So what is collaborative analytics™ and why should one care about it? If you read my blog on “Rethinking Big Analytics to handle BigData”, you kind-of get the gist on the need for some better analytics collaboration tool. BigData deluge is real, and soon our computation will not be able to catch up. Our ways to analyze the data is still old school. They are either human or machine dependent and are very isolative with collaborations only possible via manual ways. We all know that there is always someone around you who knows more that you do and could help you get to the next step. The problem is that manual ways are not efficient in helping find those people. So, what should one do?- Invent ways to make analytics discoverable, to make best practices flow and that too with minimal impact to business. A good start will be to start digging for areas that will make our analytics strategy robust and scalable with evolving technological landscape and changing customer dynamics. And if possible, keeping the strategy least invasive and in line with current business practice. No, getting whole minority report on your current business will take forever, and is not very cost effective, you need something sustainable, something that could be implemented in small scale and easy to replicate. That would make collaborative analytics possible.

For those who think your organization is doing perfect and you have the most able team to handle growing analytics capability. I would like to take a detour and point you to old PBX system. Do you remember those huge large switchboards and people manually making the connections? – Yes, that was working, but lot of other capabilities was missing that only evolved once we allowed computers to take over and help assistants. Now looks at where we are? – People taking calls from several locations, smart routing, IVR, capabilities explosion went on-and-on-and-on. Analytics needs similar outlook.

And, no this is not a Si-Fi ninja s#$t. You should be able to bake the science right in your enterprise R&D kitchen. All you need to do is, take your analytics strategy through a journey of these steps:

1. System that learns from each other: The 101 in making any analytics better is learning. Learning from other analytics, from professionals, from standards or from tools. As long as tools have the provision to learn from other analytics and leverage that knowledge to improve their own knowledge, we will be in good hands; whether learning is happening within team members or departments or across verticals. So, bake protocols that could make learning possible within analytics.

2. Makes cross platform analytics translation possible: Next stage is making your analytics ready for big-data is to facilitate the easy translation of your analytics across platforms/tools. Not clear what I am saying? Try asking your IT how long will it take from translating your computation models to business intelligence tools. Yes, that is time consuming and painfully manual process. Translation could really make use of collaborative capabilities. This will immediately solve a potential issue of saving companies millions in translating analytics quickly and letting automation do some heavy lifting in that otherwise manual and painful process.

3. Ability to analyze the enterprise analytics: Let’s take our inter-analytics learning to whole new level. What about benchmarking it?- Once you nail the art or learning from each other, the next level in your collaborative analytics foray is to use whatever you have, built a wrapper around it and thereby introduce an ability to analyze. This ability to analyze the analytics and capability to communicate the learning to analysts would take your enterprise analytics to whole new level. How to get it started? – How about you begin by creating some baseline rules and use those rules/protocols to analyze how every other analytics fair.

4. Ability to create auto sandbox for maximizing R&D: Glad, you have built the ability to learn from inter-analytics and developed the ability to analyze your enterprise analytics. Now, why don’t you take it to the next level? How about taking those analytics and wrapping them in a virtual sandbox to play with? – Yes, you heard it right. Giving your analytics a separate play area and automated script to mingle and discover the unearthed territories in your analytics. This will take your analytics to whole new level. Therefore, you could really skyrocket the possibilities by giving your analytics a sandbox.

5. Reduce the burden on people and empower people with more capabilities: Now you are at a stage, which could make you a Yoda of advance analytics. Your system could not make to facilitate analysts by acting as their super assistant taking the heavy loads off from analysts and let them focus on the fun part of their job. At this level, you have already built your system to that sophistication that doing custom help and finding ways to pitch in is not an unheard dream. This certainly brings you to big leagues on advance analytics. Now, your analytics is no more restricted to manual intervention, but could adapt itself to fill for growing and changing market needs and recommend accordingly.

6. Facilitate Personal as well as automated collaborations: Now, if you have grown your advance analytics system to this level, this step is pretty much a cakewalk. Your system has lot of ability to do smart analytics, now all you need to do is build Artificial Intelligence (AI) around to help system support manual as well as automated analytics. There are several AI channels that could be leveraged to build underground science that will feedback learning to manual as well as automated analytics making it stronger and helping it grow with growing data and competition.

7. Auto discovers and reverses engineer analytics: Collaborative analytics should also involve the ability to auto-discover and reverse engineer analytics. Sure, this is not done as business core but it does provide awesome benefits as an ancillary channel providing guiding light to businesses to learn from. Ability to take analytics or data pattern and reverse engineer it to find underlying model is a sci-fi thing. This could safeguard your business from any future pattern and let the system provide you recommendation on the fly. Ensuring your sustainable existence and providing analytics as a competitive advantage.

At the end, remember, this is not a rocket science. It is extremely edible long-term analytics strategy, which is built on common sense on how to handle traffic. World has already seen this strategy working in communication- A journey from copper communication PBX to auto-learning smart IP switch. We all have to put ourselves on this long but consistent journey to new age analytics strategy that could really work in parallel with new age data strategy. May the force be with you.