Companies have started adopting an optimised method for the optimal distribution of resources to carve the path of a company’s growth rather than relying on a trial and error method. The best method of implementation has been incorporating techniques of big data analysis. The business data acquired by large corporations over large periods of time is too complex to be processed by conventional data processing applications. There are better ways to extract useful information which can support proper decision making and help uncover patterns in an otherwise random looking data. These techniques form the core of big data analytics. There are many ways in which small and medium businesses are leveraging big data to obtain the best possible outcomes for their firms.

Key big data analytics technologies

The big data analytics technology is a combination of several techniques and processing methods. What makes them effective is their collective use by enterprises to obtain relevant results for strategic management and implementation. Here is a brief on the big data technologies used by both small enterprises and large-scale corporations.

1) Predictive Analytics

One of the prime tools for businesses to avoid risks in decision making, predictive analytics can help businesses. Predictive analytics hardware and software solutions can be utilized for discovery, evaluation, and deployment of predictive scenarios by processing big data.

2) NoSQL Databases

These databases are utilized for reliable and efficient data management across a scalable number of storage nodes. NoSQL databases store data as relational database tables, JSON docs or key-value pairings.

3) Knowledge Discovery Tools

These are tools that allow businesses to mine big data (structured and unstructured) which is stored in multiple sources. These sources can be different file systems, APIs, DBMS or similar platforms. With search and knowledge discovery tools, businesses can isolate and utilize the information to their benefit.

4) Stream Analytics

Sometimes the data an organization needs to process can be stored on multiple platforms and in multiple formats. Stream analytics software is highly useful for filtering, aggregation, and analysis of such big data. Stream analytics also allows connection to external data sources and their integration into the application flow.

5) In-memory Data Fabric

This technology helps in distribution of large quantities of data across system resources such as Dynamic RAM, Flash Storage or Solid State Storage Drives. Which in turn enables low latency access and processing of big data on the connected nodes.

6) Distributed Storage

A way to counter independent node failures and loss or corruption of big data sources, distributed file stores contain replicated data. Sometimes the data is also replicated for low latency quick access on large computer networks. These are generally non-relational databases.

7) Data Virtualization

It enables applications to retrieve data without implementing technical restrictions such as data formats, the physical location of data, etc. Used by Apache Hadoop and other distributed data stores for real-time or near real-time access to data stored on various platforms, data virtualization is one of the most used big data technologies.

8) Data Integration

A key operational challenge for most organizations handling big data is to process terabytes (or petabytes) of data in a way that can be useful for customer deliverables. Data integration tools allow businesses to streamline data across a number of big data solutions such as Amazon EMR, Apache Hive, Apache Pig, Apache Spark, Hadoop, MapReduce, MongoDB, and Couchbase.

9) Data Preprocessing

These software solutions are used for manipulation of data into a format that is consistent and can be used for further analysis. The data preparation tools accelerate the data sharing process by formatting and cleansing unstructured data sets. A limitation of data preprocessing is that all its tasks cannot be automated and require human oversight, which can be tedious and time-consuming.

10) Data Quality

An important parameter for big data processing is the data quality. The data quality software can conduct cleansing and enrichment of large data sets by utilizing parallel processing. This software is widely used for getting consistent and reliable outputs from big data processing.

In conclusion, data will be the new oil in the years to come. You will see a lot of companies making use of big data and analytical techniques to make deep understanding and provide their customers with even better products and services. And as Forbes has said, “Data Scientists will be the hottest job of the 21st Century”. Even chatbots will dominate the market with a more intelligent and smart thinking abilities due to the presence of such huge amounts of data.

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