Business analytics is today’s business need. It is defined as the process of iterative exploration of business datasets with an intension of statistical analysis (Evans & Lindner, 2012). Data are considered as information mine for data driven companies. Business analytics provides with insights that help in informed and optimized decision making to gain a competitive advantage. The process needs skilled analysts with an idea about business needs and has technology understandings, proper data quality and relevance and commitment of the organization for data based decision makings. There is a subtle difference between business intelligence and business analytics. Business intelligence evaluate past data and may be present data as well and provides an insight about present business conditions abut business analytics concentrates on evaluating past data and present one and provides an idea or insight about future events.
Consumer data are increasing exponentially now a day. Estimates show that there is a growth rate of 2.5 quintillion bytes of data per day (Paradigm Technology, 2006). Social media can be considered as the base for this data growth and provide an amazingly detailed data set that contains information regarding daily activity or even per minute activity of a person ((Kohavi, Rothleder & Simoudis, 2002)).
Business firms are interested at turning these data sources into valuable information sources and this transformation is possible through business analytics and through choosing a suitable analytic solution for every different situation (Holsapple, Lee-Post & Pakath, 2014). Data in every case cover diversified field of solutions. Each field fits to a particular source of data and to a particular type of insight.
Business firms use those insights for framing business decisions to gains a competitive advantage (LaValle et al., 2010). Few agendas like extracting relevant information, insight about predicting future events and suggesting solution for probable problems is the secret. Business analytics is the need and process of today’s data landscape.
These can be classified into the following categories:
Customer segmentation: It defines the mechanics of diving targeted customers on the basis of certain qualitative characteristics relevant to marketing which finally facilitates the company to construct tailored messages according to the preferences of different categories. An example of banking sector regarding collection of data through direct campaign can be sited here. Aim for this census is greater profits, better quality of customer communication.
Risk assessment: The segment facilitates user to detect future business problems. Goal is to detect future problems and act on them in advance. An example can be sited here regarding a banking survey (LaValle et al., 2011). Aim of this survey is to determine whether a particular person is eligible for loan facility or to calculate the probability that whether he or she would be able to pay a loan and thus calculating risk factor. Related variables are demographic factors like gender, education and others. Binary classification test is being performed here with results:
Accuracy rate is 80%, Actual risk is 0.77 and actual non-risk is 0.84. Error rate or opposite of accuracy rate is 0.19.
Churn prevention: A part of business analytics which aims at explaining when, why and which customer will end their relationship with any company (Liebowitz, 2013). Example is a study of churn of telecom customers on the basis of information of their account. Data of loyal and disloyal customers are collected with variables area code, total call in days, total call in nights, international plans and on other variables. A fusion matrix on the data will be like :
Test detects 90% of the customers who will be lost.
Forecast for sales: Analysis can be made to predict sales for a company. Few variables contain sales information for a company which can be extracted through data mining. Example regarding this matter can be predicting power demand for electric industry (Schläfke, Silvi & Möller, 2012). Variables to be studied will be Company data, demand data, weather data, social data, and calendar data.
Market survey: Analysis of market survey highlights customer requirements. Example in this case can be like analysis of wine data. Variables will be physicochemical test and sensory data outputs. The dataset variables will be fixed acidity, volatile acidity and others. Test will shoe attributes fitting customer tastes.
Financial modeling: Defines an abstract representation of Financial situations or translating hypothesis into numerical predictions. Example can be stock returns of Istanbul stocks. The dataset with variables is like:
Disruptive effect: A completely accurate forecasting is never possible in any scenario. Forecasting is of qualitative nature (Shmueli, et al., 2017). Therefore, a business scenario can always come with alternative situation regarding dependence on data interpretation.
There are few limitations of the technology as well like:
Treasuring Correlation: business analysts use large datasets to explain correlation but the fact is each and every of correlated cases are not meaningful. For example it was seen that between 2000 and 2009, divorce rates and per capita decline in the consumption of butter, both has similarly decreased (Trkman et al., 2010). But they have nothing to do with each other.
Asking wrong questions: One should properly frame a questionnaire before collecting data. Endless array of irrelevant questions will only increase cost and wastage of time.
Security: As in the case of technical fields, data sets are prone to leakage. A third party can easily get asses to someone’s personal information.
Transferability: The data set are subjected to transferability since in most of the times, the data remains stored in some cloud devices. Therefore, some technical knowledge is mandatory for the extraction of it. Again, it may become tough to transfer a data set repeatedly.
