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?