Some say architecture is destiny. But it seems as if the world of analytic databases is moving on many fronts in all directions with no particular destination. That, in fact, is probably a good working definition of “postmodern.”
That’s the only reasonable conclusion you can draw from the sprawling mess of a movement known as “NoSQL.” It defines itself by what it is not, which would be easy to do if all it were rebelling against were something well-defined, such as the need to use Structured Query Language (SQL) to access, query, update, and manipulate data, or the need to store and manage that data in third-normal-form relational databases.
But no. The NoSQL movement seems to fancy itself a catchall for all things “next-generation database,” though even there it is not clear what exactly they’re rebelling against. Check out this “NoSQL” scoping definition on the movement’s principal website: “Next Generation Databases [are] non-relational, distributed, open-source...horizontally scalable... modern web-scale databases....schema-free, replication support, easy API, eventual consistency, and more.”
Whew! If that non-definition didn’t leave you gasping for breath, the unruly menagerie of “No SQL” database approaches listed on the website will completely rob you of your oxygen supply. Apparently, this movement includes an unholy host of old and new database approaches, including wide column store/ column families, document store, key value/ tuple store, eventually consistent key value store, graph databases, object databases, grid database solutions, and XML databases.
On March 25, 2010 TIBCO Software announced that they acquired Netrics, a small, private data matching vendor. TIBCO and Netrics had a pre-existing OEM relationship that was originally announced in June 2009, where TIBCO embedded the Netrics match engine into its Collaborative Information Manager (CIM) master data management (MDM) solution.
Netrics differentiates its advanced matching engine by describing how it “Matches data based on a mathematical model that mimics human perception of similarity, identifying hidden relationships in the data.” The Netrics matching engine includes a self-learning capability that improves the confidence in its matches over time by also evaluating manual matches made by business users. Netrics business and technology approach to this market made it a ripe (and obvious) acquisition target since it developed the match engine to be completely embeddable in existing applications with the vast majority of its revenue coming from OEM and SI channels. In addition to TIBCO, Netrics current MDM OEM partners also include Data Foundations and Kalido. This bodes well for TIBCO’s ability to further integrate these capabilities into its product portfolio.
Guesstimates are often essential for market sizing and trending. To be useful, especially where primary data are lacking, they demand a valid conceptual framework.
Like you, I’m looking forward to the responses to Boris Evelson’s quick Web-based survey, which you can access from his most recent blogpost.It’s always a challenge to assess how truly pervasive BI is—and pervasive it could potentially become.
To generate a valid first approximation, Boris scoped his blog comments and quick survey to “traditional BI” applications (i.e., historical reporting, query, dashboarding). He scoped his estimate only to large enterprise and midmarket firms (i.e., those with 100 or more employees) and only to BI usage in the US.
In order to keep this task manageable, Boris excluded some use cases that are often included in the “traditional BI” category: spreadsheets and other “homegrown” analytics apps; BI embedded in line-of-business apps; and non-interactive, static, published BI outputs. He leveraged both public and Forrester-gathered primary datato gauge how many actual and potential BI users there might be.
Scoping it as he did, Boris estimated that slightly more than 1.5 million people in the US are using traditional BI applications, which is between 2-3 percent of the employees of BI-implementing firms. He suspects the actual percentage might be as high as 6-8 percent of employees, but he’s not sure. That’s why he’s running the Web-based quick survey.
For those of you unable to attend, I will summarize some of the content that I presented on SAP’s overall growth and innovation strategy. SAP has a double-barreled product strategy focused on Growth and Innovation.
The Growth strategy rests heavily on the current Business Suite, which includes the core ERP product that is used by approximately 30,000 companies worldwide. SAP claims that it touches 60 percent of the world’s business transactions, which is hard to validate but not all that hard to believe. The main revenue source today is Support, which comprises 50% of the total revenues of the company at more than 5 billion Euros annually, and it grew by 15% in 2009. Other growth engines include:
In the continuous hype cycle that is all things tech, “social” has become the latest flavor of everything.
For sure, we as IT industry analysts are major players in this cycle, albeit sometimes inadvertently. Even when we individually attempt to provide sober, nuanced, balanced, fact-based discussions of some new “social” this-or-that, we’re often stoking the popular mania. The bottom line is that yet another analyst is paying attention and tweeting thoughts on some trendy “social” topic. This fact can and often does get wrenched out of context and escalated wildly in the minds of some people who follow us.
Social CRM is still climbing the hype curve, and events such as this week’s Forrester CRM Jam on Twitter (#CRMjam, Wed. March 24- 1-3 p.m. USA EDT) will undoubtedly fuel that combustion. Of course, yours truly contributed to the buzzing conversation last week with this blogpost on the analytics component of social CRM. I hope you found that discussion useful—with enough new information to help you align social CRM with your analytics initiatives.
