Marcus Collins
BG Sr Analyst I
2 years at Gartner
27 years IT industry
Marcus Collins is a senior analyst for Gartner's Burton Group Data Management Strategies. He covers enterprise architecture, data architecture, data and information integration, database management systems and database technologies, and semantic Web. Read Full Bio
by Marcus Collins | June 22, 2010 | 1 Comment
The adoption of an event-driven paradigm and the analysis of data in motion will enable enterprises to make accurate decisions more quickly. This greater speed means that enterprises are better able to see and react to changes in the business environment and therefore gain a competitive advantage over organizations that perform the more traditional retrospective analysis of historic data. Although the benefits are great, the complexity of implementing an event-driven infrastructure and processing means that the implementation of such an initiative is fraught with technical risk. IT organizations should be in the vanguard of this adoption and lead the drive to explore areas within the enterprise where real-time event processing could be of potential benefit.
Forward-thinking enterprises are realizing that the retrospective view of the business environment—a view that is supported by traditional business intelligence—is not sufficient to meet the demands of operating in a competitive environment where decisions must be made at “Internet speed.” Compounding this issue is the fact that instrumented business processes and the increasingly sensor-enabled physical world have caused a dramatic increase in the volume of business events that are received as digital feeds. Real-time data analysis on streaming data is, therefore, quickly emerging as an important technology for IT organizations to understand.
Historically rooted in capital markets, real-time data analysis is now expanding into financial-services fraud detection, intelligence gathering, telecommunications-network monitoring, supply chain, and logistics.
Enterprises should not assume that real-time analysis is only for high volume data feeds. Analyzing low-volume events can generate significant business value. Tracking inventory movements at a palate level and on an hourly basis may be all that is required to drive increased value in a supply chain. The technical risk and cost of implementing this low-volume event scenario will be less than that required to track each inventory item, using RFID tags, as it physically moves through a warehouse. Ensure that the event rate and processing complexity is appropriate for the business process and focus on the low-risk scenarios, especially at the initial stages of the implementation.
Whilst real-time analysis is best suited for “internet speed” decisions, retrospective analysis of business event data can generate significant business value. Analyzing business events in order to understand the drivers of business change is of significant value to enterprises. Retrospective analysis can also be used as a low-risk approach and precursor to the wider deployment of real-time event processing in the enterprise.
As with all new technology, adoption initiatives start with a proof-of-concept initiative. Focus on business units with well-understood business processes where the adoption of an event-driven paradigm will yield measurable business value. As the initiative progresses and the business benefits can be shown to have been realized, use the initiative as a poster child to articulate the value of event-driven processing to the rest of the enterprise.
The real value of adopting an event-driven paradigm will be realized through the involvement of the business domain experts, especially in the programming of the real-time event processing system with the business domain knowledge and rules. The success (i.e., the “order winner”) of the real-time event processing initiative will be measured not in terms of the event throughput rates or the availability of the real-time event processing infrastructure (business will see this as an “order qualifier”). Rather, it will be measured in terms of the speed and accuracy of decision making and in the responsiveness of the enterprise to changes in the business environment.
I’ll be exploring real- analysis of data in motion in more detail in a presentation at Catalyst Europe currently going on in Prague.
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by Marcus Collins | June 21, 2010 | Comments Off
In my recent post I detailed the critical success factors for a successful analysis initiative. In a presentation at Catalyst Europe currently going on in Prague I’m going to explore these factors in more detail:
- Having the right analysis process
- Having access to the right information
- Having the right context
- Having the ability to make decisions at the right time
- Having the right leadership
- Having the right team dynamics
- Having the right people
And finally:
- Choosing the right problem
In this post I’m going to explore the “right analysis process” in more detail.
Organizations operate in a high competitive and regulated environment and so efficient and repeatable business processes are integral to an organization’s well being. This applies equally to the analysis process. The analysis process should be tailored to meet the requirements of both the organizational culture and the context of the analysis. For example, a move into or out of a market segment would require a more thorough analysis that a series of experimental what-if scenarios.
The process is shown below:
BI Process + Learning Frame the problem – requires the analyst to have a clear understanding of the problem with the problem boundaries defined and it decomposed into its constituent parts.
Design the analysis – is the selection of the appropriate technique or framework. For example, to identify the most balanced decision amongst candidates the trade study would be used.
Gather the data – identifying data that is relevant to the problem and determining the source of this information and, importantly, determining what information is not available. Data quality metrics are important as they allow the analyst to evaluating the impact of the quality metrics on the analysis output.
Execute and interpret – two different skills are used here. Execute requires an analytic discipline with a focus on quantitative fact and rule-based logic. Interpretation emphasizes human judgment. Both techniques should be used in this step of the process. The output of this step will either be actionable and the process will move onto the implementation stage or not actionable and the process will move back to the start and refine and/or reframe the original problem statement.
