Big data, Business intelligence, data quality & data governance
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Free web analytics & tag management data quality audit

Free web analytics & tag management data quality audit | Big data, Business intelligence, data quality & data governance |

Free web analytics data quality audit

Do you know for sure that you are making the right data driven decisions? Or do you just assume that your web analytics reports are based on correct data?

20% of web analytics data are wrong

Crucial for collecting web data are web analytics tags. These are placed on a website to collect data and send them to a web analytics solution. Based on these data, reports are made. But research actually shows that errors occur in 20% of all web analytics tags, seriously impacting your data quality.

The impact of poor data quality

Gartner study Measuring the Business Value of Data Quality (2011) concluded that:

  • Poor data quality is a primary reason for 40% of all business initiatives failing to achieve their targeted benefits.
  • Data quality effects overall labour productivity by as much as 20%.
  • As more business processes become automated, data quality becomes the rate limiting factor for overall process quality.

So what are the implications of tagging errors? We have made a list of frequently occurring issues, based on our experiences with our clients:

  • Incorrect attribution of orders and campaigns

To measure the success of your online campaigns, you should have insights in the entire path from an ad to an order. With these data, you can determine which campaigns lead to which orders and make decisions about budgets and optimisation. If these data are wrong, you will often end up investing in the wrong campaigns.

  • Multiple payments to affiliates

If you use affiliates for advertising on the web, incorrectly configured tags can make you pay the wrong party for a click. Double and multiple payments are very common implications of tagging errors.

  • Differences in the number of orders

In practice, it turns out that most companies have a difference of 5 to 25% in the orders in the order management system and those in their web analytics solution. This leads to multiple versions of the truth within your organisation and a mistrust of data.

Free tag audit

Want to see how the status of your web analytics data quality? Sign up for a free web analytics data quality audit with Qmon! This audit contains:

  • Auditing before a release
  • Auditing all orders, browsers, operating systems, devices and tags in your live site
  • Test both tag execution and the data sent to your analytics tool
Sign up for a free tag audit now!

You will find that Qmon doesn’t only improve your web analytics. It improves your entire online business, by simplifying data quality, securing your trust in data and creating an actionable and sustainable focus on increasing business value.

Sign up for your free web analytics audit

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Interview: Kirk Borne, Data Scientist, GMU on Big Data in Astrophysics and Correlation vs. Causality

Interview: Kirk Borne, Data Scientist, GMU on Big Data in Astrophysics and Correlation vs. Causality | Big data, Business intelligence, data quality & data governance |

We discuss how to build the best data models, significance of correlation and causality in Predictive Analytics, and impact of Big Data on Astrophysics.

By Anmol Rajpurohit (@hey_anmol), May 30, 2014. 

Kirk Borne is a Data Scientist at George Mason University. He has been at Mason since 2003, where he does research, teaches ,and advises students in the graduate and undergraduate Data Science, Informatics, and Computational Science programs. He helped to create the Data Science B.S. degree program that began in 2007. Previously, he spent nearly 20 years in positions supporting NASA projects, including an assignment as NASA's Data Archive Project Scientist for the Hubble Space Telescope, and as Project Manager in NASA's Space Science Data Operations Office. He has extensive experience in big data and data science, including expertise in scientific data mining and data systems.

He has published over 200 articles and given over 200 invited talks at conferences and universities worldwide. He serves on several national and international advisory boards and journal editorial boards related to big data. In these roles, he focuses on achieving big discoveries from big data, and he promotes the use of information and data-centric experiences with big data in the STEM education pipeline at all levels. He believes in data literacy for all. 

Here is my interview with him: 

Anmol Rajpurohit: Q1. In your keynote, you highlighted the benefits of large datasets, how Big Data can be used as a sort-of experimentation bed. Quite often, we have Big Data, but yet not necessarily all the data that we desire (for various reasons, including when the data is hard to find or to quantify). What scientific approaches do you recommend in these situations to benefit from partial, incomplete data? 

Kirk Borne: The reality here is that "partial, incomplete data" has been the norm for all of human history, and certainly for the history of science. Consequently, traditional methods of modeling and simulation are useful here -- where you build a model that represents whatever it is you are studying. The model includes parameters for things that you don't know and it includes constraints from the things you do know (i.e., from your partial, incomplete data). 

