Data Discovery Tools: also called Data Visualization Tool, sometimes also referred to as Data Analytics tools. These tools are talk of the town and reasonably hot today. With all the hype about Big-data, companies are going dandy on planning for their big-data and are on a lookout for great tools to recruit in their big-data strategy. One thing to note here is that we don’t change our data discovery tool vendors every day. Once we get into our system, it will eventually become part of our Big-data DNA Solution. So, we should put much thought into what goes in picking data discovery/visualization/analysis tool.
So, what would you do and what would you consider important while picking up your data discovery tool vendor? I interviewed a couple of data scientists and data managers in a bunch of companies and prioritized the findings with top 7 things to consider before you go out picking your big-data discovery tools vendor.
Here are my 7 thoughts not particularly in that order:
1. Not a major jump with what I already have: Yes, learning a new system takes time, effort, resources and cycles. So, the faster the ramp up or shorter the learning curve, the better it will be. Sure, many tools will be eons apart with what you are used to, but that should not deter you from evaluating it as well. Just go a bit high on the tools with minimum learning curve. One thing to check here is that you should be able to do routine things with this new tool, almost the same way as you used to doing without it.
2. Helps me to do more with my data: There will be several moments where you will realize the tools could do a lot more than what you are used to or what you are capable of. This is another check to include in your equation. More feature set, capabilities within the discovery tool. The more it will let you do stuff with your data, the better it will be. You should be able to do more fun and investigative stuffs with your data more closely and at various dimensions that it will ultimately help with better understanding of the data.
3. Integrate well with my big-data: Yes, first things first, you need a tool that at least has the capability to talk to your data. It should be able to mingle well with your data layouts, structures without having to do too much time consuming steps. A good tool will always make it almost seamless to integrate your data. If you have to jump ropes, cut corners to make data integration happen, maybe you are looking at a wrong tool for help. So, get your data integration team to work and make sure data integration is a no issue with the tool that you are evaluating to buy.
4. Friendly with outside data I might include as well: Many-times, it is not only about your data. Sometime you need to access and evaluate external data and find their relationship with your data. Those used case must be checked as well. How easy it is to include external structured and unstructured data. The bigger the product integration roadmap for the vendor, the easier it will be for the tool to connect with other external resources. Your preferred tools should be able to integrate seamlessly with data sets involved in your industry. Social data, industry data, other third party application data are some example. So, ask your vendor on how their tools mingle well with other outside data sets.
5. Scalability of the platform: Sure, the tool you are evaluating could do wonders with data and has a sweet feature set, but will it scale well as you grow. This is important consideration just like any good corporate tool considerations. If your business will grow, so will itâs data and associated dependencies, but will your discovery tool grow with it? This is one finding which must be part of your evaluation score for any tool you are planning to recruit for your big-data discovery need. So, get on call with technical teams from vendor and grill them to understand how their tool will grow with growing data. You donât want to partner with a tool that will break in future as your business grows.
6. Vendorâs vision is in-line with our vision: Above 5 measures are pretty much standard and defines basic functionalities on what a good tool should entail. Itâs also not a big surprise that most of the tools will have some or the other of their own interpretation of the above 5 points. Now one key thing to notice on strategic front will be their vision for the company and the tool. Tool can do you good today, it has boatload of features, it is friendly with your and outside data. But will it grow with a strategy consistent with yours. Yes, no matter how weird it sounds, it is one of the realities that you should consider. A vendor only handling health care will have some impact to companies using the tools for insurance sector. A tool that will handle only clever visualization piece might have impact on companies expecting some automation as part of the core tool evolution. So, it is important to understand the product vision of the tool company, that will help you understand if it will comply with your business value tomorrow or day-after or in foreseeable future.
7. Awesome import / export tools to keep my data/analysis free: Another important thing to note is stickiness with the products. A good product design should not keep customer sticky by keeping their data hostage. A good tool should bank on itâs features, usability and data driven design. So, data and itâs knowledge should be easily importable/exportable to most common standards (csv, xml etc.). This will keep the tool up with integrating it with other third party service that might emerge with emerging market. This should be a consideration as it will play an instrumental role in moving your data around as you start dealing with new formats and new reporting tools that are leveraging your data discovery findings.
I am certain by the end of 7 steps you must have thought about several more examples that one could keep in mind before picking a good data discovery tool. Feel free to email me your findings and I will keep adding it to the list.