Gartner has issued a new press release summarizing its analysis of trends in enterprise architecture (EA). Until recently EA has been strictly an information technology (IT) function that was narrowly focused on the data and technology components of the business organization. According to Gartner, however, EA is becoming a collaborative effort between IT and the business. They project nearly a third of all such effort will be collaborative by 2016, up from 9% currently.
Business intelligence can only benefit from this shift. As EA moves from a data-centric focus to a business and strategic focus, we should expect business intelligence efforts to yield greater value since they'll flow from a more sharply focused strategic vision of the business. Sharpening the strategic focus will be critical as information inputs from social networking channels both inside and outside the business multiply. Failure to do so will increase the risk of decision makers being overwhelmed by information noise.
Especially in larger and more forward-thinking organizations business intelligence and analytics are becoming less a matter of gathering data to make narrowly focused decisions and more about seeing meaning and context to enable long-term strategic planning and contingency analysis. Viewed in this context, I think the emerging trend in EA is a natural evolutionary step forward for business intelligence and analytics.
Showing posts with label analytics. Show all posts
Showing posts with label analytics. Show all posts
Wednesday, March 30, 2011
Thursday, January 27, 2011
Real-Time BI and Analytics: Flying Under the Radar
I don't normally devote a lot of space to a single company, but while researching to write about real-time business intelligence and analytics I ran across one I had never heard of before.
Vertica Systems specializes in databases that are built specifically for real-time and near real-time business intelligence and analytics. They claim database performance that is 50-1000 times faster than traditional data warehouses. How does Vertica accomplish this? In the most non-technical terms I can muster:
1. They organize the data differently than a traditional database, using a method called "columnar orientation." This makes it easier to get at just the data that are needed, faster than traditional queries.
2. They compress the data very aggressively. This allows them to, for example, store multiple copies of the same data sorted in different ways.
3. They store data for reading by queries differently than data to be written to the database, again to optimize the speed of data reads for queries.
4. They can take advantage of multiple processors on the database server.
5. Vertica wasn't designed to run on a specific kind of server hardware, but can run on anything that runs the Linux operating system. This should help hold down hardware costs.
Even though I hadn't heard of them, Vertica must be doing something right. They've quietly racked up an impressive client list that spans multiple industries. Clients include Twitter, Verizon, Bank of America, Comcast, Blue Cross Blue Shield, Sunoco, and most recently Groupon.
Vertica Systems specializes in databases that are built specifically for real-time and near real-time business intelligence and analytics. They claim database performance that is 50-1000 times faster than traditional data warehouses. How does Vertica accomplish this? In the most non-technical terms I can muster:
1. They organize the data differently than a traditional database, using a method called "columnar orientation." This makes it easier to get at just the data that are needed, faster than traditional queries.
2. They compress the data very aggressively. This allows them to, for example, store multiple copies of the same data sorted in different ways.
3. They store data for reading by queries differently than data to be written to the database, again to optimize the speed of data reads for queries.
4. They can take advantage of multiple processors on the database server.
5. Vertica wasn't designed to run on a specific kind of server hardware, but can run on anything that runs the Linux operating system. This should help hold down hardware costs.
Even though I hadn't heard of them, Vertica must be doing something right. They've quietly racked up an impressive client list that spans multiple industries. Clients include Twitter, Verizon, Bank of America, Comcast, Blue Cross Blue Shield, Sunoco, and most recently Groupon.
Tuesday, January 25, 2011
Real-Time Business Intelligence and Analytics: An Introduction
Previously we introduced the concept of a life-cycle for business intelligence data. Data are captured into transaction processing systems like the general ledger accounting system. At some interval these data are moved into a BI database and processed to make them suitable for consumption. At some point older data are archived or even removed when they are no longer useful.
At first the BI or decision support life-cycle was typically built around financial reporting, and thus depended on the quarterly or monthly accounting close. Once the books were closed for the accounting period, data were pulled from the accounting system, loaded into the BI or decision support database, reports were run, and data analyzed.
As BI moved beyond the financial realm, managers in business areas like Sales and Operations found that they could achieve competitive advantage by getting (and acting upon) more frequent updates of data as opposed to organizations that remained tied to the monthly accounting cycle. This spurred the increase in the frequency of data updates to weekly, daily, and so on. The result is an evolution toward a "24-hour BI cycle" that parallels the evolution toward a 24-hour news cycle in the news media. In some applications BI data are now updated almost continuously. Rather than being the exception, this high-frequency updating of data will increasingly become the rule.
The impetus toward real-time business intelligence and analytics has implications for both technology and business. Vendors are challenged to devise new solutions to the problems introduced by requirements for more frequent updates. With the increase in the amount and frequency of data updates, businesses need to rethink how they consume data. We'll address these issues in more detail in the next article.
At first the BI or decision support life-cycle was typically built around financial reporting, and thus depended on the quarterly or monthly accounting close. Once the books were closed for the accounting period, data were pulled from the accounting system, loaded into the BI or decision support database, reports were run, and data analyzed.
As BI moved beyond the financial realm, managers in business areas like Sales and Operations found that they could achieve competitive advantage by getting (and acting upon) more frequent updates of data as opposed to organizations that remained tied to the monthly accounting cycle. This spurred the increase in the frequency of data updates to weekly, daily, and so on. The result is an evolution toward a "24-hour BI cycle" that parallels the evolution toward a 24-hour news cycle in the news media. In some applications BI data are now updated almost continuously. Rather than being the exception, this high-frequency updating of data will increasingly become the rule.
The impetus toward real-time business intelligence and analytics has implications for both technology and business. Vendors are challenged to devise new solutions to the problems introduced by requirements for more frequent updates. With the increase in the amount and frequency of data updates, businesses need to rethink how they consume data. We'll address these issues in more detail in the next article.
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