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Analytics is Much More Than Big Data

By   /  June 20, 2018  /  No Comments

Click to learn more about author Chirag Shivalker.

BIG Data; though the term is coined everywhere and considered mainstream, is no longer a buzzword. However jazzy the concept of “Big Data” gets, simply having and collecting more data is no longer enough. We have now entered the space where business-oriented “Data Strategies” should be the focus. Data Analytics is not a luxury anymore, instead has gone mainstream. Companies can gain strategic advantage by leveraging Advanced Analytics.

BIG Data in its original form is nothing more than unstructured data and Hadoop, and believe it or not – the idea is fading fast. The Big Data revolution is progressing towards Analytics of data, and improvising the way businesses manage data collection, data entry, and data processing, categorization, and validation is expected to progress. These Data Management processes should get sophisticated and should have a clear strategy and purpose to it.

“Data Lakes” are Becoming Passive “Data Reservoirs”

Enterprises should leave the thought process of storing data for fear of missing out, as it is no more an acceptable strategy. Today, Hadoop is used to process and store data, leaving companies and organizations grappling miserably with:

  • Where to analyze the data? How to analyze data?
  • Which are the methods of analyzing data?
  • Who will analyze the data – if not done in-house?
  • How many types of quantitative Data Analysis are there?
  • What are different types of Data Analysis methods and which is the best?
  • Which method of Data Analysis addresses my organizational requirements?

Data is driving the real value for organizations, but only if it is sophisticated enough to be put to business use. This also is one of the reasons why Data Lakes are turning out to be Data Reservoirs where opportunities remain untapped. In the new prevailing paradigm, not the size of the business data, but how business data is used – counts.

Overarching Philosophy of Pragmatism

Enterprises should move towards developing and implementing new Data Strategies to remain ahead of the competition. Data Analytics can help companies in a range of different operational areas. It can enhance Data Quality and system integrity, uncover fraud and other irregularities, improve supply chain and inventory management, standardize and develop system use, and enable the benchmarking of key information.

Companies who either really work hard or have partnered Data Analytics and consulting experts are able to successfully optimize their business, create new avenues to market or create innovative new services and revenue streams. Several companies are overlooking a significant opportunity to enhance decision making and improve their overall performance through data analytics.

The key areas where companies can benefit from Data Analytics include:

  • Process very large data sets efficiently and consistently
  • Identify lack of harmony between processes and transactional data
  • Incorporate Predictive Analytics based on historical trends

With the increase in number and variety of organizations and industries looking out to deploy Data Analytics for their business, the only thing that can set them apart is the overarching philosophy of pragmatism. This happens to be one of the prominent reasons why companies are consciously building new data competence centers, not aligned to their IT departments, and instead reporting directly to the CFO, COOs or directly to the CEO in few of the cases. It is one of the ways of shirking off the conservative wisdom, challenge instinct based decision- making process; and bring Data Analytics into the heart of the business.

Heterogeneous & Agile Data Ecosystem

Companies and organizations across the globe have literally hit the size threshold of gathering the frightening amount of data, and are in dire need to create effective Machine Learning logic. Facebook, the company sitting on petabytes of pictures and profile information and with so much to do with that kind of data – introduced face recognition – pushing further the boundaries of what data analytics is capable of.

We are in an era where data projects specialize in analyzing complex graph structures, text sentiment analysis tools for analyzing customer’s support emails appropriately, and in-memory databases which provide fast access to Data Analytics – and each of this is built in accordance to specific requirements and specific Data Strategy.

Evolving technologies including Artificial Intelligence, In-Memory Databases, Key/Value Stores, Graph Databases, Stream Processing tools and many more, have successfully moved the question from technical limitations to the smart application of such technologies to create innovative insights from disparate data sources. As a ripple effect, the speed of adoption is likely to increase and data will more often form the bedrock of companies’ data and wider business strategies.

Time to Come Out of the Big Data Hype

The number of sectors or industries that have already realized that Analytics is much more than Big Data is increasing, are already headed to come out the other side of the Big Data hype. E-commerce and retail sectors have always been the front-runners in adoption and have succeeded in applying data mining in the late 1990s, while it was still a byproduct of Business Intelligence. Organizations with millions of customers and their data to be processed regularly were either equipped with tools or had business data processing partners on their side – which in a way made them enthusiastic early adopters of one of the biggest game changer of all – AI integration.

Those harping on the hype of Big Data are still struggling to comprehend what their data collection methods have to offer. Data Analytics helps companies to execute unexpected things with their databases, opening up a whole new space to become better in business with help of automated, predictive and prescriptive processes, and not merely creating reports about the history. The success rate of data projects has shifted considerably away from data gathering or data mining to the rate at which collected data is transformed into useful information. It is a process which is accelerating like anything with the democratization of data and that too with a lower barrier to entry.

 

Image Credit: Hi-Tech BPO

About the author

Chirag Shivalker is a Content Head at Hi-Tech BPO, a company thriving in the industry for more than two decades. With over a decade of experience in Data Management, Chirag regularly writes about importance of Data Management for Data Analytics and the changing landscape of the Business Process Management industry.

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