Here are more outtakes from my conversation with Rob Kugel of Ventana Research and the executive team from Star Analytics. The discussion focused on how the economic meltdown is impacting senior executives in corporate finance departments and how technology can help.
If you’re making headway on our previous post about Resolution #1: Focus attention on more strategic activities and less on transactions processing, then this next step may come naturally.
Resolution #2. Improve planning effectiveness.
Kugel points out: “Few companies have achieved a high level of maturity in their planning processes. While some point to the need to reduce the time spent on planning and budgeting, companies need to make their planning and budgeting more effective, not just more efficient. They have to use planning to gain better insight into their performance, achieve greater forecasting accuracy and improving the alignment of strategy and budgets across and within business units. The single biggest factor hindering more effective planning is the use of desktop spreadsheets to drive the process. Dedicated planning applications make it possible to do more effective planning and, in a period of high business volatility, enable companies to revise their plans more rapidly.”
Got an example of where this is working well? Let us know.
Showing posts with label data warehouse. Show all posts
Showing posts with label data warehouse. Show all posts
Thursday, March 12, 2009
Wednesday, September 3, 2008
Understanding the Impact of New Technologies on BI
"Rather than relying solely on a rigid metaphor like data warehousing, BI needs the ability to access data anywhere it can be found and to perform integration on the fly, if necessary. Locating the right information to solve problems must be a semantic process, not requiring knowledge of data structures or canonical forms."
A long-standing concern between business teams and IT is the question "Have we spent the past years creating some monolithic solution that is now unmanageable?" Can it come close to addressing the needs of the current and future business? Have the Business Intelligence (BI) systems that we've created become rigid metaphors, as Neil Raden proposes that data warehouses have become?
Implementing a BI system has always been hard and costly. But the value, once up and running, can be enormous. Insight isn't something to be taken lightly; it can change the very nature of how a business is run. It is no wonder, then, that the markets for BI and performance management solutions are expanding rapidly.
The business needs for BI are going far beyond traditional analysis and reporting; companies are now expecting information access in real-time, across global constituencies, and beyond the limitations of financial data, or for that matter, data itself. The BI systems that we built over the past decade simply may not scale to meet today's requirements.
New technologies such as software as a service (SaaS) are impacting the very nature of software development and delivery, and are also helping to shape the future direction of BI. Even large vendors such as SAP and Oracle are testing out the on-demand waters, SAP with its' 'Business by Design' on-demand solution, and Oracle delivering Oracle Hyperion On Demand. While SaaS has yet to experience broad acceptance in the marketplace, niche BI companies such as LucidEra, Adaptive Planning, Host Analytics and PivotLink are gaining customers with their on-demand offerings, and open the market to small and mid-size (SMB) companies.
Open source BI offerings may provide a clue into another alternative direction for BI markets – again with potential business benefit. As the time and cost of implementing and supporting BI solutions are reduced, so too will the bar lower to a level where SMBs can take advantage of BI, while larger companies can customize solutions and extend analytics across the enterprise. BI will broaden from the hands of the few into the hands of many.
While the new technologies broaden the BI market offerings, they exacerbate the issues companies currently face managing financial data across increasingly stratified and expanding markets. We are already dealing with a crisis in data management. Critical financial information is stored in proprietary BI systems and cannot be easily extracted or combined with operational data. Information from all source systems must be auditable and compliant. It must exist in standard structures that will support future development of combined data with other content sources. Before we can truly scale to meet the coming needs of the BI market we must create a reliable and consistent method for moving and managing data across the enterprise.
The increasingly complex business demands on BI systems are a further validation of the success and relevance of the market. We can learn from other rapidly-evolving technologies that have gone through major expansions as a result of their cumulative relevance. The Web, and Web based standards come to mind as an example of a technology that has successfully transitioned into a much higher form of relevance. Standardization, interoperability, and security are core Web concepts. Before we can hope to truly leverage new technologies, we must overcome the limitations that we've built into our existing BI systems, ensuring standardized data access mechanisms, real-time, secure access to data, and a framework that will extend into the next realm of BI technology development, and Radan's vision of "Locating the right information to solve problems must be a semantic process, not requiring knowledge of data structures or canonical forms."
