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The Storytelling Mandate of Big Data

Excerpt:
We collect data and use data effectively to enhance our experiences and tell stories, says Ramesh Jain, a UC Irvine professor and big data researcher. “But this requires understanding relationships among disparate data items. And that is where the importance of Big Data really lies,” Jain writes on his blog.

Data-powered storytelling can be broken down into two categories: micro stories and macro stories. In their 2013 piece on the topic, Jain and Microsoft researcher Malcolm Stanley theorize that micro stories can be as simple as clicking the “like” button on Facebook, posting a picture on Instagram, or posting a video on YouTube. “Micro stories reflect a person’s experience with just one small event–really a moment in the event,” they write.

Mega stories can be generated by combining micro stories into a bigger narrative. “Mega stories tell a story that could only be created by considering a large volume of relevant events in big data,” Jain and Stanley write. “All these events must be selected and aggregated based on the goal of the storyteller.”

#Social   #Storytelling   #BigData  

Featured in Data Storytelling for Disruptors
http://blog.causeanalytics.com/2014/10/data-storytelling-for-disruptors.html 

Posted by +Dan Durrant 

Cause Analytics is here to help you navigate through Business Intelligence, understand today's challenges and tomorrow's technologies.

www.CauseAnalytics.com

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Transparency inside Organisations

Here is another view of #Transparency  that has to do with opening lines of communication within an organisation. I tend to think of this more as "Visibility", but if the communication is also public facing, then it would be closer to what I think is truly Transparent.  In any case, this kind of visibility works well when different departments have access to shared #BusinessIntelligence   #Dashboards . I appreciate these four steps for building transparency (and #Collaboration ) within your organisation:
"
1. Identify what transparency means for your company.

Transparency can mean different things depending on your industry and company. Do you have a legal department that could be more communicative? Maybe there’s one department that drowns out everyone else. See where communication breaks down, and create a plan to fix it.  

2. Get your team on board.

If your team doesn’t understand what transparency is and why it’s important, it’s not going to work. Outline the benefits of transparency and how it can directly affect each department and employee. 

3. Put it into practice.

Don’t just give lip service. Once you “go transparent,” you need to develop strategies for keeping each department informed and connected. / #Executives  

4. Show results consistently.

When sharing information such as financial projects or results, don’t just broadcast the information once. You have to make a consistent effort to make information available to reap the benefits of an open culture. 
"

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Treating Your BI Project Like an Entrepreneurial Startup

Citation Mix: 
As a proponent of agile data warehousing and business intelligence, I [Ken Collier] am constantly looking for new techniques for delivering value to customers faster, adapting to their feedback, and evolving toward the right business solutions (regardless of initial requirements). I recently read the new book, Lean Startup by Eric Ries (Ries, 2011) and it has rocked my world. In the short time since this book hit the shelves in September, 2011, it has exploded in popularity. Be forewarned, this book is about entrepreneurship and high-tech startups. It isn’t about data warehousing, BI, or analytics ... or is it? / #BusinessIntelligence #DataWarehouse    #Entrepreneurs  
. . . .
Ries points out that “The fundamental activity of a startup is to turn ideas into products, measure how customers respond, and then learn whether to pivot or persevere.” Lean Startup techniques follow a Build-Measure-Learn feedback cycle. This cycle begins with an idea or hypothesis immediately followed by building a minimal viable product (MVP). Customer response to this MVP is carefully measured and the resulting data provides the basis for learning and adjustment. The goal is to move through this cycle as fast as possible, and as many times as necessary to converge on the product that customers want.
. . . 
Two critical elements of this cycle are the MVP and the validated learning that is based on scientific testing of customer acceptance. The MVP is the very smallest, fastest thing you can introduce to your customers to gauge their response. For a business intelligence “product,” this might be a disposable prototype report or dashboard mockup populated with snapshot data. For analytics, it might be a mockup of a scoring algorithm based on a rudimentary predictive model. It is the simplest version of what we think customers want, so that we can find out if our assumptions are correct. / #Dashboards   #Analytics  
. . . 
Once we have correctly discovered what customers want, then we can use agile BI techniques to build, refine, and mature the solution. Ries describes this approach as “…killing things that don’t make sense fast and doubling down on the ones that do.” This theme makes as much sense for BI directors as for startup entrepreneurs. / #BuildResilience  
. . . 
Data warehouse and BI program leaders are entrepreneurs within the enterprise. It is the job of these entrepreneurs to quickly determine which efforts are value-creating and which are wasteful. / #GrowIncome   #SaveMoney  

