equally explosive opportunity to capture, tame and corral that data to extract value from it.

So the case has been made that we have a lot of data today and we will have even way more tomorrow, but should your organization be investing in Big Data today?

In a sense, probably you already are. Enterprise Business Intelligence environments lay a solid foundation for the next phase of Big Data. EBI is an earlier iteration of Big Data and, married to tools such as Hadoop and NoSQL databases for example, enable a natural evolutionary growth curve to your mastery of your information ecosystem.

Big Data has a requirement for a new way of thinking, new tools, clustered commodity hardware and probably, substantial investment. It comes down to your business, and if there is a clear value-based case to present that data to your company’s brainpower. The actual needs for this will be radically different depending on your industry. Oil and Gas may be interested in leveraging real time alerts in wellhead data or analyzing petabyte seismic datasets.  Packaged Goods multinationals may be interested in monitoring and engaging advocates, detractors and influencers across multiple Social Media platforms, mining and understanding sentiment and identifying problem areas in real time in order to identify opportunity or identify and avert potential brand-damaging events. Financial institutions may be interested in monitoring international money traffic to identify fraud or illegal activity. Government entities may mine extremist forums, or other unstructured data traffic to identify national threats.

Big Data can serve these needs in real time, enabling rapid (or even automated) response to flagged events. Whether it is a fit for your organization today would be determined through viewing your industry and business through a critical lens on your current Information Intelligence maturity, a strategic assessment of the data and information assets currently owned or available to your organization, and a prioritization of potential initiatives. How much data you harness and convert into information should be a key outcome required from this exercise. The opportunities are legion, but initiatives should have clear objectives and success metrics understood prior to a project kickoff.
Whether it is today or tomorrow, Big Data is becoming mainstream through necessity. Whether that is a road your organization wants, or needs to drive today, is something all medium and large organizations should be considering now.

What are your thoughts on Big Data? Is your organization currently considering Big Data as a strategic imitative or Proof of Concept?

Project Management and Big Data – as a project

Post written by Jason Z., Project Manager at Ideaca. Read more about project management on his blog: Unnatural Leadership.

As part of this month’s Ideaca blogging network challenge, we were tasked with discussing our thoughts on Big Data.

This is going to be a 2 part post:

• The first part will cover how you, as a project manager, should approach a project that carries the mantle of “Big Data.”
• The second part will cover how you, as someone in a Project/Program Management Office, can use Big Data without getting snookered by the hype.

Part 1 – So you’ve been asked to “implement Big Data”… what now?

Defining Your Terms
I am going to assume that you – like me – tend to be baffled by the marketing speak until you can speak with someone intelligently about a topic. In the case of Big Data, I have heard a few definitions. The one that seems to stick the most for me is the one from Wikipedia:

• Data sets that are too big for traditional database management systems to handle
• Data sets that comprise information from multiple sources to try to infer correlation

Sounds easy enough, right?
Where it starts to get complicated (thanks Wade!) is when you try to integrate “unstructured and semi-structured data with our 'traditional' structured data.”

You will never “implement Big Data”
When it comes to Big Data, you do not implement it. You may be implementing a technology to support the analysis, but you will never actually implement this “thing.” A project of this sort relies on understanding the user requirements, selecting the right technology, and taking an exploratory approach when developing reporting capabilities.

Understanding the User Requirements
In the case of a new process and technology, such as this, your user requirements may be fairly light. "We want to correlate information from disparate sources to identify predictive trends” or “I don’t know – but I really want some cool looking reports” may be common lines that you hear. Like all projects, the user requirements are your definition of success. Because “Big Data” is still a technology in the exploratory stage, though, expecting detailed requirements may be the wrong sorts of requirements. The ones that you should be really focused on are the data sources and ensuring that the information being presented is right.

To wit, if I were to ask you to present the information on the average CEO compensation for the top 50 companies in North America, how would you start? How would you define the Top 50?  By Market Capitalization? By Environmental Performance? By Stock Price? By Revenue? What about getting access to private company information? All of the sudden, a fairly simple question about the average CEO compensation gets a little more complex.

