The scenario of Business Intelligence has shifted drastically, with news concepts, approaches and tools, and one of the most interesting and powerful is the concept of analytics.
A good definition of analytics, according Thomas Davenport and Jeanne Harris, in their excellent book Competing on Analytics: The New Science of Winning: "By Analytics we mean the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. The analytics may be input for human decisions or may drive fully automated decisions. Analytics are a subset of what has come to be called Business Intelligence: a set of technologies and processes that use data to understand and analyze business performance."
Although the idea of analytics is not so new, only lately are appearing new approaches and tools to use. Analytical cultures and processes are appearing in any business that can use extensive data, fact-based decision making and complex statistical processing. Analytics don't only will support competitive strategies, but increasingly will perform a primary role.
The important factors to apply a successful analytics in the companies are:
- The companies need to have committed executives, that should be believers in analytical and fact-based decision making, appreciate the methods and analytical tools, and willing to act on the results of analyses.
- Build a sustainable pipeline of projects and analytical technologies to analytical groups.
- Bridging business analysis and technical development
- The companies need the availability of sufficient volumes of high-quality data, to have conditions to do sophisticated analytics.
The future of Analytics
The future of Analytics will happen through three type of changes: technology-driven, human-driven and strategy-driven. The technology-driven change include pervasive BI software, more real-time analytics , more automated decisions and increasing use supercomputers optimized for business intelligence applications, more visual analytics, more mining of texts, and more prediction with less reporting. The human-driven change is mainly because the companies need greater numbers of analytically oriented people. The strategy-driven change is because the companies will need to push the boundaries of analytics in their products, service and business models. The number of changes in the analytical environment will be driven by business strategies.
Showing posts with label Business Strategy. Show all posts
Showing posts with label Business Strategy. Show all posts
Monday, June 30, 2008
Saturday, May 31, 2008
The importance of alignment between IT and the business
Nowadays, one of the most important issues in the companies is the alignment between IT and the business. It is a very difficult issue. First of all, it is necessary that the IT people know more about business, and it is also necessary the business people to know more about IT.
The big issue is because the IT and the business work with different value perspectives, on the whole, the IT uses the data to value perspective and the business area uses the goals to value perspective. Neither of these perspectives are wrong, but both are incomplete. The company needs to align the value perspective, sharing the business view with the IT view, basing IT actions on business strategies, making the IT results and business results converge to the same point of view.
The IT people need to learn more about business, know the business issues. For example, reading the documents from the company, like strategic planning, annual and strategic reports; also is important to develop interpersonal skills.
The business people need to learn what the role and the objective of the IT to the company are. Also they need to know what technologies are being used and how to use the technologies.
For the IT and the business to work together effectively, it is important define some guidelines:
- Define a business strategy and communicate to all business units of the company.
- Define a technology strategy and communicate to all business units of the company.
- Align IT and business in the levels strategic and tactic.
- Synchronize business plans and IT initiatives.
- Define a process to a continuous alignment, looking for identify gaps in IT and business process.
The BI can help the alignment
The companies should use the Business Intelligence to help the alignment between business and IT. When the company is starting a BI project, it is necessary understand the business strategy. The BI team need to study the business’ goals, and they can do that together with the business team, defining and showing how BI can help the business objectives and building together a business strategy plan.
The company also can use the concepts of operational BI to help the alignment. Operational BI can align the overall strategy with business process, using a process-centric perspective, unifying strategic and tactical business initiatives.
The IT and the business working aligned is a big challenge to the board of the companies. Consequently, the companies that solve this difficulty which is an important issue certainly achieve better results in their business.
The big issue is because the IT and the business work with different value perspectives, on the whole, the IT uses the data to value perspective and the business area uses the goals to value perspective. Neither of these perspectives are wrong, but both are incomplete. The company needs to align the value perspective, sharing the business view with the IT view, basing IT actions on business strategies, making the IT results and business results converge to the same point of view.
The IT people need to learn more about business, know the business issues. For example, reading the documents from the company, like strategic planning, annual and strategic reports; also is important to develop interpersonal skills.
The business people need to learn what the role and the objective of the IT to the company are. Also they need to know what technologies are being used and how to use the technologies.
For the IT and the business to work together effectively, it is important define some guidelines:
- Define a business strategy and communicate to all business units of the company.
- Define a technology strategy and communicate to all business units of the company.
- Align IT and business in the levels strategic and tactic.
- Synchronize business plans and IT initiatives.
- Define a process to a continuous alignment, looking for identify gaps in IT and business process.