The goal of modeling and simulation is to help identify the "best" parameters for the things that you don't know, and then apply those "best models" to your field of study in order to make inferences, predictions, and decisions.
Of course, a model is not perfect -- it is simply a partial representation of reality, from which you hope to make better discoveries and decisions than you could have made otherwise. As the famous statistician George Box said: "All models are wrong, but some are useful." That's precisely the point. The model is imperfect, but it is still useful. Similarly in the case of partial and incomplete data, our subsequent understanding, models, inferences, and predictions are imperfect, but they are still useful. 

AR: Q2. From the perspective of Predictive Analytics, Correlations are a great discovery. But, is it good enough without a proper understanding of underlying Causality? 

KB: Finding causality is good science, but in many applications it is more important to make a good decision. For example, in astronomy, scientists discovered in the 1960's that there were energetic bursts of gamma-rays coming from space. We had no idea what the cause was, but we discovered that the spatial distribution of these bursts across the sky correlated eerily well with an isotropic model (that is, the bursts were not coming from any preferred direction or location in the sky). Nevertheless, this correlation led to improved astrophysical theories, new technologically powerful scientific instruments, and further observations for several more decades before the cause was ultimately discovered in the mid-1990's. The cause was found to be from a massive star exploding (and then collapsing into a black hole), which occurs sporadically and randomly throughout the Universe. So, the correlation led to great physical models and fantastic improvements in space astronomy instrumentation, even without understanding (initally) the underlying cause. 

Similarly, in online retail stores, businesses discover correlations in customer purchase patterns, thereby enabling and empowering recommender engines to present meaningful product recommendations to their customers. These engines are not only good at recommendations, but they are also very good at generating revenue for the business. There is no hint in these models as to what causes a customer to have a preference for product A and also for product Z, but if the historical purchase data reveal that the products are correlated, then it is simply smart business sense for you to act on that correlation, even without the causal understanding.
So, I would say that we definitely want to understand causality, and we should never give up our search for the underlying causes, but let us not churn on that problem (which can lead to "analysis paralysis"), instead focus on using the discovered correlations in powerful predictive analytics applications.

AR: Q3. How has Big Data impacted the science of Astrophysics? Can you share some discoveries that were made based on Big Data? 

KB: Astronomy data collections have definitely been growing "astronomically" for many years, but the biggest and the best is yet to come, including the LSST (Large Synoptic Survey Telescope) project that will begin construction in the summer of 2014 and the future SKA (Square Kilometer Array). These petascale big data projects promise amazing discoveries. From the terascale projects of the past couple of decades, there have been many important discoveries. For example, from NASA's Kepler mission, we are discovering hundreds of new planets around distant stars via the slight variations in the time series of the stars' light emissions being tracked over several years for more than 100,000 stars. In the first large surveys of galaxies' distances in the 1980's, we found large voids in the Universe, regions that are almost devoid of massive galaxies, leading to a better understanding of the massive large-scale structure of the Universe -- it has essentially the same structure as soap bubbles: the majority of massive galaxies and clusters of galaxies reside on the surfaces of enormous bubble-like regions of space, with almost nothing within the interior regions of the bubble-like structure. We also found extremely rare ultra-luminous galaxies emitting enormous infrared radiation, caused by super-starbursts inside these galaxies -- these were discovered after studying the properties of millions of galaxies. 

More recently, we used a citizen science project called Galaxy Zoo to empower the general public to help us look at and characterize images of nearly a million galaxies (which was far more than any individual scientist or team of scientists could look at), and our citizen volunteers found totally new classes of astronomical objects (e.g., light echos from dead quasars, and also little green galaxies, which the volunteers dubbed "green peas"). In all of these cases, it was the study of very large databases of objects that led to the discovery of the surprising, interesting, new things. For me, that is the most exciting and exhilarating aspect of big data science -- discovery of the new, unexpected thing. That "novelty discovery" approach works in astronomy, but also in any big data domain! Finding the unknown unknowns is what motivates my work and it is my goal every day as a data scientist. 

The second and last part of this interview will be published soon. 

