Data Management Strategies

Enterprise Databases: Multiply and It's Tough to Prosper

Blogger: Marcus Collins
 
As a follow up to my blog post Comparing Enterprise Databases and recently published research - I was interviewed by Ann All of IT Business Edge at the Burton Group Catalyst conference in San Diego. We discussed the issues organizations experience when they try to control the growth of different databases in their application portfolios. Here's a link to the interview Enterprise Databases: Multiply and It's Tough to Prosper.

The Business Intelligence Investment: Context

Blogger: Marcus Collins
In my recent post I detailed the critical success factors for a successful analysis initiative namely:

• 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 context” in more detail. Whilst it is true that “All men are created equal”, the same cannot be said of analysis problems. It is important to determine the context of the problem as this brings with it a set of constraints and implicit assumptions that the analyst must understand and consider as they perform the analysis.The key elements of decision context are the:

• scope
• complexity
• timeliness

Scope – is it an operational decision for example analyzing customer purchases to determine customers with the potential profit; simulate supply chains to reduce overall inventory levels and order-to-delivery times? Or is it strategic in nature for example deciding whether to move into a new market or embark on a strategic acquisition? Strategic and operational decisions have a different set of characteristics:Strategic Decisions

• Long Term
• Historic data
• Internal and External perspective
• Enterprise-wide data focus (information politics)
• Focus on analytics and interpretation/heuristics
• Poor data/information quality medium/high impact on decision
• Long term feedback loop – enterprise’s goals/mission statement

Operational Decisions

• Short Term
• Real-time data
• Internal perspective
• Business unit or function data focus
• Focus on analytics; less focus on interpretation
• Poor data/information quality – high impact on decision
• Short term feedback loop – focus on operational metrics

Complexity – problem can increase in complexity quickly and expand the scope of an analysis that is quantitative and focused on operational data to one that relies on the judgment of the analyst. The key is to be cognizant that the analytics guides the decision making.

Take this sales forecast. The data shows that Q1/Q2 has seen steady growth. A simple “interpretation” might assume that Q3 would also show steady growth. But let’s assume that Q3 shows a marked drop in sales in – will Q4 follow Path A (a return to Q1/Q2 growth) or Path B (a continuation of Q3’s falling sales). To answer this question may require data outside that which is normally available from operational systems. For example, if this was a weather dependent product or service you may need weather data to see if there’s a correlation between sales and temperature. A more complex correlation might be an increase in fuel costs leading to a drop in toll road usage. In both examples the decision required an analysis of the operational data and judgment/intuition on the part of the analyst.The final aspect of context is the Timeliness of the data. I explored this aspect in this blog post – analysis when the business needs it. The traditional view of business intelligence is of operational systems feeding a data warehouse through an Extract, Transform and Load (ETL) data pipeline. Little has changed with this model since it was initially proposed in the 1980’s. Increasingly organizations are realizing that the retrospective view of data this model supports is not sufficient to meet the demands of companies that need to function at internet speed. Rather than following this one speed approach organizations should adopt a tiered approach tailored to the organization’s business requirements, competitive environment and customer demands.

In a document to be published in early June and a series of TeleBriefings on June 2 and 3, Burton Group Senior Analyst Marcus Collins will explore the analysis context and each of the other critical success factors in more detail and provide guidance on how organizations can develop a roadmap for the successful deployment of a fact-based decision making culture.

The Business Intelligence Investment: The Analysis Process

Blogger: Marcus Collins

In my recent post I detailed the critical success factors for a successful analysis initiative namely:

• 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:

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.

In a document to be published in early June and a series of TeleBriefings on June 2 and 3, Burton Group Senior Analyst Marcus Collins will explore the analysis process and each of the other critical success factors in more detail and provide guidance on how organizations can develop a roadmap for the successful deployment of a fact-based decision making culture.

The Business Intelligence Investment: Realizing the Benefits

Blogger: Marcus Collins

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.

In document to be published in early June and a series of telebriefings on June 2 and 3, Burton Group Senior Analyst Marcus Collins will explore in detail each of these critical success factors and provide guidance on how organizations can develop a roadmap for the successful deployment of a fact-based decision making culture.

Realizing the value of the BI initiative - analysis when the business needs it

Blogger: Marcus Collins

The traditional view of business intelligence is of operational systems feeding a data warehouse through an extract, transform and load data pipeline. This view has been static for many years – you know when it’s fossilized when switching the order to translate, extract and load is put forward as a major breakthrough and a competitive advantage!

Increasingly organizations are realizing that this retrospective view of data this model supports is not sufficient to meet the demands of companies that need to function at internet speed.

It’s time to lose this one speed fits all business intelligence model and adopt a tiered approach tailored to the organizations business requirements, competitive environment and customer demands.

Fast

Fraudulent activity analysis of credit card transactions 24 hours after the transaction occurred will still leave the banking organization financially exposed. The analysis needs to occur in real time so that the transaction can be stopped and the retailer informed. This real-time analysis has historically been the preserve of complex event processing (CEP) vendors in the financial services sector. With the slowdown in the financial sector we are seeing CEP vendors looking to expand out of this niche into fraud detection, intelligence gathering, telecommunications network monitoring etc.

Medium

Organizations that need to react to events or analyze the last few hours’ worth of transactions should look at solutions that update their OLAP cubes in near real-time. When you bid on Google AdWords you will want to analyze the new few hours’ worth of clickstream data to see if you bid was successful or valuable to the overall marketing agenda. This requirement has usually been partially met by traditional ETL and data warehouse vendors but we are beginning to see a number of smaller niche vendors entering this medium throughput sector.

Slow

There is still a class of analysis where daily or monthly trending is appropriate. Long term trending of inventory levels with a strategy to reduce the working capital requirements of the business,; guiding an expansion into new products, services or investment in existing products, services; gaining a deeper understand of customer behavior, demographics etc. and tailor offerings to gain maximum revenue from all customer interactions or offering tailored products to customers competitors see as too risky and unprofitable. All these require long term trend analysis and the traditional enterprise data warehouse approach is usually appropriate.

So, when you review your BI strategy or embark on a new BI initiative pay close attention to the timeliness of the data updates to ensure that they meet the needs of the business. Timeliness is just one of the factors to consider and in future blogs we look at the other aspects of BI that needs to be considered to maximize the value and realize the promise of analytics in today’s fast moving and competitive environment.

To recap - ensure that your architecture and BI products support the businesses need for speed!