Mobility and usability drive business intelligence uptake

Ulrika Hedquist looks at the emerging trends in BI tools

The worldwide number of users of business intelligence, analytics and performance management solutions will double by 2014, according to Gartner. This growth is driven by an ongoing revolution in information consumption and usability, says the research firm.

The BI market locally was worth around $57 million in 2010 with a growth rate of 19 percent, says Gartner research director Bhavish Sood. The fastest growing BI sub-segment in New Zealand, analytic applications and performance management, grew 40 percent last year, from $5.9 million in 2009 to $8.2 million in 2010, while the bigger BI platforms market grew 15 percent to $40 million in 2010.

“The combination of mobile, visual and search significantly improves the user experience of BI,” says Sood. “By making it fun and easy to use, BI [appeals] to non-traditional users — the mainstream users that don’t enjoy staring at a grid all day.”

Gartner is seeing growth in mobile BI. “In particular the larger screen size of the tablets, as well as the interactivity of the touch screen, makes mobile devices a perfect place to practice BI,” says Sood.

The advance of mobile apps will also push users to an app store to access relevant information sources, he says.

“By 2013, 33 percent of business intelligence functionality will be consumed via handheld devices.”

Another trend is search-applied BI, where users can type in key words to find relevant information. “The key word search can lead them either to previously created BI content, for example reports, dashboards and cubes, or in advanced cases, it can create new BI content based on the search key words.”

In very advanced cases, users are able to blend structured and unstructured BI content, says Sood. “In many cases, the search ability is better for finding relevant information about a particular entity such as a customer or transaction or product.”

Self-service BI

Loyalty marketing services specialist Loyalty New Zealand launched a self-service portal for its Fly Buys programme in March this year.

The Fly Buys programme has more than 70 percent of New Zealand households as active members, collecting points from over 40 businesses, according to the organisation. In addition to managing Fly Buys, Loyalty NZ does customer insights work, including analytics, modelling and reporting and loyalty marketing services.

The new portal, called the Loyalty Centre, allows businesses participating in the Fly Buys programme to log on from their desktop, access information and generate a range of reports and graphics. This helps them gain a more accurate and detailed view of how their customers are responding to promotional activity and how they redeem their points, says Loyalty NZ head of customer engagement, Chris Lamers.

The tool, which uses Oracle technology, gives customers direct access to reporting and communication tools, he says.

“In the past they would have had to send a request through to us, we would have done a lot of work on that and then sent it back to them,” says Lamers.

Loyalty NZ was previously inundated with analytics requests. It’s estimated that the self-service and automated reporting capabilities will result in annual savings of 4,000 hours.

“We have identified that, increasingly, people are wanting to make data-driven decisions and marketers are more aware of good, accurate targeting; understanding what their business performance is and understanding what marketing activities work and do not work,” says Lamers.

So far, the response from customers has been positive, he says. Fifty percent of Loyalty NZ’s Fly Buys customers are now using the portal as a tool. Take-up was immediate, he says, and the rollout required less training and support than expected.

Because the users are marketers, not data analysts, the solution has been designed to be easy to use and display the information graphically, he says.

The portal, which is connected to Loyalty’s Oracle data warehouse, also means that the organisation’s own analytics team has been able to focus more on detailed, sophisticated analysis as opposed to reporting, says Lamers.

This has resulted in better use of the team’s capabilities.

“They enjoy their job a lot more because they are now doing more meaningful analytics,” he says.

“It never ceases to amaze me how quick people are to adopt new systems,” Lamers adds. “If it’s done well, and if it is what people want, they kind of join the dots themselves and figure it out.”

The next step is to give users more access to high-end analytics and campaign development. They will be able to select and identify targets for campaign activity, says Lamers. The system will also support MIS (management information system) reporting.

“The aim is to drive more and more activity through the portal,” he says.

Advanced analytics at NZ Post

Susan Needham, manager of data solutions at NZ Post, runs the team that works with external clients. The team’s clients are across a range of industries — banking, telecommunications, retail and charity, says Needham.

“They come to NZ Post with problems such as wanting to serve their customers better or acquire new customers, or wanting to gain a better understanding of their customers,” she says.

NZ Post uses SAS software to analyse and process the “number crunching”, says Needham. The organisation has also built an in-house analytics solution called Genius, which was launched in July last year.

“[As an organisation] we need to diversify into new areas,” says Needham. “Direct mail is a growing area and we need to be able to offer our clients techniques to help them do that effectively. [To do that] you need good quality data,” she adds.

Every household in the country has a unique identifier called DPID — delivery point identifier, she says. Needham’s team often helps clients by “tidying up” their data, append DPIDs and then add other data sources to the mix. “It’s the glue that binds all the other data sources together,” she says.

The Genius system can help customers target down to individual household level. “And we can decide on the level of targeting and sophistication required,” adds Needham.

There are different segments in Genius, for example ‘suburban achievers’ or retired groups. Further targeting based on age, income and ethnicity is also possible. There are hundreds of different ways the data can be sliced and diced, she says.

A travel agent might be interested in people who are likely to take a holiday to Australia in the next year, or take a cruise. “You’ve got less people available but they are more highly targeted,” she says.

