The whole purpose of analytics is to make better decisions based on data (big/small).
The word analytics has become so broad it means anything from technology to data to predictive analytics to reporting, etc. Analytics in itself adds no value; it’s the decisions that analytics helps make and enable drive the actual business value. More specifically, what’s value proposition of data analytics, and how to fulfill its purpose?
The word analytics has become so broad it means anything from technology to data to predictive analytics to reporting, etc. Analytics in itself adds no value; it’s the decisions that analytics helps make and enable drive the actual business value. More specifically, what’s value proposition of data analytics, and how to fulfill its purpose?
Top-down support for analytics is crucial for analytics success: In order to make decision analysis more practical, data-driven analytics should be driven by top-down so that right patterns, right insights, and right decisions are made smoothly. Also, you need data-driven organizations across departments, business units and cross-functional. These need to be started sooner than later and bring as part of organizational culture. And that is why decision science or analytics should not be a function of its own in an organization. It needs to be integrated with the commercial or operational functions of a company. There needs some emphasis on the talent needed to sell the analytics up to the ladder to those who can change the course of the ship. Otherwise, all you have is a pretty report coupled with conservative behavior.
Monetization of analytics efforts is very essential. The insights obtained from data mining needs to be converted into an actionable plan and results/ outcome validated in terms of financial figures. Historical data is always relevant and has potential value, but the analysis of the data has no value unless someone decides to act on it. Many information-savvy organizations have invested in tools and created analytic teams to make informed real-time decisions. As more often big data analysis are near analysis & insight of individual or of the group be it internal or hired group! The whole point is to make a decision and to establish a course of action. Otherwise, it is futile to run any analysis at all. To deliver value from analytics, organizations must turn information into knowledge, insight, and wisdom, enabling making the right decisions at the right time.
Analytics is and never was a magic wand anyway, it is just another way to make an informed decision. Data is like the raw diamond, raw diamonds get their use when becoming treated, processed into industrial diamonds. Only treatment makes them valuable, able to serve a purpose - help to make more effective decisions to create better business values.
The whole purpose of analytics is to make better decisions based on data (big/small). You can call it with any name like decision management/theory/science/technology/engineering. Analytics has no value until they inform decisions, which further helps optimize various business management, directly or indirectly related to long-term revenue and organizational agility, ultimately build a highly intelligent and high mature digital organization.
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