In response to my colleague Stephen Pace’s blog post about the lack of a “magic quadrant” for data warehouse automation, we’re happy to report that industry analyst coverage of this growing market segment has finally occurred!
I recently read a great blog post on smartdatacollective.com, From Master Data to Master Graph by Peter Perera. I found myself agreeing with almost everything in the post, particularly once I realised he was using terminology slightly differently to how we would here at Magnitude Software (in particular I suspect he and I think of different things when we refer to “MDM Applications”). What’s interesting is that although I’m in broad agreement with the arguments made, I’m not yet convinced with his conclusion of the post (which as I understood it is that graph database technology is the best foundation for master data management systems). Read more
Lately we’ve been spending a fair amount of time talking to banks and investment firms about their information management challenges. One industry insider recently summed it all up for me by stating that organizations in this industry are motivated by one of two things – fear or greed. It might sound a bit crass, but that’s not a bad thing and I think it reflects the reality of the market we all, not just banks and investment firms, operate in.
In a recent TDWI interview, Jonathan Geiger, executive VP of Intelligent Solutions Inc, discussed how to improve the process of requirements gathering for BI projects. Several things immediately stood out from the interview as his suggestions were quite consistent with the methodology and technology at Kalido.
It’s the end of October now which means that the autumn trade show season is drawing to a close. This year we sponsored and exhibited at the TDWI World Conference in San Diego (and in Chicago earlier in the year), and the Teradata PARTNERS Conference in Nashville. Once again we gathered survey responses in our booth, so I took a look at how things have changed, or not, since last year’s update.
Kimball Group puts out some fantastic design tips, and one I especially liked in the last year was one from Joy Mundy (Design Tip #158 Making Sense of the Semantic Layer). The arrival of this in my inbox was extremely timely because I was at the time involved in a disagreement with a BI consultant who was currently specialized in one of the new breed of BI tools. His position was that semantic layers were unnecessary and the realm of “traditional, IT-centric reporting environments.” If I didn’t understand that, well, I just didn’t understand the niche that particular product was fulfilling towards the goal of enabling ad-hoc end user reporting without IT involvement. The argument started because I was inquiring about the capability of third-party solutions like Kalido (now Magnitude DIW) of preconfiguring the semantic layer for the tool rather than having to manually configure it. Read more
“I can explain it to you, but I can’t understand it for you.”
A good friend of mine recently alienated the person that pays his salary with the above statement. While you might question his wisdom – I know I did (after a quiet chuckle) – I’m reminded of how often the same thought has gone unsaid when business and IT “collaborate” on traditional data warehouse projects. The business is baffled by the technical jargon that IT uses, and the IT team wishes the business could articulate their requirements in a form from which they can build the solution. The lack of a common language to explain what each side means hinders this understanding. Read more
I took my first Kalido training class in October of 1997 and I’ve been involved with Kalido ever since. Because I’ve used Kalido for so long, it’s often easy for me to forget all of the things people had to do manually prior to Kalido coming around. I recently saw a book by Oracle ACE Kent Graziano called “A Check List for Doing Data Model Design Reviews” on Amazon, so I grabbed a copy. Read more
Many companies are in search of the holy grail of being a more agile business. But, those same organizations are trapped in traditional decision support cycles and have fallen into a rut of “business as usual”. Knowledge workers are capable of answering any questions rapidly for which supporting data is available and accessible. They are also capable of expressing their requirements for additional data that’s needed to support their next generation of questions – or are they? Read more