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Answer Upon - Information Quality & The Balanced Scorecard
Dayton OH sure has a lot going for it. Everyone likes the Big Air Show and Aviation Conference. Wow, what a show. If you are an aviation buff you should not miss it. In fact our Commander in Chief also visited there for the Air Show event signifying the Wright Bros. Makes me feel comfortable that everyone is Pro-Dayton and that means jobs and a strong economic future too.Ohio is getting some juice politically and in the last election it proved to be the final battleground, many knew it would. Ohio has always been big in politics. It is also good to see that the political and government structure is working hard in Dayton to promote a positive and seamless goal with the Dayton Chamber of Commerce and Economic Development Association to revitalize downtown. It is looking
Transforming Data into Information:
Raw data is not information. Data becomes information when it is analyzed and transformed using the scorecard information goals as a guide. Data must be grouped and placed into context. It must be compared to other data and to older versions of the same data. How does the data relate to other aspects of the scorecard,
Entrepreneurs Know Fixed Costs Will Eat Them Alive -- number nineteen in a series taken from:How to Evaluate and Profit from a Business Opportunity - The Entrepreneur's GuideBy Art ConsoliFixed costs; those costs which are attributable to commitments for equipment, buildings, employment contracts, notes payable, and other items of this type require payment regardless of sales or profits, or the availability of cash to make the payments. The term is very descriptive and defines a heavy responsibility on the business owner.The documents that cover such agreements can include language that give the other party very strong rights to payment and the ability to take possession of other property the business might own.Successful entrepreneurs know
Today, efficient internal business processes, good customer relations, and long-term strategic investments in technologies and employees are what make companies successful.
The balanced scorecard proposes that companies be viewed and measured in each of four major dimensions1:
· The traditional financial dimension
· The customer dimension
· The business process dimension
· The learning and growth dimension
The organization's overall vision and strategy are particularized for the various organizational structures within each of these dimensions-e.g., accounting for the financial dimension, marketing and customer support for the customer dimension, order and warehouse management for business processes, and human resources and business development for the learning and growth dimension. Metrics for gauging performance against the specific strategic goals of the organization's structures are then devised.
Finally, data is collected and analyzed on an ongoing basis to evaluate performance against the goals and to provide decision makers with the information needed to identify problems and trends and to make adjustments while the data is hot. Quality information is the foundation of a balanced scorecard.
Getting Good Raw Data:
Producing quality information begins with getting good raw data. Above all, good data is data that is directly related to the larger informational needs as determined by the scorecard. That is, just because it is true and accurate does not mean it is good data. The data must be relevant to the strategies within the scorecard dimensions. Rarely is there a shortage of data. Often we are overwhelmed with data, much of it not relevant or helpful, and we are forced to do our own faulty filtering. As a result, insignificant or misleading pieces of data are emphasized, and poor decisions are made.
Good data must be accurate and fresh. Getting "clean" data is often the greatest impediment to effectively using the sophisticated business intelligence tools now available. It is estimated that over 25% of critical data used by major corporations is flawed due to human data-entry errors and a lack of corporate data standards2.
Companies are spending much more money and effort analyzing data than they are on ensuring its accuracy.
“Flawed data not only compromises customer service, marketing campaigns, and supply-chain forecasting, but skews the scorecard and misleads decision makers.”
A number of data-cleansing software packages are available, but data quality degrades quickly over time. Obtaining clean data should be viewed as an aspect of ongoing business processes rather than as a one-time operation of IT organizations. Business units must take ownership of their data and treat it as a critical resource and product of its operations.
Transforming Data into Information:
Raw data is not information. Data becomes information when it is analyzed and transformed using the scorecard information goals as a guide. Data must be grouped and placed into context. It must be compared to other data and to older versions of the same data. How does the data relate to other aspects of the scorecard, a
Handling of heavy machinery is a task that requires specially designed equipment. Heavy machinery like pneumatic conveyors, milling machines and drill jigs are used in places like farms, docks and construction sites. It is difficult to transport this equipment from one place to another. This is when the powerful material handling machines like tractors, bulldozers, trucks and trailers are used.The equipment used for handling heavy machinery varies, depending upon the location. Industrial trucks and tractors are used to handle heavy machinery and move material around warehouses, storage yards, factories, or construction sites. A typical industrial truck, often called a forklift, uses a hydraulic lifting mechanism and forks to move large and heavy objects. Industrial tr
· The learning and growth dimension
The organization's overall vision and strategy are particularized for the various organizational structures within each of these dimensions-e.g., accounting for the financial dimension, marketing and customer support for the customer dimension, order and warehouse management for business processes, and human resources and business development for the learning and growth dimension. Metrics for gauging performance against the specific strategic goals of the organization's structures are then devised.