The same will be true of your Big Data project. Start by understanding that to present the information your users want, you will either have to ask a whole lot of detailed questions, or provide a platform to enable them to answer their own questions.

Understanding the available technology
As Project Managers, we know that when we are asked to Implement something, it’s never that simple. Understanding what the technology can and cannot do is critical to ensuring that your project can meet the user’s definition of success.

One might want to satisfy the guiding principles of a company’s Enterprise Architecture. A quick scan of the landscape will reveal that tools like SAP HANA, Oracle’s Exadata, and Amazon’s AWS can all fulfill the technology requirements quite nicely and potentially support a company’s Enterprise Architecture. However, since this is a new application of technology, fulfillment of requirements needs to trump Enterprise Architecture.

Take an Exploratory and Iterative Approach to reporting
Some organizations will judge success of your project by its ability to deliver a load of reports. If this sounds like your organization, be realistic as to what can be delivered. Deliver a robust and reliable dataset, some transactional reports, and one report that really helps demonstrate the art of the possible.

Smarter organizations will judge the success of your project by its ability to deliver analytic capabilities to the user base. The robust and reliable dataset is still mandatory, but the ability for users to generate their own reports will satisfy all of the “what about …?” requirements that would blow your project budget and schedule out of the water.

In the end… it’s the people that matter
If we believe all of the marketing hype, Big Data will help us explore all the myriad of ways our world is constructed. But from the perspective of a Big Data as a project, an empowered user base will produce much more value than some canned reports.

Have you been asked to “implement big data”?
If so, what did your project look like? Let me know in the comments down below. Stay tuned for another post on making the most of Big Data in a PMO.


Special thanks to Wade Walker and Chris Sorensen for keeping me honest with this post.

Just Imagine...

Post written by Chris S., BI Consultant at Ideaca. Read more about BI on his blog: The Outspoken Data Guy.

For quite some time I have been imagining what the possibilities of Big Data might be. I am certainly no expert in the area but being the data guy that I am, I often wonder what might be able to be done with data that may be being collected at any point in time. Face it, we are so connected now that our every move generates some form of data and often multiple pieces of it.

For example, if a marketer wanted to know everything about Chris Sorensen in a given day, chances are that most of that data is logged somewhere. What time I leave my house is available via my cell phone, my driving directions and speed are also available there as well. When I sit on the train I surf the web, send emails and organize my task list, all of these actions generate recorded data. What time I log into work, how often I am active on my computer and what I am do all day long is logged. Where I shop, what I buy (if I have a rewards card) is all tracked. My Facebook views, tweets all contain things that could be used to build a personality model of myself and my habits.

It is not really that big of stretch to think that this data could be used in one gigantic model to predict my next move and perhaps even entice me to make a different one. Maybe instead of stopping at Home Depot to get my painting supplies, an app could suggest the best place for me to go based on what I am doing. Sound like a stretch? Not really…Think about the labor that gold miners went through just to get a few stones. Now gigantic machinery does the same thing. The same thing is happening with Big Data where machines are able to gather information from a variety of sources and store large volumes of it in order to form predictive models. We are only at the tip of the iceberg but just imagine what the possibilities might be

The importance of a shared vision

Post written by Jason Z., Project Manager at Ideaca. Read more about project management on his blog: Unnatural Leadership.

In a post from my series “Advice for Junior PMs," I touched on the concept of saying what you mean when working with your project team. The same concept should be applied when communicating outside of your project team.

There’s a fairly common graphic that gets passed around IT departments, and it’s somewhat self-deprecating. It shows that project teams tend to not understand what the customer needs – which is endemic of lacking a shared vision.

This graphic makes me cringe every time I see it.

As we all know, a project is a temporary group activity designed to produce a unique product, service or result. However, more often than not, project teams take an “I know best” view of the world when designing solutions for their customer.

