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7 key features of big data analytics tools to take into account

(Image credit: Image source: Shutterstock/ESB Professional)

Gone are the days when big data analytics was considered as a mere buzzword, now it has successfully ended in becoming a new fact of business life. The trend is being warmly welcomed by organisations across the globe. For those who have no idea regarding big data analytics, it is the process of examining large data sets containing a variety of data types, starting from uncovering hidden patterns to unknown correlations, market trends, customer preferences and so forth.

The situation before Big data 

Like I said before, big data describes large volumes, both in the structured and unstructured format generated day in day out. With such technology, one cannot just make better business decisions but will also be able to make some strategic moves. Earlier people used different kinds of system or should I say business intelligence solutions to extract, transform and load data to obtain important reports. The only problem was that the database technology was unable to handle multiple continuous streams of data at a time. As a result, it couldn’t modify the input info in real-time, and the reporting tools couldn’t handle anything other than a relational query on the backend.

With the introduction of big data solutions, reporting interfaces, extraction capabilities, automatic file, optimised and highly indexed data structures, and Cloud hosting also came into existence. Companies can now make better decisions to increase the effectiveness of sales and marketing.  

Its benefits include:

#1 All questions are answered-  Managing business or business procedures involves much of answering questions in terms of what do customer want, who are your best and loyal customers, why do people think of choosing different brand or solution, etc. Let’s do some activity, try and figure out who were the 10 worst customers? Before big data, it could take up to 60 days to figure out the answer but after the evolution of such technology, answering these questions become a relatively straight-forward process. In fact, the whole process of answering complex questions can be shortened from months and weeks to days and even hours or minutes.

#2 Gain confidence with accurate data- Incorporating big data into your question/answer process can offer you a complete view of answers but more kind of an accurate view. Previously, taking decisions on the basis of manual data always carried an inherent risk that false or incomplete data could lead to uninformed or even misinformed decisions. With the emergence of big data technology, businesses can gather data from a huge number of sources, reducing the risk of siloed, valuable information. Using inaccurate data isn’t just an inconvenience but even can mislead you to a great extent.

#3 Empower employees- Do you what differentiates millennials from older generations? It is the way how each party uses technology. For instance, millennials wish to answer questions like, what does the flag of the European Union look like. And find information on the instant basis whereas older generations who have succeeded in developing near-native fluency in technology are still used to doing things the old and conventional way. 

Being a complex field to understand, big data analytics technologies on their own aren't sufficient to handle the task. In order to carry out a successful initiative, one requires well-planned analytical processes and people with the talent and skills to succeed. Buying additional tools isn’t a compulsion unless and until you have these seven features in your existing tools that won’t just reduce the effort but even increase your business growth.

1.    Embeddable results- With big data, additional value can be gained only when the insights gleaned from data models can help support decisions made while making use of other crucial applications. The must-have features in a big data analytics tool include the ability to create insights in a format that it is easily embeddable into a decision-making platform. Besides, it should also be able to apply these insights in a real-time stream of event data to make in-the-moment decisions.

2.    Includes an expanded set of basic charts- Apart from statistical graphics (line, bar, scatter, histogram, bubble, boxplot, your tool must have an additional ability to visualise hierarchies (treemap), financial (stock charts), longitudinal, geospatial (maps) and network data are essential.

3.    Summaries can be generated easily- Of course, as a business person, you will be exploring data sets that contain many observations (rows) and variables (columns). SAS visual analytics is such a feature that produces a quick summary (average, min/max, histogram) for each variable and displays the results in a compact, scrollable format. This is done entirely through a GUI and doesn’t require any coding.

4.    Data exploration- Data analytics is such a term which involves an ad hoc discovery and exploration phase of the underlying data on the frequent basis. With data exploration, organisations can easily understand the business context of a problem and formulate better analytic questions. Features that help streamline this process can reduce the effort involved in testing new hypotheses about the data to weed out bad ones faster and streamline the discovery of useful connections buried in the data.

5.    Scalability- This should be a must-have feature in your big data tool. Professionals, in general, have this tendency to develop and test different data models on small data sets for long durations. But as for analytics models, they require to run economically and often must end up delivering quick results. This requires that these models support high levels of scale for ingesting data and working with large data sets in production without exorbitant hardware or cloud service costs.

6.    Simple integration- Another interesting feature to take into account. Simple integrations make it way easier to share results with other developers and data scientists. As a result, data analytics tools must support easy integration with existing enterprise and cloud applications and data warehouses.   

7.    Security- This has to be one of the massive priorities for companies investing in big data analytics tools, especially those based on the cloud. Consider tools that comprise of an extensive bunch of security provisions. For this, you can even seek help from a big data service provider.

In a nutshell

The increasing adoption of big data analytics solution clearly states that big data is way beyond a fad. It has become a business practice which is here to stay for the long run.

Vikash Kumar, Manager, Tatvasoft Australia (opens in new tab)
Image source: Shutterstock/ESB Professional

Vikash Kumar is an Online Marketing Manager at, a Software development and Enterprise level Mobile application Development Company. Apart from his profession, he also has a passion of blogging in which he shares his expert advice on Enterprise Solution and cloud services.