Most master data management (MDM), data quality, and accompanying data governance efforts prioritize customer, account, and product data over all others. Certainly, industry-specific exceptions exist; for example, energy, utility, and oil and gas companies place a high priority on asset and location data domains, while investment management firms prioritize securities. But exceptions aside, a recent Forrester survey of 298 business process management (BPM) and MDM professionals across industries found that 83% prioritized customer data, 61% product data, and 53% account data. And coming in at 44%, the next highest priority: the red-headed stepchild of the MDM “party” (pun intended — apologies for that), employee data!
It’s no surprise that customer/account and product data-centric MDM programs get the lion’s share of funding, executive sponsorship, and prioritization within most organizations. This data is the lifeblood of your customer engagement and supply/distribution chain, with quantifiable impacts to both top- and bottom-line success, and can be positioned as a major competitive differentiator. But even more relevant, those MDM efforts are often driven by sales, marketing, finance, operations, or risk management functional organizations — all of which are typically better funded than many human resource (HR) teams, especially when it comes to IT budgeting. Of course, this isn’t always the case, and many large enterprises spend millions of dollars optimizing their HR systems infrastructure. Applications supporting learning management, performance and talent management, recruiting, time and attendance, benefits administration, compensation planning and analysis, and organizational charting and employee directories all require high-quality employee and organizational data.
“… and they lived happily ever after.” This is the typical ending of most Hollywood movies, which is why I am not a big fan. I much prefer European or independent movies that leave it up to the viewer to draw their own conclusions. It’s just so much more realistic. Keep this in mind, please, as you read this blog, because its only purpose is to present my point of view on what’s happening in the cloud BI market, not to predict where it’s going. I’ll leave that up to your comments — just like your own thoughts and feelings after a good, thoughtful European or indie movie.
First of all, let’s define the market. Unfortunately, the terms SaaS and cloud are often used synonymously and therefore, alas, incorrectly.
SaaS is just a licensing structure. Many vendors (open source, for example) offer SaaS software subscription models, which has nothing to do with cloud-based hosting.
Cloud, in my humble opinion, is all about multitenant software hosted on public or private clouds. It’s not about cloud hosting of traditional software innately architected for single tenancy.
This is a very smart move by Oracle. Until the Siebel and Hyperion acquisitions, Oracle was not a leader in the BI and analytics space. Those acquisitions put them squarely in the top three together with IBM and SAP. However, until this morning, Oracle played mostly in the traditional BI space: reporting, querying, and analytics based on relational databases. But these mainstream relational databases are an awkward fit for BI. You can use them, but it requires lots of tuning and customization and constant optimization — which is difficult, time-consuming, and costly. Unfortunately, row-based RDBMSes like IBM DB2, Microsoft SQL Server, Oracle, and Sybase ASE were originally designed and architected for transaction processing, not reporting and analysis. In order to tune such a RDBMS for BI usage, specifically data warehousing, architects usually:
Denormalize data models to optimize reporting and analysis.
Build indexes to optimize queries.
Build aggregate tables to optimize summary queries.
Build OLAP cubes to further optimize analytic queries.
Whoa! Hold your horses. If this is indeed a key challenge that you’ve tried to address in the past without much success, consider switching jobs. This is not a joke. Business intelligence (BI) is an employee market right now; a key challenge for most BI employers is finding, recruiting, and retaining top — or actually any, for that matter — BI talent. Consider that IBM BAO alone added more than 4,000 (!) BI positions in just over a year! Every other major, midsize, and boutique BI consultancy I talk to is struggling to find BI resources. So if you’ve been fighting this uphill Sisyphean battle for a while, consider new channels for your noble efforts.
Now, some more practical advice — albeit not as exciting. Start from the top down. In a few minutes I am getting ready to talk to yet another large client whose CEO does not “get” BI. Can you rightfully blame him/her? Yes and no. Yes, because how can you manage any business without measurement and insight into your internal and external processes? So if your CEO didn’t learn that in his/her MBA 101, suggest that he/she look for another job. And if you’re still standing after that and have suffered only a mild concussion, consider that many BI projects have been less than successful, and ROI on BI — one of the most expensive enterprise apps — is extremely difficult to show. So can you really blame your CEO?