BIG DATA Analytics: What is it & Why it matters?

When we hear the term “BIG DATA” our team usually thinks that there is going to be a lot of work involved! However, it does not have to be that way. For SmiForce, BIG DATA Analytics means being able to take large volumes of data and analyze it automatically to give our customers significant value from it. The aim of analyzing all this data is to identify patterns, insights, and connections that might otherwise be lost, and that might provide actionable solutions to the organization.

There are many benefits that BIG DATA Analytics can bring to your organization:

1. Cost reduction. SmiForce’s cloud-based analytics brings significant cost advantages when it comes to storing large amounts of data – plus they can identify more efficient ways of doing business.
2. Faster & better decision making. With the speed of SmiForce architecture and Performance analytics, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately – and make decisions based on what they’ve learned.
3. New products and services. With the ability to gauge customer needs and satisfaction through analytics comes the power to give customers what they want. SmiForce has proven that with BIG DATA Analytics, more companies are able to evolve practices and services through our detailed analytics in order to meet your environment needs.
4. Save on resources and time. SmiForce’s services bring automated data analytics to your team. With analytic modules built for many scenarios within various business industries, teams will be able to save time by NOT having to manually identify trends, patterns, and insights to help transform the organization.

So how do you know if “BIG DATA Analytics” is worth the investment in your organization? Well, you and your team need to ask yourself:

1. How long does it take your team to analyze data your data today?
2. Are you working in “real-time” or using “static” data sets?
3. Do you have to invest significant resources in your current analytical process?
4. Are you proactive or reactive in your environment?