Thursday, 21 November 2013

Tableau adds integration with R

http://blog.revolutionanalytics.com/2013/10/tableau-adds-integration-with-r.html


Tableau, the popular interactive data visualization tool, is coming out with a new 8.1 update, and it will include integration with the R language. Access to R is a feature that has been requested by Tableau users for some time, and was met with rapturous applause when it was announced at the recent Tableau customer conference.


When released, Tableau 8.1 will make it possible to use R packages and functions in Tableau calculated fields. This will unlock all of the capabilties of R when creating Tableau charts. For example, you will be able to:


  • Use results of R functions to annotate points on a chart (e.g. use the mvoutlier package to color-code or filter outliers in a Tableau scatterplot).

  • Use the output of R’s statistical functions to in charts, for example to color points by cluster using R’s k-means clustering function.

  • Create interactive applications based on custom R scripts. For example, a business analyst could enter parameters to control an R script developed by a data scientist. The business analyst will see the results directly in Tableau without the need to know the R language themselves.

You can see a quick example of how this works in the video below:



This connection brings all of R’s data analysis cababilities to Tableau, bringing almost limitless statististical capabilities to data visualizations in Tableau. Tableau 8.1 with R integration is currently available for beta test, and you can read more about the new R functionality at the Tableau blog linked below.


Tableau blog: Tableau 8.1 and R

Tuesday, 20 August 2013

Top BI Trends 2013

It’s Going to be a BIG (Business Intelligence Growth) Year


What a year 2012 was for business intelligence! The staid old world of databases is developing faster and faster, with startups addressing new data problems and established companies innovating on their platforms. Web-based analytics tools are connecting to web-based data. And everything’s mobile.


With all the attention organizations are placing on innovating around data, the rate of change will only increase. So what should you expect to see?


1. Proliferation of data stores.


Once upon a time, an organization had different types of data:
CRM, point of sale, email, and more. The rulers of that organization worked very hard and eventually got all their data into one fast data warehouse…


2013 is the year we will recognize this story as a fairy tale.

The organization that has all its data in one place does not exist. Moreover, why would you want to do it? Big data could be in places like Teradata and Hadoop. Transactional data might be in Oracle or SQL Server. The right data stores for the right data and workload will be seen as one of the hallmarks of a great IT organization, not a problem to be fixed.


2. Hadoop is real.


Back in 2008 and 2009 Hadoop was a science project. By 2010 and 2011 some forward-thinking organizations started doing proof-of-concepts with Hadoop. In 2012, we saw the emergence of many production-scale Hadoop implementations, as well as a crop of companies trying to address pain points in working with Hadoop. In 2013, Hadoop will finally break into the mainstream for working with large or unstructured data. It is also becoming more “right-time” for a faster analytics experience.


3. Self-reliance is the new self-service.


Self-service BI is the idea that any business user can analyze the data they need to make a better decision. Self-reliance is the coming of age of that concept: it means business users have access to the right data, that the data is in a place and format that they can use, and that they have the solutions that enable self-service analytics. When all this happens, people become self-reliant with their business questions and IT can focus on providing the secure data and solutions to get them there.


4. The value of text and other unstructured data is (finally!) recognized.


One of the subplots of the rise of Hadoop has been the rise of unstructured data. Emails, documents, web analytics and customer feedback have existed for years, but most organizations struggled enough to understand their structured data that unstructured data was left alone. In 2011 and 2012 we saw more techniques emerge to help people deal with unstructured data, not least of which is a place to put it (Hadoop). With the explosion of social data like Twitter and Facebook posts, text analysis becomes even more important. Expect to see a lot of it in 2013.


5. Cloud BI grows up.


Cloud business intelligence as your primary BI? No way! Not in 2012, at least. There are cloud BI services, but with important limitations that have made it difficult to use the cloud as your primary analytics solution. In 2013 we expect to see the maturation of cloud BI, so that people can collaborate with data in the cloud, just like they collaborate on their Salesforce CRM or help desk data.


6. Visual analytics wins Best Picture.


For years visual analytics has been the Best Documentary of business intelligence: impressive, but for the intellectuals and not the mass audience. But people are finally beginning to realize that visual analytics helps anyone explore, understand and communicate with data. It’s the star of business analytics, not a handy tool for scientists.


7. Forecasting and predictive analytics become common.


Much like visual analytics, forecasting used to be seen as the domain of the scientist. But everyone wants to know the future. Forecasting tools are maturing to help businesses identify emerging trends and make better plans. We expect forecasting and predictive analyses to become much more common as people use them to get more value from their data.


8. Mobile BI moves up a weight class.


Last year we predicted that Mobile BI would go mainstream—and it did. Now everyone from salespeople to insurance adjusters to shop floor managers use tablets to get data about their work right in the moment. To date mobile BI has been lightweight—involving the consumption of reports, with a bit of interactivity. But the tremendous value that people have seen in mobile BI is driving a trend for more ability to ask and answer questions.


