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The BI Server wouldn’t have any use for any more query threads, as it can’t really do anything with them – the exception to this being queries that generate multiple physical SQLs, for example to join data from multiple sources together and return a single set of data to the user, for which the BI Server could benefit from a higher CPU count if each of these queries in turn led to lots of threads being used – but two queries, in themselves, don’t neccessarily require two CPUs, because of course the BI Server, and the underlying CPUs, are themselves multi-threaded.

To conclude then – all things begin equal, the BI Server should make use of all of the CPUs that the underlying operating system presents to it, with the OS itself deciding what threads are scheduled against which CPUs. In-theory, all CPUs on the server are available to each BI Server component, but each OS is different and it might be worth experimenting if you’re sure that certain CPUs aren’t being used – but this is most probably unlikely and the main reason you’d really consider vertical scale-out of BI Server components is for fault-tolerance, or if you’re using a 32-bit OS and each process can only see a subset of the total overall memory. And, bear in mind that however many CPUs the BI Server has available to it, for queries that send just a single SQL statement down to the underlying database server, adding more CPUs or faster CPUs isn’t going to help as only a single (or so) thread will be needed to send the query from the BI Server to the database, and it’s the database that’s doing all of the work – all that this would help with is compilation and post-aggregation work, and enabling the server to handle a higher number of concurrent users. Invest in a better underlying database instead, sort out your data model, and make sure your data source back-end is as optimised as possible.

Social channels are inherently built for sharing and engagement, making them the perfect place to cultivate valuable business relationships. Integrating social into every marketing campaign you run can move you from a company-to-buyer marketing model to a peer-to-peer influence model. This not only builds trust and brand loyalty, but also positively impacts ROI.

It can be tempting to jump right in to all the social media sites out there and start posting away. However, before you publish that first nugget of social marketing content, you need to develop your plan.

goals and metrics

Many marketers fall into the trap of thinking that social media campaigns can be dealt with on an ad hoc basis, but this couldn't be further from the truth. You don't want your company's online personality to come across as erratic or disjointed, so create a policy that guides those who are participating in the social marketing effort and be sure those guidelines are enforced.

Once everyone is on board, encourage them to create engaging content. A good starting place is to ask your team members to answer some of the most frequently asked questions they receive on the various social channels. If everyone is a content creator, you'll never be short of ideas.

Word-of-mouth is incredibly powerful and the "share" button on every social media channel allows you to tap into millions of different networks. One of the best ways to interact with your audience is by giving them content they genuinely want to share with their networks. Peer recommendation is extremely valuable because people believe their friends much more readily than a company or marketer.

A "Refer-a-Friend" campaign promotes a compelling offer via email marketing and social networks, then grants access to special offers for both the referrers and those referred. Using these campaigns will allow you to gather important metrics, like tracking who the biggest influencers are.

A "Social Sweepstakes" campaign allows your entrants to spread the word on your behalf. Through the sweepstakes entry, you gain important user data like who is sharing and where they are sharing most.

Finally, a "Flash Deal" campaign is similar to Groupon. Flash deals offer a limited amount of deals for a specific time period through your social platforms. If you use these campaigns, be sure to let participants track the deal's progress! These campaigns are fun and viral ways to spread brand awareness and boost new customer numbers with sharing.

make sure your shares are measurable. Monitoring social share numbers is not only an easy way to tell what's working and what's not, but also allows you to see your ROI by showing how far your social reach is in relation to how much time and resources you've put in.

Google Alerts and search functions, or enterprise level software like Viral Heat or Radian6.

Once you hear what people are saying, you can engage them with relevant responses.

Social has evolved into much more than just a channel or tactic and should be an ever-present strategy in all aspects of your marketing. Ultimately, if you come up with a plan, encourage creative content, incorporate social marketing into every stage of your funnel, and measure your results, you'll start to see your social efforts move the ROI needle in the right direction.

Every company has hundreds of millions of records about their sales, expenses, employees and so on, with dozens of insights yet to be discovered through simple comparison or triangulation of relevant data.

