Big Data use cases too compelling to ignore, argues HP’s Wood
An increasing amount of companies have recognised that there a number of different types of data within this whole ‘big data wave’ that has dominated IT discussions for some time now. However many organisations are still struggling with three main buckets of data.
In the first instance there’s internal business transactions — here we’re talking about customer relationship management (CRM) and enterprise resource planning (ERP). A lot of larger organisations in particular are finding it difficult to get the answers out of those systems in a time that’s fast enough for the business to react to it.
Then there’s structured machine data, for example, good old fashioned ideas like log files, then more emerging areas like sensors embedded into retail environments, cars, manufacturing facilities and so on. Structured information is being produced everywhere.
Finally comes unstructured human information. Take marketing executives — for them, one of the major parts of their job right now is to know what people are saying about their brand, product or service on those incredibly vast resources of human opinion that are Twitter and Facebook, or other corners of the online world such as TripAdvisor.
People rarely venture on to any of those platforms to put across a plain, vanilla review. They either love or hate something and you need to be able to decode the sentiment and be able to react quickly. As with the other two buckets of internal and structured data, you can now look to HP’s HAVEn platform to attack this wave of data.
HAVEn lets you collect, index and query massive volumes of data enabling faster, better decisions. It allows companies to anticipate customers’ needs, then design more relevant products and services and gain valuable insight to secure your enterprise.
Containing the ability to first catalogue massive volumes of distributed data, a combination of tools are available under the HAVEn banner. These include Autonomy IDOL to process and index data, while ArcSight Logger allows you to collect and unify machine data and the HP Vertica analytics platform helps you monetise all of this real-time data.
Since the launch of HAVEn, we’ve seen a common set of uses cases emerge across all the main verticals, whether that’s finance, retail, public sector, manufacturing and so on. One of the first use cases is to understand your customer better.
Take one of our major US clients, the huge racing organisation, NASCAR. It realises it has an amazing, engaging product for those people who are actually at the racetrack but that’s combined with huge revenues from TV coverage alongside merchandising. It needed to know exactly how people are enjoying its NASCAR experience whether at home or watching live, as well as what elements are not so good in addition to the areas it is not capitalising on as much as it could.
Once it knows all of the above, it can react to it, for instance telling its TV partner to change the coverage to emphasise particular elements it knows people are talking about and are interested in. Even the most leftfield of elements can be utilised: take even a recent rain delay for a race that went on for several hours. With TV ratings tanking completely, it saw that people who were actually at the racetrack were talking about this innovative, slightly bizarre machine that hoovers the rain up from the racetrack.
It told the TV people about this, and they in turn ran a 20-minute segment on the machine, completely re-engaging the TV audience as it did so! That’s the type of hidden, unknown elements that this analysis can break down.
Another use case is simply the desire to design your product a lot better by understanding the usage model. One prominent example of this is the fact that more and more companies are engaging with their customer base primarily through mobile apps — whether that’s online gaming companies, retailers or even financial institutions.
Lots of businesses are buying into the HAVEn platform to do immediate analytics of these apps — understanding people’s experience and their journey through an application which is now quite often the face of an organisation.
Another massive opportunity for big data is internal operations and processes. Let’s think about the IT department as a good example, there’s an enormous amount of transactional data that is collected by IT. When something goes wrong in your network for example application log files, network log files as well as other system management tools can help you diagnose what exactly happened.
Previously, a lot of the data that which was collected was frequently just thrown away and never used. However, a platform like HAVEn you can actually analyse all of that information in real time and indeed flip this process around and give predictive analytics on the IT operation to prevent further issues.
“HAVEn lets you collect, index and query massive volumes of data enabling faster, better decisions. It allows companies to anticipate customers’ needs, then design more relevant products and services and gain valuable insight to secure your enterprise”
As part of the HAVEn platform HP has built a tool called Operations Analytics — containing analytics engines like Autonomy, Vertica and ArcSight Logger. Using a combination of those tools we then build any number of applications on top to perform a specific task. In a lot of cases that’s what we we’ve done for IT managers and built a really impressive set of user interfaces and reporting tools.
Beyond the IT department though, there are other processes where HAVEn can help as well. This runs from automation tools for doctors to help breakdown and analyse the make-up of their day, to tracking mortgage application data with a bank, or intelligently analysing the swathes of applications and processes contained within a public sector organisation.
On a customer-by-customer basis, any combination of HAVEn tools can be used. Whether you need to understand the journey a customer takes through your website or are analysing voice recordings from your helpline [which the Autonomy Idol engine can break down to decipher sentiment, saving enormous amounts of work previously done manually], the use cases are continually developing.
For companies interested in getting started, HP has data scientists who can help people to understand what the potential value of their data is, and indeed what are the right questions to be asked in order to get the value from the data.
We’ve got the ability for people to use a HP-hosted version of HAVEn on a trial-like basis to see if they can extract value out of particular types of as well which is an initiative that’s gaining a huge response.
It’s something I’m sure a lot of companies who have seen so much discussion about big data can utilise and really begin to see just what all the fuss has really been about.
See the big picture in Big Data: http://tinyurl.com/n2565vx
Dan Wood is director for big data solution marketing with HP.