Neil Raden, Intelligent Enterprise, Business Intelligence 2.0
A long-standing concern between business teams and IT is the question "Have we spent the past years creating some monolithic solution that is now unmanageable?" Can it come close to addressing the needs of the current and future business? Have the Business Intelligence (BI) systems that we've created become rigid metaphors, as Neil Raden proposes that data warehouses have become?
Implementing a BI system has always been hard and costly. But the value, once up and running, can be enormous. Insight isn't something to be taken lightly; it can change the very nature of how a business is run. It is no wonder, then, that the markets for BI and performance management solutions are expanding rapidly.
The business needs for BI are going far beyond traditional analysis and reporting; companies are now expecting information access in real-time, across global constituencies, and beyond the limitations of financial data, or for that matter, data itself. The BI systems that we built over the past decade simply may not scale to meet today's requirements.
New technologies such as software as a service (SaaS) are impacting the very nature of software development and delivery, and are also helping to shape the future direction of BI. Even large vendors such as SAP and Oracle are testing out the on-demand waters, SAP with its' 'Business by Design' on-demand solution, and Oracle delivering Oracle Hyperion On Demand. While SaaS has yet to experience broad acceptance in the marketplace, niche BI companies such as LucidEra, Adaptive Planning, Host Analytics and PivotLink are gaining customers with their on-demand offerings, and open the market to small and mid-size (SMB) companies.
Open source BI offerings may provide a clue into another alternative direction for BI markets – again with potential business benefit. As the time and cost of implementing and supporting BI solutions are reduced, so too will the bar lower to a level where SMBs can take advantage of BI, while larger companies can customize solutions and extend analytics across the enterprise. BI will broaden from the hands of the few into the hands of many.
While the new technologies broaden the BI market offerings, they exacerbate the issues companies currently face managing financial data across increasingly stratified and expanding markets. We are already dealing with a crisis in data management. Critical financial information is stored in proprietary BI systems and cannot be easily extracted or combined with operational data. Information from all source systems must be auditable and compliant. It must exist in standard structures that will support future development of combined data with other content sources. Before we can truly scale to meet the coming needs of the BI market we must create a reliable and consistent method for moving and managing data across the enterprise.
The increasingly complex business demands on BI systems are a further validation of the success and relevance of the market. We can learn from other rapidly-evolving technologies that have gone through major expansions as a result of their cumulative relevance. The Web, and Web based standards come to mind as an example of a technology that has successfully transitioned into a much higher form of relevance. Standardization, interoperability, and security are core Web concepts. Before we can hope to truly leverage new technologies, we must overcome the limitations that we've built into our existing BI systems, ensuring standardized data access mechanisms, real-time, secure access to data, and a framework that will extend into the next realm of BI technology development, and Radan's vision of "Locating the right information to solve problems must be a semantic process, not requiring knowledge of data structures or canonical forms."
Wednesday, June 18, 2008
Thoughts from ODTUG Kaleidoscope 2008
Kaleidoscope 2008 is getting into full swing, and I've already found some nuggets of wisdom from the speakers. Ron Moore, one of the first Essbase certified consultants in the world and founder of Marketing Technologies Group (http://www.mtgny.com/), is a speaker at Kaleidoscope this year. In conversation with him about blending analytic and relational data, he said that you:
"Just can't get by on a hammer or a saw - you need both."
From the perspective of corporate data, that just rings true. Companies can't simply rely upon cube data from their analytic applications in order to make the best business decisions, and they can't rely solely upon relational data – they need both. And to create a central data store requires cooperation between the business owners (of analytic data) and IT owners (of the relational data stores).
Many companies experience a disconnect between the Finance and IT groups, as their goals can appear to be at odds. While Finance teams often implement the business systems, it is IT who inherits the issues of compliance, maintainability, and governance for those systems. But in order to successfully integrate corporate data to provide a holistic information view, the project requires the cooperation of both groups.
