Strategy to build a Big Data Solution
One of the biggest topic past this year “The Big Data”, is growing and when entire core is running behind the big data hype, everyone cares is getting their hands dirty with tools to get to those big-insights. Big Data is nothing without big insight and the price to get those big insights is even bigger. Business struggle a lot to get their lights up , let alone sleeping with one eye open if being run over the competition. So, why do all those awesome tools that radically change businesses rarely require minimal learning? Why tools are not build around ease of its use and adoption by businesses?
No doubt the currently available big data tools are awesome. These tools could help business enormously. But the bigger question is, by when the businesses will be able to get this tools rolling to its full capacity.
To go further, let’s talk about the five basic steps that could help in easy and early adoption:
1) Improve time to adoption
A product could start being high on Time to Adoption but could work its way downwards to make sure they build something which is easily adoptable by their prospective businesses. This metric will also help businesses understand which product is having its shortest route to their business.
2) Adoption with existing resources
Another important metric for product managers and clients is to figure out how much of client’s existing infrastructure and resources will be utilized. This will give a key perspective into how to go about designing the product. The right product will make integration faster and offload client’s nightmare and adoption troubles.
3) A hug to other tools
If your product strategy believes in keeping clients hostage in your system, you should ignore it by all means. But if you care to grow with your clients, give it a long and hard look. An easily integratable tool will always find easy adaptability as well as more market share. At the end, you should know it is very difficult to be doing everything and let your client explore better alternatives as well. This strategy will keep tool companies real and relevant. On the other side, client will not have nightmares to not adopt the best tool for their business if some other tool could compliment your offerings.
4) Basic Proficiency level
This is another good indicator for measuring a good tool. Not to anyone’s surprise, this indicator will suggest how fast could one attain substantial knowledge to start using the tool. These metrics on the other side suggest clients that how much will they need to plan their resources for adoption. A longer learning cycle driven tools should be evaluated accordingly. This will also help product managers in building tool that could really speed up the adoption.
5) Data driven Product Strategy
It is to have data do the most of the talking and leaving minimum things for the gut to figure out. Having a data driven product approach will truly help build something, which is loved by clients and in-demand; which is closer to client’s problem and which is showing better return on investment for product managers as well as clients. Such metrics find it easy when it comes to selling to clients or using for budget approval.
Certainly, these 5 steps are not the end, but just the beginning. These 5 metrics could be bench-marked and segmented across various other vertices as well as for better and effective results. Something that could really help mass businesses gain access to the top of the line product without investing their years into adoption without much momentum.