Monthly Archives

February 2019

BI Project Success

By | Data & AI | No Comments

What makes for BI Project Success? I read with interest the results from the BI Survey 2018 – particularly its results on the subject of Success Factors in implementing BI Applications. I took particular interest in two interlinked themes, speed and competency of implementation.

Speed of Implementation

Speed is a critical part of achieving BI project success. It was very clear from the results that Enterprise BI Platforms took over 2 times as long to implement as Self Service solutions. This in itself isn’t overly surprising. An Enterprise platform is typically selected because the problem it is trying to solve is more complex. Self Service BI Solutions work excellently for targeted problem solving but in my experience struggle as the data landscape gets more complicated.

However what I thought was the most interesting finding was that the longer a program runs, the harder it is for it to deliver value. This seemed to be a universal effect regardless of project type or expected value. This led the survey takers to the conclusion – that I agree with – that smaller, more focused projects are more likely to deliver value. This is why we embrace Agile delivery methodologies at FTS Data & AI.

Competency of implementation

Tying in with speed of implementation is the competency of it. The more capable the company was in delivering BI programs, the faster they delivered – by a factor of over three times. Shorter implementations had less issues in delivery as well. This could well be a reflection of the higher competency teams delivering results more quickly and capably, resulting in better BI project success.

Implementation time by best-in-class companies in median months

Implementation time by best-in-class companies in median months (credit: BARC)

The competency was also impacted by the support from vendors and implementers. Vendor support had a big impact on project outcomes, with good support correlating well with project outcomes. This works the other way as well – poor support led to worse outcomes, so tool selection criteria clearly should look at local support. I would draw again on my comments on the Australian experience with Microstrategy, where I have had customers move away from that platform due to an inability to get local support for it.

Implementers also had a significant impact – projects with excellent partner support did significantly better than those without. It is also worth noting that the wrong choice of partner can lead to outcomes that are actually worse that using no partner at all. The survey team advised picking a specialist partner over a generalist firm – which I believe ties in to the above effect of vendor support – some vendors rely heavily on partners to deliver on their behalf (e.g. Microsoft) so when choosing a partner, a strong track record with your chosen vendor platform should be a key criteria.

They also advise road testing partners with a proof of concept. I support this approach as a successful relationship with a partner needs to be evaluated and there’s nothing quite like getting hands on together to properly evaluate their competency, commitment and understanding of your specific needs.

Takeaways for BI Project Success

My key takeaways from these survey results are:

  • Focus on smaller delivery cycles for better outcomes
  • Ensure vendor support is a key factor in tool decisions
  • If you need help, the right delivery partner will make a big difference

Microsoft Lead Gartner 2019 Magic Quadrant for Analytics and BI

By | Data Visualisation | One Comment

Hot BI news is the recently announced 2019 Gartner Magic Quadrant for Analytics and BI and Microsoft was the clear leader, driven in part without doubt by the explosive growth of PowerBI .

2019 Gartner Magic Quadrant BI & Analytics

2019 Gartner Magic Quadrant BI & Analytics

PowerBI is the story

PowerBI is advancing at a rate which its key competitors – Qlik & Tableau – are struggling to match. Further underpinning Microsoft’s lead is its ongoing investment in the underlying Azure Data & Analytics Platforms which give it an edge that competitors just can’t match.

One thing I am frequently hearing from the market is that other platforms are struggling to match PowerBI’s compelling price point – some might cynically say Microsoft are using their deep pockets to undercut everyone else. However in a recent conversation I had with a long standing Cognos customer, once they understood what the product could do – and how much cheaper and faster it would be – it drove them to reconsider their strategy.

I freely admit was initially cynical about Self Service BI a few years ago as it rapidly became transparent that for all the slickness of creating great looking reports the tools were still beholden to a clean set of well managed data. Now modern data platforms are reducing the cost and complexity of providing this data, I am now holding the position that self service BI can really deliver on its value rather than just provide a final polish to a Data Warehouse – as long as it is paired with such a platform.

