Big Data is a common expression under which all kinds of processing techniques for large volumes of data are grouped, apart from traditional analytics tools. This concept comprehends many ideas and approaches, but they all have a common goal: extract valuable information from the data, so that it can be helpful for taking better decisions.
It is commonly acknowledged that Big Data is formed by 4 V’s:
- Volume-based value: The more comprehensive your 360-degree view of customers and the more historical data you have on them, the more insight you can extract from it all and the better decisions you can make in the process of acquiring, retaining, growing and managing those customer relationships.
- Velocity-based value: The more customer data you can ingest rapidly into your big-data platform and the more questions that a user can pose more rapidly against that data (via queries, reports, dashboards, etc.) within a given time period prior, the more likely you are to make the right decision at the right time to achieve your customer relationship management objectives.
- Variety-based value: The more varied customer data you have – from the CRM system, social media, call-center logs, etc. – the more nuanced portrait you have on customer profiles, desires and so on, hence the better-informed decisions you can make in engaging with them.
- Veracity-based value: The more consolidated, cleansed, consistent current the data you have on customers, the more likely you are to make the right decisions based on the most accurate data.
Every Big Data-oriented project has 3 technological layers
How does it work?
Big Data solutions aim to extract valuable information by analyzing large data sets. This analysis is based on mathematical techniques, generally based on probability and statistics, and it could involve various data science fields such as data mining, machine learning, data visualization and time series analysis.
Via appropriate data treatments, any type of data is susceptible to be analyzed: databases, numerical registers, free text, activity in a social network, audios, images, videos. We often found scenarios that have a diversity of data in different formats and these scenarios can also be treated through information integration strategies, hence enriching the solution.
Depending on the information obtained, three types of analytics can be distinguished:
Descriptive Analytics insight into the past
Descriptive analytics summarize past data and make it something that is interpretable by humans. The past refers to any point of time that an event has occurred, whether it is one minute ago, or one year ago. Descriptive analytics are useful because they allow us to learn from past behaviors, and understand how they might influence future outcomes.
Predictive Analytics understanding the future
Predictive analytics has its roots in the ability to “predict” what might happen. These analytics provide companies with actionable insights based on data and provide estimates about the likelihood of a future outcome. It is important to remember that no statistical algorithm can “predict” the future with 100% certainty. Companies use these statistics to forecast what might happen in the future.
Prescriptive Analytics advise on possible outcomes
Prescriptive analytics allows users to “prescribe” a number of different possible actions towards a solution. Prescriptive analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made. At their best, they predict not only what will happen, but also why it will happen providing recommendations regarding actions that will take advantage of the predictions.