What’s the perfect database management system (DBMS) architecture, where analytics is concerned? It seems as if everybody in business intelligence (BI), data warehousing (DW), and related areas has their own opinion on this topic. In fact, it’s more than just opinions. In the database wars, we have huge communities of vendors and users with substantial investments in one or more approaches—from traditional relational DBMSs to column-oriented, in-memory, dimensional, inverted indexing, and other approaches.
In the analytic database wars, new architectures are springing up everywhere, each with its own devotees and differentiators, and each with a go-to-market message that takes potshots at established approaches. The “No SQL” movement is just the latest coalition to emerge in this titanic struggle, and is essentially a loose coalition of diverse approaches rallying around a common theme: that traditional RDBMSs and their SQL-based access languages are unfit, or at least, maladapted to the new world of cloud, social networking, and Web-oriented analytics applications.
DW industry analysts such as myself are of course embedded in these wars—we’re the neutral observers often caught in the crossfire. Of course, this is not a new battle. The columnar database industry has been around for many years and positioned itself as the chief high-performance, low-footprint analytics contender to traditional row-based RDBMSs. And columnar has steadily encroached on relational’s turf, not only through established columnar-based DW vendors such as Sybase, Vertica, and ParAccel, but also through recent adoption (albeit in limited fashion) by the likes of Oracle, SAP, and others into their analytics architectures.
Self-service analytics is one of my core coverage focus areas. It applies not just to business intelligence (BI) but also to advanced analytics.
When, a few months ago, I uttered the immortal phrase “roll over rocket scientists,” I was referring more specifically to the need for pervasive self-service tools for predictive analytics and data mining (PA/DM). Considering that my recently published Forrester Wave on PA/DM Solutions primarily addressed the traditional requirements of “rocket scientist” experts in statistical analysis, I did not put a huge emphasis on data mining features geared to business analysts, subject matter experts, and other “non-technical” information workers.
As I’ve stated in that blogpost and the follow-on podcast, the core problem with today’s PA/DM offerings is that many of them are power tools, not solutions that have been designed for the mass business market. Vendors such as SAS Institute, IBM/SPSS, KXEN, Oracle, Portrait Software, Angoss, FICO, and TIBCO Spotfire provide data mining specialists with feature-rich algorithm-powered solutions for modeling, scoring, regression, and other core PA/DM functions. Their core, traditional user base consists of statisticians, mathematicians, and other highly educated analytics professionals.
Social networks have their foundations in the space-time continuum—you know, the funky coordinate system that Einstein was so keen about.
Social network analysis is all about looking for patterns of “proximity” among people, considered in their cultural capacities as influencers and followers, innovators and imitators, first-movers and late adopters. Down deep, I consider social network analysis an important new branch of decision support systems as a discipline. The core question is: What unique situational chemistry causes various people, individually or collectively, to make various decisions at various places and times?
That’s where space and time enter the social network analysis equation. It’s not enough that I look up to your shining example and take my lead from what you say and do. It’s just as important that we be in the same city, neighborhood, or room. More than that, it’s important that you and I actually cross paths in order for you to actively influence me to buy that latte, or for you to calm me down and thereby stop me from storming out the door and severing my relationship with a retailer who has ignored my complaints one time too many.
What the business world needs now is a bigger, badder, more powerful social media dashboard for customer relationship management (CRM). It almost goes without saying that TweetDeck just won’t cut it.
Ideally, the social media dashboard would provide a CRM-integrated interface for monitoring what customers are saying about you in Twitter, Facebook, and other communities. It would also allow you to aggregate high-level customer satisfaction metrics; to flag smouldering issues surrounding defective products and poor customer service; to respond inline through these channels; and to escalate issues internally to the appropriate parties. In other words, it would be, per my colleagues Bill Band and Natalie Petouhoff, a true “customer business intelligence (BI)” dashboard.
As you develop your company’s social CRM strategy, you must provide social media dashboards to all roles that participate in the customer lifecycle. Whether you’re a brand manager who simply wants to listen into social networks to track awareness, sentiment, and propensities, or a sales person who is interested in identifying and qualifying leads, or a customer service rep who wants to interact closely with established customers, a social media dashboard will soon become a core productivity tool.
Business processes don’t execute only at the application layer. Just as thought processes aren’t entirely divorced from the synaptic firings of the underlying neurons.
Most business activity monitoring (BAM) tools I’ve come across only operate at the business level. In other words, they are geared to monitoring, tracking, correlating, visualizing, and analyzing those metrics that come from business process management (BPM) platforms, enterprise resource planning (ERP), and other application platforms. That’s essential, but it’s only half the battle of process optimization. To deliver the promised service levels, BAM dashboards should integrate closely with business service management (BSM) dashboards, thereby mapping application services to the underlying server, storage, network, and other infrastructure components. In this way, IT can provide full-stack visibility, provisioning, and control over every component that affects every step of every business process. If you need a deep drilldown on BSM, check out the excellent research by my colleague Peter O’Neill, who specializes in this area.