Analytics is evolving at a rapid pace than ever and there are newer paths being developed and researched in recent times. These include but are not limited to:
1) Development of data management platforms which have opened new possibilities to measure/collect both structured and unstructured data adding new storage and retrieval possibilities leading to new integrations, the most recent being Cloud based data storage and retrieval
2) As DMP’s have matured overtime, new tools in the business intelligence environment have correspondingly evolved taking visualization and reporting to the next level eg: Tableau, PowerBI, Zoho etc.
3) Using statistical tools and machine learning techniques is in these days across various organization. This involves taking decisions based on data and continuously automating the cycle to decision actions based on advanced tools. It also involves taking not only specific actions which are true/false but training computers to take actions like a human brain does with cognitive intelligence. Automated machine learning is automating exploratory data analysis, pre-processing of data, hyper-parameter tuning, model selection and putting models into production.
As the Analytics landscape evolves new techniques are being evolved to minimize the gap between data events and actions related to it, eg: in a product SAAS model- if a customer starts to engage less with a product and falls into the category of segment of customers which might churn due to less engagement due to various reasons, action on such event can be triggered automatedly and damage control can be done at an appropriate time. While this may appear to be a straightforward solution, there is never a singular problem that may causes customer churn; rather there are a number of aspects to consider simultaneously and over time that cause the undesirable outcome eg: – customer churn may occur due to one contact (customer contact) in an account posting a negative social post in addition to less engagement with a product. Overdrive stage has not yet reached in Analytics, with focus moving on making computers act like humans and making them smarter to take actions/decisions to enhance business value and AML environments, there is still more milestones to reach here.
Another piece of the analytics story is Sales & Marketing Analytics, it involves providing intelligent data stories for customers to maintain their relations with the business. Historically, self-serviced BI tools have been available for sales to do BI data slicing; however, the limitations to technical acumen characteristic of sales can give rise to challenges in interpreting information. Various organizations have analysts on the sales & marketing disposal/support to keep the flow of such intelligent conversations/insights so that sales pitches can seamlessly include analytics. Recently, companies have started investing in different analytics mix with a recent one being use of Artificial Intelligence and one of its branch is NLP (Natural language processing), It may sound complex, but the idea behind it is quite simple. Breaking it down, we have “natural,” referring to how humans interact with each other; “language,” which is text-based in this case; and “processing,” which brings it all together, imagine a chat-bot being used by a sales executive using text inputs to help get inputs to sell to a customer, these could be virtual analysts who have been fed with both data intelligence and continuous learning using AMT models. Eg: an existing customer who needs to be renewed a subscription or same customer who needs to be influenced to upgrade their subscription, the ChatRoBots which on the basis on certain parameters can provide specific data metrics which the sales can use to provide impactful sales inputs to sell.
Final summary:
Organizations need experienced experts who have the drive and vision to execute this hybrid analytics environment as outputs within an organization from Sales, marketing and product development. Data resides practically in every corner of the business life-cycle in both internal and external environment and using a hybrid analytics model which includes flexible data management models and new analytic designs is the need of the hour. Success of an organizations depends not on building a single model for a business data solutions but rather continuously experimenting with different models/approaches in line with the dynamism inherent in today’s business environment.