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Mary Meeker: Mobile devices equal big data devices

Mary Meeker: Mobile devices equal big data devices | Big data, Business intelligence, data quality & data governance |
MAY 29, 2014

Mary Meeker: Mobile devices equal big data devicesKleiner Perkins Caufield & Byers report sees mobile devices at the center of a slew of personalized data-harvesting trends

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Mary Meeker, of Kleiner Perkins Caufield & Byers, has delivered the latest edition of her annual "Internet Trends" report, a series she has churned out since 2001. The touchstones for this year's report all involve familiar terms -- mobile devices, big data, cheaper processing power -- but one finding revolves around the way connected and instrumented mobile devices create data from user behaviors, as opposed to just providing data for user consumption.

First, the basics: Overall, Internet usage continues to grow -- 2.6 billion users as of the end of 2013 -- but its growth rate has started to flatten, and much of the growth is in markets that Meeker describes as "more difficult to monetize," such as India, Indonesia, and Nigeria. Most of that Internet usage is shifting to mobile devices, with the lion's share of engagement taking place on smartphones rather than tablets despite the latter's booming sales. Even there, it's starting to level off: Smartphone subscriber growth is flattening, with the majority of growth taking place in what Meeker calls "underpenetrated markets" like China and Brazil.

But Meeker notes how mobile devices are not being used as mere consumption devices. "People enabled with mobile devices and sensors [are] uploading troves of findable and shareable data," says the report. Meeker also sees this as part of the way our newly found big data-gathering abilities (thanks to cloud computing being cheaper than ever) are being refashioned more as big problem-solving methodologies. The push is toward figuring out what specific problems to solve with all these harvesting tools and the data they gather.

As promising as such a view is, it's also an experimental one, with the applications, user behaviors, the harvested data, and the potential problems to be solved all in flux. In messaging, for example, all-in-one apps like Facebook are being replaced with more utility-specific applications like Snapchat, a process Meeker describes as "unbundling." Meeker also notes the rise of what she calls "invisible apps," such as Foursquare Swarm or Dark Sky, that gather data passively in the background based on a user's behaviors, both online and in the real world, and notify the user only when needed.

Elsewhere in the report, Meeker examines how education and health care are being reshaped by technology. Both have become costly affairs; the former is doing a poorer job of preparing people for the realities of the modern job market, and most of the cost of the latter stems from management of chronic conditions due to behaviors that engender health risks (bad diet, lack of exercise).

In both cases, Meeker sees connected technology as a reformative influence. Education is being reshaped via cheaper online courses, and the "consumerization of health care" allows patients to not only more closely manage their own conditions, but give more detailed feedback about the quality of their care providers. There's still room for skepticism, given the newness of those fields, and Meeker seems to implicitly understand that, as she characterizes her findings as "green shoots data."

This story, "Mary Meeker: Mobile devices equal big data devices," was originally published Get the first word on what the important tech news really means with the InfoWorld Tech Watch blog. For the latest developments in business technology news, follow on Twitter.

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Top Ten Big Data Analytics Tips

Top Ten Big Data Analytics Tips | Big data, Business intelligence, data quality & data governance |

Analytics is a powerful tool for any company to understand customer better and make decisions based on facts and numbers, not just emotions or feelings. But how to get it right and get the best out of analytics capabilities? Data experts from major brands shared their tips on best practice work with business analytics.

1.    Keep It Simple (10 times more so than is necessary to get your point across). 
Alex Uher at L’Oreal Paris advises: “Keep it simple. Ridiculously simple. Ten times more simple than what you think necessary. Just about then, you are actually getting your point across in a way that people are starting to follow you”.

2.    Hypothesize/Put Problem First

3.    Don’t Assume Data is Good – Check/Validate!

4.    Automate repeat tasks & Carve out time to go exploring 
Jonathan Isernhagen at Travelocity believes that “Automate anything you do more than once. It’s very easy to fill your time with routine pulls of data which lie just beyond the reach of the visualization tools available to business stakeholders.”

5.    Set a Data Strategy – don’t just collect data for the sake of collecting it 
Farouk Ferchichi at Toyota Financial Services says: “Ensure there is a purpose you understand of why analytics is valuable to the organization. Purpose can be a business sponsor like discovering new ways (i.e. products, markets, etc.) to increase revenue, retention, profit, or control costs. So ask the tough questions and align with executives mandates.”

