Here’s an excerpt:

600 people reached the top of Mt. Everest in 2012. This blog got about 9,700 views in 2012. If every person who reached the top of Mt. Everest viewed this blog, it would have taken 16 years to get that many views.

Click here to see the complete report.

• How business analytics can integrate data from across a organisation to deliver self-service reporting and analysis.

• How the power of business visualisation can transform how we see, discover and share insights hidden in our data.

• What pressures and market conditions are driving us to adopt mobile analytic reporting.

• How and why SAS can help us move from insight to performance.

The session was well attended with a number of different industries and organisations represented.

The key takeaways for me:

1. ‘Business Analytics’ is about discovering why an event occurred, and not simply reporting on it, which falls more into the ‘Business Intelligence’ way of thinking. Business Analytics helps predict what will happen in the future. (http://www.sas.com/businessanalytics/)
2. If an organisation is looking to compete in their marketplace with ‘Competitive Advantage’, Business Analytics is a key enabler.
3. SAS have developed a great Business Analytics value chain; Analysis -> Forecasting -> Predictive Modelling -> Optimisation (of business processes).
4. There are challenges to be faced and resolved on the Business Analytics journey.

a. Data Governance and Data Quality – As with any data project, if data quality is an issue the business insights you’ll generate will be at best low value and at worst wrong. A Business Analytics project, not unsurprisingly, needs good quality data.

b. One version of the truth – Integrated data ensures consistency of the insights generated and provides an easy access path to data.

c. Operationalisation of intelligence – Reducing the risk of having business insight locked up in individual resources, operationalisation of intelligence ensures insight generated can be used across the organisation.

d. Big Data!

There was a great case study,which for me really highlights the power and competitive advantage of Business Analytics.

State Fleet of New South Wales, Australia, have been able to accurately set the lease price of the 12,000 cars per year they lease to NSW public sector workers, by using ‘Predictive Analytics’ to accurately forecast the residual/sale value of a car at the end of the lease period. With this capability they are saving millions of dollars in potential losses. With a fleet of over 25,000 cars, every 1% error in the calculated vs. actual end of lease sale value of their fleet of cars will cost State Fleet over $3 million.

No seminar/presentation would be complete without a section and discussion on Big Data, and SAS gave us their take on Big Data, including what SAS see as the fourth V of big data – Value. When you think about it, this really is far more important that Velocity, Variety and Volume. Big Data ‘Value’ for SAS means focusing effort on analysis of data where high value insights can be generated. SAS further define Big Data as being able to perform analytics in a much shorter timescale than previously possible – ‘High Performance Analytics’ – so it’s not all about how big the data set is you have for your Big Data initiatives. You can still be doing ‘Big Data’ with a small amount of data; where the data set contains a large amount of untapped high value business insight but requires a high level of processing to unlock that value.

We had some practical demonstrations of current and new SAS tools that support Business Analytics – visualisation, mobility and reporting.

•  SAS Visual Analytics (http://www.sas.com/technologies/bi/visual-analytics.html) – a high-performance, in-memory solution for exploring massive amounts of data very quickly. The perfect tool for your Big Data projects.

•  SAS Mobile Business Intelligence (http://www.sas.com/technologies/bi/mobile/index.html ) - via Roambi™ ES for SAS, organizations can deliver real-time analytics to Apple iPhones and iPads, empowering users to monitor key metrics and make informed decisions wherever they are. A great move by SAS in partnering with Roambi (http://www.Roambi.com) to provide a visually appealing and market leading MobileBI experience.

•  SAS Social Media Analytics (http://www.sas.com/software/customer-intelligence/social-media-analytics/index.html) – integrates, archives, analyses and enables organizations to act on intelligence gleaned from online conversations on professional and consumer-generated media sites. It enables an organisation to attribute online conversations to specific parts of the business, allowing accelerated responses to marketplace shifts. ‘Sentiment Analysis’ made simple.

•  SAS Office Analytics (http://www.sas.com/technologies/bi/office-analytics.html) – connects analytics data with Microsoft Office products (Excel, PowerPoint, Word and Outlook) to produce consistent views of data, automate reporting and add analytical insights while keeping information consumers within their interface comfort zone. The ability to directly access and view analytics from within Outlook looked very good, and provisg business users with the option to remain within a tool they are familiar with like Excel, can only be a good thing to further drive analytics uptake in the organisation.