9. Collaboration is not a feature, it’s a reality.


Business intelligence solutions have often talked up their collaboration features. In 2013, that’ll no longer be good enough. Collaboration must be at the root of any business intelligence implementation, because what is business intelligence but a shared experience of asking and answering questions about a business? In 2013, business will look for ways to involve people all around their organization in working together to understand and solve problems.


10. Pervasive analytics are finally…pervasive.


As an industry, we’ve talked for years about terms like “pervasive BI” or “BI for the masses”. There’s a whole market for data that is outside of the market for “business intelligence.” When we talk more about data, and less about software categories like BI, we get to the crux of maximizing business value—and fast, easy-to-use visual analytics is the key that opens the door to organization-wide analytics adoption and collaboration.


So there you have it, the future in 10 points.


These are the trends we see in talking with customers about what they’re doing today and where are investing for the future. The good news is that investment is most often being driven by a desire to take good initiatives farther, not a sense of frustration with failed initiatives. Perhaps the new technology and investment of the last few years is finally starting to pay off. No matter what, you can expect lots of change in business intelligence in 2013.

Monday, 19 August 2013

Business Intelligence Startup Looker Raises $16M From Redpoint, First Round

http://techcrunch.com/2013/08/13/business-intelligence-startup-looker-raises-16m-from-redpoint-first-round/


Looker, a business intelligence startup founded by an early lead engineer from Netscape and LiveOps, has raised $16 Million in series A funding, led by Redpoint Ventures with First Round Capital, who invested in the company’s $2 million seed round, also participating. This brings the company’s funding total to $17.7 million.


Founded by Lloyd Tabb, Looker is aiming to disrupt the business intelligence space by turning what used to be programming queries into those based on natural language. Most legacy BI systems are based on SQL and require users to have engineering and programming expertise to formulate queries to pull out the data and make sense of these insights. Looker allows anyone to easily query these large data sets.


As the company explains, it’s like putting a data scientists within your browser. Part of the approach includes a proprietary language called LookML, which simplifies the process of scripting and recycling SQL queries. Using Looker and LookML, organizations can distribute Looker queries across all functions of the business, including sales, marketing and more. The Looker can be deployed in hours.


After operating in stealth for a year, Looker launched earlier this year with more than 20 paying customers. Since launch the company has more than doubled their customer base, with more than 40 paying customers now using Looker.


The company is raising the money primarily to expand its sales team and keep up with customer demand, as well as to hire more engineers and invest in infrastructure.


Helping businesses gather more insightful information and understanding around data isn’t a new trend. Good Data, Domo and a number of others are growing fast in the business intelligence world. Not only are we going to see more entrants into this space, but we’re likely to see more acquisitions by incumbents as well. All in all, it makes for a super interesting future for many of these companies.

Sunday, 28 July 2013

Business Intelligence Definition and Solutions

from http://www.cio.com/article/40296/Business_Intelligence_Definition_and_Solutions


What is business intelligence?


Business intelligence, or BI, is an umbrella term that refers to a variety of software applications used to analyze an organization’s raw data. BI as a discipline is made up of several related activities, including data mining, online analytical processing, querying and reporting.


Companies use BI to improve decision making, cut costs and identify new business opportunities. BI is more than just corporate reporting and more than a set of tools to coax data out of enterprise systems. CIOs use BI to identify inefficient business processes that are ripe for re-engineering.


With today’s BI tools, business folks can jump in and start analyzing data themselves, rather than wait for IT to run complex reports. This democratization of information access helps users back up—with hard numbers—business decisions that would otherwise be based only on gut feelings and anecdotes.


Although BI holds great promise, implementations can be dogged by technical and cultural challenges. Executives have to ensure that the data feeding BI applications is clean and consistent so that users trust it.


What kind of companies use BI systems?


Restaurant chains such as Hardee’s, Wendy’s, Ruby Tuesday and T.G.I. Friday’s are heavy users of BI software. They use BI to make strategic decisions, such as what new products to add to their menus, which dishes to remove and which underperforming stores to close. They also use BI for tactical matters such as renegotiating contracts with food suppliers and identifying opportunities to improve inefficient processes. Because restaurant chains are so operations-driven, and because BI is so central to helping them run their businesses, they are among the elite group of companies across all industries that are actually getting real value from these systems.


One crucial component of BI—business analytics—is quietly essential to the success of companies in a wide range of industries, and more famously essential to the success of professional sports teams such as the Boston Red Sox, Oakland A’s and New England Patriots.


With an analytical approach, the Patriots managed to win the Super Bowl three times in four years. The team uses data and analytical models extensively, both on and off the field. In-depth analytics help the team select players and stay below the NFL salary cap. Patriots coaches and players are renowned for their extensive study of game film and statistics, and Coach Bill Belichick reads articles by academic economists on statistical probabilities of football outcomes. Off the field, the team uses detailed analytics to assess and improve the “total fan experience.” At every home game, for example, 20 to 25 people have specific assignments to make quantitative measurements of the stadium food, parking, personnel, bathroom cleanliness and other factors.


