The convergence of machine learning, artificial intelligence, analytics and business intelligence, statistics & decision science will make a direct impact on building an intelligent business.
With the information-based analysis, and application of intelligence, it is the aggregation and assessment of information that creates the intelligence required to define the rules and strategies driving the business forward effectively.
Analysis: Big Data can be considered to be a set of technologies that are used to develop a data focused infrastructure targeted at solving business problems: Methodologically, business intelligence is the process of transforming data into information and transforming that information into knowledge/insight/foresight in better management and governance. Truly modern analytics would be able to see through the hallucination of the entheogens in order to make logical deductions. Businesses need insights that drive real value, data science is only one of many enablers, and decision science could put more focus on decision analysis.
For problem-solving, improvement and innovation. working on the organizational aspects of creating incentives, re-engineering processes, and leveraging collaboration tools to make information flow relevant to specific groups and individuals, and running a digital organization as a “living organic system,” not just the sum of mechanical pieces. Information based analysis helps to develop new strategic insights (not just reports) based on new and consistently changing and streaming data, very dynamic, predictive and prescriptive in nature, mainly used for text/sentiment analytic, machine learning and adaptive modeling.
Data analysis involves quantitative reasoning and interpretive power to capture business insight: Data cleansing, transformation and sorting are vital in the data world, it helps to put things in perspective for business to read between the lines with accuracy and clarity of information that is needed for making effective decisions with consistency. "Data" is scattered, and needs cleansing and improvement. Data cleaning and data management has a deep business purpose to turn data into information, refine information into business insight.
Technically, cleansing the data is often the most difficult and time-consuming part of data science. Understand your data needs, remove redundancy, and collect your data more responsibly. It requires referential integrity, to ensure information synchronization. Data cleansing has always been a challenge. Data analysis involves quantitative reasoning and interpretive power to capture business insight. So it’s important to know your business, know your data. It is best to solve the problem as early on as possible at the source that would be more ideal, keep fine-tuning business processes and improving organizational effectiveness and efficiency.
Information analytics is the systematic and methodological selection of data to track the behavior of past decisions to support future business directions: How you frame a decision largely determines the kinds of alternatives that will emerge from the choice process of decision analysis. The key is to orient the analysis and decision variables tied to forecasted outcomes with uncertainty. The whole purpose of decision analytics is to make better decisions based on data big and small. 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. Leverage good information, processes, and other decision tools as a "corporate knowledge base" to help make better decisions and solve problems effectively.
High intelligent organizations have a clear understanding of how customer-centric approaches enhance the business model and extend profitability. Business Intelligence as a data process, offers slice-and-dice, drill-down, and trend analysis capabilities. Everything from how pricing is affecting close times and support calls to referral business activity triggered by high customer satisfaction, etc. Business leaders/managers are provided scoreboard/dashboards or some other tools that are drillable and specific for tracking the progress of business management and calculate relevant return on investment, etc.
Analysis: Big Data can be considered to be a set of technologies that are used to develop a data focused infrastructure targeted at solving business problems: Methodologically, business intelligence is the process of transforming data into information and transforming that information into knowledge/insight/foresight in better management and governance. Truly modern analytics would be able to see through the hallucination of the entheogens in order to make logical deductions. Businesses need insights that drive real value, data science is only one of many enablers, and decision science could put more focus on decision analysis.
For problem-solving, improvement and innovation. working on the organizational aspects of creating incentives, re-engineering processes, and leveraging collaboration tools to make information flow relevant to specific groups and individuals, and running a digital organization as a “living organic system,” not just the sum of mechanical pieces. Information based analysis helps to develop new strategic insights (not just reports) based on new and consistently changing and streaming data, very dynamic, predictive and prescriptive in nature, mainly used for text/sentiment analytic, machine learning and adaptive modeling.
Data analysis involves quantitative reasoning and interpretive power to capture business insight: Data cleansing, transformation and sorting are vital in the data world, it helps to put things in perspective for business to read between the lines with accuracy and clarity of information that is needed for making effective decisions with consistency. "Data" is scattered, and needs cleansing and improvement. Data cleaning and data management has a deep business purpose to turn data into information, refine information into business insight.
Technically, cleansing the data is often the most difficult and time-consuming part of data science. Understand your data needs, remove redundancy, and collect your data more responsibly. It requires referential integrity, to ensure information synchronization. Data cleansing has always been a challenge. Data analysis involves quantitative reasoning and interpretive power to capture business insight. So it’s important to know your business, know your data. It is best to solve the problem as early on as possible at the source that would be more ideal, keep fine-tuning business processes and improving organizational effectiveness and efficiency.
High intelligent organizations have a clear understanding of how customer-centric approaches enhance the business model and extend profitability. Business Intelligence as a data process, offers slice-and-dice, drill-down, and trend analysis capabilities. Everything from how pricing is affecting close times and support calls to referral business activity triggered by high customer satisfaction, etc. Business leaders/managers are provided scoreboard/dashboards or some other tools that are drillable and specific for tracking the progress of business management and calculate relevant return on investment, etc.
There is a world of difference between the best tools and the worst, and this is where the focus should be in terms of quality, not just on how good is the source data, but how good is the processing and merging; how good the data analysis team with complementary skills and expertise. The convergence of machine learning, artificial intelligence, analytics and business intelligence, statistics & decision science will make a direct impact on building an intelligent business.
1 comments:
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