10 Key Technologies that enable Big Data Analytics for businesses
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 consider 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 being “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:
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 utilised 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.
These
databases are utilised 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 utilise the information to their benefit.
4) Stream
Analytics
Sometimes
the data an organisation 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
utilising parallel processing. These softwares are 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 about Big Data management. With proper management of Big Data,
your business will be more productive and efficient.
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