Saturday, November 22, 2014

Advanced Analytics, Which Stage are you in?

Data, is the raw material from which businesses will extract the nuggets of insight.

Companies are collecting more and more data now. Ability to meaningfully analyze and create a strategy around the analysis determines which companies are more profitable. The data is there but how do you use it to solve either strategic concerns or operational issues through analytics? Which stages are advanced analytics currently in, and what’re the emerging technology trends in driving its maturity?

Analytics is the competitive advantage for business’ growth. Advanced Analytics should be and surely are implemented in most of the large corporations around the world, from a cross-industrial perspective, many have embraced it and are taking advantage, and others will give it a try. The small to mid size companies require the same type of KPIs and actionable intelligence as the large ones, at a different scale, but they certainly have most of the same objectives and experience the same external/internal constraints, so modeling /predicting, is key to their survival and success. Advanced analytics solutions will give all kinds of businesses the capability to anticipate the future. This is where they can really differentiate themselves from their competitors. In order to achieve this, they need to build an analytical roadmap and invest in software, people, and infrastructure. They need to pay particular attention to their Data, the raw material from which they will extract these nuggets of insight.

Data quality and data management is key factor for analytics success. 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. An incredible amount of analytics nowadays is combining data from multiple sources and seeing the connections via a graph-oriented database. The ranges in applications go from leveraging social information and demographics for customer service, marketing, human capital management, preventing insider or poorly priced trading in financial services, relating genomic data to clinical and post-clinical drug evaluation. These are difficult to implement with relational SQL. We can't discuss Advanced Analytics without putting context around technology trends, such as; unstructured and very large datasets, or Cloud services, or the IOT, or stats packages & functions, etc. 

There are four stages of Analytics that are currently underway, different organizations perhaps are in the different stage of analytics journey, from leveraging hindsight to forecast the future; from diagnosing the problems to suggesting actions. "Execution Support" is important, either under the "prescriptive" umbrella, or as a new step. Once data are 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 implementing these strategies.with emergent digital technologies, analytics tools  today is lightweight, but smarter:
1) Stage 1 - Descriptive - Typically the domain of Data Warehouse as the repository and the traditional BI tools. The data is never current so users are trying to make current decisions based on historical data. 
2) Stage 2 - Diagnostic - The nimble analytics service provides more "Agile" capabilities to ingest large datasets, and visualize the results. For most organizations there's still a long way to go. 
3) Stage 3 - Predictive - this is where large, unstructured datasets (Big Data) and stats functions such as 'R' converge. Organizations can process internal & external datasets to make intelligent predictions of future outcomes. 
4) Stage 4 - Prescriptive - this is where Predictive Analytics & Machine Learning converge. The datasets drive the Analytics, which in turn refine the dataset, so the Prescriptive Analytics become refined and more accurate over time.

Analytics is more pervasive in modern businesses. However, there’re mix bag of stories of big data experiment. Some leading organizations achieve high ROI by deploying their data successfully; on the other hand, the failure rate of analytics project is still considerably high, that makes many executives hesitate to invest in. It is fair to say now it’s reaching an inflection point for organizations to unleash their business intellectual potential via advanced analytics, but it still takes time for getting technology maturity, methodology/process maturity and talent maturity.


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