Needham has been a SAS software user since about 1987, she says. “I’ve used other analytic software out there, but I have to admit SAS is my favourite in terms of the level of sophistication available and the ease of which you can access data from a variety of different data sources and manipulate that to get something that is usable for the client.”

Genius has given the organisation more precision. Previously targeting was more of a “broader brush”.

The system is updated every quarter and once a year there is a bigger update. Genius incorporates data from NZ Post’s latest “lifestyle survey”, says Needham. The voluntary survey recently attracted criticism from privacy groups. Two reports carried out for the privacy commissioner said the company’s 2009 survey, distributed to 800,000 letterboxes and via email, breached privacy principles and was unfair in terms of marketing industry standards. However, based on customer feedback and advice from the privacy commissioner, NZ Post made changes to the 2011 survey, making it clear the survey is voluntary and that the information collected will be rented to other companies.

Every five years when the Census comes out there will be a bigger update as well, Needham says.

“The more data we can feed in, the better.”

Needham’s team has 13 members — four in Auckland and the rest in Wellington. The team in Wellington is more focused on data capture and data quality, while the Auckland team does advanced analytics.

The team members in Auckland generally have a degree in maths or statistics. But a degree on its own is not that useful, says Needham. “You need people who have a broader perspective so they can understand what the numbers are telling us rather than just looking at the numbers. There is a real shortage of people with those sorts of skills in the marketplace.”

Cloud opportunities and challenges

Interest in exploring opportunities for BI is increasing among Asia-Pacific organisations, but there are a few things to consider, says Gartner. Research by Bhavish Sood and Eric Thoo says that the higher the authority of the “business intelligence competency centre (BICC) executive sponsor, the better the chances of BICC initiatives being successful”.

CIOs seeking to justify IT investments in BI and information management (IM) capabilities must also articulate “incremental value propositions, to develop maturity and leverage best practices”, write Sood and Thoo. “Leaders must identify synergies between opportunities presented by a BI strategy, tied to specific outcomes where the business has identified improvement to be critical to success.”

Data quality issues should also be addressed as “an absence of data quality capabilities will limit the value of BI and IM, and raise costs, putting business objectives at significant risk of outright failure”.

Sood and Thoo also recommend leaders of BI and IM initiatives “take preliminary steps now to identify where SaaS and cloud-based services offer opportunities and challenges for managing and using data”.

“They should understand what the drivers are and determine scenarios that might or might not derive benefit from ‘as a service’ models, or complement presently established capabilities,” say the analysts.

Operational intelligence

Christchurch-based Jade Software is seeing an emerging area of business intelligence — operational intelligence, says its CEO, Craig Richardson.

Jade has a presence in the criminal justice market, where its solutions are helping law enforcement groups with investigations, and also in the financial services space, says Richardson. Operational intelligence is used in areas such as fraud detection and anti-money laundering on the detection side. On the customer and product side it is used for, for example, pattern-based strategy, looking at characteristics of customers to find out what the likelihood is they would be interested in a particular product.

“That is where we are seeing the most interest, but it’s probably the least developed market as well,” he says. “There is an opportunity to make [this technology] very usable - the advanced analytics tools in the market at the moment are quite difficult to use and require specialist knowledge,” he continues. “We are trying to hit that balancing point where the tools are usable and useful but not a ‘black box’.”

Jade is also seeing a shift from mobile and web analytics to what is called “user intelligence”, says Richardson.

“If you think of the mobile phone as a social probe, we now have the capability to know where you are, who you are, who you are with and potentially what you need,” he says.

This gives “a lot of context”.

“If we can mash that up with information such as what plan you are on - if you are a telco customer; demographics information and the social network view of you and people like you, and then predict behaviour — we think that is a big opportunity,” Richardson says. “But it’s really early days.”

Jade has been experimenting and prototyping in these two areas with a couple of customers, he says. One project is around fraud and money laundering, the other in the mobile space around user intelligence, looking at ‘what is happening right now’ and ‘what is happening next’, rather than ‘what has happened’, he says.

None of these tools are new. The algorithms and code used for pattern-based strategies have been around for a long time, he says, but, particularly in the mobile space, smart mobile growth gives access to context. “And context is a game changer if you can execute on that context quickly.”

With Jade’s Joob mobile cloud platform, which connects to any business system and any device, the company is targeting the increasing penetration of smartphones and the advanced analytics trend, says Richardson.

The platform has three stages, he says: porting the business system to mobile — this is the phase the company is in with most of its customers at the moment; modernisation — this includes collecting multiple services from multiple systems into one app on the mobile device and starting to make mobile much more useful in terms of interacting with businesses; and lastly, user intelligence.

“Once you have got customers interacting, transacting and communicating across our middleware platform, there are opportunities to do some smart things with a particular customer or group of customers.”

Jade has been working on the components of the operational intelligence technology for about 18 months. New Zealand is “quite rich” in terms of its talent in the advanced analytics area, particularly at the Canterbury University and the University of Waikato, Richardson says. These universities are “hotspots of strength” around machine learning and semantic search technology, he says.

“We’ve been tapping into PhD students [from the universities] and looking for ways to really utilise and commercialise some smart stuff in New Zealand.”

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