A strong project manager will not only sit with their customer to understand what is required, but will bring the whole project team along to understand as well. We all have our own perceptions and filters, and as a result may play broken telephone.

At this point, you may be asking if a shared vision is different from the project scope statement. It is, in that the shared vision is what the customer will see as the product, service, or result of the project, whereas the project scope is everything that will be delivered (including training, documentation, organizational change management).

To create a shared vision of what the project will produce (be it a unique product, service, or result):

1. Bring everyone to the table to ensure open communication

1. Define what is to be produced in simple language – do not say “we are going to produce a tree swing,” and leave it there, say “we are going to produce a tree swing, which is comprised of a tire hanging from a sturdy branch of a large oak tree by a piece of polyester rope.”

1. Involve the customer in design meetings. Subject Matter Experts (SMEs) should definitely lead, but should be eliciting feedback so that the customer’s requirements are re-confirmed by the team.

1. Revisit the shared vision often. Ask your customer at difference acceptance testing points if what is being developed meets the shared vision.

Most importantly, communicate the shared vision often. Use it as the first line in your status reports, use it as part of your elevator speech, and when people ask you what you are working on, relay your project’s shared vision.

What are your tips for creating a shared vision? What have you seen work well? Do you have any stories of spectacular failures? Share your tips and stories below!

Standards = Starting Point

 Post written by Wade W., BI Consultant at Ideaca. Read more about BI on his blog: Pragmatic Business Intelligence.  

In a data migration project, standards are synonymous with quality.

Every developer has a different philosophy of what works. Many say that it is easier to develop with “what I know," which sounds a lot like “quick and dirty."

The definition of, or existence of Standards of Development and Naming Conventions provide guidelines within which developers should be expected to work. Without these, your environment quickly becomes rife with development packages, interfaces and jobs with different naming conventions, different approaches and widely varying levels of development quality.

I think it is common that, lacking a mentor or some kind of guidance, developers new to data migration start the same way – monster jobs, lack of flexibility, lack of clarity…and lack of documentation. Result: effectively, unmaintainable, throw-away jobs.

The good news is, as discussed, there is a remedy: Take the time to define standards, or work with a supplier who uses a proven methodology based on established standards and quality-centric processes…ideally processes that can be templated and reused.

Re-usability of processes (i.e. “templates") should be your objective. Ensure that in your environment, your team lead is responsible to establish a set of skeleton templates (say 5-10?) that 95% of all your data migration mappings can be based on. “Skeleton” templates means that they are pre-populated with the parameters (that’s “placeholders” for the project-specific values) – these skeleton templates contain no table structure information – just as much development that can be reused in all cases.

Once you have this in place, you can quantifiably calculate substantial cost savings just from having these templates in place… from every project.

Really.

Sliced or Shaved? Avoiding spreading your BI team too thin

 Post written by Chris S., BI Consultant at Ideaca. Read more about BI on his blog: The Outspoken Data Guy.

As a consultant with a background in Agile, I often get questions about how Agile can be used to solve certain problems that people are having with their Business Intelligence Programs.

I recently sat with a client to listen to some of the issues that they are currently having with their BI program. One of the biggest issues that this client is facing is what I would classify as a simple supply and demand problem. Basically their team of around 8 people cannot keep up with the demands of developing and sustaining their BI/DW environment in what is a large organization. The main question for me was could Agile help solve this problem. In my experience, Agile cannot solve the problem directly but it can be used to highlight the root cause.

This is a very common problem that BI programs face. It is the fact that teams are often small relative to the size of an organization and are also too small to manage the tasks that they need to perform to grow and maintain a BI portfolio. And in certain circumstances it is compounded by the fact that teams are often staffed with the wrong skills sets needed to grow and manage a BI offering.

So how can Agile help?

