World over, enterprises are spending more than their budgeted share to understand all about their data. This is a universal problem when comparing the ways and methods used to collect data might have been ancient to today’s advanced technologies. This creates a pressing need for enterprises and analysts to either have an “in-house tech team” come and ‘patch’ their systems or have a separate process for data updations, consolidations etc. Whatever the method maybe – it is costly and time consuming.
In gearing up for data analytics, an understanding of the following points will be really helpful to arrive at a better conclusion of things. After all, it will still cost money and that’s a big point.
- Be aware of technology changes!
Technology is a great disruptor and it’s this disruption that has caused enterprises to keep adjusting their technology stack to arrive at a steady state of operations. Depending on how your business is run, there may be areas where legacy frameworks are to be supported. The Cloud framework is a top bet as it is the platform of the future and a lot of enterprises (atleast mainstream) enterprises have moved to the Cloud giving them low costs (Opex), high availability (no showdowns), rapid scalability (easily ramp up your services up and down according to business need)
- Choose the right tool
This is more specific to your business. If you experience huge volumes of data, you could be looking at a big data solution. If you need to create customized reports from large datasets, then it would be more of visualization and reporting features with a solid modelling framework driving the whole process.
- Models make all the difference
So you have the data, you know the kind of report your need. So how do you go about doing it? That’s where models come in. Data models define how data is connected, stored and how they are processed within the system. And, that’s why this is important to link this with the kind of processing you envision to achieve. You have tools that are exclusively based on modelling techniques like decision trees, regression analysis, association rule analysis, time series analysis etc. Advanced versions also include machine learning, integration with R etc
- Support for availability
Depending on the size of your organization, this may ramp up or down. It’s always prudent to think of the future. This is always driven by the growth of your organization. A cloud based approach would serve best because of its low start-up cost, ease of use and high availability (99% of time). This also has to do with the size of the organization. Larger organizations will have huger data overloads, would also need more processing time and power.
Last but not least, budget. This is where everything falls flat. Different vendors have different mechanisms when coming to cost. They could have it spread against different editions of the software or for the support and maintenance given etc. The costs will reflect according to the size and scope of the support being rendered. There could also be free and open source versions for some requirements as well.
We hope these tips will help you understand what lies ahead in your quest to purchase an analytical platform for your organization. We will be glad to offer you a demo of our platform and showcase how it can be beneficial for your enterprise in the long run.