Environmentally friendly business intelligence - or Green BI - is kind of an ambiguous term. What does it mean? There are 2 main ways of approaching the topic of green BI - the physical environmental impact of implementing a BI solution - and using BI to analyze your footprint.
Business leaders acknowledge that proactively addressing corporate social responsibility (CSR) issues can result in real business benefits. An IBM global survey of more than 250 executives showed that 68% are already focusing on CSR activities to create new revenue streams, and 54% believe CSR gives them a competitive advantage.
Physical impact of implementing BI
It is commonly thought that an environmentally-friendly IT department can save you money, at least in the long run. Cloud computing infrastructure is noted for being more environmentally friendly than in-house infrastructure, with no need for new systems or IT equipment inside your company's walls. So exactly how can the cloud make your organization more green?
1. Energy consumption: since cloud-based business intelligence solutions are delivered through a scalable environment, energy consumption is reduced at the source. At the same time, your company's energy expenses will be reduced.
2. Decommissioning: eventually, every on-premise server you buy will have to be replaced, creating a future waste disposal challenge. By using the SaaS model, you won't have to buy (and later get rid of) any IT equipment. So that means you will also enjoy waste disposal cost savings.
3. Staff: keeping your IT needs means you won't have to hire resources as aggressively. Not having to increase the number of employees also translates to a lower rate of energy consumption, not to mention lots of other cost savings.
President Barack Obama expressed a desire for the Federal Government to spend over 150 billion U.S dollars on green technology at the beginning of his term, and he wanted a significant amount of this money to be spent on green IT and green computing. The idea was to practice green IT initiatives, especially those concerned with improving the environmental sustainability of enterprise data centers.
Using BI to analyze your footprint
Regulatory mandates to reduce greenhouse gas emissions worldwide are compelling companies to look for new approaches to carbon management-from sourcing and production, to distribution and product afterlife. Much of the opportunity to address CO2 emissions rests on the supply chain. Reducing the supply chain's carbon footprint is essential.
So how would you use BI to analyze your footprint? Reduce greenhouse gas emissions and environmental impact across your business activities by monitoring your carbon use. Streamline business processes to improve overall efficiency, enhance quality, and add or change activities that help you become more environmentally friendly. Use dashboards to estimate emissions across business travel, distribution, energy, manufacturing etc. Collate data from company-wide systems into one place so you can comprehensively report your CO2 emissions to key stakeholders in an auditable and consistent manner.
Unfortunately, a lot of companies still have little or no actionable data about their environmental footprint. Although organizations may have the means to track cashflow, they often have little business intelligence or transparency into the most critical sustainability issues. The C-Suite is increasingly calling for environmental performance indicators to be included on existing executive dashboards - it is not enough anymore to be the fastest and cheapest: companies must strive to be the greenest and cleanest as well. What are the benefits of using BI to measure your "greenness"? Helping reduce your ecological footprint and energy costs, improving employee morale, developing competitive advantage and leadership, enjoying lower cost of compliance and strengthening your brand image are a few of them.
What do others have to say about Green BI?
William Laurent, a leading expert in information strategy and governance has talked about the Seven Pillars of Green BI:
"Green business intelligence breaks down into seven related, yet importantly distinct components. The Seven Pillars of Green BI provides the actionable "green knowledge" that will best assist global enterprises in monitoring, managing, and implementing their environmentally-wise future."
The Seven Pillars of Green BI
* Manufacturing Consumption Footprint (i.e. Resources Used in Production)
* Manufacturing Output Footprint (i.e. Waste Created from Production)
* Operational Consumption Footprint
* Operational Output Footprint
* Product Consumption Lifecycle (i.e. Customer Consumption)
* Employee Social Footprint
* Green BPR (i.e. Green Reengineering of Business Processes, Procurement Patterns, etc.)
To sum up
What is the future of Green BI? It could be the introduction of new types of key performance indicators (KPIs) and dashboards that will measure environmental performance and compliance. These dashboards will be able to regulate, track and ration energy, while monitoring and integrating power usage statistics with pricing strategies and carbon-footprint data.