With proper tracking and monitoring of what the team does on a daily basis, teams can begin to gather data on what types of work the team is doing on a daily basis. What we often find is that at a certain point new development will stop coming from small teams charged with both the development and sustainment of a program as they cannot keep up with both. The ironic thing is that most BI managers have no real data to back this up. So taking advantage of some of the rigor around agile in terms of tracking what is done on a daily basis and how slowly new work burns down, one can begin to understand and report better on how time is spent and in fact how little time is available to delivering new functionality.

Social Analytics meet Business Intelligence

 Post written by Wade W., BI Consultant at Ideaca. Read more about BI on his blog: Pragmatic Business Intelligence.

If your company is a well-known brand, somebody, somewhere is publicly talking about it. Right now. It may be on your own Social channels, in an Internet forum, a blog or other user-generated content site. Social Media Monitoring, which put simply is keeping a constant eye on Social sites including Twitter, Facebook and hundreds of other platforms to monitor what is being said about your brand, has become a necessity for most large organizations, and it is an art and a science to manage this well.  Manage it badly, and you can have a catastrophic image issue (i.e. the 2010 Nestle Palm Oil debacle on Nestle’s own  Facebook page).  Handle it well, and you can cement a solid relationship with existing clients and convert new clients to your brand (i.e. HP’s little-known but truly brilliant efforts to provide temporary replacement HP hardware to certain individual users on social platforms complaining of broken computers).

From a commercial aspect, companies are increasingly looking at Social Media to contribute to driving revenue, largely through lofty concepts such as “engagement” and “conversion.” Social is unique in not only the speed of the communication, but also the intimate nature of content.  In addition, and importantly, what companies must understand is that in the Social realm, the customer controls the conversation. The implication here is a fundamental paradigm shift for Customer Relationship Management and Marketing, to understand the customer on a personal level, and to handle – with great sensitivity – both the positive and negative sentiment expressed on Social platforms.

(Social) Business Intelligence
A growing and compelling new flavor of Business Intelligence is attempting to tap into the unstructured content on social platforms and attempt to structure that data into a format that can be analyzed and mined using new methods such as Sentiment Analysis, which measures the aggregate sentiment across user posted content. Social Business Intelligence uniquely sits in the convergence of Knowledge Management, Social Media Monitoring, Collaboration, Social Networking, Analytics , Customer Relationship Management (CRM) and Business Intelligence (BI).

Social Business Intelligence is at a unique convergence point between several key technologies.

First, there is an important roadblock to get out of the way. Today there are a selection of tools to do everything I am discussing below in one way or another.  With a simple sentence I have rendered technology irrelevant for the purposes of this blog. So let’s focus on what Social BI is, how it is done and what it means because that is what is important to business.

I’m not really a catch-word kind of guy, but this is Big Data in its truest form. There are thousands of platforms and sites, of course, but if we only talk about  the current Big Guys (Facebook, Twitter and Foursquare for example), this would add up to billions or trillions of conversation segments over a given  (even conservative) time horizon. To put this in context: that customer data warehouse you have built over all these years probably doesn’t come close…

Social Business Intelligence offers both Internal and External Opportunity
There are both internal and external opportunities to be realized through Social Media Business Intelligence, and many tools are evolving to support these, some even going so far as to adopt a “Facebook-like” or “Twitter-like” interface, mimicking social interaction and Social Networking site features.

Social Business Intelligence applied internally to an organization could be termed Social Collaboration. For example, certain tools might feature collaborative review where colleagues can ask questions and link those answers to specific reports, or collaboratively comment and markup objects such as Business Intelligence ad-hoc analytics,  graphs or reports. This functionality to comment in real time on powerful business intelligence (even if it is only based on Traditional data sources that exclude Social Media data) has the potential to add value to interpretation of the reports that companies produce and use today to base key decisions upon, thereby potentially improving decisions made from today’s Decision Support Systems. Many traditional software vendors already have adopted such functionality.

