Business Intelligence Blog from arcplan
11Apr/120

Collaborative BI: Today & Tomorrow

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Collaboration is becoming an increasingly important facet of our business interactions. For years, research groups like Gartner , Ventana Research and the Aberdeen Group have provided insight and predictions about this phenomenon, and today we’re seeing how collaboration within the business intelligence space has moved from knowledge sharing and self-service BI to a whole new level of innovative decision-making for the business team. So let’s take a look at some of the shifting points of view about Collaborative BI and where it’s headed in the future.

Web 2.0 technologies and the social media boom have had a tremendous impact on what business users expect out of their business applications, especially in the collaborative space. Collaborating does not simply mean exchanging emails, making calls or holding meetings to facilitate decision-making (though they are the most-used ways according to Wayne Eckerson’s Collaborative BI report). These days, Twitter, LinkedIn, Facebook and YouTube have taught us how to share, rate, like, comment on, and especially make use of user-generated, helpful information. In our work lives, business users have learned to embrace information from various data sources – both formal and informal – as well as perform ad-hoc analyses without help from IT and share this information with colleagues. Collaborative BI as it exists presently is about facilitating the innate desire of business users to collect and share the information necessary for their everyday decision-making, while at the same time preventing duplicate work and allowing colleagues to draw on each other’s strengths. Users have an expectation that social media concepts will be available to them in their business environment, and so many Collaborative BI systems, like our own arcplan Engage, incorporate rating, tagging, etc.

However, we’re seeing a shift in how analysts define Collaborative BI and they are now calling for an even higher level of engagement.

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9Mar/120

Big Data for Manufacturers: Customer Feedback Should Influence R&D

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In 2001, The McKinsey Global Institute published a comprehensive report on big data, Big Data: The Next Frontier for Innovation, Competition, and Productivity, which explores the value that companies across various industries may yield as result of the big data explosion. So far we’ve explored the impact of big data on retail and healthcare companies, but today I’ll explore how big data analytics impact the manufacturing industry.

The manufacturing sector stores more data than any other sector, according to the McKinsey report. Manufacturers will likely get the most benefit from big data analytics since they have so much “raw material” to work with (from machinery metrics to sales systems). Manufacturing is a relatively efficient industry, with many advances made over the last few decades to streamline processes and improve quality through management practices like lean & six sigma (and lean six sigma!). But big data can be the impetus for the next wave of improvements in manufacturing, especially in R&D.

Research and Development
Streamlining the R&D process results in greater efficiency and reduced costs for US manufacturers and is important for products to be competitive in the global economy. But in 2012 and beyond, manufacturers should be going further, leveraging big data to influence design decisions. This means incorporating customer feedback into the process, designing products and adding features that customers actually want. McKinsey calls this “design to value” or “value-driven design.”

Surveys: I’ve taken consumer surveys that ask questions like “How much more would you be willing to pay for x feature?” and I now understand why companies are asking this. They are culling data from consumers about what features are desired and if they are included in the product/service, what is the value, i.e. how much are people willing to pay for it. Gathering concrete insights is one step toward big data analytics influencing R&D. Manufacturers should be listening to what consumers want and refining their designs accordingly. It’s just smart business.

Here’s a concrete example: Domino’s Pizza. You might not think of Domino’s as a manufacturer, but it is – the company is a serious dough manufacturer, producing and distributing dough to more than 5,000 US stores.

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2Feb/120

Collaboration – the Future of Decision Making?

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Perhaps every generation says this at least once, but I believe we’re in the midst of a very interesting time. The world is getting more social everyday with Facebook, Twitter, and LinkedIn, where we can find old friends, colleagues and even relatives online with a single click. We may even find new people to follow through social media tools’ recommendations and can form relationships online and offline with them. Hundreds of millions of users are making decisions online all the time – who to follow, what content seems interesting, what topics to promote.

Our social media feeds make it obvious who to engage with about a particular topic – a friend may post frequently about sports and you can go to him with thoughts or questions – but that type of insight is not widely available at the place where we spent most of our time: work. We lack intelligence when it comes to the enterprise decision making process. It follows that we should apply the same principles of social media in our corporate environments to identify which colleague can help us make decisions. Applying social media functions that allow users to rate, tag, and comment about corporate content is the answer. Enterprises gain insight into the most used reports and dashboards at the company, report authors get instant feedback and enhancement requests from users, and users gain from the existing expertise of colleagues.

This idea has led to a new category of business intelligence software that Gartner describes as Collaborative Decision Making (CDM) and Collaborative BI. Gartner considers such platforms an emerging trend to fill the gap in decision support for tactical and strategic decisions most often made by knowledge workers.

“By 2013, 15% of BI and analytic applications will combine BI, collaboration and social software in decision-making environments.” – Gartner Group

BI vendors are following this path, creating matching solutions that serve as an interface to your wealth of corporate data. Is the market ready to deploy these solutions?

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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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