Posted by +Dan Durrant 

Cause Analytics is here to help you navigate through Business Intelligence, understand today's challenges and tomorrow's technologies.

www.CauseAnalytics.com

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More Data, Less Time Adding to IT Pressures

Citation Re: #Managers #BusinessIntelligence   #OnDemand  
New research from the Aberdeen Group shows 28 percent of business managers want a business intelligence (BI) report on their desk within an hour of an event. If that's not bad enough, 18 percent -- nearly one in five managers -- want that report within "minutes," the report says. Another 42 percent are content if the information find its way to them within a day.

Re: #DecisionMaking  
And that time window is narrowing. Citing a separate study that has yet to be published, Aberdeen says 64 percent of business managers "have seen their decision window shrink in the last 12 months."
. . . 
Re: #InformationTechnology   #Dashboards  
IT organizations are typically responsible for collecting the data, assembling it into usable reports, populating some charts, and delivering the results to the business managers. But simply adding a column to a BI report takes the IT department eight days on average, according to Aberdeen's survey. To build a new dashboard giving clarity on the data can take a month.

Worse, a work order for such a change generally would be added to the backlog of work facing the IT department. And the research firm reported in March that backlogs for BI projects averaged 143 days.
. . 
Re: #SelfService  
Aberdeen argues that this "self-service" approach is preferable for two reasons. First, it shifts the burden away from the IT staffers who are "so overburdened that they cannot realistically achieve this quest anyway." Second, it represents "the only hope for those managers to gain the insights they need in the time required to support their decisions." #SaveTime  

Posted by +Daniel Durrant 

Cause Analytics is here to help you navigate through Business Intelligence, understand today's challenges and tomorrow's technologies.

www.CauseAnalytics.com

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9 most common #BusinessIntelligence misconceptions

Gartner recently predicted that Business Intelligence (BI) and Analytics will remain a top focus for CIOs through 2017. 
. . . 
The only problem: Many businesses still don’t quite understand BI. 
. . . 
1. BI doesn’t have value -- “I believe the biggest misconception of BI by CEO’s is the belief that it doesn’t have value,” says Steve Cross, CEO of Infinite Synergy.  / #GrowIncome  
. . . 
2. BI is only for (fill in the blank) organizations with (fill in the blank) -- “Every organization can embrace effective BI targets regardless of size, budgets or tenure,” explains Sara Handel, Business Intelligence Services Lead at Excella Consulting. 
. . . 
3. Measuring data = successful BI -- “True BI uses data, collected from different sources and analyzed in different ways, to make informed decisions over time. If you are just running the same report and looking at the same numbers but aren’t actually using that information to make decisions or alter your strategies, then you aren’t doing BI.” -- Sara Handel / #DecisionMaking  
. . . 
4. BI = reports and dashboards -- "Despite the fact that reports and dashboards are standard tools of a classical BI, they do not define the value by themselves” says Sergii Shelpuk, Data Science Group Director, SoftServe. “BI is a solution for delivering the right information to the right people at the right time." / #Dashboards  
. . . 
5. More data = better BI -- “This is flat out wrong,” says Anita Andrews, VP, Client Analytics Services at RJMetrics. “The larger community is so quantity obsessed (of course it’s fun for some to solve the computational and processing problems associated with massive data sets), but it’s actually the case that if you can double your revenue with one column of data, you’d do it in a heartbeat.” / #BigData  
. . . 
6. All BI tools are the same -- "Organizations set themselves up for failure when they solely buy into the hype of a BI tool. The best tool for your team just may be the underdog,” says Takashi Binns, Senior Manager, Solutions Delivery at arcplan. / #SelfService  
. . . 
7. We need ‘big data’ for BI to be meaningful -- "Your company may not even have – and may never have a need for – big data. Business leaders can glean insight into business operations by performing various analytic techniques on their regular data to improve business processes, efficiency and profitability.” -  Takashi Binns / #SaveMoney  
. . . 
8. Business intelligence is an IT job -- “Historically, running queries and generating reports for the business team were back-office activities assigned to the IT department,” says Binns. “With little communication between these two groups, obtaining valuable information was hit or miss." / #InformationTechnology  
. . . 
9. BI Software = BI Strategy -- "While the BI software should provide timely information to support decisions, these decisions are ultimately made by humans. In addition to software, a successful BI implementation must include systematic changes to an organization’s culture, making them more data-driven. Strong cultures are identified through strong patterns of accountability which encourage and reward data-driven decision making,” says James Ficarra & Adam Rene, Senior Project Managers & Engineers at ExcelAutomationHelp.com. / #Collaobration   #HumanResources  

