It is a journey to build a solid analytics capability in the overall organization. It needs hard work, persistence, resilience, a deliberate strategy, and a comprehensive platform to make it happen.
Companies across industrial sectors need to develop a comprehensive analytics framework for foreseeing business opportunities/risks, overseeing what has happened or is happening in their business, capturing customer insights, understanding talent via empathy, diagnosing problems accurately, leveraging efficient analysis tools or algorithms and prescribing some information-based solution to certain problems effectively. From standardization to personalization, analytics helps us run a smart business to connect the mind and touch the heart.
It’s the historically long journey of data based analytics evolution with an enhanced “Descriptive- Diagnostic - Predictive - Prescriptive analytics cycle”: Traditional business intelligence is heavy-duty with the domain of data warehouse as the repository and tools to analysis based on historical data to generate statistics or business hindsight, that’s technically a necessary step to make contemporary businesses more information based and data savvy. Nowadays, with lightweight tools along with structural or dynamic processes, the nimble analytics service provides more "Agile" capabilities to ingest large datasets, and visualize the results. Analysis becomes popular or even over-popular, influencing our career path and personal life; business model and management disciplines.
Diagnostic analysis enables the business to dig through the root cause of many thorny problems, organizations integrate big data into strategic decision systems for supporting corporate strategic decision and execution to achieve sustainable profit market shares. To move steps further, predictive analytics which relies on analysis of the past as well, allow the management forecast and well prepare for the upcoming trends or risks. The quality of your data defines the quality of your predictions. The true power of Predictive Analytics comes when you integrate the insights into your business processes, digitizing the touch points of customer experience, making seamless business transactions and effortless organizational transformation. At this stage, the Prescriptive Analytics become refined and more accurate over time.
Manage quality data and high quality analytics application portfolio: Good quality data, either big, wide or deep, with quality data management is a key factor for analytics success. Businesses need to refrain from seeking to access/aggregate analyze data until such time as you are able to translate business priorities into people-centric initiatives. Good businesses do analysis, but truly smart businesses run a highly intelligent people centric business in which smart tools are the means, not the end, regardless of investigative, descriptive, predictive and prescriptive, and a balanced analytics app portfolio is built with a shortened delivery cycle to enable capability-based strategy management. Like any other engineering discipline, look for something in an analytics initiative, you could reuse before inventing from scratch; both in the need for a systemic view of a business and delightful perspectives of customers.
The ranges in analysis based applications across industrial boundaries go from leveraging social information and demographics for customer experience, marketing/finance/medical analysis, talent management, relating genomic etc. Once data is aggregated, interpreted and strategies defined based on the insights, the right tools can make the right data available at the right level of the organization to implement these strategies effectively. Fundamental point is still about what is needed for successful analytics applications. The ability to connect various sensor systems effectively at the bottom of the stack, and then to quickly and easily identify relationships, for forensic and predictive needs, at the top of the stack, is key. Businesses that invest in analytics initiatives need to see clearly tangible and intangible business benefits in numbers and other key parameters such as decision-making, out-hustling the competition, and how they can differentiate in the competitive landscape, etc.
Cultivate top analytics talent teams: Regardless how powerful your tools are, people are the analytics masters. Real data scientists understand the needs of normal people and find together with them a viable solution. Data knowledge is theoretical until you know where and how to express and implement it, and business domain knowledge is equally as important as technical knowledge. Competent analytics experts can properly interpret data and recognize useful patterns. Domain knowledge will help them know when the data doesn't make sense. As with any engineering or science related job you need to learn to think and be intelligent in order to be good at your job.
Great analysis professionals should be able to take any problem and translate it to the abstract domain of analytics and data science, and they can contribute to the business growth from data mining, data analysis, and modeling. Collectively, the data science teams and community need to realize that they are there to support the decision makers in the organization. Analytical algorithms and data scientists are not mutually exclusive, but they are absolutely complimentary. It’s the concerted effort of business leaders/professionals and engineer/analysis professionals to run a smart business to engage employees and delight customers.
Overall, it is a journey to build a solid analytics capability in the overall organization. It needs hard work, persistence, resilience, and a deliberate strategy to make it happen. Great leadership, right expertise, strong management discipline and strategic flexibility, etc, are all crucial components to improve business intelligence, people-centricity and maturity.
It’s the historically long journey of data based analytics evolution with an enhanced “Descriptive- Diagnostic - Predictive - Prescriptive analytics cycle”: Traditional business intelligence is heavy-duty with the domain of data warehouse as the repository and tools to analysis based on historical data to generate statistics or business hindsight, that’s technically a necessary step to make contemporary businesses more information based and data savvy. Nowadays, with lightweight tools along with structural or dynamic processes, the nimble analytics service provides more "Agile" capabilities to ingest large datasets, and visualize the results. Analysis becomes popular or even over-popular, influencing our career path and personal life; business model and management disciplines.
Diagnostic analysis enables the business to dig through the root cause of many thorny problems, organizations integrate big data into strategic decision systems for supporting corporate strategic decision and execution to achieve sustainable profit market shares. To move steps further, predictive analytics which relies on analysis of the past as well, allow the management forecast and well prepare for the upcoming trends or risks. The quality of your data defines the quality of your predictions. The true power of Predictive Analytics comes when you integrate the insights into your business processes, digitizing the touch points of customer experience, making seamless business transactions and effortless organizational transformation. At this stage, the Prescriptive Analytics become refined and more accurate over time.
The ranges in analysis based applications across industrial boundaries go from leveraging social information and demographics for customer experience, marketing/finance/medical analysis, talent management, relating genomic etc. Once data is aggregated, interpreted and strategies defined based on the insights, the right tools can make the right data available at the right level of the organization to implement these strategies effectively. Fundamental point is still about what is needed for successful analytics applications. The ability to connect various sensor systems effectively at the bottom of the stack, and then to quickly and easily identify relationships, for forensic and predictive needs, at the top of the stack, is key. Businesses that invest in analytics initiatives need to see clearly tangible and intangible business benefits in numbers and other key parameters such as decision-making, out-hustling the competition, and how they can differentiate in the competitive landscape, etc.
Great analysis professionals should be able to take any problem and translate it to the abstract domain of analytics and data science, and they can contribute to the business growth from data mining, data analysis, and modeling. Collectively, the data science teams and community need to realize that they are there to support the decision makers in the organization. Analytical algorithms and data scientists are not mutually exclusive, but they are absolutely complimentary. It’s the concerted effort of business leaders/professionals and engineer/analysis professionals to run a smart business to engage employees and delight customers.
Overall, it is a journey to build a solid analytics capability in the overall organization. It needs hard work, persistence, resilience, and a deliberate strategy to make it happen. Great leadership, right expertise, strong management discipline and strategic flexibility, etc, are all crucial components to improve business intelligence, people-centricity and maturity.
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