3 techniques for a good Big Data data analysis procedure

Nowadays companies generate and use data at unprecedented speeds. But many companies that are faced with this large amount of data they don't use a good data analysis procedure and they are unable to take full advantage of the vast amount of information available to them.

Too much information without a data analysis procedure correct avoid clear decisions. When you have so much data, need more than just data. You want to know if it is the correct data to answer your question. You need to draw accurate conclusions of your data and you need your data to help you make decisions.

In summary you need to analyze your data with a data analysis procedure the right tools and the right tools. Thus, what was a huge volume of disparate information becomes a clear decision point.

1.Make sure you have the right equipment

This means that Your analytics team shouldn't have just IT staff, experts and statisticians. You need to make sure that some of them can compile deep insights from the data and make additional recommendations. In other words someone who understands not just numbers, but also the strategic implications.

Inclusive may involve an external specialist, since a fresh look is often needed from someone outside the company. to devise innovative ways to use data. Workers and people who use data on a daily basis may have somewhat myopic or tunnel vision of information. The only problem with this option is that there is often a shortage of qualified personnel, and the ones that are often expensive. You need to weigh the cost-benefit of hiring an outside consultant and negotiate a performance-based fee with them..

2. You need a data analysis procedure that gives answers to people

Your organizational or business data analysis procedure, must answer specific questions, measurable, clear and concise. These questions should qualify or disqualify possible solutions to specific problems or possibilities..

To make good decisions, the system should be able to answer questions like “which customer group is using a product” Y “what factors are driving customer growth and retention”

Decisions are made based on those recommendations on issues associated with the business. And often the decision-maker doesn't have enough computer skills, hence the system must be able to explain the results in a non-technical way, which introduces an additional layer of complexity to the data analysis procedure. The need to explain implies that the data analyst tends to deliberately select more basic models over more precise but more complex ones. The system must be able to reach high-level conclusions in the style of “why” O “What”, which is quite far from the raw data.

3. You also need a data analysis procedure for the machines.

In this circumstance, the final decision maker and data consumer is a machine (a computer). Data analysts build complex models containing large data sets and attempt to extract subtle signals through machine learning and sophisticated algorithms.. They tend to work in areas like algorithmic trading, online content with targeted advertising, personalized product recommendation, etc.. They are digital models that get high and then act on your own, make recommendations, select ads to automatically display or trade on the stock market.

Data analysts producing analytics for computers. must have math skills, computational and statistics, remarkably solid, since they must build models that can make high-quality predictions very quickly. They must create sophisticated models that squeeze every last drop of performance.and regularly operate with clearly measurable and unambiguous metrics, as clicks, earnings and purchases. Its value lies in taking advantage of its technical virtuosity in millions of situations in which even Small aggregate earnings on millions of users and trillions of events can lead to big earnings.