Big data is everywhere. What really delivers value from big data is the analytics applied to the data. Without analytics, which involves examining the data to discover patterns, correlations, insights, and trends, the data is just a bunch of ones and zeros with limited business use.
To answer the age old question, “what’s in it for me?”, analyzing information using big data analysis tools enables organizations to make better-informed business decisions such as when and where to run a marketing campaign or introduce a new product or service.
Data analytics is a broad field. To break it down, there are four primary types of data analytics: descriptive, diagnostic, predictive and prescriptive analytics. Each type has a different goal and a different place in the data analysis process.
- Descriptive analytics helps answer questions about what happened. These techniques summarize large datasets to describe outcomes to stakeholders. By developing key performance indicators these strategies can help track successes or failures. Metrics such as return on investment are used in many industries. Specialized metrics are developed to track performance in specific industries. This process requires the collection of relevant data, processing of the data, data analysis, and data visualization.
- Diagnostic analytics helps answer questions about why things happened. These techniques supplement more basic descriptive analytics. They take the findings from descriptive analytics and dig deeper to find the cause. The performance indicators are further investigated to discover why they got better or worse.
- Predictive analytics helps answer questions about what will happen in the future. These techniques use historical data to identify trends and determine if they are likely to recur. Predictive analytical tools provide valuable insight into what may happen in the future and its techniques include a variety of statistical and machine learning techniques such as: neural networks, decision trees, and regression.
- Prescriptive analytics helps answer questions about what should be done. By using insights from predictive analytics, data-driven decisions can be made. This allows businesses to make informed decisions in the face of uncertainty. Prescriptive analytics techniques rely on machine learning strategies that can find patterns in large data sets. By analyzing past decisions and events, the likelihood of different outcomes can be estimated.
There are various tools in data analytics that can be successfully deployed in order to parse data and derive valuable insights out of it. The computational and data-handling challenges that are faced at scale mean that the tools need to be specifically able to work with such kinds of data.
The advent of big data changed analytics forever, thanks to the inability of the traditional data handling tools like relational database management systems to work with big data in its varied forms. Even data warehouses could not handle the immense size of the data stored by many firms.
Melody K. Smith
Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.