Companies today have to contend with large volumes of data increasing in giant leaps. Managing data becomes critical to the business, from storage to processing and deriving relevant information for decision-making, giving birth to the term Big Data.
What is Big Data?
Big Data pertains to the large volumes of data that cannot be easily structured and queried using standard relational database techniques. Data sizes usually range from a few dozen terabytes to many petabytes in a single data set, beyond the ability of commonly used software tools to capture, organize, manage, and process within an acceptable period of time.
Big data is also used to describe the exponential growth, availability and use of both structured and unstructured information. It is considered a high volume, high velocity, high variety information asset that requires new forms of processing to enable improved decision making, insight discovery and process optimization. Read more on big data and data structuring below.
Dimensions of Big Data
Big Data is characterized by several dimensions that are critical to the challenges in handling them. These are:
- Volume. Accumulated data over time from sources like transactions, social media, and sensor data are collected.
- Variety. Data coming in all types of formats, text documents, meter-collected data, video, audio, to databases, etc.
- Velocity. How fast data is generated and how fast it must be processed to meet requirements.
- Variability. Inconsistent data flows with periodic or irregular peaks, event-triggered or influenced by social media.
- Complexity. Large data volumes coming from multiple sources, required to be linked, matched, cleansed and transformed across systems.
Major Challenges in Big Data
1. Shortage in Big Data skills: There is a shortage of skilled data scientists and managers, especially those who are capable in applying predictive analytics to big data. This requires an analytics and data-science capability, needing both software development and IT systems management skill-set.
2. Unorganized system to using big data: There are a large number of companies still using disparate systems for gathering data, not managing log data, or employing antiquated tools and spreadsheets in managing their data, with no comprehensive approach to centralize the information.
3. Increasing data volume from new data sources: Until recently, traditional data capacity was able to cope with the pace of growth in data sets. But with the rise of social networks, real time consumer behavior, mobility, sensor networks and other new data generating sources, organizations’ data capacity began to overflow.
4. Deriving relevant insight from Big Data: It is essential to convert data to significant insights to be useful. Decisions and execution based on discoveries about customer trends or other revelations about market conditions usually deliver better revenue and profit results.
Advanced Analytics on Big Data
With mounting challenges on Big Data, analytics arise to support organizations in better data management.
Big data analytics is the process of examining large amounts of a variety of data to uncover hidden patterns, unknown correlations and other useful information. It relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.
Advanced Analytics is a grouping of analytic techniques used to predict future outcomes. It is the process of obtaining an optimal and realistic decision based on existing data. It includes:
Predictive analytics-a branch of data mining concerned with the prediction of future probabilities and trends.
Simulation-performing what-if analysis by building on predictive models and capturing interactions among different variables.
Optimization-finding an alternative with the most cost effective or highest achievable performance under the given constraints, by maximizing desired factors and minimizing undesired ones.
Big data analytics can be done with the software tools commonly used as part of advanced analytics disciplines. These are:
- Information management for big data. A more comprehensive data management/data governance approach provides a strategy and the solutions to manage and use big data more effectively.
- Real-time streaming for big data. Analyze high-volume data sets in real-time with the help of Kafka mirrormaker which allows the replication of clusters, thus making them accessible at different destination points. All data metrics can be traced with the help of Kafka monitoring which will enhance the real-time processing of big data and make it accessible for final use.
- High-performance visual analytics. Explores huge volumes of data in mere seconds, quickly identify opportunities for further analysis, and aid in the decisions that will create organizational gains.
- Flexible deployment models. Brings choice, analyze billions of variables, and deploy solutions best suited to the organization’s requirements, e.g., in the cloud, on a dedicated high-performance analytics appliance or within your existing IT infrastructure
The Value of Big Data
The primary goal of big data analytics is to help companies create more precise models of their business environment, applied throughout the business, to aid in informed decisions. The impact of big data rests on the organizations’ recognition of its ultimate value in generating higher quality insights that enable better decision making, interest, and revenues and profit.