Why You Need A Methodology For Your Big Data Research

A research methodology can help big data managers collect better and more intelligent information. Businesses that utilize big data and analytics well, particularly with the aid of research methodology, find their profitability and productivity rates are five to six percent higher than their competition.

Businesses may view that substantial increase in effectiveness and immediately seek to expand big data management, but without a proper research methodology, the time and monetary investment required for successful big data management may not pay off. Many companies that fail to get the most out of their big data falter because they lack a plan for how big data, analytics and any relevant tools interact.

A prudent business should involve data scientists, tech professionals, managers and senior executives when establishing a big data methodology, with these roles combining their expertise to create an all-encompassing plan. Project initiation and team selection are critical parts of a successful research methodology because it highlights decisions a business must make and how those impact end goals for faster growth or greater profit margins.

Areas a big data methodology should address include selecting ideal analytic tools and models, identifying which internal and external data to integrate and developing an organizational structure to accommodate this data flow with goals in mind.


Assembling and Integrating Data

Big data can be the lifeblood of strategic decisions that can influence whether a company will profit or experience losses. Especially in today’s digital age, many businesses are drowning in a large quantity of data, struggling to identify relevancy. The amount of data is especially overwhelming today due to the influx of social media platforms, which provide insight into customer data and behavior that is technically outside of the company.

Assembling data and knowing which data to prioritize is a big aspect of establishing a methodology and may point to a need for further investments in new data capabilities. Short-term options include outsourcing issues to data specialists, though this can be costly and can feel too hands-off for some businesses. Internally, a company can strive to consolidate analytical reports by separating transactions from other data. They can also attempt to implement data-governance standards to avoid mishaps regarding accuracy and general compliance.

Utilizing Analytic Models and Tools

While the integration of data is vital when establishing a methodology, that integration won’t have much value if advanced analytic models are not in place to help optimize results and predict outcomes based on that data. A methodology needs to identify how models create business value, such as how data regarding customer buying histories can influence what types of discounts they receive via email.

Additionally, the methodology should utilize analytic models to help solve optimization issues regarding data storage in general. Models that can separate superfluous information from meaningful data that can impact a business’ bottom line can have a massive impact on productivity and results. Tools that help integrate data into daily processes and business actions can provide an easily comprehensible interface for many functions, from employee schedules to decision making on which types of discounts to offer.


Industries will vary regarding their core areas of focus. For example, a transportation company will rely more on GPS and weather data than a static storefront, while a hospital will require data on drug efficacy. Regardless, aspects of importance should be at the forefront when analyzing big data, particularly how they interact with productivity on a daily and long-term basis.

Methodology Planning Challenges

An effective big data research methodology will help address some common planning challenges for business, notably aligning investment priorities with strategy, focusing on frontline engagement and balancing speed with cost.

Frontline engagement and general efficiency can increase if a methodology is capable of detecting unusual data segments, helping alert researchers of areas that require manual analysis alongside pre-existing machine learning and automated transactional data. Big data research methodology should be ready to identify abnormalities, with a plan in place how to address them.

Additionally, a methodology that disregards responsible big data research can run into legal and ethical issues down the line regarding data sharing and the use of data about people, especially in social network maps. As a result, the methodology should regard efficiency and productivity, while also being mindful of ethics.


An ethical methodology for big data research that assembles and integrates data into an organized system with relevant analytics tools can provide businesses with an increase in productivity and profitability.

Kayla Matthews has been writing about smart tech, big data and AI for five years. Her work has appeared on VICE, VentureBeat, The Week and Houzz. To read more posts from Kayla, please support her tech blog, Productivity Bytes.