The BI can help the alignment
The companies should use the Business Intelligence to help the alignment between business and IT. When the company is starting a BI project, it is necessary understand the business strategy. The BI team need to study the business’ goals, and they can do that together with the business team, defining and showing how BI can help the business objectives and building together a business strategy plan.
The company also can use the concepts of operational BI to help the alignment. Operational BI can align the overall strategy with business process, using a process-centric perspective, unifying strategic and tactical business initiatives.
The IT and the business working aligned is a big challenge to the board of the companies. Consequently, the companies that solve this difficulty which is an important issue certainly achieve better results in their business.
Saturday, March 8, 2008
The concern about data quality
Lately, the concern about data quality in the companies is growing, the called dirty data or bad data.
The dirty data are the data considered incomplete, inaccurate, incorrect, outdated, redundant or misleading.
All the companies around the world have problems with the data quality, to a greater or lesser degree, and a lot of companies ignore the issue of data quality or have underestimated the importance.
The poor data quality takes significant costs to the companies, and obviously, this reflected in the profit.
Nowadays, in the age of globalization, with a lot of companies acquiring others companies around the world, those problems are aggravated. Culture different and the complication to integrated data of companies, resulted in numerous ways of entering shipping, billing, invoice and other key data. This kind of problem with inaccurate data is typical across all industries.
Especially when you are talking about Business Intelligence and Data Warehouse. To be a successful BI, you need to have data accurate, because you can have a Data Warehouse correctly modeled , a well implemented BI using good tools, but if your data are not accurate, you will make decisions using unreliable information.
Data Governance
It is very important the companies create a policy of Data Governance, but is not so easy.
First of all, you have to know your data: checking what you have, if have IT projects duplicating information, which data is inaccurate, which data is reliable and which is not. It is important to communicate to stakeholders about the value of create a policy of data governance and the cost of not having it to the company. Successful data governance depends on sponsorship in management level, preferably the CIO. You need to treat your data as a valuable corporate asset.
Focus on create data quality processes, a master data management solution and establish a culture of data governance. Ongoing training both IT and users, always showing that the measures of data governance are helping to improve your data quality.
Define a data cleaning process, considering that to clean the data is very expensive to the companies. The better way to clean your data are define strategically, IT together with the business users, which data need to be clean based on the business impact. If the cost of dirty data on the business impact are greater than the effort and cost to clean, this data certainly should be clean.
To keep the data cleaned, is very important controlling who accesses the data, and which level of manipulation have, allowing only each person responsible for each process has access to input, update and delete the data in this process.
After all, the companies need to use data quality tools, to create mechanism of standardization and validation, so they can ensure that the new data entering in the systems are clean.
The dirty data are the data considered incomplete, inaccurate, incorrect, outdated, redundant or misleading.
All the companies around the world have problems with the data quality, to a greater or lesser degree, and a lot of companies ignore the issue of data quality or have underestimated the importance.
The poor data quality takes significant costs to the companies, and obviously, this reflected in the profit.
Nowadays, in the age of globalization, with a lot of companies acquiring others companies around the world, those problems are aggravated. Culture different and the complication to integrated data of companies, resulted in numerous ways of entering shipping, billing, invoice and other key data. This kind of problem with inaccurate data is typical across all industries.
Especially when you are talking about Business Intelligence and Data Warehouse. To be a successful BI, you need to have data accurate, because you can have a Data Warehouse correctly modeled , a well implemented BI using good tools, but if your data are not accurate, you will make decisions using unreliable information.
Data Governance
It is very important the companies create a policy of Data Governance, but is not so easy.
First of all, you have to know your data: checking what you have, if have IT projects duplicating information, which data is inaccurate, which data is reliable and which is not. It is important to communicate to stakeholders about the value of create a policy of data governance and the cost of not having it to the company. Successful data governance depends on sponsorship in management level, preferably the CIO. You need to treat your data as a valuable corporate asset.
Focus on create data quality processes, a master data management solution and establish a culture of data governance. Ongoing training both IT and users, always showing that the measures of data governance are helping to improve your data quality.
Define a data cleaning process, considering that to clean the data is very expensive to the companies. The better way to clean your data are define strategically, IT together with the business users, which data need to be clean based on the business impact. If the cost of dirty data on the business impact are greater than the effort and cost to clean, this data certainly should be clean.
To keep the data cleaned, is very important controlling who accesses the data, and which level of manipulation have, allowing only each person responsible for each process has access to input, update and delete the data in this process.
After all, the companies need to use data quality tools, to create mechanism of standardization and validation, so they can ensure that the new data entering in the systems are clean.
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