6.    In a rapidly expanding field, work with people on the leading edge 
As per Anthony Palella at Angies List: “Work with people who are able to work on the leading edge …the people who are helping expand the envelope.”

7.    Be a Skeptic about models etc. 
Sofia Freyder at MasterCard says: “Double check your results using data from different sources. Make sure it makes sense. In case of discrepancies use it directionally. Reach out to experts to obtain their opinion.”

8.    Look for the pragmatic and cost effective solutions 
Deepak Tiwari makes an example: “You can probably do machine learning and neural networks to solve a lot of problems but a linear regression might sometimes be enough.”

9.    Don’t torture Data – in the end it will confess.

10.  Think like a Business Owner – what would you like to know?

Read detailed tips here:

This topic is being discussed in more depth at the Useful Business Analytics Summit in Boston, June 10-11. Join us:

Tags: AnalyticsBigBusinessDataIntelligencePredictivemodelling

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Top Three Business Benefits from Big Data

Top Three Business Benefits from Big Data | Big data, Business intelligence, data quality & data governance |
Kirk Borne, Ph.D. speaks with Anametrix CEO Pelin Thorogood, this time identifying the areas where he thinks business will benefit most from big data.
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Is Your Decision-Making Out Of Control?

Is Your Decision-Making Out Of Control? | Big data, Business intelligence, data quality & data governance |

Decisions are the life-blood of business. The right decisions can lead to tremendous successes, while the worst decisions can bring entire companies down.

In business, we make decisions, big and small, all the time. The question is: Are these decisions as good as they could be? In most cases the answer would be no!

Major decisions in business are often made on no more than hearsay, rumors and gut feel. In many ways it might feel like decision-makers are making the right choices, steering the company into a successful future, when in reality they are making decisions whizzing through the air in free-fall while wearing snorkeling goggles that distort their view.

Read full article from Bernard Marr

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Generating Data on What Customers Really Want

Generating Data on What Customers Really Want | Big data, Business intelligence, data quality & data governance |

At a fundamental level, the decisions managers make about revenue and profits fall into two categories—those related to growth and those related to cost reduction. Both types are meant to increase margins. But how data are used in each decision-making process is completely different.

Cost reduction data are precise. Firms know their cost structure very well and can compute with a reasonable level of certainty the savings each alternative being considered will generate. Managers use cost-related data as an objective input for decision making, since they consider it both reliable and reasonably predictable.

Managers do not treat growth-related data in the same way, and for good reason. Growth alternatives usually involve either launching new products or entering new markets, and these are activities where uncertainty is high. Here data are used mainly as a tool for persuasion among managers. A stereotypical example would go more or less like this: Managers gather the data that reinforce their own point view on what they believe is the right business decision. During a meeting they all explain their various rationales and present their various data points. Usually, they end up reaching a consensus that they all consider makes sense. Then they leave the room still thinking that their own alternative was best but that in life you have to make tradeoffs.

Most growth-related decisions in management are made this way because managers do not have data that reliably predicts how new customers will react to their offerings or how any customer new or old will react to innovative offerings. If only Coca Cola would have predictably known that its customers would not embrace New Coke! But its executives lacked a way to generate predictable information on that score or tell them that the data they’d generated from their taste tests would ultimately not be relevant.

Disruptive innovation practitioners have just such a tool for reliably predicting customers’ behavior. It’s a methodology that uncovers what in disruptive innovation parlance is called a person’s “job to be done.” Briefly, the idea is this: Consumers don’t go to the store to buy products. They go to the store to buy something that will enable them to get some important job done in their lives. The classic example, attributed to HBS professor Ted Levitt, is that people don’t want to buy a quarter-inch drill; they want something that will make a quarter-inch hole. Making a quarter-inch hole is the job to be done. The product that does that job most reliably, easily, conveniently, and less expensively is the tool they will be most likely to purchase for that job.

Work out your customer’s jobs-to-be-done, disruptive innovation practitioners have found, and you will generate data that more reliably predicts what a customer will buy and why. How do you do that? Traditionally, corporate innovators are told to conduct ethnographic studies, starting with no preconceptions, and to observe customers’ behavior and frustrations with the same open mind that start-ups employ. Human nature being what it is, that’s a hard thing to do.