Altis has been actively engaged in a number of successful Business Analytics projects over the last few years, and this seminar has strengthened my understanding and belief that successfully establishing and embedding Business Analytics within an organisation can generate massive competitive advantage.

I look forward to sharing our success stories and blogging further about our thoughts on how to deliver insights via Business Analytics. Altis expects 2013 to be another growth year for Business Analytics, with the smart companies using it to their advantage.

From time to time, ETL will highlight data quality issues.  There is often a choice between fixing the issue at the source or in the ETL processes.

In this blog I argue that the issue should be fixed at the source, but if that isn’t practical I provide  some guidelines to ensure the best outcome.

Correcting the issue at the source is the gold standard as this will not only correct the data itself, but may address the root cause of the underlying problem (be it process, standards or a technical error). It’ always worthwhile aiming for the problem to be fixed at the source, for the following reasons.  In other words … Go for Gold!

It stays fixed

If the issue is fixed at the source, it can prevent similar or related data quality issues from re-occurring. A one-off correction made within the receiving system is no guarantee that the data won’t be reloaded or re-submitted incorrectly in the future, and  the issue will re-appear. Correct it at source and it stays fixed.

It stays fixed for everyone

If the issue is fixed at the source, not only is it fixed for the warehouse, it is also fixed for the source system and any other upstream users of the data. Never underestimate the breadth of impact a data quality issue may have. Imagine a spider’s web with lots of tangled threads that radiate out from the centre and you’re looking at a how data quality issues can thread through an organisation . Enforcing a solution at the source means everyone benefits from a consistent and correct view of the data.

It’s usually cheaper

Even though it may appear easier to just patch in the warehouse, appearances are often deceptive.  It’is worthwhile keeping the following saying in mind:

“If you want quick and dirty, we can guarantee the dirty but not the quick”.  One example of hidden costs is reconciling the source and the target.

The reality is though, sometimes we are forced to apply a patch in the data warehouse .  The root cause may involve external systems over which we have little control.  The time needed to co-ordinate the fix may be long and complex, meanwhile we need to do something to keep the data flowing.

In this case we need to settle (at least temporarily) for silver, but here are some steps to ensure that the fix is as effective as possible:

Raise the Issue

Ensure the issue is communicated so that other users of the data are aware of it.  Those responsible for the root cause should know so that it at least doesn’t increase in impact.  Highlighting the impact of data quality issues may be all that’s needed to get the ball rolling towards addressing it.

Isolate the fix

Minimize the flow-on effect of the issue.  In other words, avoid becoming part of the problem.  Once the root cause is fixed, it should be easy to remove the temporary fix. Don’t comprise on your standards to attain the best data quality possible. Remember – go for gold!

Revisit the Issue

Keep an eye on progress towards fixing the root cause.  Don’t drop the issue because the symptoms have disappeared for the moment.

Keep aiming for gold in data quality!

Simon McAlister

In a global survey of 600 executives this month by Capgemini and the Economist Intelligence Unit, nine out of 10 respondents identified data as being the fourth factor of production – as fundamental to business as land, labour and capital.

18 June 2012

Author:
Paul Taylor

Publication:
Financial Times

Companies are awash with data, some generated by their customers or systems, some by third parties. These data are growing so fast – by about 2.5 exabytes a day – that 90 per cent of the stored data in the world today has been created in just the past two years, earning it the geeky moniker “big data”.
Whether big data becomes an organisation’s greatest asset or one of its gravest liabilities depends on the strategies and solutions it puts in place to deal with the epic growth in data volumes, complexity, diversity and velocity.
This message seems to be getting through. Among the survey’s other findings, respondents said the use of big data has improved businesses’ performance, on average, by 26 per cent and that the impact will grow to 41 per cent over the next three years.
Almost 60 per cent of companies said they planned to make a bigger investment in big data over the next three years, suggesting that the era of big data and big data analytics has already arrived.

To read the full article on FT.com, please click here: Crunch time for big data

Related links:

• Press Release: Capgemini report shows rising impact of Big Data on decision-making

First, what is Big Data? In John Bantleman’s recent blog Raw is More, he defines Big Data using the criteria of volume, velocity, variety and value.  This is a great definition and captures exactly why the hype, buzz and excitement around Big Data will be with us for some time – businesses now have the means to collect, store and analyse huge volumes of data, from varied sources, at high frequency, in a very cost efficient manner – and this hasn’t been possible before.