In the future, a large amount of green intelligence budgets will be at stake when it comes to measuring carbon footprints and emissions. Tax breaks and compliance penalties associated with environmental sustainability will make their way to the forefront of company concerns.
Thursday, October 13, 2011
Tuesday, October 11, 2011
50 Business Intelligence Failures
1. Inadequate integration knowledge on Business Intelligence and Decision Support System Level. Dashboards are not good solutions.
2. Not seeing complete Business Intelligence picture.
3. No vision of what is final goal of BI tool. What are final outcomes and how will they be used for company prosperity and competitive advantage.
4. Demands are created faster than Management Information Systems can absorb, implement and stabilize = Generating to big cumulated requests.
5. No or little relation between financial statements and non financial key performance indicators.
6. Lack of integrative systems in Legacy level.
7. No or inadequate Mater Data Management solutions.
8. No or inadequate Data Quality processes.
9. Minimizing data quality issues by customer.
10. Too high expectations from Business Intelligence. It is not an Expert System.
11. Business Intelligence solutions are specialised. Can not cover everything.
12. Out of the box solutions cover minor part of your current process and required functionalities. Be prepared to change processes more then to customize out of the box solution.
13. Too dynamic complex market, like telecommunications.
14. No internal technical knowledge on management and on expert level
15. No internal dedicated team of experts to cooperate with vendors.
16. No internal resources to handle knowledge generation.
17. No internal structure to handle development of Business Intelligence layer.
18. Wrong project lead.
19. Too many internal enemies.
20. Too ambitious management.
21. Too naive management and project lead.
22. Wicked management. Political games can destroy any project.
23. Political games on management level. It is easy to declare "not needed any more".
24. Lack or inadequate internal marketing.
25. Wrong scheduling of modules. What comes first, next and at the end matters.
26. Too long implementation time. Many other projects with significant impact happen during implementation time. Each impact could mean new code writing and starting from beginning in certain segments.
27. Wrong vendor selection.
28. Selection of wrong software solutions.
29. Not enough resources to finish project. Especially in cases of additional costs that can double or triple initial investment.
30. Lack of data definition or poor methodology.
31. Too little integration with comptrolling. Comptrollers stay in their world with separated solutions.
32. Internally made solution, more likely to fail than vendors solution.
33. There is no one stop shop vendor solution. Each solution should be compared with leader in particular segment.
34. Business Intelligence project should evolve.
35. Can outsource the whole thing. Company must avoid the temptation of outsourcing everything and only things that are not core competences.
36. Just give me the dashboard. Companies must have a solid and stable BI infrastructure before implementing dashboards.
37. Information chasm between financial and non financial systems is too big.
38. Inadequate consultancy.
39. Trusting consultants too much.
40. Start with internal development without asking users what they need.
41. Mega requesting appetite. Data Warehouse/BI must cover all requests without any exemptions.
42. Include all departments and business units, especially call on workshops as many people as possible. Nobody should say later that was not informed. Some call it spam with project but don't believe to gossips.
43. Jumping into BI and Data Warehouse project without own IT administrators. Why should you have them since it is out of the box solution.
44. Leave all operative work to consultants.
45. Believing without any doubt to presented models and presentations of vendors.
46. Scheduling and starting in parallel major upgrades of legacy systems.
47. Not giving up from processes and not willing to change them.
48. Modifying project scope and making false promises just to enter into company.
49. Insufficient customer specification and lack of vendors notification about it.
50. Staying too tight to signed specification. Not allowing single change.
2. Not seeing complete Business Intelligence picture.
3. No vision of what is final goal of BI tool. What are final outcomes and how will they be used for company prosperity and competitive advantage.
4. Demands are created faster than Management Information Systems can absorb, implement and stabilize = Generating to big cumulated requests.
5. No or little relation between financial statements and non financial key performance indicators.
6. Lack of integrative systems in Legacy level.
7. No or inadequate Mater Data Management solutions.
8. No or inadequate Data Quality processes.
9. Minimizing data quality issues by customer.
10. Too high expectations from Business Intelligence. It is not an Expert System.
11. Business Intelligence solutions are specialised. Can not cover everything.
12. Out of the box solutions cover minor part of your current process and required functionalities. Be prepared to change processes more then to customize out of the box solution.