In order to harness the value of the content in the cube data, we need to synthesize it within relational data sources. Ultimately, a standard rows and columns relational database is the only common ground to which every application available to the information technology industry can have ubiquitous access. Trying to achieve the bridge in the absence of relational technology creates the potential for a myriad of misinterpretation and eventually the value of the cube data would get lost in translation.
Once the cube data is exported into a relational structure, the infrastructure and data are in place to provide information access to stakeholder groups across the business. A potential next project step, incorporating data from other sources such as ERP or CRM systems or operational data stores, creates a centralized, single version of the truth for all stakeholders to work from, and provides a comprehensive, holistic view of the business that includes both operational and management data.
The benefits of this level of integration to a company are myriad. But as Ron Moore so succinctly put it, it takes both the hammer and the saw to make this kind of effort successful. Finding the tools and technologies that can help bridge the inherent gap between groups is imperative to success.
As Kaleidoscope continues through the week, I'm sure that I'll hear more about opportunities to create mutually beneficial projects between Finance and IT groups and the tools to support them. With technical and thought leaders from across the country here in New Orleans, I'm sure I'll find a few more nuggets of wisdom to share this week.
"Just can't get by on a hammer or a saw - you need both."
From the perspective of corporate data, that just rings true. Companies can't simply rely upon cube data from their analytic applications in order to make the best business decisions, and they can't rely solely upon relational data – they need both. And to create a central data store requires cooperation between the business owners (of analytic data) and IT owners (of the relational data stores).
Many companies experience a disconnect between the Finance and IT groups, as their goals can appear to be at odds. While Finance teams often implement the business systems, it is IT who inherits the issues of compliance, maintainability, and governance for those systems. But in order to successfully integrate corporate data to provide a holistic information view, the project requires the cooperation of both groups.
In order to harness the value of the content in the cube data, we need to synthesize it within relational data sources. Ultimately, a standard rows and columns relational database is the only common ground to which every application available to the information technology industry can have ubiquitous access. Trying to achieve the bridge in the absence of relational technology creates the potential for a myriad of misinterpretation and eventually the value of the cube data would get lost in translation.
Once the cube data is exported into a relational structure, the infrastructure and data are in place to provide information access to stakeholder groups across the business. A potential next project step, incorporating data from other sources such as ERP or CRM systems or operational data stores, creates a centralized, single version of the truth for all stakeholders to work from, and provides a comprehensive, holistic view of the business that includes both operational and management data.
The benefits of this level of integration to a company are myriad. But as Ron Moore so succinctly put it, it takes both the hammer and the saw to make this kind of effort successful. Finding the tools and technologies that can help bridge the inherent gap between groups is imperative to success.
As Kaleidoscope continues through the week, I'm sure that I'll hear more about opportunities to create mutually beneficial projects between Finance and IT groups and the tools to support them. With technical and thought leaders from across the country here in New Orleans, I'm sure I'll find a few more nuggets of wisdom to share this week.
Friday, May 30, 2008
Ready for Operational Business Intelligence? A Good Read…
Operational reporting isn't new; I remember slogging through reams of mainframe-generated reports in my very first job out of college, many years ago. So why is operational Business Intelligence (BI) garnering so much headline real estate these days?
Expanding beyond traditional, strategic business intelligence, operational BI provides an unprecedented level of business insight to support the management of daily operations. And as we look at ways to extend our existing investments in analytic applications, this seems a natural evolution. But introducing operational BI into your organization comes at a cost. The impact on your data warehouse environment can be enormously disruptive if not approached with the understanding that this is a broad-scale project that will have significant impact (and benefits to be realized) across the organization.
Claudia Imhoff, recognized thought leader and expert on business intelligence, has published a new white paper on The Ever-Evolving Data Warehouse: Dealing with Changes and Pressures for BI Today that discusses the impacts of operational BI on the data warehouse environment. Some of her key takeaways regarding the impacts to data warehouse include:
Expanding beyond traditional, strategic business intelligence, operational BI provides an unprecedented level of business insight to support the management of daily operations. And as we look at ways to extend our existing investments in analytic applications, this seems a natural evolution. But introducing operational BI into your organization comes at a cost. The impact on your data warehouse environment can be enormously disruptive if not approached with the understanding that this is a broad-scale project that will have significant impact (and benefits to be realized) across the organization.