Finally, data is collected and analyzed on an ongoing basis to evaluate performance against the goals and to provide decision makers with the information needed to identify problems and trends and to make adjustments while the data is hot. Quality information is the foundation of a balanced scorecard.
Getting Good Raw Data:
Producing quality information begins with getting good raw data. Above all, good data is data that is directly related to the larger informational needs as determined by the scorecard. That is, just because it is true and accurate does not mean it is good data. The data must be relevant to the strategies within the scorecard dimensions. Rarely is there a shortage of data. Often we are overwhelmed with data, much of it not relevant or helpful, and we are forced to do our own faulty filtering. As a result, insignificant or misleading pieces of data are emphasized, and poor decisions are made.
Good data must be accurate and fresh. Getting "clean" data is often the greatest impediment to effectively using the sophisticated business intelligence tools now available. It is estimated that over 25% of critical data used by major corporations is flawed due to human data-entry errors and a lack of corporate data standards2.
Companies are spending much more money and effort analyzing data than they are on ensuring its accuracy.
“Flawed data not only compromises customer service, marketing campaigns, and supply-chain forecasting, but skews the scorecard and misleads decision makers.”
A number of data-cleansing software packages are available, but data quality degrades quickly over time. Obtaining clean data should be viewed as an aspect of ongoing business processes rather than as a one-time operation of IT organizations. Business units must take ownership of their data and treat it as a critical resource and product of its operations.
Transforming Data into Information:
Raw data is not information. Data becomes information when it is analyzed and transformed using the scorecard information goals as a guide. Data must be grouped and placed into context. It must be compared to other data and to older versions of the same data. How does the data relate to other aspects of the scorecard,
Many business owners opt to incorporate their business to protect themselves and the business from unanticipated losses and liabilities. Both small as well as large businesses can be incorporated. It is possible to incorporate businesses in any state in the United States, regardless of where the business is operated. Many business owners choose to incorporate their businesses in Delaware or Nevada, as they are very corporate-friendly. Nevada has highly favorable corporate laws. Many new business owners and small business owners lack adequate knowledge about the corporate laws prevailing in Nevada. The most frequently asked questions about Nevada corporations, include what are the different types of corporation entities, the process of incorporation, and what are the advantag
Getting Good Raw Data:
Producing quality information begins with getting good raw data. Above all, good data is data that is directly related to the larger informational needs as determined by the scorecard. That is, just because it is true and accurate does not mean it is good data. The data must be relevant to the strategies within the scorecard dimensions. Rarely is there a shortage of data. Often we are overwhelmed with data, much of it not relevant or helpful, and we are forced to do our own faulty filtering. As a result, insignificant or misleading pieces of data are emphasized, and poor decisions are made.
Good data must be accurate and fresh. Getting "clean" data is often the greatest impediment to effectively using the sophisticated business intelligence tools now available. It is estimated that over 25% of critical data used by major corporations is flawed due to human data-entry errors and a lack of corporate data standards2.
Companies are spending much more money and effort analyzing data than they are on ensuring its accuracy.
“Flawed data not only compromises customer service, marketing campaigns, and supply-chain forecasting, but skews the scorecard and misleads decision makers.”
A number of data-cleansing software packages are available, but data quality degrades quickly over time. Obtaining clean data should be viewed as an aspect of ongoing business processes rather than as a one-time operation of IT organizations. Business units must take ownership of their data and treat it as a critical resource and product of its operations.
