It’s an exciting time for business. Companies have either amassed a tremendous amount of data or are increasing their efforts to ingest data available from this digital ecosystem that we’re part of to find value and new profit streams. No matter how we look at it there is a need to make sense out of all this.

Time to move from spending precious time for defining Big Data and actually taking steps of doing something with the data available. Before we do this though we need vision. On an earlier post I added the 5th V for Big Data Vision. This is an integral component to be on the right path of profitability. Enter the 5 ways to getting to insights fast.

  1. Modernize data access

More and more as we engage on this strategy there is going to be a need for data engineering. Perhaps one of the most important layers. Unfortunately there are many moving parts in Big Data and being an open source technological framework there is a high curve of learning. A good practice is to do an assessment so you have the roadmap blueprinted.

  1. Data Management & data processing

This step depends on such factors as organizational structure, capabilities to ingest and process a variety of data with velocity. Chances are that in order to be efficient and to accommodate the business demands you have to move swiftly. As such you’ll need solutions. A good model is to look for managed services. It is important that you alleviate all stakeholders as much as possible.

  1. Predictive insights

A managed services model per above will allow you to focus on how to improve the business. A lot of companies today are focusing on the reporting. This is all good but elaborated spreadsheets don’t allow you to have time to explore where the hidden revenue streams are. Time to put the thinking cap on. The paradox here is the human capability.  Predictive modeling is where the action should be focused. A good rule here is that this is a continuous process so don’t treat this steps as one and done.

  1. Problem solving selection

Big Data methodology can help you solve many problems. Improve product recommendations to clients, adjust content creation based on listening ingestion from social feeds, prevention in maintenance or in fighting diseases or customize customer experience, etc. If you’re starting you need to test the waters. At this point you have heard the terminology POC (Proof of Concept) and Use Case (the larger version of the problem you have to solve).  A good rule start with a modest problem that can lead to the full use case solution.

  1. Anticipate Scalability

Revert back to the vision. You will grow and not all companies have the same capabilities. Your historical data will grow, the demand from the business units will grow, and your team will grow. At this stage what you have to keep in mind is that Big Data should be tailored to the exact needs of your organization. Stay away from one size fits all models. Choose nimble technology partners because there is a learning curve and more importantly a high degree of customization.

In conclusion there is a dependency to external partners and also a high degree for teamwork. You have to remember and constantly evaluate if at the end of the day are my problems Big Data problems and that you shouldn’t do Big Data because it’s fashionable but because you have a need to extract value for your business. On my next post I would be writing about the 5th V in Big Data stay tuned.