Top 10 Analytics Trends 2016
- 1. The Analytics Landscape: Top 10 Trends Niranjan Krishnan Director & Head - Innovation Lab Tiger Analytics Contact: [email protected] May 1, 2016
- 2. 1. The 5 Forces 2. The Top 10 Trends Agenda
- 3. Analytics Landscape: The Five Forces
- 4. Five Forces shaping the Analytics Landscape Analytics Data Storage Capacity Computing Power New Methods & Tools Business Usage Data Growth ▪ Storage capacity expanding ▪ Cost falling ▪ Processing power increasing ▪ Cost falling ▪ High-impact innovations ▪ Open Source collaboration ▪ Shorter time-to-market ▪ Increasing adoption ▪ Growing needs ▪ Expanding possibilities ▪ Volume, variety and velocity of data increasing ▪ Cost of data creation falling
- 5. Top 10 Analytics Trends
- 6. Data Enrichment ushers in better forecasts and sharper insights 1 -- Forecasting Analytics is (still) the critical path to planning and operational excellence. -- Internal systems (e.g. CRM, EDW) do not contain all the pieces needed to form a full picture of business scenario. -- External data from public sources enriches internal data and enables more robust predictions.
- 7. Unstructured data opens new pathways to business value creation 2 -- Data no longer needs to be structured, formatted and linked to be useful. Intelligence can be gleaned from virtually any type of data e.g. text, image, audio, video. -- Text Analytics powers several business decisions.
- 8. 3 Speech and Voice Analytics come into their own -- Following the footsteps of Text Analytics to enable better business decisions.
- 9. Data Lakes begin to fill up and yield prize catches 4 -- Hyper-massive datasets and the powerful business intelligence they provide are steering corporations towards Data Lakes in the Cloud. -- Cloud offers a compelling option to both store and process massive amounts of data efficiently.
- 10. Big Data and Internet-Of-Things (IOT) Analytics make great strides 5 -- Big Data is not a passsing fad - it is here to stay. -- Enormous amount of data generation is happening without human involvement or touchpoints. -- Sensor data, machine-to-machine data and network data are emerging as data goldmines. -- Analytics-of-Things taking of as a field
- 11. Analytic tools proliferate at a rapid pace 6 -- Expanding data storage options e.g. CRM, EDW, Cloud, calls for a wider set of tools. -- New tools offer advantages of speed, scale and flexibility.
- 12. Open Source Tools steal a march over paid licences 7 -- Cost is not the only advantage of Open Source. -- Open Source tools are often more modular and scalable. -- Provide cutting-edge data processing, analytic and visualization techniques. -- BUT . . . documentation is scant and there is no support other than online communities. Selection of right analytic tools is a difficult decision for companies -- Problems of plenty calling for good alignment of business needs, system constraints and tool capabilities.
- 13. New visualization tools carve out their own niches in the market . . . even as MS Excel rules Visual Analytics 8 -- Interactive visualizations are replacing static reporting. -- Unifocal insights are giving way to What-If Analysis and Multi-criteria Decision Making.
- 14. 9 Full-pipeline Analytics Automation gets more popular -- Data Preparation i.e. pulling, merging, cleansing and aggregating, remains the greatest bottleneck to analytics. -- Prompts a move away from adhoc-ism and towards automation. -- End-to-end automation of data extraction, preparation, analytics and reporting boosts productivity of analytics teams. -- Enables users to spend less time in getting analytic insights and more in putting them to work.
- 15. 10 New Age Decision Systems gain clout -- Early Warning Systems help businesses anticipate and adapt to change. -- Real time, self-learning systems powered by artificial intelligence have low latency feedback, automated recalibration and large-scale deployment. -- Test and Learn systems enable controlled experiments in the market. New ideas are tried out on a limited basis and their impact is measured and confirmed before full-scale deployment.
- 16. Contact: Niranjan Krishnan Director & Head – Innovation Lab [email protected] www.tigeranalytics.com
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