Here, instead, is a simplified version of a methodology for identifying a customers’ job-to-be-done that starts with information about your own product. Since product information is not part of a job-to-be-done, the information about your product will drop out of the process in step 6, so that it doesn’t distort your results. But in this way, you can make your way to a new insight by starting in familiar territory. (And you’ll not keep the people you’re interviewing wondering what all your questions are about during the entire interview process.)

Step 1. Prepare a list of the key characteristics of your offering. List at least 10 of them. Your product or service may be faster than your competitor’s. Or cheaper. Or have a better screen resolution. Or have leather seats. Or a battery that lasts for many days. Or connect to the internet and let you play with other people online. Let’s say you sell cars. Your list might include characteristics such as speed, gas consumption, how little it pollutes the environment, number of doors, colors, type of seats, cup holders and amenities inside the car for the driver and the passengers.

Step 2. Interview at least 10 consumers and 10 nonconsumers about the various features connected to your offering. Nonconsumers are people who are not buying either your offering or your competitors’. So in this case you would be interviewing both people who drive cars and people who could drive cars but chose not to. The interviews can be anonymous, but you need to record the entire conversation. For each of the characteristics listed above ask three questions. First, “Where are you when you are using this feature?” Second, “When you use this feature, what are you really trying to do?” And third, “If this feature were not available, what would you be using instead?” Now, here’s an important part: for consumers, you need to ask the second two questions without reference to your product. So, to return to the car, let’s say you asked: “Why do you sit in the passenger’s seat of your own car?” and the answer was: “I am a salesman, and in between meetings I work in the car.” Then you ask: “When you do that what are you trying to accomplish?” He answers: “I try to have an environment that mimics my office space so I can concentrate and work comfortably for a while.” Then the third question: “If you could not do that what would you do instead?” He replies: “In the car I use the cup holder for my coffee, and in the passenger’s seat I can work with my laptop and recharge my phone. If I could not do that I would go to a cafeteria, but it is difficult to concentrate there. It’s noisy and I waste time locating one.”

Step 3. Transcribe the recordings. It is important that you do not miss anything. The transcript must end up looking like an interview, faithfully recording exactly what was said, complete with pauses. Being systematic about when the data stop is important for the statistical analysis used in step 5.

Step 4. Codify the transcripts by tracking all the meaningful nouns, verbs, adjectives, and adverbs. To continue our example we would extract from the sentence: “I try to have an environment that mimics my office space” will result in the following codes: “try,” “environment,” “mimics,” “office,” and “space.” With this you create a table in which you count the number of instances of each word (so if in the entire first transcript the word office is only repeated once you would have a 1 in the first row). Complete the table until all the sentences from all the transcripts have been codified. There are software tools available to do that more easily.

Step 5. Group the codes. Using the statistical technique of cluster analysis, group the codes based on their proximity. That is, it measures how many times each word appears close to one another (that’s why the pauses matter). Let the software that you choose determine the optimal number of clusters. The end result is that all your codes will now appear in groups.

Step 6. Remove descriptive data so you have only prescriptive data left. To do this, you remove all the groups that contain information about your product, in this case, all the groups that contain the word “car.” The end result is a series of groups that each contains a number of different code words. Within each group, these codes refer to the customer’s way of thinking and the portions of the context that the customer considers relevant to deciding on which product alternative to buy. If you compare how product performs in relation to the concerns expressed in each group, your next product improvement will become compellingly clear, not only to you, but also to your colleagues. In this example, it would become easy to make the case for focusing an innovation effort on helping salespeople become more productive and work more comfortably in their cars. Better electrical outlets, perhaps, so people can charge more than one item at a time? Storage for computers or samples? Something that mimics a desk more effectively? The point here is this is a fruitful avenue for further attention since most car models sell fewer than 100,000 units per year, but millions of people work in their automobiles.

During the 1950s Edward Deming and others developed such tools as statistical process control charts and total quality management techniques that have made the cost-reduction data we use today predictable and reliable. Before that, though, data about cost reduction was as unreliable as growth related data are today. Now the first tools are starting to emerge that add predictability and reliability to growth related decisions. Jobs-to-be-done is one such tool. Once managers learn how to compute a job-to-be-done by themselves, their growth related decisions will become much more objective and less opinion based.

