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Why is prescriptive analytics across industries so evident and wide spread? Why is big data the talk of the town? Why do companies invest heavily in prescriptive and predictive analytics?
Before we answer all these questions, let us start from the basics. What led to the emergence of Big Data?
The increasing amounts of data throughout industries spread all over the globe have led to its emergence. It is simply a cumulation of immeasurable size of data which has a scale that is too varied too large to comprehend. And unconventional technologies cannot deal with this data efficiently.
In the year 2000, 800,000 petabytes of data were stored in the world and this number is expected to reach 35 zettabytes by 2020. This data consists a diverse range of data types such as texts, weblogs, GPS locations, sensor information, graphs, videos, audio and everything online.
Talking about Prescriptive analytics,it can be defined as something that involves bringing out optimal planning decisions given the predicted future and addressing questions such as “what shall be done in this case?” and “why shall it be done?”
In industries like logistics, predictive analysis or analytics is done with respect to operational performance metrics such as delivery service levels and impact analysis of external factors such as exchange rates, fuel prices, and carrier rates.
Other practical applications include collaborative Sales and Operations Planning (S&OP) by connecting statistical forecasting methods for demand planning. There are techniques such as analytic hierarchy process and game theory that have been described to lie within the ‘Source’ classification.
The development of big data analytics and especially prescriptive analytics across industries has time-warped into the future already. Here are some prime examples of how prescriptive analytics is changing the face of various industries across the globe.
Prescriptive Analytics in Banking
In a recent Crowe Horwath LLP webinar involving bank executives from a broad array of organizations, most of the participants 63 percent of them said that they were interested in moving beyond descriptive and diagnostic data studies. They also said that they were exploring more advanced analytics or already implementing more advanced projects.
Source: Crowe Horwath LLP webinar survey, June 29, 2017
Banks today encounter a small hesitancy and uncertainty considering they have the needed technological capacity, proper governance, and adequate resources.
Phase 1: Data Discovery
Tasks- Understand the data available
• Gather data and get data linkages
• Understand data quality and data usage
• Review segmentation already being done
Output: Comprehensive data inventory compiled
Phase 2: Exploratory Data Analysis
• Analyze the data
• Perform descriptive statistics of Data trends, Outliers and errors and Business insights
• Explore the data
Output: Data analysis completed
Phase 3: Model Development
• Design models and define inputs and output
• Apply supervised learning algorithms
• Develop and test models
• Develop models with and without segmentation
Output: Models developed and tested
Phase 4: Finalization
• Perform iterative testing
• Validate and finalize models
• Identify and present key insights
• Operationalize Insights
Output: Models finalized and updated, key insights developed
Prescriptive Analytics in Supply Chain
Prescriptive Big data analytics solutions has the ability to work across all supply chain management levers. It conveys information from one aspect to another but the overall aggregation requires accuracy, timeliness, and consistency.
Marketing: Intimacy with customers is a prerequisite for your business. It can be achieved through evergoing sophisticated methods of customer data analysis. This stage includes the data from social media, mobile apps, or loyalty programmes; all of them being the enablers for the sentiment analysis.
Procurement: Data complexities on this side might arise from globalised purchasing strategies with thousands of transactions. In this lever, a strong connection with internal finance reporting led to adopt measures on spend visibility data, to achieve granular levels on aggregated procurement patterns.
Warehouse: Warehouse management has experienced a shift in the paradigm by modern identification systems after successful introduction of RFID.
Transportation: Managing and coordinating in real-time with operational data relies on mobile and direct sensing over shipments that are integrated into in-transit inventory. This allows tracking of estimated lead times based on traffic conditions, weather variables, real time marginal cost for different channels.
Prescriptive Analytics in Healthcare
Prediction and apt prescription is most useful when conveyed precisely into clinical action. Prediction should carefully link to clinical priorities and measurable events, such as cost effectiveness, clinical protocols, or patient outcomes.
Hospitals today make use of the predictor to drive decisions from updated dashboard and foresee associated cost simulation, real-time hospital census bed counts, pending medication reconciliation, or adjusting order sets for education material and in-home follow-up.
Hospital staff is now able to determine those exact patients who are at highest risk of readmission and take necessary action in order to mitigate this risk.
Prescriptive Analytics in Retail
Research estimates that Walmart collects around 2.5 petabytes (1 petabyte = 1,000,000 gigabytes) of information every hour regarding transactions, customer behavior, location and devices . Gartner estimates that there will be 20 Billion (13.5 Billion in the consumer sector) devices connected in the “Internet of Things”.
Source: Retail Analytics Landscape
When you multiply ‘People x Products x Time x Location x Channel’ data, this eventually brings up an unfathomable amount of data. The magic lies in weaving these data sets together and bringing out required results. The ones who are able to execute this task are the real winners in retail industry.
Conclusion
With correct guidance and digital transformation consulting, various sectors spread across major industries have been able to harness the magnificent power of big data. Big data is subtle, it doesn’t work on the same plain as AR or IoT, Big data is the king maker and plays the biggest role behind the curtains on the gargantuan stage of progressing technologies.
“Companies like pepsico are all working on moving from the traditional descriptive and diagnostic analytic capabilities to prescriptive analytics. adopting prescriptive is critical for supply chains to gain a competitive advantage now and in the future.” Leslie Keating, Former Svp Supply Chain, Frito-lay
