Business Intelligence Blog from arcplan

Careers in Business Intelligence: What Makes BI an Attractive Field


Along with the demand for big data, better data, and the need for greater insight into company operations comes the need for analytic professionals who can effectively leverage this data to maximize business benefits. But it’s not always easy to find and hire these types of people. Within the past few years, we’ve seen titles such as BI Analyst, Data Analyst, Data Scientist, and Big Data Engineer emerge in job listings as companies seek out much-needed expertise to wrangle their growing amounts of information. As a matter of fact, McKinsey and Company’s often-quoted 2011 report on big data predicted that  by 2018, the US alone faces a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts to analyze big data and make decisions based on their findings. The bright side of this situation is that job seekers with analytic talent and business acumen have a tremendous opportunity to make a positive impact for business teams. BI is a growing field that shows no signs of slowing down, so let’s take a closer look at what makes it attractive to job seekers as well as students deciding what course of study to pursue.

Continue reading this post >>


Invest in Good Data Before Big Data


Big data is without a doubt 1 of the top 5 BI trends of 2012. The hype around big data has driven many companies to hoard massive amounts of structured and unstructured information in the hope of unearthing useful insight that will help them gain competitive advantage. Admittedly, there is significant value to be extracted from your company’s growing vault of data; however it is data quality – not necessarily quantity – that is your company’s biggest asset. So here are 3 reasons why you should devote more of your IT budget to data quality:

1) Because good data quality sets the stage for sound business decisions.
Sensible business decisions should be based on accurate, timely information coupled with the necessary analysis. Decision-makers need to be equipped with facts in order to plan strategically and stay ahead of the competition – and facts are entirely based on having correct data. Though it’s not as “sexy” as big data, mobile BI, or cloud, data quality should be the foundation of all of these other initiatives.

Admittedly, achieving data quality is tough. Gartner analyst Bill Hostmann says, “Regardless of big data, old data, new data, little data, probably the biggest challenge in BI is data quality.” It crosses department lines (both IT and business users must take responsibility), and processes that have multiple levels of responsibility often suffer from the “everyone and no one is responsible” conundrum. It’s also a complex process that requires laying out common definitions (what is a customer, what are our conventions for company names – Inc. or no Inc. – for example), performing an initial data cleanse, and then keeping things tidy through ongoing data monitoring, ETL, and other technologies.

But ensuring that your data is timely, accurate, consistent, and complete means users will trust the data, and ultimately, that’s the goal of the entire exercise if you see this first reason as the most important. Trusting the data means being able to trust the decisions that are based on the data. Clean up the data you have in place, then you can move on to a strategy that incorporates additional sources of big data.

2) Because you have to.

Continue reading this post >>


When Analytics and Collaboration Intersect


Fueled by the big data hype and the need to extract greater business value from data, investment in business analytics software is on the rise. Many companies have begun to tap into the potential of big data analytics and this number is predicted to increase according to recent reports by the International Data Corporation (IDC). IDC forecasts that the market will continue to grow at a 9.8% compound annual growth rate through 2016 to reach $50.7 billion. Perhaps to a less aggressive extent, interest in Collaborative BI is also on the rise, with top performing companies incorporating collaborative techniques to share knowledge throughout the enterprise according to Aberdeen’s extensive 2011 research report on Collaborative BI. The demands for agile insight and self-service are changing the landscape of BI, driving the need for Collaborative BI, which uses social functionality to improve business decision-making. Separately, the benefits of deploying analytical tools and taking advantage of collaborative techniques are appealing for any organization seeking streamlined operational success – but the payback of merging these initiatives could be even more rewarding.

Analytics is gaining traction in the BI arena due to the need to explore massive amounts of varied information (what we now call big data), extract valuable insight, and quickly deliver these insights to the users who need it. Initiatives geared toward improving analytics utilize technology that gathers and organizes data from disparate data sources and provides a platform for in-depth analysis, yielding benefits such as improved business operations and agility, increased sales, and lower IT costs. So it’s no wonder that organizations are making significant investments in the analytics market.

Collaborative BI, on the other hand, seems to be the new kid on the block…

Continue reading this post >>


The Practicalities of Moving BI Into the Cloud: Part I


Many (if not most) companies are evaluating the benefits and risks of cloud-based solutions this year. In fact, marketing research firm IDC predicts that businesses will spend $22.6 billion on cloud services by 2015. However, there is one area that has fallen behind the cloud – business intelligence. But it’s ready to emerge. Even organizations with traditional (hosted on-premise) BI systems in place can make the move. Let’s consider the practicalities of doing so.

Organizations that have deployed business intelligence have first-hand knowledge of the complexities of such a system – the vast network of linked parts and pieces, from data warehouses to ETL applications, OLAP servers to analytical dashboards. It’s a jungle out there and it’s clear that it can’t continue this way for much longer. A more repeatable and sustainable model for business intelligence must emerge – one that reduces the complexity while maintaining security and enhancing ease of use.

The Data Question
For services like CRM and document collaboration, the roadmap for moving to the cloud has already been established by companies like and Google. But for BI, it’s not as clear. The sensitivity and volume of data as well as the inherent complexity of BI systems have left executing a cloud-based BI strategy more of a dream than a reality.

Many believe that the next logical step in BI’s evolution is moving it to the cloud. However, when looking at the characteristics of a modern day BI deployment, it’s easy see how getting there is complicated.

Let’s take a look at just one aspect of a cloud BI deployment: the amount of data that would need to be moved, stored, and processed. There’s a reason we’re all talking about big data these days – according to April Adams, research director at Gartner, data capacity in enterprises is growing at 40% to 60% year over year…

Continue reading this post >>


Big Data FAQs – A Primer


The big data trend promises that harnessing the wealth and volume of information in your enterprise leads to better customer insight, operational efficiency, and competitive advantage. The marketing hype around big data and the pace of studies, analyst reports, and articles on the subject can be mind-numbing for companies that want to take advantage of big data analytics but do not know how to separate fact from fiction and determine real use cases for their business. So here’s a big data primer for those just getting in the game.

1) What exactly is big data?
“Big data” is an all-inclusive term used to describe vast amounts of information. In contrast to traditional structured data which is typically stored in a relational database, big data varies in terms of volume, velocity, and variety. Big data is characteristically generated in large volumes – on the order of terabytes or exabytes of data (starts with 1 and has 18 zeros after it, or 1 million terabytes) per individual data set. Big data is also generated with high velocity – it is collected at frequent intervals – which makes it difficult to analyze (though analyzing it rapidly makes it more valuable). Additionally, big data is usually not nicely packaged in a spreadsheet or even a multidimensional database and often includes unstructured, qualitative information as well.

2) Is it a new trend?
Not exactly. Though there is a lot of buzz around the topic, big data has been around a long time. Think back to when you first heard of scientific researchers using supercomputers to analyze massive amounts of data. The difference now is that big data is accessible to regular BI users and is applicable to the enterprise. The reason it is gaining traction is because there are more public use cases about companies getting real value from big data (like Walmart analyzing real-time social media data for trends, then using that information to guide online ad purchases). Though big data adoption is limited right now, IDC determined that the big data technology and services market was worth $3.2B USD in 2010 and is going to skyrocket to $16.9B by 2015.

3) Where does big data come from?
Big data is often boiled down to a few varieties including social data, machine data, and transactional data…

Continue reading this post >>