Big Data delivers ground-breaking capabilities, but achieving the desired result demands a tactical vision of creating business value. Over time, Big Data has progressed from just another technology term into an essential business strategy. However, somewhere between the conceptual pledge and pragmatic implementation, business leaders faced a hard-hitting realization: garnering value from Big Data requires technologies, strategies, and tools. And not all organizations get the results that big data promises.

The worldwide Big Data market is estimated to generate $103 billion by 2027. Considering data is the newfound gold and is evolving fast, it is unsurprising that Netflix is saving $1 billion a year on customer retention by leveraging Big Data.

The Impact of Big Data on Your Deliverables

Understandably, companies had been stockpiling tons of data into the databases, not knowing how to use it until the concept of Big Data evolved. To that end, it’s noteworthy that poor data quality can cost organizations more than $14.2 million per year. Moreover, it could lead to flawed business strategies and affect decision-making, eventually deteriorating productivity and market reputation.

Before putting Big Data to work, consider its flow across many sources, locations, owners, users, and systems, as it provides a perspective to the system and adds value to the brand.

Understand Your Customers Better

Data creates the ability to understand customers better and provides a higher-resolution view of their values, preferences, behaviours, and activities. Through data accumulation and analysis, businesses can be relevant in the market. And by infusing more data sets into customer profiles, customers’ views can become clearer. Without data, the messages are less targeted, affecting conversion rates.

Attribution Model

Any of the three models can be adhered to – time decay, linear, or last interaction. However, data remains pivotal to all. Remember, the modern buyer journey is long, with multiple touch points and several channels. Each of these adds value to the experience. From the first touch to conversion points and abandonment to the last touch, along with other interactions in between, reveal the strong areas and pinpoint the spots that need an update.


Once you have data sets providing high-resolution customer views, it’s important to customize and make them more relevant and actionable. Leverage Big Data by following analysis with customized messaging, which resonates as it is appealing. The yesteryear’s distant approach no longer works. Data availability makes way for personalized campaigns.


Testing is invaluable to delivering business insights with confidence. Customers give businesses the direction to move ahead and introduce value propositions. Favorably, Big Data offers transparency, which changes the testing environment and enhances the ability to deliver instant solutions to the markets. It must be noted that audience fragmentation makes testing evaluation outcomes more challenging, but Big Data is a sheer winner.

Product Development

Customer sentiment, customer preferences, geographic disparity, and latent opportunities are key and embedded in the collected data. As such, data supports multiple points in the product development process where failure or success is concerned. It pushes businesses to deliver personalized solutions that meet user requirements across various segments in a specific domain.

Predict & Forecast

Predictive capabilities improve across business verticals when data support expectations. Forecasting also becomes clearer when data sets are segmented, and end-user behavior is analyzed. Moreover, data accumulation gives real-time updates and facilitates better decision-making.

How To Initiate Big Data Implementation for Success

Organizations should encourage well-defined, constructive, and competent leadership to realize successful Big Data implementation and nurture a data-driven decision-making culture in an enterprise. Stewardship for making Big Data integral to business operations should comprehend that leadership-driven initiatives help organizations make the data commercially worthwhile.

Set Clear Business Goals

The data initiatives are often scattered in massive systems and exist in silos. No doubt, collating useful data from an overwhelming resource pool is challenging. However, if the aim is to make an aspect of the data useful for decision-making, it stands to reason that the decision-makers (humans) must comprehend the value of the data.

The greatest challenge is clearly articulating the value of Big Data and deriving actionable data points. This approach is a process that takes time and primarily requires a well-thought-out strategy. Once the domain analysis has been implemented, the stakeholders can concentrate on the technical aspects.

Choose An Appropriate Technology and Simple Architecture

Pick an appropriate technology with simple architecture so it is easier to infer. Successful projects start with simple and solid technologies with clearly separated concerns so complicated implementations and modifications can be carried out quickly and efficiently.

To ingest data into a data warehouse, you need a few things – a data staging repository, event infrastructure, data pipeline tech, and multiple databases. To turn the existing data into several actionable data points, you need a universal programming language like Python, robust reporting systems, BI systems (self-service), and data science software for data analysis.

A modern architecture like AWS, SQS, SNS, AWS Lambda, and scalable databases like Snowflake or Postgres can add value.

Experiment With Big Data Pilots

Begin by identifying the critical business issues and the importance of Big Data as a solution. Once the problem is identified, bring several Big Data aspects into the lab where the pilots can be engaged before investing in modern technology.

Such programs provide a massive collection of tools and expertise, which are valuable for an organization without spending fortunes on talent or IT. Work with pilots to reduce the cost of technology investments at the lower level.

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