Why don’t we focus on this? I think because it’s very difficult to do – being successful in this “data translator” role requires a unique set of skills and knowledge, the combination of which I call the BASE skillset: Business understanding Ability to synthetize and simplify Storytelling skills Expertise in data visualization

Business Understanding This one seems obvious, but it doesn’t mean simply understanding the financials of a business. Rather, it means truly knowing the operational details, the incentives, the install base, market growth, penetration, the competition, etc. An analyst can’t just know the technical aspect of a report or the math behind the numbers, but what is truly driving a pattern in terms of product quality, competition, incentives and/or offerings. The best analysts are able to mathematically isolate the key levers of a trend and then suggest actions to react to or take advantage of those trends. Ability to Synthetize and Simplify This is, in my opinion, the most underrated and underappreciated skill. Combing through thousands of data points and netting out 3-4 key issues in under 10 minutes, and then communicating these to a group of execs with very different analytical skills, is truly difficult. The key is to make it simple but not simplistic, which means you still capture the complexity even as you get to the few core insights. It requires a very thorough effort to gather all the relevant information before categorizing, prioritizing and deciding if it is significant. After a while, you become an expert and can sniff things out quickly. At the same time, there is the danger of missing anomalies when you jump to conclusions based only on a summary look.

Storytelling Skills There are stages that should be followed when explaining complex ideas, something data translators are frequently expected to do. The best storytellers start by giving context and trying to couple the current discussion to something the audience already knows, ensuring the story is well structured and connected. We have to move from a “buffet style” business review with thousands of numbers packed in tables to a layered approach that will guide the audience to focus first on the most relevant messages, diving deeper only when necessary. Minto Pyramid Principles, which are built around a process for organizing thought and communication, are helpful in making sure you really focus on what is important and relevant, versus being obsessed in telling every fact. Expertise in Data Visualization I am glad to finally see so much focus on Information Visualization and I believe this is correlated to the explosion of data. Traditional methods of organizing data do not facilitate an intuitive understanding of key information points or trends. For instance, the two examples below contain data on car sales across the U.S. The first, an alphabetized list, is much less intuitive than the second, which shows those sales on a map in Power View. With Power View, right away you can identify the states with the highest sales: CA, FL, TX, NY. (Workbook available here)

There is no better way to see patterns or trends than data visualization, making expertise in this area – both technical and analytical – critical for data translators.

Absolute path to VBoxManage is necessary unless it’s in the Windows $PATH.

vertical database

open schema

more complex queries

Entity: The entity represents Magento data items

Attribute: The attributes represent data items that belong to an entity.

Value: The value is the simplest to understand as it is simply a value linked to an attribute

The attribute or parameter:

The potential benefits of business intelligence programs include accelerating and improving decision making; optimizing internal business processes; increasing operational efficiency; driving new revenues; and gaining competitive advantages over business rivals. BI systems can also help companies identify market trends and spot business problems that need to be addressed.

BI data can include historical information, as well as new data gathered from source systems as it is generated, enabling BI analysis to support both strategic and tactical decision-making processes.

BI programs can also incorporate forms of advanced analytics, such as data mining, predictive analytics, text mining, statistical analysis and big data analytics.

In many cases though, advanced analytics projects are conducted and managed by separate teams of data scientists, statisticians, predictive modelers and other skilled analytics professionals, while BI teams oversee more straightforward querying and analysis of business data.

Business intelligence data typically is stored in a data warehouse or smaller data marts that hold subsets of a company's information. In addition, Hadoop systems are increasingly being used within BI architectures as repositories or landing pads for BI and analytics data, especially for unstructured data, log files, sensor data and other types of big data. Before it's used in BI applications, raw data from different source systems must be integrated, consolidated and cleansed using data integration and data quality tools to ensure that users are analyzing accurate and consistent information.

In addition to BI managers, business intelligence teams generally include a mix of BI architects, BI developers, business analysts and data management professionals; business users often are also included to represent the business side and make sure its needs are met in the BI development process.

To help with that, a growing number of organizations are replacing traditional waterfall development with Agile BI and data warehousing approaches that use Agile software development techniques to break up BI projects into small chunks and deliver new functionality to end users on an incremental and iterative basis.

consultant Howard Dresner is credited with first proposing it in 1989 as an umbrella category for applying data analysis techniques to support business decision-making processes.

Business intelligence is sometimes used interchangeably with business analytics; in other cases, business analytics is used either more narrowly to refer to advanced data analytics or more broadly to include both BI and advanced analytics.