Transforming Data into Information:
Raw data is not information. Data becomes information when it is analyzed and transformed using the scorecard information goals as a guide. Data must be grouped and placed into context. It must be compared to other data and to older versions of the same data. How does the data relate to other aspects of the scorecard,
When you are getting started it's so hard to understand the integral role packaging has to play in marketing and selling your product. Put simply, it’s one of the most important product decisions you will have to make. There is a universe of packaging suppliers, materials and even regulations. Not to be overwhelmed, it is easy to navigate if you take it one step at a time. It is a process just like any other component in product development. They key is to know which packaging factors will influence your product’s success --or failureHere are 5 vital things you need to know as you start on your journey that packages your product to sell.1. You can't have a product without a package.Just think about potato chips and eggs for example. How could you sell the
Good data must be accurate and fresh. Getting "clean" data is often the greatest impediment to effectively using the sophisticated business intelligence tools now available. It is estimated that over 25% of critical data used by major corporations is flawed due to human data-entry errors and a lack of corporate data standards2.
Companies are spending much more money and effort analyzing data than they are on ensuring its accuracy.
“Flawed data not only compromises customer service, marketing campaigns, and supply-chain forecasting, but skews the scorecard and misleads decision makers.”
A number of data-cleansing software packages are available, but data quality degrades quickly over time. Obtaining clean data should be viewed as an aspect of ongoing business processes rather than as a one-time operation of IT organizations. Business units must take ownership of their data and treat it as a critical resource and product of its operations.
Transforming Data into Information:
Raw data is not information. Data becomes information when it is analyzed and transformed using the scorecard information goals as a guide. Data must be grouped and placed into context. It must be compared to other data and to older versions of the same data. How does the data relate to other aspects of the scorecard,
We all know that a good first impression has a long lasting impact. Human beings have the inherent nature of building up their perceptions on the basis of visual stimuli. This particular aspect of human nature is applicable in all walks of life. Be it dressing up smart for an interview/ presentation, wrapping a gift in an elegant paper or even a good handshake with a pleasing smile while meeting a person…the ways we adopt to impress is beyond the scope of compilation. We all strive to make that good first impression to make things work in our favor. It is often believed that a good first impression is all about being flamboyant and colorful. This notion is not necessarily true coz strong messages if communicated in a simpler way can serve the purpose in the best possible mann
Transforming Data into Information:
Raw data is not information. Data becomes information when it is analyzed and transformed using the scorecard information goals as a guide. Data must be grouped and placed into context. It must be compared to other data and to older versions of the same data. How does the data relate to other aspects of the scorecard, and what trends are emerging overtime?
“Transforming data involves not only analyzing and distilling it into useful information related to the organization's strategic goals, but requires that the information be presented in a form that is best for the audience.”
Delivering the Data:
Information must be delivered to the right persons at the right time. This may seem obvious, but often it is the most difficult part of the information generation process, particularly for large organizations. For these organizations, delivery may mean more than simply making the information available. Such mass delivery can result in the information being overlooked. Targeted delivery to only those people who are responsible for acting on the information focuses the information process and highlights the importance of the information. If no one is directly responsible, why is the information being generated?
In addition, the persons receiving the information must understand how it relates to their objectives. Without direct relevance to their organization, work, and goals, the information is little more than interesting reading material.
Finally, the timing of delivery is a crucial consideration, which may determine the type of data collected. If an operations manager needs to predict sales quantities in the future so that materials can be ordered in advance, historical sales data may not be as relevant as proxy information that is highly correlated to sales. Timing may also affect data analysis when there are data dependencies.
What Information Quality Means for My Organization:
No matter what your organization does, it relies on information to make strategic and operational decisions, which ultimately determine its level of success. This information most likely includes financial data. It should also include a broader spectrum of information related to other dimensions of the organization. Whatever the breadth of the information, it must be directly related to strategic goals or it is a distraction.
The quality of the information must similarly be measured by how directly it relates to the strategic goals. Data which is not culled and cleaned as guided by the strategic goals will distract and mislead decision makers. And data analysis and delivery, which transform data into actionable information, can only be effectively performed when strategic goals are understood. The quality of the information generated by your organization is ultimately measured by its overall relation to the organization's goals.
1 http://www.balancedscorecard.org
2 "Getting Clean," CIO Insight, August, 2004.
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