One of the things that always stuns me in IT is how people don't appear to like change. Whether it was the EAI folks pushing back on Web Services in 2000 in favour of their old-school approaches. The package guys pushing back against SaaS or now the BI guys pushing back against the new wave of BI technologies and approaches the message is always the same:
We are happy doing what we are doing,
I can smell a change coming, the last few years have seen cloud and SaaS on the rise and seen a fragmentation in application development (thanks in a large part to the appalling stewardship of Java) and a real focus of budgets around BI and 'vanilla' package approaches. Now this is a good thing, both because I jumped out of the Java boat onto the BI boat a few years ago but also because its
The end of the next Software Development wave will be when Software development against 'eats itself' as it did with with technologies like Hadoop showing a new value in information, with platforms like SFDC showing new pre-build services, where people like GoodData have turned BI into SaaS. So we will see the same evolution again and a new generation of commoditisation which drives
This is the stage at which software development begins to commoditise itself, its no surprise that underneath all that Salesforce.com scripting lurked rather a lot of Java code. This wave sees the rise of the libraries, the utilities and above all the commoditisation of software in a way that enables the majority of developers to be useful in the enterprise. This was the goal of Spring, JEE
The problem with Wave 1 was that it didn't scale, I mean sure lots of the personal developers claimed it did scale, often laughing at large scale developments and going 'Me and four mates could do that in a couple of weeks' often they attempted to do that and suddenly realised that when you get a few people together it gets a bit more complicated and when that few gets over 20 it begins to (more...)
This is the wave we are in at the moment and its the wave that we last saw in the late 90s, this is where technologies enabled single people to build small specific things really quickly. Java and its applets really were the peak of this first wave back then but now we are seeing people use technologies such as R, Python and others to create small solutions that offer really good point value.
Is your data science providing you enough indications that challenge your existing compensation strategy? Does it reveal that the art of compensation distribution performed by your managers is not in accordance with your compensation strategy? Old habits die-hard, so you need to make sure that your plan for data-driven decision-making is not getting overridden by compensation managers’ belief system and they are not ignoring data science recommendations.
Even today challenge is to effectively distribute (more...)
There continues to be a disproportionate amount of hype around 'NoSQL' data stores. By disproportionate I mean 'completely and utterly out of scale with the actual problems of the vast majority of companies'. I wrote before about 'how NoSQL became more SQL'. The point I made there is now more apparent the more I work with companies on Big Data challenges.
There are three worlds of data
Past few months I have been meeting with clients and discussing their potential need of Big Data. The discuss gets to the bottom of , do they really need the Big Data ? The below link to my ITNext article talks about As big data goes bigger,IT managers are challenged with the task of identifying data that qualifies for big and finding appropriate solutions to process it.
Click Here To Read Full Article (more...)
Which came first Big Data or Fast Data? If you go from a hype perspective you'd be thinking Hadoop and Big Data are the first with in-memory and fast coming after it. The reality though is the other way around and comes from a simple question:
Where do you think all that Big Data came from?
When you look around at the massive Big Data sources out there, Facebook, Twitter, sensor data,
While doing a comparison analysis for building a reference architecture for Big Data technology stumbled on a very impressive Open source Big Data Technology mashup . Thanks to http://www.bigdata-startups.com/ . The most impressive part of this mashup is breaking the whole Big Data operational paradigm into multiple stages and giving available opensource technology.
Hope This Helps
Sunil S Ranka
“Superior BI is the antidote to Business Failure”
The game Cluedo (or just plain Clue in North America) is about discovering which person committed the murder, in what room using what. What is amazing is that in IT we have the easiest game of Cluedo going and yet over and over again we murder the poor unfortunate business in the same way, then stand back and gasp 'I didn't know that would kill them'.
I talk about the EDW, the IT departments
I’m wearing a Nike Fuelband – one of those fitness/activity tracker gizmos. Nike is offering both a website and an app showing my daily activity. As a customer, I am expecting these two to contain the same data. After all, my bank balance is the same in my mobile banking app, in an ATM or in a web browser.
Unfortunately, Nike does not have a proper infrastructure behind their gadget, so the numbers do not (more...)
Last few weeks I have been engaged with a customer, helping them them with remediation of Endeca project. During remediation, faced a typical challenge, where all the graphs and EQLs were erroring out. After doing some research found out that its a known issue . I spent good amount (more...)
There is an interesting article on Forbes where Paul Sonderegger from Oracle is making the case that you have to jump onto the “Big Data” bandwagon without delay if you want to avoid your big-data-using competitors crushing you.
But he would say that, wouldn’t he?
In reality, most companies already (more...)
The Oracle guys running the Big Data 4 the Enterprise Meetup
are always apologetic about marketing. The novelty is quite amusing. They do this because most Big Data Meetups are full of brash young people from small start-ups who use cool open source software. They choose cool open source software (more...)
On his recent Forbes report, Greg Satell lays down 5 steps to get Big Data working in your business. The first four are very well captured, but it was the fifth that really caught my attention: “Adopt a Big Data Mindset“. This is exactly where I want to drill in (more...)
Thank you all who attended my sessions at NYOUG Fall Conference this morning. I appreciate spending you most precious commodity - your time
- with me. I sincerely hope you found both the presentations enlightening as well as entertaining.
Please see the details of the sessions below along with the (more...)
Yesterday's UKOUG Analytics event
was a mixture of presentations about OBIEE with sessions on the frontiers of data analysis. I'm not going to cover everything, just dipping into a few things which struck me during the day
During the day somebody described dashboards as "Fisher Price activity centres for managers". (more...)