Data collection hassle: Tools and energy that is being engaged in finding a dataset are imprecise at times. For example, a search engine that is famous for it changes in the data set for tweaks and updates is Google. A data set can change here completely if being searched after a good interval of time. A mea ningless dataset will obviously yield a meaning analysis.
The video explains about tracking whys of business through business analytics. Few general questions regarding the field are what business house are tracking, what are KPI that is questions related to what. Signal of answer is present in some external data as well along with internal data. Business analytics can synthesis the whole external data and extract information about business key drivers and a combination of the key drivers which results in answers for business whys. Business why’s can be listed as why are customer making certain decisions and why are things important. Business analytics report are prepared in such a way that it can be a source of information for a person who is not used to with analytics as well as for an analyst. Relevant user information is the factors affecting their business structure. Again an analyst will be in need of the process of the whole analysis.
The significance of the video lies in explaining exact needs of analytics in business. It displays a certain key point for any business house as gets confused by majority. It also says that reports based on business analytics are always readable anyone from a common people till analysts.
Conclusion
It can be concluded that business analytics is a useful tool for the present data landscape. Data driven decision making is need of time to gain a competitive advantage. Business analytics is the tool for gaining insight from datasets. It can be issued in various sectors like finance sector, sales prediction, market prediction, customer segmentation and risk assessment, responsible for construction of sales strategy, make investment decisions and in other crucial management works. The process has certain disadvantages as well. Since it is a complete data driven process, proper data collection and storage of data is an important criteria. There are few more problem regarding data related matters like transferability of data and security of it.
Reference
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Ferreira, R. P., Martiniano, A., Ferreira, A., Ferreira, A., & Sassi, R. J. (2016). Study on daily demand forecasting orders using artificial neural network. IEEE Latin America Transactions, 14(3), 1519-1525. ( Taken from: https://ieeexplore.ieee.org/abstract/document/7459644/)
Holsapple, C., Lee-Post, A., & Pakath, R. (2014). A unified foundation for business analytics. Decision Support Systems, 64, 130-141. ( taken from: https://www.sciencedirect.com/science/article/pii/S0167923614001730)
IBM. (2018). Understanding the “why” of business analytics. Retrieved from https://www.youtube.com/watch?v=2zla8pw5-dw
Fernandes, P. Vinagre and P. Cortez. A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News. Proceedings of the 17th EPIA 2015 – Portuguese Conference on Artificial Intelligence, September, Coimbra, Portugal. ( taken from: https://link.springer.com/chapter/10.1007/978-3-319-23485-4_53)
Kohavi, R., Rothleder, N. J., & Simoudis, E. (2002). Emerging trends in business analytics. Communications of the ACM, 45(8), 45-48. ( taken from: https://link.springer.com/chapter/10.1007/978-3-319-23485-4_53)
LaValle, S., Hopkins, M. S., Lesser, E., Shockley, R., & Kruschwitz, N. (2010). Analytics: The new path to value. MIT Sloan Management Review, 52(1), 1-25. (taken from: https://search.proquest.com/openview/354c77b4761408883652a7244c4e2803/1?pq-origsite=gscholar&cbl=26142)
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT sloan management review, 52(2), 21. (taken from: https://search.proquest.com/openview/863e390a27e9a69d59ee5ffe2204f293/1?pq-origsite=gscholar&cbl=26142)
Liebowitz, J. (Ed.). (2013). Big data and business analytics. CRC press. (taken from: https://books.google.co.in/books?hl=en&lr=&id=Oq9znTz7FGoC&oi=fnd&pg=PP1&dq=Liebowitz,+J.+(Ed.).+(2013).+Big+data+and+business+analytics.+CRC+press&ots=JSZHwFRCzt&sig=IdcwQajm9A2tf2B0wK39vNQRnRI#v=onepage&q=Liebowitz%2C%20J.%20(Ed.).%20(2013).%20Big%20data%20and%20business%20analytics.%20CRC%20press&f=false
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Trkman, P., McCormack, K., De Oliveira, M. P. V., & Ladeira, M. B. (2010). The impact of business analytics on supply chain performance. Decision Support Systems, 49(3), 318-327. (taken from: https://www.sciencedirect.com/science/article/pii/S0167923610000680)
Vera-Baquero, A., Colomo-Palacios, R., & Molloy, O. (2013). Business process analytics using a big data approach. IT Professional, 15(6), 29-35. ( taken from: https://ieeexplore.ieee.org/abstract/document/6560078/)
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