Contributed by Mayank Kumar, Co-founder and MD, UpGrad
Big Data is the hottest trendsetter in the industry now. Today, organizations and institutions across the world are leveraging data to power their entire infrastructure, from enhancing business operations to boosting revenues and sales. As more and more companies are joining the Big Data bandwagon, the competition in the market is soaring high with every passing minute. Thanks to the Internet, and the surge of social media and IoT, consumers are now aware of the latest trends in the market, what services/products can optimize their utility, and where can they access them. When dealing with such smart consumers of a ‘connected world,’ companies can no longer afford uncertainty and indecision.
Organizations and companies need to identify their target audience and then strive to have a clear understanding of the pain points of their customers. To help achieve these two fundamental goals, companies are leveraging Big Data (both structured and unstructured) along with data science technologies. It is data science that makes it all happen – it allows companies to extract meaningful information from massive datasets and make sense of it so that businesses can use those insights to their advantage.
Data science has, thus, become the lifeline of Big Data. As a result, the demand for skilled and professional data science experts is increasing exponentially across all sectors of the industry. If you wish to begin a career in Big Data, there are plenty of online data science courses on the Internet today.
While tapping into Big Data can unravel secrets related to consumer and market trends, taste and preference patterns of consumers, and their interaction with your brand, you need to understand one thing – these patterns and behaviors are ever-changing. Thus, if you wish to stay relevant in the market, you need to possess ‘Agility,’ for it is now the official order of the day.
What does the ‘Agile Approach’ exactly mean?
IT companies deal with huge amounts of data on a daily basis. Everyday data scientists and analysts leverage various statistical and scientific methods to test the hypotheses around the information buried in the data to approve or reject them. Based on the outcomes of these validations, they come up with new hypotheses or new ways to utilize the insights gained. However, the uncertain nature of Big Data projects often makes them challenging. The key is to chalk out such a delivery method that is able to cope with the specific requirements of the changing trends of Big Data.
Project management pertains to the planning, delegation, monitoring, and control of every aspect of the project. It motivates the participants to perform better and achieve the objectives within the expected parameters set for time, cost, quality, benefits, and risks.
Essentially, software projects include piling up one assumption on the other and so on. Throughout the planning process, it is assumed that every aspect of the project will go as planned, including the padding. Further, it is assumed that all the implemented functionalities will provide the expected business value. The Agile manifesto focuses on validating the hypotheses or assumptions as early as possible in the product delivery lifecycle, thereby minimizing the risk exposure of the project as it progresses. The fundamental aim of the agile approach is to create software devoid of defects.
The Agile Approach is a synergistic, cohesive, and time-tested approach to software development and business process management that fosters collaboration between the various teams of an organization to develop innovative software applications. The method also focuses on continually testing the products by assessing the customer interactions and dynamically enhance and refining them to better suit the latest consumer and market trends.
How can agile project management be combined with big data?
Applying the Agile methodology to Big Data can be very advantageous as it will allow data scientists and analysts to extract valuable insights from vast datasets quickly. By encouraging the collaboration of cross-functional teams within a company, the Agile approach will allow the data and management professionals to implement those insights in ways that can enhance customer satisfaction, optimize business operations, boost sales and revenue, and most importantly enhance and streamline business decisions. As all hands on the deck come forward, a company can not only craft well-designed business strategies, but it can also transform the plan into reality in accordance with the dynamic business environment.
In the Agile methodology, it is assumed that each line of a code does not have any bugs. The code is developed and deployed in smaller increments so that the end result is a working and production ready software. So, every line of the code and its architecture and design are continually validated each time a new increment is added to it. Apart from the obvious benefit of early validation, Agile also enables data professionals to learn from consumer feedback and improve the product according to the feedback as and when necessary, without having to alter the process or start from scratch.
In a research study it was found that the transition to Agile methodology helped mitigate a number of business problems.
Here are some other benefits of applying the agile approach to Big Data projects:
- Declutters an organization’s information and management domains.
- Allows IT companies to prioritize data transformation strategies
- Rapidly generates data-driven insights that further creates the scope for fresh business opportunities.
- Facilitates easy access to data from multiple databases and business units.
- Promotes cohesion among cross-functional units of a company, thus, bringing in an increased visibility and transparency within the organization.
The unique ability of early validation and to change direction to incorporate the necessary changes into a product are what make Agile compatible with Big Data and data science technologies. It is all about keeping pace with the dynamic data and business landscape to deliver cutting-edge, quality products.
The opinions expressed within this article are the personal opinions of the author. The facts, opinions and language in the article do not reflect the views of CISO MAG and CISO MAG does not assume any responsibility or liability for the same.