I recall the days during the first dot-com boom, where trying to capture and store all the detailed data generated by people browsing a website – capturing every click, interaction and page viewed, over a period of more than a month was near-on impossible.  A client involved in providing share trading services couldn’t hold more than 14 days’ worth of detailed browsing data – so think how difficult it was to generate insights into user behaviour.  With the arrival of Big Data, this problem is no longer present; it’s possible to save the detail data for much longer.

So where does an Enterprise Data Warehouse (EDW), fit into the picture? Are we now witnessing the demise of an EDW, to be replaced by ‘Big Data’ systems? In short … no. For an organisation to get value out of their data they must be able to generate insights, quickly, effectively and for as many user groups as possible. For this you need a well-structured Data Warehouse.

In a recent Australian CIO article, ‘Five things CIOs should know about Big Data’, the misinformed idea  is presented that in some way ‘Big Data’ allows an organisation to forgot all the hard work and thinking that goes into creating a well-constructed Enterprise Data Warehouse (EDW), The article suggests that a Big Data implementation will enable;

1. Access to data by more than just a handful of highly paid and hard-to-find Data Scientists. Untrue – you will need even more sophisticated data analysis if your data is not structured in a logical way – a skill most people in the organisation do not have.
2. Support for all the business questions that can be thrown at it, unlike an EDW, and without the need for any structure. Untrue – A well-designed dimensional data model at the core of the EDW supports a variety of business questions being asked, and the data model doesn’t prevent, limit or second guess those questions.  Structure to data actually makes it easier to navigate the data and generate insight.  Good luck if you need to navigate your unstructured Big Data store, without your expert guide available!
3. As much detail data as the underlying infrastructure can support. True, but you still have to have the means and capability to access that data.

The article goes even further suggesting that ‘You can use a [Big Data repository] as a dumping ground, and run the analysis on top of it, and discover the relationships later.’  I’ve seen ‘data dumps’ and they are not fun to use for anyone. They typically suffer from extremely poor data quality, poor performance and lack of control – all of which is the reason we’ve spent 20 years refining the approach to supporting business in generating insight from Data Warehouses!

We believe that both ‘Big Data’ and Enterprise Data Warehousing need to co-exist, supporting the need for organisations to generate insight from all data. Big Data provides the deep analytical capability to generate insight from huge volumes of data and transactions that you just wouldn’t need to make available to everybody on expensive hardware, whereas an Enterprise Data Warehouse is bringing insight from data to as many business users as possible, in a structured and planned way.

Is there a meeting point in the future? We believe there could be – a ‘Big Data 2.0’ where an Enterprise Data Warehouse can take advantage of the infrastructure approach that ‘Big Data’ uses.  In the meantime if your ‘Big Data’ vendor tells you that you don’t need that Data Warehouse any more, come and talk to us at altis Consulting for a more rounded and balanced view.

• Have immediate access to time-critical information from any company terminal around Australia;
• Reduce decision making by cutting down on the number of hours spent every week on paperwork; and
• Improve visibility of performance and drive productivity and efficiency at each terminal location.

The need for Data Integration has never been greater. Organizations are faced with an overwhelming growth in data, an increasing range of diverse data sources and the continued proliferation of systems within the enterprise.

In this blog I will talk about the hot topics and key trends for Data Integration in 2012 and beyond.

First, a definition – ‘Data integration refers to an organisation’s ability to gather data and information residing in multiple sources, combine it and provide a unified view to support business goals and initiatives’.

So what are the Data Integration hot topics and trends?
• Data Integration Platforms – the right mix of capabilities
• Managing social data
• Profiting from ‘Big data’ and analytics
• Driving efficiency from Cloud Data integration
• Data quality – the essential ingredient when taking data from multiple sources.

This week we will focus on trends in Data Integration Platforms.
Data Integration requires a range of capabilities including Extract, Transform and Load (ETL) working with complementary capabilities for data sharing via XML messages using an Enterprise System Bus (ESB) and data federation (or virtual data integration) utilising Enterprise Information Integration (EII).