13. Too dynamic complex market, like telecommunications.
14. No internal technical knowledge on management and on expert level
15. No internal dedicated team of experts to cooperate with vendors.
16. No internal resources to handle knowledge generation.
17. No internal structure to handle development of Business Intelligence layer.
18. Wrong project lead.
19. Too many internal enemies.
20. Too ambitious management.
21. Too naive management and project lead.
22. Wicked management. Political games can destroy any project.
23. Political games on management level. It is easy to declare "not needed any more".
24. Lack or inadequate internal marketing.
25. Wrong scheduling of modules. What comes first, next and at the end matters.
26. Too long implementation time. Many other projects with significant impact happen during implementation time. Each impact could mean new code writing and starting from beginning in certain segments.
27. Wrong vendor selection.
28. Selection of wrong software solutions.
29. Not enough resources to finish project. Especially in cases of additional costs that can double or triple initial investment.
30. Lack of data definition or poor methodology.
31. Too little integration with comptrolling. Comptrollers stay in their world with separated solutions.
32. Internally made solution, more likely to fail than vendors solution.
33. There is no one stop shop vendor solution. Each solution should be compared with leader in particular segment.
34. Business Intelligence project should evolve.
35. Can outsource the whole thing. Company must avoid the temptation of outsourcing everything and only things that are not core competences.
36. Just give me the dashboard. Companies must have a solid and stable BI infrastructure before implementing dashboards.
37. Information chasm between financial and non financial systems is too big.
38. Inadequate consultancy.
39. Trusting consultants too much.
40. Start with internal development without asking users what they need.
41. Mega requesting appetite. Data Warehouse/BI must cover all requests without any exemptions.
42. Include all departments and business units, especially call on workshops as many people as possible. Nobody should say later that was not informed. Some call it spam with project but don't believe to gossips.
43. Jumping into BI and Data Warehouse project without own IT administrators. Why should you have them since it is out of the box solution.
44. Leave all operative work to consultants.
45. Believing without any doubt to presented models and presentations of vendors.
46. Scheduling and starting in parallel major upgrades of legacy systems.
47. Not giving up from processes and not willing to change them.
48. Modifying project scope and making false promises just to enter into company.
49. Insufficient customer specification and lack of vendors notification about it.
50. Staying too tight to signed specification. Not allowing single change.
Sunday, October 9, 2011
Designing The Ultimate Business Intelligence Tool
A short time ago I was contacted regarding a blog by Jaime Brugueras(1), discussing what he feels is lacking in the current crop of Business Intelligence (BI) tools. I was asked to provide my feedback via blog post and hopefully start up a discussion.
Everything which Brugueras describes in his blog would comprise the ultimate BI tool. He clearly highlights key pain points felt by all levels of user and creates the framework by which these could be addressed. In spite of his observations, I feel that the nature of the market and current BI tool landscape prevents these recommendations from being realized.
The core argument of Brugueras' blog can be summed up as follows: Business Intelligence tools need to be easier to use, more comprehensive in nature, predictive of future outcomes and cheap enough that even the smallest businesses can afford it. He goes on to advocate for tools that are more sophisticated than a team of IT professionals could program, but simple enough for a lay person to configure.
He notes that "tools available today are relatively complex and require some level of programming", yet a few lines later declares that "an effective BI tool allows for seamless integration of data across... CRM, accounting and point-of-sale software". He laments, however, that current BI tools are "unable to integrate data from all sources".
While multiple tools may output into similarly-formatted CSV files, the database from where these exports originate don't all have the same schema. There exists no universal key to link multiple data sources, and few companies are willing turn themselves into information providers by facilitating others usage of their data.
Many would rebut my previous statement by calling my attention to APIs; how companies like Google, foursquare and Twitter make their data available to the outside world. They would be correct, save for one key point: the user accommodates, as opposed to dictates, the format of the API. If you don't like how Twitter has named a particular variable, then too bad for you. A company like Google isn't going to change their data structure simply because you ask nicely.
