Many organizations are concerned that the amount of amassed data is becoming so large that it is difficult to find the most valuable pieces of information.
Challenges
- What if your data volume gets so large and varied you don’t know how to deal with it?
- Do you store all your data?
- Do you analyze it all?
- How can you find out which data points are really important?
- How can you use it to your best advantage?
Until recently, organizations have been limited to using subsets of their data, or they were constrained to simplistic analyses because the sheer volumes of data overwhelmed their processing platforms. But, what is the point of collecting and storing terabytes of data if you can’t analyze it in full context, or if you have to wait hours or days to get results? On the other hand, not all business questions are better answered by bigger data.
The solution
You now have two choices :
- Incorporate massive data volumes in analysis. If the answers you’re seeking will be better provided by analyzing all of your data, go for it. High-performance technologies that extract value from massive amounts of data are here today. One approach is to apply high-performance analytics to analyze the massive amounts of data using technologies such as grid computing, in-database processing and in-memory analytics.
- Determine upfront which data is relevant. Traditionally, the trend has been to store everything (some call it data hoarding) and only when you query the data do you discover what is relevant. We now have the ability to apply analytics on the front end to determine relevance based on context. This type of analysis determines which data should be included in analytical processes and what can be placed in low-cost storage for later use if needed.
If in doubt about your big data strategy, contact TheMarketsTrust. Our experts will help you make the right choice.