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5) While 81% of execs said they believe that social media is an essential ingredient in delivering great customer experiences, 35% of responding companies still do not have social media for sales channels, and another 35% still do not have social media for customer service. QUESTION: How do you plan to close that dangerous gap?

Most Data Professionals work in teams of up to five people. “Companies are starting to realize that Data is key to their success. The majority of them, though are not growing their Data Science teams fast enough to win. This maybe because they don’t want to or because they can’t. This is an alarming trend though and only software can come to the rescue,” noted Aziza.

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.

Beware the Three-Headed Geek-Monster: Tools, Performance, and Mathematical Perfection Many things can sabotage the use of analytics within the workplace. Politics and infighting perhaps; a bad experience from a previous “enterprise, business intelligence, cloud dashboard” project; or peers who don’t want their “dark art” optimized or automated for fear that their jobs will become redundant.

Not all hurdles are within your control as an analytics professional. But some are. There are three primary ways I see analytics folks sabotage their own work: overly complex modeling, tool obsession, and fixation on performance.

In other words, work with the rest of your organization to do better business, not to do data science for its own sake.

Data Smart: Using Data Science to Transform Information into Insight by John W. Foreman. Copyright © 2013.

1) Your First-Party Data: The primary and most valuable data set you can access, first-party data encompasses transactional and other customer-level profile information you have on your customers. It could also include your own off-line segmentation analysis that allows you to map a customer to a customer profile around which you build your marketing programs. This can also include your analytics or other on-site tracking data, which can deliver behavioral insight to your consumers. This data can be difficult to export from its current environment due to the ad hoc nature of the data, but, if possible, look at ways to make this information accessible to your digital sites. 2) Third-Party Data: A consumer's broader Web browsing and buying history can now be accessed in session to provide you with more context on their likes and habits. Data management platforms (DMPs) and other data aggregators are accelerating this offering and, just as importantly, the availability of this type of data. This is invaluable in the context of new visitors who you know nothing about historically. 3) Real-Time Behavior: Let's not forget what our customers are telling us with each click. We get enamored with our predictive modeling to the point that we do not see the tell-tale signs as they are happening. Take the time to stop, look, and react. Your analytic tools, personalization tools, and other software-as-a-service (SaaS) platforms can help you trigger alternate site experiences based on every click you see.

five broad ways in which using big data can create value

Leading companies are using data collection and analysis to conduct controlled experiments to make better management decisions

others are using data for basic low-frequency forecasting to high-frequency nowcasting to adjust their business levers just in time.

big data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services.

Fourth, sophisticated analytics can substantially improve decision-making

big data can be used to improve the development of the next generation of products and services.

The use of big data will become a key basis of competition and growth for individual firms.

For example, we estimate that a retailer using big data to the full has the potential to increase its operating margin by more than 60 percent.

The computer and electronic products and information sectors, as well as finance and insurance, and government are poised to gain substantially from the use of big data.