Posted by +Dan Durrant 

Cause Analytics is here to help you navigate through Business Intelligence, understand today's challenges and tomorrow's technologies.

www.CauseAnalytics.com

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The Future of Information #Dashboards

Last year was an awesome one for the data visualization industry. With more and more businesses realizing the value of data visualization for deriving critical business insights, the year ahead looks very promising, too. IIA (International Institute for Analytics), an independent research firm focusing on the use of analytics, recently issued its nine analytics predictions for 2014.

Among other things, it predicted that the use of data visualization will increase both for low and high complexity analytics. However, Thomas H. Davenport, Co-Founder and Research Director at IIA, had a caveat for those trying to use visualization in instances involving highly complex, multivariate statistics: “That gets tricky because humans don't comprehend things in more than two dimensions or, at most, three dimensions.” / #DataVisualisation  
. . . . 
Here are five changes on the horizon that will deepen user experience with data.

1. More Independent Software Vendors will Include Powerful Information Dashboards as Part of their Key Offering / #Dashboards  

2. Information Dashboards Catering to the Specific Needs of the On-the-Go User / #Mobile  

3. Information Dashboards with High Location Intelligence Quotient / #Maps  

4. Dashboards Catering to the Here and Now / #RealTime  

5. Information Dashboards That Can See The Future / #PredictiveAnalytics  

Posted by +Dan Durrant 

Cause Analytics is here to help you navigate through Business Intelligence, understand today's challenges and tomorrow's technologies.

www.CauseAnalytics.com

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Mindful decision-making with clear data

+David Amerland summarises interesting views on mindfulness and decision-making in a hyperconnected world.
as well as some other summaries worth reading on the original

"We live in a hyperconnected world of analytics, big data, semantic search, semantic technologies and ever speeding up processes. We cannot survive alone (http://goo.gl/yFRTrB). We would like to think that we are capable of making the right decision every time. Yet our mental defenses are not yet formed, our heuristics already out of date. A ‘small’ thing could easily lead us down more disastrous paths (http://goo.gl/zyBa3N) where we accept unpalatable situations or action because we have been compromised. "

+Paul Simbeck-Hampson leaves his insights in the comments for us to chew on...

"The mention of analytics, big data and semantic search wets my Sunday insight appetite!

"Process is required, and it all begins by harvesting data from a multitude of currently unconnected, or partly connected sources. The data then needs enriching and cleaning before it can be modelled in collaboration with those knowledge experts who seek the outcomes. At this point it can be visualised within dashboards in ways never seen before. Self reflection occurs at first, but soon after the desire to involve others is awakened. It is the collaborative thinking around newly formed insights based on newly combined and modelled data sets, presented in easy to adjust views that ultimately lead to the kind of breakthrough decision making that 21st century enterprises not only desire, but desperately need.

"The process of working with data in this way leads to an improved ability to derive better meaning, which in turn will help us to think (clearly) before we act (decide).

"Without such a process it is not possible to gain the insights required and as a result, human instinct plays too heavy a role in the decision making process, which inevitably leads to an increase in regrettable decisions. "
. . . . 
"Collaborative sensemaking that utilises unclean data as its basis for discussion will result in unclear sense."
. . . . 
'Like this quote via +Jonathan Belisle "see decisions not as final choices but as experiments" - this resonates especially as decision data is always changing and updating..."

#DecisionMaking #BigData    #DataVisualisation   #Collaboration   #Dashboards  

Posted by +Dan Durrant 

Cause Analytics is here to help you navigate through Business Intelligence, understand today's challenges and tomorrow's technologies.

http://www.CauseAnalytics.com

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Jaguar Land Rover builds data sharing infrastructure
(Nov 2013)

Jaguar Land Rover (JLR) revamped its product lifecycle management (PLM) system around a data-sharing standard to improve car development after Ford sold the company in 2008 for £1.15bn to Tata Motor.