Claudia Imhoff, recognized thought leader and expert on business intelligence, has published a new white paper on The Ever-Evolving Data Warehouse: Dealing with Changes and Pressures for BI Today that discusses the impacts of operational BI on the data warehouse environment. Some of her key takeaways regarding the impacts to data warehouse include:
- Scalability
- Performance
- Continuous availability
- Ability to combine different sources of data
Wednesday, April 2, 2008
The Challenge Remains the Same
In the recently published "Cost Cutting in Data Management and Integration, 2008," Gartner emphasizes the need to reduce costs while continuing to support BI initiatives. Sound familiar? The operating motto for CIOs and IT teams for the past several years: Do more with less.
With recent downturns in the US economy, the business needs for the data coming from BI systems have become critical to companies’ maintaining their competitiveness. When there is less money in play, companies have to understand all facets of their business: where they are making money and where they are leaving opportunities for improvement on the table, to plan continuously, and to have the frameworks in place to change directions quickly in order to stay at the forefront of their markets. There is a pressing need for accurate, reliable data from which they can analyze and gain the insight needed to make the best business decisions possible. This source data comes from the same IT team tasked with reducing their costs.
Balancing the business need for insight into the organization and the IT need to reduce costs is where innovation can occur. Gartner’s report provides a number of recommendations for cutting costs within data management initiatives, ranging from the common-sense Optimize Data Integration Tools Licensing to recommendations that are complex and require creativity within the IT teams to successfully achieve: projects like Perform Operational Database Consolidation and Perform Data Mart Consolidation. Gartner estimates the payback from data mart consolidation as a savings of “approximately 50 percent of the total cost allocated to supporting their disparate data marts if they consolidate those marts into an application-neutral data warehouse.”
Easily said, but how to get from here to there? The solution lies in feeding a central application-neutral data warehouse with business critical data from application-specific data marts. To do this requires using intelligent data movement applications to ensure that this feeding of the warehouse is both seamless (does not interfere with the data mart applications), highly dynamic (so the data is refreshed in near real-time), persisted (ensuring a physical copy of calculated or aggregated data is available in the warehouse), and auditable (for data governance and regulatory compliance).
There are myriad opportunities to reduce costs within the IT department. The challenge is to develop innovative solutions that meet the requirements of data governance, security and accuracy, and still provide business users with the data they need to drive the company forward.
With recent downturns in the US economy, the business needs for the data coming from BI systems have become critical to companies’ maintaining their competitiveness. When there is less money in play, companies have to understand all facets of their business: where they are making money and where they are leaving opportunities for improvement on the table, to plan continuously, and to have the frameworks in place to change directions quickly in order to stay at the forefront of their markets. There is a pressing need for accurate, reliable data from which they can analyze and gain the insight needed to make the best business decisions possible. This source data comes from the same IT team tasked with reducing their costs.
Balancing the business need for insight into the organization and the IT need to reduce costs is where innovation can occur. Gartner’s report provides a number of recommendations for cutting costs within data management initiatives, ranging from the common-sense Optimize Data Integration Tools Licensing to recommendations that are complex and require creativity within the IT teams to successfully achieve: projects like Perform Operational Database Consolidation and Perform Data Mart Consolidation. Gartner estimates the payback from data mart consolidation as a savings of “approximately 50 percent of the total cost allocated to supporting their disparate data marts if they consolidate those marts into an application-neutral data warehouse.”
Easily said, but how to get from here to there? The solution lies in feeding a central application-neutral data warehouse with business critical data from application-specific data marts. To do this requires using intelligent data movement applications to ensure that this feeding of the warehouse is both seamless (does not interfere with the data mart applications), highly dynamic (so the data is refreshed in near real-time), persisted (ensuring a physical copy of calculated or aggregated data is available in the warehouse), and auditable (for data governance and regulatory compliance).
There are myriad opportunities to reduce costs within the IT department. The challenge is to develop innovative solutions that meet the requirements of data governance, security and accuracy, and still provide business users with the data they need to drive the company forward.
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