I continue to maintain my (unpopular in some circles) position that Qlik has nothing unique to offer any more and is doomed to irrelevance unless it innovates or at the very least catches up with its competitors. Tableau remains a solid tool for self service analytics, but the absence of an underlying data platform is going to start hurting it before too long. I would expect it’s longevity to be tied to being acquired by someone suitably huge.

I also note the absence of any serious competition from the other two cloud megavendors. Google offers Data Studio & Amazon has Quicksight – but neither rate a mention. I would watch this space carefully as the pace of innovation by both companies is fierce and Google in particular has strong AI / ML capabilities. Both are also ramping up their own data platform services.

Outside the Big 3

If you are on any of the legacy on premise tools in the 2019 Gartner Magic Quadrant for Analytics and BI such as Cognos/IBM, Oracle or SAS then i’d be seriously considering where you go next. The pace of innovation in the cloud is hard to ignore and users risk lagging behind their competitors if they cling to these.

SAP and Salesforce have a strong story within their own source but have their weakness in using data from outside the native platform. Doing anything in SAP is horribly expensive, and my conversations with Salesforce BI users have not left a very positive impression of the tools’ capabilities.

The remainder either are strong in their niches and / or have minimal presence in Australia (Microstrategy is basically unsupported here as there’s no people doing it) so i’ll not pass comment on them.

Where to from here?

If you have reviewed the 2019 Gartner Magic Quadrant for Analytics and BI and decided you want to know more about PowerBI and the Microsoft Data Platform, we can help.

Full disclosure – FTS Data & AI are a Microsoft Gold Partner so this post is a bit biased. However if you are not using PowerBI and are looking at migrating to a more cost effective platform, want to understand how cloud capabilities are transforming data and analytics – or work for Qlik and want to lure me into a dark alley – please contact us.

FTS Data & AI are Microsoft Gold Partners Data Analytics

Microsoft Gold Partners Data Analytics

 

SSIS Integration Runtime Connectivity Testing

By | Data Platform | No Comments

SSIS Integration Runtime Connectivity Testing is hard as there is no physical Azure VM to log in to as part of the Azure Data Factory (ADF). While behind the scenes there is effectively a VM spun up there is no way to access it.

The scenario our Data Platform team faced was reasonably simple – we needed to connect to a 4D database that sat behind the Storman application that our customer used so that we could extract data for their various workloads. Because 4D is not supported by the Generic ODBC source in Azure Data Factory, we needed to use the 4D ODBC driver. This meant using SSIS to leverage the driver.

The client is well managed in terms of security so the target system can only be accessed within their network. Their Azure network was connected to theirs and properly secured, so part of the setup of the SSIS Integration Runtime in Azure Data Factory is to ensure that it is joined to the virtual network.

Houston, we have a problem

SSIS Azure Data Factory

SSIS Azure Data Factory

However, despite all this – we couldn’t get the ODBC connection to work when deployed. Due to stability issues our first suspect was the driver – after all it frequently crashed Visual Studio 2017 / SSDT and configuration was a pain. Also, initially we couldn’t connect on our dev machines as we weren’t on the clients VPN (easily fixed, fortunately). Then we had the wrong target server (again easily fixed).

Once we got on to ADF of course our debugging options got more limited as we now were having to do SSIS Integration Runtime Connectivity Testing without all the tools available on our desktops . Initially we struggled because the runtime was very slow at sharing its metadata (package paths, connection managers, etc.) so we weren’t initially sure it was even able to work with the driver. Eventually we got enough metadata to start playing with the JSON of the task to configure it. However we continued to get errors in ADF that were’t really helping.

Our breakthrough came when we remembered we could just connect to the more familiar and mature environment of the SSIS catalog that is deployed alongside the runtime. We configured the package correctly, ran it and got a more manageable ODBC error – “Cannot reach Destination server”. A quick ping from our desktops proved the server could be pinged, so as a test we used a simple package with just a script task to ping the server. This worked just fine on our desktop, but when deployed the script task reported failure.

So a quick connectivity test helped pin it down to probable network config issue. Now it’s in the Infrastructure teams hands to ensure everything is configured correctly, but at least we have (for now at least) got SSIS & the ODBC driver off the list of probable causes of the issue. It’s also taught us a few things about SSIS Integration Runtime Connectivity Testing.