A true Data Integration solution needs to include all these different capabilities to provide a complete platform to support all of the organisation’s Data Integration business initiatives e.g. Data Warehousing, Master Data Management, Enterprise Application Integration (EAI), SOA and Data Migration.
Companies who are successfully managing their data and achieving efficiency and effective business results are recognising that to succeed in Data Integration no single capability will address all business Data Integration initiatives. For example, an Enterprise System Bus (ESB) is not the right capability for bulk movement of data and complex transformations; this must be performed using an Extract, Transform and Load (ETL) capability.
Many Data Integration projects have failed due to an incorrect selection of the right Data Integration capability. With a Data Integration Platform in place, with the right mix of capabilities, this should no longer be an excuse for a failed project.

Next time we will look at trends in Social Data.

Data Quality Blog – A Never Ending Story

                         Where’s a spell-checker when you need it?

I was recently invited to provide one or more data quality anecdotes for a chance to win some glorious prize. I was hooked, more for the opportunity to contribute an anecdote and less for the prize. I was also amused that there’s still such widespread appeal for these embarrassing events and, more worryingly, that there are still plenty of embarrassing events happening.

I decided that a blog focussing on data quality stories would provide some insight into how data quality issues can impact business and why it’s important we don’t just accept poor data but we actively work to fix the problems (and stop these lifelong embarrassing moments).

This blog is the first in a series that will focus in a light-hearted way on DQ anecdotes and stories, to examine how such errors occur and what can be done to prevent them in the future.

To set the scene let’s look at what the term Data Quality means. My colleague Veronica Coyne recently identified the dimensions of Data Quality, in her blog ‘What Does Data Quality Mean?’ (http://altis.com.au/what-does-data-quality-mean/) Veronica agreed with the importance of the dimensions identified by Larry English (http://www.infoimpact.com/) these are:

• Existence and Completeness
• Validity (conforms to business rules)
• Accuracy (correctly represents characteristic of real world object or event)
• Precision (values are correct to the right level of detail)
• Non-duplication (each record appears only once in a data store)
• Equivalence (of redundant or distributed data)
• Timeliness (for all knowledge workers)
• Currency (data is up-to-date)
• Presentation Clarity (data is presented in a way that clearly conveys the truth)
• Relevance
• Definition Conformance (the value of an attribute is consistent with the attribute’s definition, e.g in type and format).
• Plus her own useful additions:
• Auditable and
• Trusted.
• Consistent

The purpose of Veronica’s message was that measuring data is necessary to managing it and for that you need to have clearly defined measures or in this case data quality dimensions. My purpose is to look at what happens when you get it wrong and then focus on the next step “What do we do about it?”

These blogs are to help highlight the DQ dimension(s) embedded and show their potential impact on your business.

A tip for the uninitiated here: A DQ occurrence is hardly ever likely to have a single outcome. The chances are you get it wrong once you get it wrong over and over.

Data Quality Felix

Here’s my first example – ok it’s not real, it’s a TV commercial, but you can easily see how real this example could be:

Remember this ad from a couple of years back? A certain large bank had Tony Barber accompanied by an obvious senior manager cum trophy holder; photographers, media hack and cheer-squad. They knock on the door of some house and tell the unsuspecting door-opener that he was being presented with an award because the bank was the umpteenth winner of Home Lender of the Year. The joke was they had the wrong person – the guy they want is next door. Poor data quality – you betcha! Did the management at the bank know they were making a joke out of poor data quality?  Seems to have eluded their management – or did it?

I wondered how the experience of Altis consultants could contribute a number of real-life anecdotes. When you work in the DW/BI space long enough you can’t help but capture interesting stories along the way. That said, you may only need to be a new entrant to pick up a story or two, because the issue’s not going away. It’s almost as if (poor) data quality is an implied cost of doing business and organisations have given up trying to address it and simply, even happily, accept the situation.

To set the scene, here are some references to locations where you can see various data quality results posted.

Now it’s over to our Altis story-tellers to come forth with their tales. Stay tuned.

Larry English – Information Impact

Daragh O Brien – IQ Trainwrecks

Darryl Collins – Data Quality Management

About the author:

Paul Priestly is a Senior Consultant with Altis Consulting and has over 20 years’ experience in Information Management and IT project management within public and private sector organisations nationally and internationally. Paul specialises in strategic initiatives for DW/BI, data quality, MDM and data security.

