Startups

How Startups can leverage the Power of Big Data Analytics

By | Shantanu Chaturvedi | Director of Technology Stepout Solutions

Global size of data is increasing at a breakneck speed every moment, and the aggregate volume of global business activities is also rising rapidly. The two plain facts culminate to bring a third natural conclusion before us, that the business world in near future is going to have a lot to do with a gigantic amount of data, also known as the ‘Big Data‘ than the mankind could ever imagine.

 While a lot has already been said and explored about the implications and importance of Big Data for big manufacturers, service providers and business giants, somehow the benefits of Big Data to small business enterprises and startups have left far behind in the discussion, though there are a number of ways in which startups can make it real big with the help of Big Data.

For example, Big Data may help online e-commerce startups in receiving immediate business inferences by leveraging data warehousing and analytics solutions. Hence, they may be able to manage vast business datasets, modify their systems infrastructure and develop dedicated solutions for their purpose. Apart from that, analysis of customer purchase behavior data may enable them offer relevant product recommendations and offers to increase sales. According to a research paper titled ‘An Overview of Big Data for Growth in SMEs’, the Oxford Economics Survey had indicated in the year 2013 that technology and innovation must be strategic priorities for SME growth, and Big Data must be one of the key drivers of it.

There are a few important strategies and policies that when followed properly, may be of great use to startups in leveraging the potential of Big Data analytics to the fullest. Let’s have a glance at them.

  • Organizational Initiation into Big Data:

Despite all the ripples being made by the Big Data phenomenon across the business world, a large section of organizations and especially startups has still not been formally introduced to the concept. A business study titled ‘How can SMEs benefit from Big Data’ reveals that the e-skills UK8 survey suggested startup representatives in general have an extremely low understanding of Big Data analytics, while around 30% to 40% representatives of larger organizations are found to have a sound understanding of the topic. Because data accumulation, warehousing and mining required for Big Data analytics must be carried out in all the vital departments of an enterprise, it becomes imperative that the startup workforces take proactive steps in educating their executives in every department about Big Data and its implications in their business activities.

For example, a garments export unit may induce its marketing and sourcing executives into applications of Big Data in taking better business decisions through online tutorials, interactive seminars or full fledged crash courses, conferences and events focused upon the topic. Additionally, regular quizzes, tests and activities related to the Big Data awareness may also be organized within the enterprise premises, in order to encourage the employees embrace and adopt the core concepts with more interest and enthusiasm.

  • Big Data Customers, Insights and Sourcing

    The first thing a startup interested in leveraging Big Data for its benefit needs to understand is that not every customer is a Big Data customer. The startups with higher activity and engagement upon the online and social media platforms are more likely to catch hold of a higher number of data customers. For example, if a shoe company carries out more of its sales through traditional sales chain, its data customer base will be relatively less than its competitor that works hard to engage with its target customer base through social media platforms, and prefers to boost its online sales generating data footprints by offering discounts and gift vouchers to its online customers.

    Secondly, the possible insights to be generated through Big Data depend upon the nature and scope of data generated as well. For example, if a publishing enterprise selling a large number of books to a variety of audience in online mode is gathering all the sales data but is not structuring it properly, will find it very difficult to pull usable insights out of its database even if it deploys a cutting edge data analytics solution.

A well structured, appropriately categorized, highly contextual and clean data is the best friend of a Big Data analytics solution, and enterprises need to know this fact well beforehand.

Apart from these two factors, Big Data sourcing too remains a very crucial aspect for any startup trying to delve into the arena. Building its own data collection infrastructure is unarguably the most preferred and helpful method to receive highly relevant and meaningful business insights, but it costs a lot.

Therefore, sourcing Big Data may be left to other third party vendors too, who may do the same for you at much cheaper costs. The sourced datasets may be hosted upon secure web service spaces, and finally analyzed by data scientists and analysts with the help of various solutions.

  • Identification of Big Data Requirements of the Organization:

    Usually, Big Data is misunderstood to be nothing else but a huge amount of data that will work on its own to benefit the companies of all sizes and sectors in a similar manner. The reality is way much far from it, for a matter of fact.

An appropriate analysis of Big Data that is relevant to the needs of an enterprise depending upon its nature and scale of business is what will do the needful, but prior to that the enterprise must carry out a deep enquiry into its peculiar Big Data requirements. For example, the Big Data requirements of an Indian automobile manufacturer aiming to expand its business in South African market will be in stark contrast with the same of an American pharmaceutical company vying to boost its penetration in the South Asian markets.

The biggest concern while dealing with the Big Data is formulating an optimized strategy involving less human labor, time and expense and yet analyzing the gigantic datasets to bring out relevant, meaningful and sensible business insights. Therefore, assessing the requirements is first and major step in moving ahead.

  • Hiring Big Data Experts:

    As of now, it still remains a daunting task to derive useful business insights out of Big Data, and definitely, requires a lot of training and meticulous experience for one to turn an expert in Big Data Analytics. For a startup that is planning to venture out in Big Data, therefore, it will be advisable to go for hiring the professional skills of an outside expert in the field, rather than creating an in house analysts team that will cost them much higher. Later on, though, the investments may be made to develop a team inside the organization itself at an appropriate point in time.


About the Author –

Shantanu Chaturvedi is director of technology at Stepout Solutions. Visualr, a data visualization tool by Stepout Solutions helps in exploring business insights by converting data into information. You can connect with him on LinkedIn to know more about his work.

 

 

Show More

Related Articles

Leave a Reply

Your email address will not be published. Required fields are marked *

Back to top button