Citation:
Dave Sharrat, senior manager PLM at Jaguar Land Rover said: "We started with existing applications like CAD (computer-aided design), CAM (computer aided manufacturing) and CAE (computer aided engineering) and created a technical architecture to build a new PLM system with single version of the truth. Where appropriate, this is connected to our ERP system."
. . . . 
He said the company needed simple integrated technology, which could provide a single source of the truth for the design of new cars.
Applications in car design and manufacturing are generally semi integrated. Information is often passed from one application to another, but JLR wanted the data to reside in one place, within the application that created it, and provide APIs so other applications could access it.
. . . .
While the whole PLM programme will take three years to complete, he said JLR is starting to see the benefits now, by exposing some of the PLM data in visualisation tools. "We’ve implemented control systems that constantly monitor quality [assurance] data from our production plans using dashboards on an iPad."

#DataWarehouse   #APIs   #Dashboards   #Mobile #DataVisualisation    #Manufacturing   #Collaboration  

Posted by +Dan Durrant 

Cause Analytics is here to help you navigate through Business Intelligence, understand today's challenges and tomorrow's technologies.

www.CauseAnalytics.com

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Why Automated Semantics Will Solve The BI Dashboard Crisis

Do we really need so many dashboards?

Dan Woods answers, "no. In fact, if we can figure out a way to have fewer dashboards each of which answer more questions — which is essentially the victory of QlikView and Tableau — we cannot only reduce the number of dashboards but also bring BI to a whole new population to the world of users." In 7 statements he lays out the logic for why "combining natural language and semantics can create dashboards that can answer many questions so that we can get by with many fewer of them."

1) Most ways that BI is consumed is based on static queries or models of information that are created by hand.
2) Even when these models are configurable, such as parameterized reports or dashboards, severe limits are placed on the amount of data included and the way it can be displayed.
3) Systems such as QlikView and Tableau have dramatically expanded the size of the model that can be represented using a single interface, but even these systems are still based on static models.
4) Because the amount of available data will become overwhelming. Manual modeling just won’t be able to keep up.
5) Therefore, the next generation of BI will be powered by automatically created semantic models.
6) Natural language provides the simplest way for users to express their desires in terms of a question or a request.
7) Many systems (IBM Watson, Microsoft’s Q&A in Power BI for Office 365, DataRPM) have shown that natural language can be parsed and connected to semantic models and lead to visualizations that can then be refined.

#Dashboards   #Search   #DataVisualisation  

Posted by +Dan Durrant 

Cause Analytics is here to help you navigate through Business Intelligence, understand today's challenges and tomorrow's technologies.

www.CauseAnalytics.com

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What needs to happen for BI to tackle the next wave of adoption?

"BI systems in use now mostly represent what I call Bottom of the Funnel technology. The funnel is a concept used by marketing to explain the journey of a user in their understanding of a technology and also to organize the different ways of attracting them."
....
"The reason that most BI represents Bottom of the Funnel technology is that it is created for a specific context. A lot is known about the end-user and what they want, just like at the bottom of a marketing funnel. Dashboards to support a specific role in a business process, advanced analytical environments, complicated multi-sheet spreadsheets, and thick reports all fall into the bottom of the funnel category. They are super helpful for a small segment of the population."
....
"The perfect Top of the Funnel, aka as TOFU, BI would be like HAL in 2001: A Space Odyssey. You could just ask questions and get answers. We are a long way from that sort of experience, but some huge progress has been made in the past few years. At the most ornate level is IBM’s Watson, of course, but we must be able to do more with less to really solve the TOFU problem."
....
"As I pointed out in 'How Semantics Can Make Data Analysis Work Like Google Search', DataRPM has many elements of this in place. Microsoft PowerBI for Office 365 is also exploring what can be done with queries expressed in natural language. Such systems are in their early days, but the best ones already work well enough so it is clear they will get better as the ability to automatically create semantic models of data and connect them to both natural language and to visualizations improves."

#Dashboards   #DataVisualisation   #Search  

Posted by +Dan Durrant 

Cause Analytics is here to help you navigate through Business Intelligence, understand today's challenges and tomorrow's technologies.

www.CauseAnalytics.com
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