Implement – in context of business intelligence this is the implementation of business change. A key recommendation here is that the analysis is not the act of making decisions; rather it is a single factor in the justification for a decision. The actual decision will be a combination of quantitative information, qualitative information and human judgment.
Measure – the traditional IT view of measurement focuses on efficiency and cost. Within the context of the business intelligence initiative the focus should shift to measuring business value.
Continuous learning – the analysis process is iterative and key to the success of this is culture of continuous learning. Organizations should encourage a culture of learning through both successes and failures.
The right analysis process is just one aspect of business intelligence initiative. As you develop your business intelligence strategy ensure that each of the critical success factors has a specific focus. In a presentation at Catalyst Europe currently going on in Prague I will explore these critical success factors of the business intelligence strategy in more detail.
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by Marcus Collins | June 10, 2010 | 1 Comment
IBM recently published a report that states that while 75% of organizations recognize the opportunity for analytics only about 15% have a well developed analytic capability. This mirrors the finding of a 2008 report published by Accenture.
The current economic climate and highly competitive nature of today’s business environment requires that the traditional approach of making decision, based solely on intuition and gut instinct, be replaced by one where a rigorous information and fact based analysis process guides the decision making.
Given this landscape, what can organizations do to ensure an effective business intelligence initiative? As we analyzed both the successes and failures of business intelligence projects, a number of critical success factors emerged:
- Having the right analysis process
- Having access to the right information
- Having the right context
- Having the ability to make decisions at the right time
- Having the right leadership
- Having the right team dynamics
- Having the right people
And finally:
- Choosing the right problem
None of these factors involve tool deployment – many organizations have already made the investment in the analysis tools – rather they focus on the softer process and organizational aspects.
The key recommendations are that analysis should follow a clearly defined process with a focus on the reliable delivery of business value; that the analysis initiative should be led by the business, with strong involvement from IT and that organizations should embark on a proof of concept to both refine the internal processes and as a poster child for a wider deployment.
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by Marcus Collins | June 1, 2010 | Comments Off
Bold breakthroughs or incremental innovation – which model should enterprises follow when implementing data management initiatives?
It seems that most of the reports I write have a recommendation along the lines of …
“As with all new technology, adoption initiatives start small. Focus on business units where the adoption of “the new technology/process” will yield measurable business value. As the initiative progresses and the business benefits can be shown to have been realized, use the initiative as a poster child to articulate the value of the new technology/process” to the rest of the enterprise.”
I’ve always wondered if this is the right approach or if a big-bang would be more effective.
I got an interesting perspective from an article in the Harvard Business Review – Block-by-Blockbuster Innovation. The article explains that many CEO’s want breakthroughs not incremental innovation (I’ll explain the differing innovation models in a later post). The author recommends thinking of innovation as a pyramid. The base of the pyramid covers the small but numerous ideas; middle covers new-opportunity areas; the top includes the small number of big-bets about the future direction of the enterprise.
The lesson I take from this – don’t underestimate the importance of small-scale change.
Many of the innovations I write about – database archiving; cloud databases; real-time analytics etc. – have the potential to yield major business value when deployed across the enterprise. But this benefit is not without cost – technical and business risk. To mitigate these risks enterprises should consider treating all innovation as if they were incremental. Start with a small scale pilot, measure the success or failure and then leverage the successes (and lessons learned) as the initiative is rolled-out to a larger audience.
So don’t be surprised if I continue to use the start small recommendation in future papers. Enterprises need a mix of big-bets and incremental innovation and both should follow a similar model if they are to deliver real business value.
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by Marcus Collins | June 1, 2010 | Comments Off
Welcome to my blog on the Gartner Blogging Network. I’ve been blogging for quite a while on my Burton Group service but I’m new to Gartner and GBN. In the spirit of the “hello world” posts by many of my teammates here’s an introduction about me. I …
… am an analyst in the Data Management Strategies service in the IT Practitioner part of Gartner
… cover a diverse set of topics from database technology (where the rate of innovation shows no sign of slowing); business intelligence (technology and increasingly organizational issues the deployment of BI is raising); SAP (where the acquisition of Sybase shows that this area too is undergoing change); data and database architecture and cloud computing and the noSQL movement (databases and BI in the cloud)
… cover organizational issues the changes database technology are imposing on the operational part of IT (i.e., the database administrator)
… and interested in the changes that technology and the democratization of IT (and especially business intelligence) is having on the way decisions are made in enterprises
… joined Burton group 2+ years ago – prior to becoming an analyst I was a data management practitioner and enterprise architect. I’ve worked in a variety of industries including oil exploration and production, retail and logistics, pharmaceuticals and scientific computing
… have an MBA from the Durham Business School in the UK (I now live in the Boston USA area), which I find useful for bringing a business viewpoint to topics which can easily become technically focused and allows me to bring an international perspective on issues
… look forward to speaking with you about the fast moving data and information management area!
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