Business Intelligence Blog from arcplan
24Jul/120

When Analytics and Collaboration Intersect

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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…

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13Mar/120

Analytics – Not Gut Feeling – Should Drive Business Decisions

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You know the gut feeling that leads you to take a different route to work or accept one job over another? Those gut feelings may have led you on the right path, but they’re personal decisions where you have only so much information (a traffic report on the radio or both companies’ financials) and you would expect to make your decision based on gut instinct. These personal choices affect only you and potentially your family in the case of a new job. But relying on gut instinct alone in your business life is a mistake – there’s simply too much supporting evidence to take into account when making business decisions (decisions that affect much more than just yourself). Why play Russian roulette with these decisions when you’re surrounded by analytics?

Sound business decisions are based on facts, data analysis, trend spotting, or other complex calculations, and yes – a bit of intuition. But your instinct should be used as an indicator, not the basis for your decisions. In every business there are variables and unique scenarios that make planning and analysis imperative; neglecting these factors could have serious implications. Consider this example: The 2010 Report of Anton R. Valukas examined the demise of Lehman Brothers, a formerly dominant global financial institution that went bankrupt during the recent financial crisis. It revealed that the company excluded some assets from routine stress performance calculations (meaning the company couldn’t know how much money it was in a position to lose because it was not performing what-if analysis) and valued some real estate investments on a combination of financial projections and “gut feeling” according to a Lehman Brothers vice president. In essence, the company’s business practices lacked analytic insight, or at least the will to get it. There is no doubt that Lehman Brothers had access to multitudes of data on its assets, on the market, and on its level of risk. Armed with this information, I’d hope executives would have made better choices, taken on less risk, and valued their assets more realistically.

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3Aug/110

Social Media & Business Intelligence: Friends or Frenemies?

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Social media monitoring is an emerging trend that’s here to stay as the popularity of sites like Twitter, Facebook, and Google+ increases. Business intelligence is past the trend phase – it’s commonplace at companies large and small, who will spend nearly $11 billion on it before the year is over, according to Gartner. These are two powerful segments of the analytics market and the question that’s begging to be asked is: are social media and business intelligence friends or frenemies? Do they have to play along to keep the peace or do they actually go hand-in-hand?

For us at arcplan, social media and BI are two sides of the same coin, two pieces of the puzzle that is your business. For a complete picture of your customers, your brand, and your position in the market to emerge, you need information that’s collected from social sites and from your corporate data sources. After all, your data warehouse isn’t going to tell you that the off-handed Twitter comment you made last week contributed to a drop in sales unless you can associate your sentiment analysis to your sales data.

We’re all striving to “do more” with our data – to roll out ad-hoc reporting to our business users so they can take ownership of analysis, make data easier to access so we can rely on IT less, create high-level dashboards for our executives, build scorecards to manage our KPIs, master every chart type… but we haven’t truly begun to do more with data until we incorporate information from outside traditional BI data sources into our everyday analysis. And be aware, the amount of data we’re talking about can be huge. That’s why some in our industry call this Big Data – but this is a story we will review in another article.

It’s important to understand how you, as an organization, can structure social information and associate it to the other data you have about your customers. All kinds of companies – B2C and B2B – are seeing the need to better understand all dimensions of their customers – not only demographic information and purchase history, but also what they’re saying in the social space.

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