Of course, where Social Business Intelligence as a disruptive technology becomes particularly interesting is when we start gathering and analyzing that unstructured user-generated content, or even more compelling, when we combine it with our existing “traditional” Enterprise Analytics environments. This empowers organizations to produce new innovative products that target user segments more accurately and respond better to customer support or relationship development opportunities. The value of Social Business Intelligence is not really “about” the frequency of words and phrases users post on social platforms. The value is in segmenting, categorizing, mining and understanding the aggregate of the users’ behavior, and the sentiment of those posts across products, segments and channels.

Social Media has its own unique segments, which include Employees, Partners, Influencers, Detractors and Advocates. We can analyze social network traffic, understand and identify our segments, and tailor personalized/semi-personalized interaction to individuals or one of these segments,  flagging key comments, monitoring Likes, +1’s, trending subjects and use of hashtags, enabling rapid and targeted response to user comments to avert public relations crisis, measure success of our Social Marketing programs or capitalize on new opportunities.

It’s all about the conversation. And you don’t control it.
Again, companies need to understand that the customer controls the conversation. However, the tools exist that can arrange and present structured knowledge from unstructured noise, providing key information input to areas such as Marketing and Manufacturing to be responsive and agile, acting on data that correlates highly to real-life fact.

At its root, Social Media is about the conversation. This implies new requirements for how to manage our link to the customer, and how to most effectively target and market to them. Increasingly, consumers are mistrustful of the marketing messages and advertising. They are more likely to find more relevance and see more value in the reviews and purchasing of their friends and peers.

Social Business Intelligence in Practice
I thought to finish, I would provide two examples that support the claim that through mining user-generated content, we can correlate with very high level of confidence, to known and validated facts.

Google Flu Trends
An example of single-source user generated content analysis is  Google Flu Trends.  Google has been analyzing aggregated web search terms to see if it is possible to correlate geographic frequency of user search terms on Google’s search engine to real data on flu epidemics.

While I recognize this is not Social Media  per se, this example is very relevant to the argument that user-generated content can be tied to sentiment and can also be used as a predictor for future events, when we clearly understand and define the objective, then identify and measure indicators supporting that objective.

Google’s site http://www.google.org/flutrends/ca/#CA provides up-to-current-day results to allow tracking of current and developing flu incidents and epidemics. In addition, on this site there are historical graphs over a multi-year period for regions around the globe that prove, using known, validated historical data, that reality and future events can indisputably be predicted by user-generated content.

United Nations Global Pulse.
Between 2009 and 2011, the United Nations and SAS studied how Social Media and other user-generated content from public internet sources such as blogs, Internet forums, and news published in Ireland and the US could be correlated to validated statistics and leveraged as a compliment and an qualitative indicator of real-life events.

For Global Pulse, the focus was on employment status. To summarize from the document found at http://www.sas.com/resources/asset/un-global-pulse.pdf,  the UN identified keywords indicating changes in employment status (i.e.”fired”), level of anxiety (i.e.  “depressed”) or economic indicators (i.e. loss of housing or auto repossession, cancellation of vacations) in order to  monitor sentiment.  The results were astonishing. The analysis of sentiment allowed them to predict  increases in unemployment as much as four months in advance of an uptick in unemployment claims with a 90-95% level of confidence. Further, they were able to predict precisely, again with a 90-95% confidence, how long after an uptick in unemployment that there would be an increase in clear economic indicators in the form of talk of loss of, or negative changes to housing, changes of transport method or cancellation of travel plans.

These two examples underscore that user-generated content in the social realm represents a new and potentially highly accurate source of knowledge when tied to clearly defined objectives and supporting metrics (leveraging appropriate keywords). Indeed, Social Media Business Intelligence has the potential  to facilitate very personal customer understanding and when backed by a well defined strategy, to strengthen the relationship with our customer, avert PR disasters and increase customer engagement and conversion.

What are your thoughts? Is the world ready for Social Business Intelligence? Has your company thought about imposing order and structure to the chaos that is Social Media user-generated Content?