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Dec 12

10 technologies required to support big data analytics for a business

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There are several processing methods and techniques combined when it comes to Big Data analytics technology. The way they are collectively used by enterprises is done so to attain results that are classed as relevant for strategic implementation and management. Even though there is a lot of enthusiasm for investment and people are keen to elevate the influence of data to change the enterprise, in terms of triumph, the results vary.

Many businesses are still struggling to forge a culture that can be classed as ‘data-driven.’ However, these large transformations do take time. While the majority of companies aspire to become ‘data-driven,’ a much lesser number has had this ambition fulfilled. During this time of Big Data evolution, technology is not a limitation for most organisations. Cultural challenges, such as a lack of or a resistance to understanding, and how to manage during change and organisational alignment, are the biggest issues being faced during this time. The following lists 10 of the technologies that are key to facilitate Big Data for a business.

 

1. Predictive Analytics

Predictive analytics are used to help a business evade risks when it comes to making decisions. There is a hardware and a software solution for predictive analytics, so it can be used to utilise evaluation, deployment and discovery of predictive situations by processing large data. This data can then be used to help a business prepare itself for what may come in the future, and it can also help them resolve problems by understanding and analysing them.

 

2. Stream Analytics

In some cases, multiple platforms are used to store data that a company requires for processing. This data can also be stored in different formats on these platforms. Stream analytics software is extremely useful when this Big Data requires filtering, aggregating and analysing. Stream analytics can also be used to allow external data sources to connect and integrate it into the application flow.

 

3. Knowledge Discovery Tools

Knowledge discovery tools are used to allow a business to source Big Data (both structured and unstructured), which is normally stored on a number of bases. These sources come in the form of assorted file systems, DBMS, APIs and other related platforms. Without exploration and knowledge discovery tools, a business would not be able to segregate and apply the relevant information to benefit themselves in the future.

 

4. NoSQL Databases

To utilise efficient and reliable data management, NoSQL databases are used. NoSQL databases are required for manual sharding and distributed caching. Features of these databases include dynamic schemes and auto-sharding, replication and integrated caching. These databases are set across a large scale of storage nodes. The data found here is stored as relational database tables, key-value pairings and JSON docs.

 

5. Data Virtualisation

Data virtualisation is used to enable an application to collect data without applying any technical restrictions. These include data systems, the actual location of the data and other familiar aspects. Apache Hadoop and additional distributed data stores use this for instantaneous (or close to real-time) admission to data that has been stored across several platforms. Data virtualisation has actually been classed as a very commonly exercised Big Data technology.

 

6. Distributed Storage

To reduce the risk of autonomous node failures and corruption or loss of the biggest sources of data, data can be replicated and contained in distributed file stores. Sometimes, the replicated data are also copied to allow low latency fast access on big computer networks. Normally, these will be non-interpersonal databases. Examples of distributed databases that are classed as limited are Amazon’s Dynamo, Windows Azure Storage and Google’s Bigtable, which is a lot more than a distributed file system or partner-to-partner network.

 

7. Data Integration

When organisations need to handle Big Data, one of the main operational challenges they face is how to manage terabytes (or petabytes) of records in a manner that is deemed useful for client deliverables. To ensure that data can be streamlined across large numbers of big data resolutions, data incorporation tools should be used. Examples of Big Data solutions include Hadoop, Apache, Amazon EMR, Apache Pig, Hive, Apache Shark, MongoDB, Couchbase and MapReduce.

 

8. In-Memory Data Fabric

Data fabric is a set of data services that deliver a number of consistent abilities across selected endpoints, which span on-premises and various cloud environments. This technology is used to distribute a large quantity of data across resource systems, like Solid State Storage Drives, Flash storage, and Dynamic Ram. In turn, in-memory data fabric enables low expectancy processing and access to Big Data on the nodes that are connected.

 

9. Data Pre-Processing

This is a software solution that is used to manipulate data into formats that are consistent, and it is possible to utilise it to aid future analysis. The preparation tools used for this data help to speed up the process involved in data sharing by cleansing and formatting data sets that are considered to be unstructured. The main limitation involved in data pre-processing is that basically all of the tasks can’t be programmed and they need human supervision. This is usually a time-consuming and tedious task.

 

10. Data Quality

The data quality is an essential parameter when it comes to processing Big Data. Cleansing and enhancement of sets of big data can be done by data quality software. The software does this by applying parallel processing to the Big Data sets. The use of this software is high, and is mainly used to collect reliable and consistent outputs from processed Big Data.

 

How is Business Analytics Involved?

Business analytics are the frequentative, systematic exploration of a business’s data, with extra emphasis on the use of statistical analysis. Corporations that are trying to commit themselves to making decisions that are ‘data-driven’ will use business analytics. Data is treated like a commercial asset in data-driven organisations, and the ways it can be turned into an economical advantage are actively studied by them. The quality of data, the skills of the analysts and the level of commitment the organisation has for the use of data to make decisions for the business will all determine how effective business analytics will be.

 

Forms of Business Analytics

There are three different types of business analytics. Descriptive analytics are used to track KPIs (key performance indicators), which helps the business understand what its state is at present. Then there are predictive analytics, which are there to analyse the trends in the data. These can be used to evaluate the probability of future results. Finally, there are prescriptive analytics. These produce recommendations on how to deal with similar circumstances that may be faced in the future by using records of performance from the past. If this interests you, then you may want to consider a business analyst career.

To conclude, the use of Big Data isn’t something that is considered to be new, and it is currently being used by many businesses all over the world. Big Data is commonly used to develop operational efficiency and to help a company make an informed decision that is based on the most recent information. Without a doubt, the use of Big Data will continue to grow and it will proceed to play a vital role in a variety of industries. In order to get the best out of Big Data, it is important to make sure employees have been fully trained and are aware of Big Data management. When this is done correctly, a business can become the most efficient and productive it has ever been.

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