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How P&G Presents Data to Decision-Makers

How P&G Presents Data to Decision-Makers | Big data, Business intelligence, data quality & data governance |
When it comes to data visualization, it's not how creative you are, but how common.
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Teradata: We've got the most complete Big Data analytic tool in the market today

Teradata: We've got the most complete Big Data analytic tool in the market today | Big data, Business intelligence, data quality & data governance |
The data warehouse vendor, whose first ever data warehouse debuted over 20 years ago, announces the QueryGrid for seamless, easy data analytics.
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Nederland heeft chief data officers nodig

Nederland heeft chief data officers nodig | Big data, Business intelligence, data quality & data governance |
Dit jaar heeft Said Business School (University of Oxford) aan het Executive MBA-programma - naast Finance, Marketing en Operations management - een nieuwe, tiende kernmodule toegevoegd: decision and...
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Why do we need Data Governance? - watch YouTube movie

2 Minute video on why organizations need Data Governance

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84% Of Enterprises See Big Data Analytics Changing Their Industries' Competitive Landscapes In The Next Year

84% Of Enterprises See Big Data Analytics Changing Their Industries' Competitive Landscapes In The Next Year | Big data, Business intelligence, data quality & data governance |

87% of enterprises believe Big Data analytics will redefine the competitive landscape of their industries within the next three years. 89% believe that companies that do not adopt a Big Data analytics strategy in the next year risk losing market share and momentum. 

Via John Lasschuit ®™
John Lasschuit ®™'s curator insight, October 31, 2014 3:29 PM

By Louis Columbus: #BigData #Analytics now seen as essential for competitiveness

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Two reasons why big data is so big for telecoms operators - YouTube

There are two main reasons why big data is so 'big' for operators: a technological reason and a commercial one. In this video, Justin van der Lande, Principa...
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How to prep your big data architecture for better analytics

How to prep your big data architecture for better analytics | Big data, Business intelligence, data quality & data governance |

collect vast amounts of data that may help them develop new insights about their business and customers, but just because they have the data in-house doesn't mean they're ready to analyze it.

"To enter the brave new world of big data analytics you'll probably need to extend your data warehouse environment and make some adjustments to your data warehouse architecture," Russom said.

The point of adding more data platforms to the data warehouse environment is to deal with the diversity of data types and analytic algorithms.

Variety is likely the main reason that traditional data warehouse architectures aren't up to the big data challenge. A lot of data being collected is unstructured, including social media posts or natural language from health records. Similarly, many analytic tools aren't designed to pull data from traditional relational databases. The velocity piece of big data is also a challenge for legacy databases. Relational databases may struggle to keep up with streaming data from machine sensors, for example.

For these reasons, Russom recommended implementing a new data architecture that can keep up with big data demands. This is how businesses move beyond just managing data to extracting value.

"I regularly tell people to never be content to manage big data as a cost center," Russom said. "We want to be sure that we get some business value out it."

Analytics is necessary to pull useful information from big data sets. But Russom said there isn't one tool that performs all analytic functions. Analyzing data starts with capturing it. Then an organization needs to explore it to see what's there. From there, the real analysis happens. Finally, the analysis output must be put into a visualization so executives can make sense of it.

Each step in the process may demand its own tool, Russom said, which may require an organization to build new features into its existing data warehouse architecture. Some data management professionals may scoff at this idea, as it will inevitably add greater complexity and make the architecture more difficult to manage. But Russom said data managers worry too much about this.

"The point of adding more data platforms to the data warehouse environment is to deal with the diversity of data types and analytic algorithms," he said. "Managers have been dealing with complexity for years, so I think the vast majority of professionals are coping with it quite easily."


See how big data is changing the role of the data architect

Learn why marketing and advertising are the biggest users of big data

Read other tips on planning for big data analytics

Russom provided more tips for managing a growing data warehouse architecture:

  • Determine business drivers for new data marts or applications before implementing them.
  • The physical layout of the warehouse (for example, where data is stored) often is not as important as the conceptual or logical level, which determines how applications function alongside each other.
  • Be aware that the leading barriers to successful architecture development are skills and staffing.
  • Address other barriers, like executive sponsorship and funding, early in the project.
  • Establish data warehouse standards, but be open to exceptions.

