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?

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

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?

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?

You've Collected Data...But Now What?

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

The list of technologies that allow us to capture vast amounts of data is quite extensive. This list varies in magnitude of use and exposure within organizations. Companies today can, and most often do, use multiple means of collecting data, such as: Spreadsheets, Databases, Operational specific Software, Enterprise Systems; ERP, CRM, HRM, Various Portals; Personal Portals, News Portals, Enterprise Information Portals, Self-Service Portals, e-Commerce Portals, Collaboration Portals… And the list goes on and on.

It is very evident that companies are really good at collecting data. Whether the data management function within an organization is primitive or advanced, gathering data in spreadsheets or in elaborate enterprise systems and databases: the majority of organizations are great at data collection. Hard copy, Soft Copy, e-Copy, web displayed; data in all forms, shapes and sizes is being collected at an enormous pace. If you can write it, print it, draw it, type it, sketch it, draft it, and capture it, you can rest assured it is being gathered.

The question is not what data to capture next, but now that we have all this data, NOW WHAT?  

Once data is collected, do organizations use it in the most efficient way? The overarching question is: now that you have all this data, what value are you getting from it? The following are five steps that will assist organizations in gaining the most value out of their data.

STEP 1 – IDENTIFY YOUR VALUE DRIVERS

Before we can successfully answer the question of value derived from data, we need to understand what the value drivers are for an organization. Are the value drivers; profitability, reputation, market share, productivity, customer service? The list can certainly be expanded upon. Getting value out of your operational data is imperative, but if you don’t link the data that you are capturing with the value drivers of the organization, you could be spinning your wheels and not realizing the full potential of your systems and efforts.

STEP 2 – LINK DATA TO YOUR VALUE DRIVERS

The next step to ensuring you are making intelligent decisions based on relevant information is to verify that all data captured is linked to the value drivers of your organization. Every piece of information that is collected and processed is intended to provide new intelligence, thereby improving the positive outcomes of critical operational decisions. The way to optimally perform this is by linking significant data retrieval and performance functions to your value drivers. Furthermore, these links can be expanded upon where multiple associations exist.

Dissecting the specific data captured will allow organizations to assess data accuracy, timeliness, depth, and most importantly the interconnection with various other data sets and systems. The key is to ensure that crucial data is modeled to display how it is gathered, at what interval, and how data from one source is related to data in another.

STEP 3 – ANALYZE

Now that you have modeled all significant operational data, you will be able to focus on the highest impacting pieces. By designing new processes or re-engineering solutions, you will be able to increase the usefulness of the information. The analysis will be focused on interconnecting data, assets, management, and operational systems. This exercise will require a thorough look at the data to ensure that standards are in place and the collection of information is from across the entire organization in order to ensure corporate-wide accurate reporting. The outcome from the analysis is to design a roadmap that will focus on operational improvements tied directly to the value drivers of the organization. This can be initiatives such as: identifying ways to increase production, improve safety records, decrease maintenance costs, improve asset visibility, reduce compliance risk, and much more.

STEP 4 – SOLUTIONING

After defining opportunities to improve operations, organizations need to devote some time to developing a realistic plan of achieving these goals. A key step in the Solutioning process is developing the overall vision and detailing the various components of development in palatable sizes ready for execution. Increasing the capabilities of the organization through the design of new automated systems or enhanced analytics, processes and interfaces are just some of the improvements that can be realized. If structured properly, these enhancements can provide the organization significant wins by capitalizing on the information captured along the way.

Information Technology has assisted organizations in navigating from simple and non-existent data management environments, to an optimized level where data can be used for benchmarking and analysis to drive their strategic and operational initiatives. This cannot be successfully done however without ensuring that all data captured provides value and that value is something that drives the automation, analysis and design of advanced systems and integration opportunities. The following diagram depicts the stages of data management and provides a visual of where organizations currently are and how far they may have to go in order to achieve the most optimal level of data management:

IDC Executive Brief

The Current reality of Analytics in Large Canadian Enterprises: IDC Canada Maturity Model

How do you measure up?

July 2012

Sponsored by Ideaca

Adapted from Canadian Business Analytics Landscape, 2012, by Nigel Wallis  

IDC #CA0ECA12

In 2011, more than a trillion gigabytes of information was created and replicated globally, which means it grew by a factor of nine in just five years. Being able to deal with this onslaught and successfully deliver the right information to the right people at the right time is a competitive business advantage. That's why the market for analytics software is bigger than one might imagine. In Canada, organizations spent $923 million in 2011, 12% more than the previous year. IDC anticipates that by 2015 the Canadian analytics market will be north of $1,200 million, meaning the analytics sector is growing much faster than the software market as a whole.

In spring of 2012, IDC surveyed 100 business and 100 IT leaders from Canadian firms with $100 million or more in revenue. IDC spoke with director, VP, and C-level executives to better understand how businesses were integrating analytics into their competitive strategies.

IDC Canada Business Analytics Maturity Model

In order to better understand how analytics is moving from hype to reality, IDC developed a maturity model from the data in the study. Our aim was to identify which cultural and technological choices and decisions determine analytical competency.

To Download a copy of the entire Executive Brief please click here.



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BI and Analytics Top Technology Priority for CIOs in 2012

Gartner's recent worldwide survey to over 2000 CIOs conducted in the fourth quarter of 2011, noted that analytics/business intelligence was the top-ranked technology priority for 2012. Gartner notes that "CIOs are combining analytics with other technologies to create new capabilities" and that increasing enterprise growth is their top priority. 
 
Some questions to keep in mind as investments in BI and analytics continue to grow in 2012:

• How can we manage Operational and Enterprise BI initiatives without impacting the business user community?
• Can we embrace BI-in-a-box and maintain the integrity of one version of the truth?
• What does the future of BI hold for information integrity and decision making accountability?

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