When you consider that every company out there has their own special flavor of API, the dream of "an effective BI tool (that) allows for seamless integration of data" simply goes up in smoke. Every time a provider comes along, you will either need to adapt your BI tool to accept their data, or ask them to conform to your standard. The former is far more likely than the latter, but doing the former requires programmers and programmers cost money.
It is not difficult to see the relationship between cost and compatibility. Being more compatible requires a larger programmer base, which in turn requires more capital. The resulting tool would have to be heavily ad-supported, or sell at a price point sufficient to cover ongoing development costs. Very quickly you enter into the realm of enterprise-level solutions, where even the traditionally free Google Analytics has started charging $150,000/year for a Premium service level. This price point is clearly far outside the realm of affordability for the majority of small businesses.
The author advocates for an all encompassing, low-cost solution aimed at the SMB market. He wants a tool that is user friendly, inexpensive and easy to use, featuring automatic integration with multiple data sources that is predictive of future outcomes. He acknowledges that end-users "are not likely to be able to program their needs", yet advocates the development of modular software in anticipation of every possible need. These BI tools must be action oriented, distilling complicated tasks like customer retention, inventory management and social media communication down to the simple click of a button. While wonderful in concept, I don't believe that all will ever be within a single tool.
Apple products, be it the iPhone, iPad or iPod, are famous for working well together. Apple accomplishes this by controlling every step of the process, and knowing exactly what goes into every piece of hardware. They can then perfectly tailor their software to work within the hardware's specifications, producing a very attractive product offering. However, a Mac falls flat when trying to run software originally written for a PC. With Apple reticent to license out their iOS to third party developers, don't expect to see this product-line unity augmented or replicated anytime soon.
There are only two ways that Brugueras' ideal BI tool could come to pass: A unifying open-source project of unprecedented scope or one single (for profit) company willing to take users cradle-to-grave for all their business software needs.
While I don't think that a massive open-source project could appear, I make a point to never say never. The cynic in me just doesn't see it, though. A single company creating a full-feature, A-to-Z Business Intelligence tool isn't very likely, either. The barrier to entry isn't so high that somebody wouldn't try and come up with a "me too" product offering.
I'm not here to preach about what my ideal Business Intelligence tool would be, because I don't believe that there can ever be a one-size-fits-all tool. You'll never get enterprise-level features in a cheap/free product, either because of the associated development costs or due to the simple inability for small businesses to devote the required time to such a far reaching tool.
Take your prototypical small business as an example, where employees generally wear more than one hat. It's likely that your "web guy" will not only be responsible for website design and SEO, but also for PPC and SEM and possibly copy writing for both the online channel and traditional corporate communications. Ask yourself, will this overworked individual be able to devote the time required to use a highly-sophisticated BI tool?
Much like clothing, creating something that is one-size-fits-all typically results in a garment which is a poor fit for 99% of the population. When you buy a suit, most people require that the pants be hemmed or the jacket taken in. Few people are truly "off the rack", so why do we expect the same from our BI tools?
There exist tools that work very well for small business, which scale well and can accommodate the business as it grows. They don't have the features of an enterprise-level tool, but small businesses don't have the bandwidth to use all those features anyway. Rather than devote time to chasing the dream of the perfect tool, end-users should focus on better expressing their BI tool needs. The corporate world has been quite successful at recognizing needs and designing products to meet them. I have every confidence that several different vendors will step up and provide targeted solutions, provided the requirements are clear and properly documented by the eventual end-users.
Everything which Brugueras describes in his blog would comprise the ultimate BI tool. He clearly highlights key pain points felt by all levels of user and creates the framework by which these could be addressed. In spite of his observations, I feel that the nature of the market and current BI tool landscape prevents these recommendations from being realized.
The core argument of Brugueras' blog can be summed up as follows: Business Intelligence tools need to be easier to use, more comprehensive in nature, predictive of future outcomes and cheap enough that even the smallest businesses can afford it. He goes on to advocate for tools that are more sophisticated than a team of IT professionals could program, but simple enough for a lay person to configure.
He notes that "tools available today are relatively complex and require some level of programming", yet a few lines later declares that "an effective BI tool allows for seamless integration of data across... CRM, accounting and point-of-sale software". He laments, however, that current BI tools are "unable to integrate data from all sources".