Ed Burns is site editor of SearchBusinessAnalytics. Email him at and follow him on Twitter: @EdBurnsTT.

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Big Data - "Dangerous (feat. Joywave)" [Official Music Video] - YouTube

. EXCLUSIVE- watch us drift a 200 ton Mining Truck to demonstrate how you can have Scale AND Agility . What hap...
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Visual Analysis Best Practices - Tableau guidebook

Visual Analysis Best Practices - Tableau guidebook | Big data, Business intelligence, data quality & data governance |
How to get your visualizations from good to great
Bringing your visualizations from “good” to “great” takes time, patience, attention to detail, and some basic knowledge of visual analysis best practices. Luckily, we have compiled an important list of techniques to get you started.
Read this guidebook and learn:
  • Why you should always start with questions
  • How to choose the right chart type
  • The ins and outs of creating effective views
  • How to design useful and engaging dashboards
  • The importance of perfecting your work (and tips!)
Ronald van Loon's insight:

Get the free guidebook ;

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MEPs tighten up rules to protect personal data in the digital era

MEPs tighten up rules to protect personal data in the digital era | Big data, Business intelligence, data quality & data governance |

MEPs inserted stronger safeguards for EU citizens’ personal data that gets transferred to non-EU countries in a major overhaul of the EU’s data protection laws voted on Wednesday. The new rules aim both to give people more control over their personal data and to make it easier for firms to work across borders, by ensuring that the same rules apply in all EU member states. MEPs also increased the fines to be imposed on firms that break the rules, to up to €100 million or 5% of global turnover.


The EU’s 19-year-old EU data protection laws urgently need updating to keep pace with the progress of information technologies, globalisation and the growing use of personal data for law enforcement purposes.

"I have a clear message to the Council: any further postponement would be irresponsible. The citizens of Europe expect us to delivera strong EU wide data protection regulation. If there are some member states which do not want to deliver after two years of negotiations, the majority should go ahead without them", explained rapporteur for the general data protection regulation, Jan Philipp Albrecht (Greens/EFA, DE).

"Allow me to express my dissatisfaction and frustration about the fact that it is the Council, or at least some member states, which are preventing us from achieving the goal that we had set, namely to have the data protection reform package passed by the end of this Parliament’s mandate", said rapporteur for the directive on the protection of personal data processed for law enforcement purposes, Dimitrios Droutsas (S&D, EL).

Data transfers to non-EU countries

To better protect EU citizens against surveillance activities like those unveiled since June 2013, MEPs amended the rules to require any firm (e.g. a search engine, social network or cloud storage service provider) to seek the prior authorisation of a national data protection authority in the EU before disclosing any EU citizen’s personal data to a third country. The firm would also have to inform the person concerned of the request.

Deterrent fines

Firms that break the rules should face fines of up to €100 million, or up to 5% of their annual worldwide turnover, whichever is greater, say MEPs. The European Commission had proposed penalties of up to €1 million or 2% of worldwide annual turnover.

Better protection on the internet

The new rules should also better protect data on the internet. They include a right to have personal data erased, new limits to “profiling” (attempts to analyse or predict a person's performance at work, economic situation, location, etc.), a requirement to use clear and plain language to explain privacy policies. Any internet service provider wishing to process personal data would first have to obtain the freely given, well-informed and explicit consent of the person concerned.


The data protection package consists of a general regulation covering the bulk of personal data processing in the EU, in both the public and private sectors, and a directive covering personal data processed to prevent, investigate or prosecute criminal offences or enforce criminal penalties (law enforcement).

Next steps

The European Parliament voted on its first reading of the draft legislation, in order to consolidate the work done so far and hand it over to the next Parliament. This ensures that the MEPs newly elected in May can decide not to start from scratch, but instead build on work done during the current term.

The draft regulation was approved by 621 votes to 10, with 22 abstentions.

The draft directive was approved by 371 votes to 276, with 30 abstentions.


Procedure: Co-decision (Ordinary Legislative Procedure), first reading

REF. : 20140307IPR38204
Updated: (12-03-2014 - 18:01
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