I am excited to formally announce the opening of the London office for Altis Consulting. We are taking our specialist expertise in business analytics, business intelligence, data management and data warehousing to the UK market and I am proud to say I will be leading the UK practice.

Our goal is to offer a service that is vendor-independent, more flexible and offers greater value than our competitors.

With a blend of expertise across numerous products in the Data Warehouse and Business Intelligence space, combined with a deep commitment to the skills of our staff, we believe Altis is uniquely positioned to offer this service to UK clients.

Many BI projects end up as shelf-ware and never fulfill their potential. But our approach is different. Unlike other data management consultants we are not tied to specific vendors, so we can provide completely impartial advice, services and training – we make sure we are up to date with the offerings provided by a whole range of leading vendors. This enables our clients to better address key challenges and realise their objectives through better reporting and utilisation of business metrics. We pride ourselves on taking a less technical approach, and because we are privately owned we are more flexible, agile and provide less expensive consultancy than the larger companies in the UK.

Although we have experience across all sectors our initial focus in the UK will be on the Construction industry, Utilities and Higher Education, where there is huge demand to report and analyse information that can be applied to drive efficiencies in these industries.

I have over 15 years of experience in the Data Warehouse and Business Intelligence fields, having worked both client-side and for a number of consultancies including CMG (now owned by Logica). For the last six years I have been at Altis in Australia, but as I am originally from the UK I was the logical choice to head up the Altis expansion.

Altis provides an exceptional level of training to our employees and clients. Our philosophy is based on looking after our staff so they are better equipped to look after our customers. Our partnership with experts in the field such as Kimball University, for leading Data Warehouse training, and Stephen Few, one of the World’s leading experts on data visualisation and the author of several well-known books including ‘Show us the Numbers’, enables us to provide training in data warehousing and business intelligence that is second to none. All of our staff are offered the chance to attend these courses to ensure they are up to date with leading best practice, and we will be making the best use of these strategic partnerships in the UK market.

I am also excited about the opportunities for staff who may want to live and work in the UK and for our UK staff looking for opportunities in Australia. We see this cross-fertilisation of talent as one of the key benefits of this move.

Check out the press release at sourcewire.

Being aware of and managing subtle cultural differences can make all the difference when managing a successful Global project.

 Recently I held a post project review session as part of closing out a global Business Intelligence project. The objective was to share and collate feedback on what had gone well or not so well and why – so this could be passed on to future project teams. At the end of the call, I found I could summarise the type of responses I would get on a topic from each region:

• One region was very positive and seemed careful to respect any ‘organisational rankings’ within the team in terms of their feedback.
• Another region’s feedback was extensive and mostly negative.
• The other region has some no-shows and virtually no comments or feedback despite during the project being the hardest region to implement.

 Perhaps if you’ve also worked on global teams you can guess which region was which?

 For me it was a perfect summary of the subtle cultural differences we deal with on global projects. There is definitely a downside to global projects. We have calls at unsocial hours (it’s always someone’s bed time, someone else’s waking up time and another’s lunchtime). The 24hr turnaround always means tasks take longer than expected to complete. And the effort of communication is always so much higher.

 On the other hand, diverse teams can generate improved productivity, creativity and results. There can also be a sense of excitement and discovery working with global partners which can drive team performances.

 So are you getting the most out of diverse teams?

Below are some tips to make the pain worth it!

•  Try to include a budget for an onsite team meeting. This can be a real team-building exercise where you can agree how you will work together, how to exploit each team’s strengths, and it’s amazing what a difference it makes talking to someone over the phone when you can picture their face.
• Agree the processes for working together, how you will report, resolve conflict and what is expected from each participant and region.
•  Spend significant time upfront sharing each region’s vision for success.
•  Avoid difficult topics (including introducing anything new) via email; remember 70% of communication is non-verbal, and subtleties are better communicated when you see each other.         
•  Utilise technology such as web cameras, online conferencing with video, as well as custom conference call facilities. These are definitely a step up from phone calls and can be used to substitute for travel.
• Involve the regions as much as possible in planning and talk about how you will communicate. Explain and use techniques like sharing personal anecdotes at the beginning of a meeting as ice- breakers.

So those are my experiences and tips, I’d be very interested in any stories or tips you can share at http://altis.com.au/getting-the-best-out-of-global-projects/

Mythili Baker
Principal Consultant