In retail, Wal-Mart uses vast amounts of data and category analysis to dominate the industry. Harrah’s has changed the basis of competition in gaming from building megacasinos to analytics around customer loyalty and service. Amazon and Yahoo aren’t just e-commerce sites; they are extremely analytical and follow a “test and learn” approach to business changes. Capital One runs more than 30,000 experiments a year to identify desirable customers and price credit card offers.


Who should lead the way?


Sharing is vital to the success of BI projects, because everyone involved in the process must have full access to information to be able to change the ways that they work. BI projects should start with top executives, but the next group of users should be salespeople. Because their job is to increase sales and because they’re often compensated on their ability to do so, they’ll be more likely to embrace any tool that will help them do just that—provided, of course, the tool is easy to use and they trust the information.


With the help of BI systems, employees modify their individual and team work practices, which leads to improved performance among the sales teams. When sales executives see a big difference in performance from one team to another, they work to bring the laggard teams up to the level of the leaders.


Once you get salespeople on board, you can use them to help get the rest of your organization on the BI bandwagon. They’ll serve as evangelists, gushing about the power of the tools and how BI is improving their lives.


How should I implement a BI system?


When charting a course for BI, companies should first analyze the way they make decisions and consider the information that executives need to facilitate more confident and more rapid decision-making, as well as how they’d like that information presented to them (for example, as a report, a chart, online, hard copy). Discussions of decision making will drive what information companies need to collect, analyze and publish in their BI systems.


Good BI systems need to give context. It’s not enough that they report sales were X yesterday and Y a year ago that same day. They need to explain what factors influencing the business caused sales to be X one day and Y on the same date the previous year.


Like so many technology projects, BI won’t yield returns if users feel threatened by, or are skeptical of, the technology and refuse to use it as a result. And when it comes to something like BI, which, when implemented strategically, ought to fundamentally change how companies operate and how people make decisions, CIOs need to be extra attentive to users’ feelings.


Seven steps to rolling out BI systems:


  1. Make sure your data is clean.

  2. Train users effectively.

  3. Deploy quickly, then adjust as you go. Don’t spend a huge amount of time up front developing the “perfect” reports because needs will evolve as the business evolves. Deliver reports that provide the most value quickly, and then tweak them.

  4. Take an integrated approach to building your data warehouse from the beginning. Make sure you’re not locking yourself into an unworkable data strategy further down the road.

  5. Define ROI clearly before you start. Outline the specific benefits you expect to achieve, then do a reality check every quarter or six months.

  6. Focus on business objectives.

  7. Don’t buy business intelligence software because you think you need it. Deploy BI with the idea that there are numbers out there that you need to find, and know roughly where they might be.

What are some potential problems?


User resistance is one big barrier to BI success; others include having to winnow through voluminous amounts of irrelevant data and poor data quality.


The key to getting accurate insights from BI systems is standard data. Data is the most fundamental component of any BI endeavor. It’s the building blocks for insight. Companies have to get their data stores and data warehouses in good working order before they can begin extracting and acting on insights. If not, they’ll be operating based on flawed information.


Another potential pitfall is BI tools themselves. Though the tools are more scalable and user friendly than they used to be, the core of BI is still reporting rather than process management, although that’s slowly beginning to change. Be careful not to confuse business intelligence with business analytics.


A third impediment to using BI to transform business processes is that most companies don’t understand their business processes well enough to determine how to improve them. And companies need to be careful about the processes they choose. If the process does not have a direct impact on revenue or the business isn’t behind standardizing the process across the company, the entire BI effort could disintegrate. Companies need to understand all the activities that make up a particular business process, how information and data flow across various processes, how data is passed between business users, and how people use it to execute their particular part of the process. And they need to understand all this before they start a BI project, if they hope to improve how people do their jobs.


What are some benefits of business intelligence efforts?


A broad range of applications for BI has helped companies rack up impressive ROI figures. Business intelligence has been used to identify cost-cutting ideas, uncover business opportunities, roll ERP data into accessible reports, react quickly to retail demand and optimize prices.


Besides making data accessible, BI software can give companies more leverage during negotiations by making it easier to quantify the value of relationships with suppliers and customers.


Within the walls of the enterprise, there are plenty of opportunities to save money by optimizing business processes and focusing decisions. BI yields significant ROI when it sheds light on business bloopers. For example, employees of the city of Albuquerque used BI software to identify opportunities to cut cell phone usage, overtime and other operating expenses, saving the city $2 million during three years. Likewise, with the help of BI tools, Toyota realized it had been double-paying its shippers to the tune of $812,000 in 2000. Companies that use BI to uncover flawed business processes are in a much better position to successfully compete than companies that use BI merely to monitor what’s happening.


More tips for getting BI right


  • Analyze how executives make decisions.

  • Consider what information executives need in order to facilitate quick, accurate decisions.

  • Pay attention to data quality.

  • Devise performance metrics that are most relevant to the business.

  • Provide the context that influences performance metrics.

And remember, BI is about more than decision support. Due to improvements in the technology and the way CIOs are implementing it, BI now has the potential to transform organizations. CIOs who successfully use BI to improve business processes contribute to their organizations in more far-reaching ways than by implementing basic reporting tools.