Information is not for its own sake, but to drive needed changes and produce high performance results coherently.
It’s always crucial to understand business analysis impact on business optimization, customer satisfaction, or what turns into certain actions with the ultimate goal of producing high performance outcomes.
It’s important to leverage good information, processes, and other analytics tools as a "corporate knowledge base" to improve manageability: Predictive analytics refers to future-oriented analyses that can be used to help drive changes and improvements in business practices. Do "what-if" analysis to understand diverse future scenarios in order to prepare for change smoothly. Predictive analysis involves techniques such as regression, forecasting, simulation, and risk analysis, search for value position. It helps to determine future outcomes.
With rising customer expectations, the need for sentimental analysis is actually more important than ever before. Let the data do the talk. Complexity, data quality, skill shortage, etc, are all roadblocks to successful analytics. Every organization must address their common and unique skills requirements and map that out through some sort of skills/competency matrix within their organization to improve business analytics capacity.
It’s the science to leverage information analytics for improving the quality of business decision-making: The amount of data required for a decision, and the amount of information generated by a decision, can both be measured in bits & bytes. Fact-based decision making is an activity that does require concrete buy-in from all stakeholders, data quality is key. Because many people that are going to make the decisions based on "why", do not have time to understand the process behind delivering the "why". They just need to know what happened and what caused it.
Technically, there are three general pillars in Decision Science: Modeling, simulation and optimization. Information Analytics in real-time coupled with a powerful rules engine poses a challenge to the market with the prize being people-centricity. The typical challenge seen with the traditional decision-making analytics approach is to arrive at insights. The better decision engineering approach is to embed analytics in decision making systems to improve business performance, reduce risk, and encourage good behavior.
It’s crucial to apply an integral management discipline for managing analytics initiatives: There's a need for a change management component in each and every business analytics initiative. Ofen, data quality and information refinement takes a lot of effort, and it’s always important to keep information fluid across functions and build an interdisciplinary team for producing great outcomes. In addition, the creative human interpretation process is needed to tell a most likely story about the analysis results.
Accountability is needed because far too often people in Analytics simply provide their portion of the work and walk away. Once the numerical part is over, so is their part in the effort. Senior leadership gets frustrated about resource waste, lack of complete solution or conclusion. They get all of these promises of closure once the root cause is identified but then there is no follow-through on the remediation of key business issues.
Regardless of how big or complex data is, it does not make sense if it is not converted into usable information for harnessing communications and fostering changes. It’s always crucial to refine information, identify, analyze and solve issues for the better of the company and its stakeholders including customers. To put simply, information is not for its own sake, but to drive needed changes and produce high performance results coherently.
It’s important to leverage good information, processes, and other analytics tools as a "corporate knowledge base" to improve manageability: Predictive analytics refers to future-oriented analyses that can be used to help drive changes and improvements in business practices. Do "what-if" analysis to understand diverse future scenarios in order to prepare for change smoothly. Predictive analysis involves techniques such as regression, forecasting, simulation, and risk analysis, search for value position. It helps to determine future outcomes.
With rising customer expectations, the need for sentimental analysis is actually more important than ever before. Let the data do the talk. Complexity, data quality, skill shortage, etc, are all roadblocks to successful analytics. Every organization must address their common and unique skills requirements and map that out through some sort of skills/competency matrix within their organization to improve business analytics capacity.
It’s the science to leverage information analytics for improving the quality of business decision-making: The amount of data required for a decision, and the amount of information generated by a decision, can both be measured in bits & bytes. Fact-based decision making is an activity that does require concrete buy-in from all stakeholders, data quality is key. Because many people that are going to make the decisions based on "why", do not have time to understand the process behind delivering the "why". They just need to know what happened and what caused it.
Technically, there are three general pillars in Decision Science: Modeling, simulation and optimization. Information Analytics in real-time coupled with a powerful rules engine poses a challenge to the market with the prize being people-centricity. The typical challenge seen with the traditional decision-making analytics approach is to arrive at insights. The better decision engineering approach is to embed analytics in decision making systems to improve business performance, reduce risk, and encourage good behavior.
Accountability is needed because far too often people in Analytics simply provide their portion of the work and walk away. Once the numerical part is over, so is their part in the effort. Senior leadership gets frustrated about resource waste, lack of complete solution or conclusion. They get all of these promises of closure once the root cause is identified but then there is no follow-through on the remediation of key business issues.
Regardless of how big or complex data is, it does not make sense if it is not converted into usable information for harnessing communications and fostering changes. It’s always crucial to refine information, identify, analyze and solve issues for the better of the company and its stakeholders including customers. To put simply, information is not for its own sake, but to drive needed changes and produce high performance results coherently.
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