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.
In spite of the investment enthusiasm, and ambition to leverage the power of data to transform the enterprise, results vary in terms of success. Organizations still struggle to forge what would be considered a "data-driven" culture. Of the executives who report
starting such a project, only 40.2% report having success. Big transformations take time, and while the vast majority of firms aspire to be "data-driven", a much smaller percentage have realized this ambition. Cultural transformations seldom occur overnight.
At this point in the evolution of big data, the challenges for most companies are not related to technology. The biggest impediments to adoption relate to cultural challenges: organizational alignment, resistance or lack of understanding, and change management.
Here are some key technologies that enable Big Data for Businesses:
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. Such data can help companies to be prepared for what is to come and help solve problems by analyzing and understanding them.
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 on 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, Big Data is already being used to improve operational efficiency, and the ability to make informed decisions based on the very latest up-to-the-moment information is rapidly becoming the mainstream norm.
There's no doubt that Big Data will continue to play an important role in many different industries around the world. It can definitely do wonders for a business organization. In order to reap more benefits, it's important to train your employees in Big Data management. With proper management of Big Data, your business will be more productive and efficient.
The article was originally published here