While multiple tools may output into similarly-formatted CSV files, the database from where these exports originate don't all have the same schema. There exists no universal key to link multiple data sources, and few companies are willing turn themselves into information providers by facilitating others usage of their data.
Many would rebut my previous statement by calling my attention to APIs; how companies like Google, foursquare and Twitter make their data available to the outside world. They would be correct, save for one key point: the user accommodates, as opposed to dictates, the format of the API. If you don't like how Twitter has named a particular variable, then too bad for you. A company like Google isn't going to change their data structure simply because you ask nicely.
When you consider that every company out there has their own special flavor of API, the dream of "an effective BI tool (that) allows for seamless integration of data" simply goes up in smoke. Every time a provider comes along, you will either need to adapt your BI tool to accept their data, or ask them to conform to your standard. The former is far more likely than the latter, but doing the former requires programmers and programmers cost money.
It is not difficult to see the relationship between cost and compatibility. Being more compatible requires a larger programmer base, which in turn requires more capital. The resulting tool would have to be heavily ad-supported, or sell at a price point sufficient to cover ongoing development costs. Very quickly you enter into the realm of enterprise-level solutions, where even the traditionally free Google Analytics has started charging $150,000/year for a Premium service level. This price point is clearly far outside the realm of affordability for the majority of small businesses.
The author advocates for an all encompassing, low-cost solution aimed at the SMB market. He wants a tool that is user friendly, inexpensive and easy to use, featuring automatic integration with multiple data sources that is predictive of future outcomes. He acknowledges that end-users "are not likely to be able to program their needs", yet advocates the development of modular software in anticipation of every possible need. These BI tools must be action oriented, distilling complicated tasks like customer retention, inventory management and social media communication down to the simple click of a button. While wonderful in concept, I don't believe that all will ever be within a single tool.
Apple products, be it the iPhone, iPad or iPod, are famous for working well together. Apple accomplishes this by controlling every step of the process, and knowing exactly what goes into every piece of hardware. They can then perfectly tailor their software to work within the hardware's specifications, producing a very attractive product offering. However, a Mac falls flat when trying to run software originally written for a PC. With Apple reticent to license out their iOS to third party developers, don't expect to see this product-line unity augmented or replicated anytime soon.
There are only two ways that Brugueras' ideal BI tool could come to pass: A unifying open-source project of unprecedented scope or one single (for profit) company willing to take users cradle-to-grave for all their business software needs.
While I don't think that a massive open-source project could appear, I make a point to never say never. The cynic in me just doesn't see it, though. A single company creating a full-feature, A-to-Z Business Intelligence tool isn't very likely, either. The barrier to entry isn't so high that somebody wouldn't try and come up with a "me too" product offering.
I'm not here to preach about what my ideal Business Intelligence tool would be, because I don't believe that there can ever be a one-size-fits-all tool. You'll never get enterprise-level features in a cheap/free product, either because of the associated development costs or due to the simple inability for small businesses to devote the required time to such a far reaching tool.
Take your prototypical small business as an example, where employees generally wear more than one hat. It's likely that your "web guy" will not only be responsible for website design and SEO, but also for PPC and SEM and possibly copy writing for both the online channel and traditional corporate communications. Ask yourself, will this overworked individual be able to devote the time required to use a highly-sophisticated BI tool?
Much like clothing, creating something that is one-size-fits-all typically results in a garment which is a poor fit for 99% of the population. When you buy a suit, most people require that the pants be hemmed or the jacket taken in. Few people are truly "off the rack", so why do we expect the same from our BI tools?
There exist tools that work very well for small business, which scale well and can accommodate the business as it grows. They don't have the features of an enterprise-level tool, but small businesses don't have the bandwidth to use all those features anyway. Rather than devote time to chasing the dream of the perfect tool, end-users should focus on better expressing their BI tool needs. The corporate world has been quite successful at recognizing needs and designing products to meet them. I have every confidence that several different vendors will step up and provide targeted solutions, provided the requirements are clear and properly documented by the eventual end-users.
Subscribe to:
Posts (Atom)
