Are you data-flooded, data-driven, data informed? Are you outcome oriented, insight driven or hindsight driven?
Are you a firm where executives claim – “Data is our competitive advantage.” Or sprout analogies like, “data is the new oil”.
The challenge I found in most companies is not dearth of vision… everyone has a strategy and a 100,000 ft general view of the importance or value of data. Every executive can parrot the importance of data and being data-driven.
The challenge is the next step….so, how are you going to create new data products? How are you going to execute a data driven strategy? How are you going to monetize data assets? What are the right business use cases to focus on? How to map the use case to underlying models and data requirements? What platform is a good long-term bet? The devil is in these details.
Everyone is searching for new ways to turn data into $$$ (monetize data assets). Everyone is looking for new levers to extract value from data. But data ingesting and modeling is simply a means to an end. The end is not just more reports, dashboards, heatmaps, knowledge, or wisdom. The target is fact based decisions, guided machine learning and actions. Another target is arming users to do data discovery and insight generation without involving IT teams…so called User-Driven Business Intelligence.
In other words, what is the use case that shapes the context for “Raw Data -> Aggregated Data -> Intelligence -> Insights -> Decisions -> Operational Impact -> Financial Outcomes -> Value creation.” What are the right use cases for the emerging hybrid data ecosystem (with structured and unstructured data)?
I see this every day at clients. Many organizations flounder in their Analytics, Data Science and Big Data efforts not because they lack smart talented people or analytics capability but because they lack clear objectives, leadership, experimental mindset or multi-year roadmaps in converting noisy hybrid data into useful signals.
So the first question is: What do you really want to achieve? Increased customer loyalty? Better customer engagement? A greater share of wallet via cross-sell? New customers? Lower attrition? Cheaper and faster data processing? In other words, what is the use case? As the old adage goes: if you don’t know where you are going, any road will get you there.
Starting with a clear objective is essential in order to pick the right tool to solve the right problem. Some clarity is necessary to drive proof of concepts or even select a technology stack to experiment with.
Big Data Analytics promise: enable “data monetization” through more timely, more accurate, more complete, more granular, more frequent decisions. So, what exactly are the types of business problems big data analytics likely to solve? For this you need a mini-MBA in Big Data Use Cases.
Use cases described here are meant to stimulate ideas of how to apply iterative big data analytics in your own organization and enable your own analytics center of innovation.
Some interesting Big Data use cases I have come across include:
First, let’s define what makes data Big to set some context.
We live in a world of data: transactions, feedback, and realtime interaction with customers, partners, suppliers, and employees. Big data is where the volume, velocity, variety, verticalization (context) and value of the data itself is now part of the problem.
3 reasons why we are generating data faster than ever:
• Processes are increasingly automated • Systems are increasingly interconnected • People are social and increasingly generate data exhausts by interacting online
In addition to brick, click and mobile business app transactions, the new variable in the mix is Human generated data — explosive growth of blogs/reviews/messages/emails/pictures. The Twitter firehose alone generates 7+ terabytes — 10s of millions of tweets per day and is growing rapidly. Facebook is estimated to generate 10+ terabytes a day.
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