The term business analytics has started gathering momentum after the publication of Tom Davenport’s book “ Competing on Analytics: The New Science of Winning” ( 2007). According to Watson (2011), the adoption of mathematical and statistical analysis on business data to support the decision-making process can be traced back to the 1960s. According to Evans (2013), Business Analytics involves different kinds of software, tools and approaches that seek to among other things:
- analyse and deeply describe a phenomenon (descriptive analytics)
- support forecasts and predictions (prescriptive analytics)
- provide effective action patterns (prescriptive analytics
The last step in the evolution of Business Analytics is concerned with the diffusion of Big Data Analytics. Big data is defined as volume, variety, velocity, value, and veracity (5Vs) of data available inside and outside organizations Big Data Analytics makes easy the analysis of previously unmanageable large quantities of unstructured datasets, that comes from different sources like from outside the company and social networks like Facebook, Twitter, LinkedIn and personal blogs. The prominent 4 Vs distinguish Big Data Analytics from previous Business Analytics and these are:
Volume is a large quantity of data. There is not a predetermined number that defines “big” data. What is considered “big” today could be “medium” tomorrow and maybe “small” in the close future. Velocity means that data can be acquired, stored, and analyzed in real-time. It allows a quick understanding of the business environment to become more agile than competitors. Variety is related to the different types of data (structured and non-structured) and sources. Value means the value-added to the business due to the understanding of previously unknown insights from data. Veracity ensures that the data collecting methods and data processing techniques will provide reliable data.
Volume, velocity, and variety depend on the veracity, which will define the value. There are four steps to big data processing: (1) acquisition, which encompasses data captured and acquired from many different sources; (2) access, which includes data indexing, storage, sharing, and archiving, usually based on specific software framework for integration and organization; (3) analytics, which is related to data analysis and manipulation; and (4) application, which means making decisions and taking actions
There are quite several performance management systems and all of them seeks to among other things:
- set up a dashboard of measures able to support day-to-day decision making
- describe the firm’s objectives, encourage coherent behaviours and measures results
The performance management system is made up of the following characteristics:
- The integration of financial and non-financial measures
- Combination of the internal and external orientation of the measures
- Inclusion of forward-looking perspectives
- identification of causal relationships
- between the different measures and perspectives.
According to Silvi (2015), Business Performance Analytics can be applied to the performance management systems in several ways.
- It can help manage the information overload from the perspective of decision-makers
- through mathematical and statistical analysis it reduces the complexity of available data and leads to the understanding of the main levers to act on for improving the financial performance of the organisation
- It helps in understanding the causal interdependencies between strategic impact factors
- It can support the definition of a holistic view of the organisation and its different inputs, processes, outputs and outcomes connected by critical interdependencies
- In this way, it improves the strategic and operative planning and measurement by providing a quantified view of the relationships between the different inputs, activities and outputs of specific sub-processes or even entire business units.
Moss, L.T. and Atre, S. (2003), Business intelligence roadmap: the complete project lifecycle for decision-support applications, Addison-Wesley Professional.
McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D. and Barton, D. (2012), “Big data.
The management revolution”, Harvard Business Review, Vol. 90
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