Big Data

How To Harness Big Data And Effectively Use It

Steve Sangapu
Steve Sangapu

In a recent guide, we took a deep dive into the world of data science and big data analytics and shared some ways through which companies could pivot to becoming a data-driven company. But should you? Is big data analytics really worth it for a legacy business? And what if your competitors are not data-driven either?

In short, yes. With almost everyone adopting data science strategies in some form in 2020, big data analytics is no longer a “nice-to-have” – it’s necessary to stay competitive, especially for a legacy business. And if you’re competing with non-tech companies, you can leverage big data analytics to gain an early advantage over competitors.

But getting to a point where you can effectively use data to make strategic decisions isn’t straightforward. Fortunately, there are hundreds of companies that have already harnessed big data analytics and there’s a lot that can be learned from them. So in this article, we’ll take a look at some of the use cases and how companies can effectively tame big data.

1. Product Development and Optimization

Companies like Amazon and Netflix completely rely on big data analytics to develop and optimize their products. By monitoring and analyzing customer behavior, needs, and wants they know where and what changes to make to their platform. For instance, recommendations are a crucial part of both their products and by monitoring how users react to AI-generated recommendations, they can make big improvements.

However, the use of big data to innovate and optimize products extends far beyond what Amazon and Netflix are doing. Companies can set up automated pipelines that capture, sort, and process large datasets and use it to make data-driven changes to their product.

2. Customer Acquisition and Retention

One of, in not the biggest, use cases of big data analytics is customer experience. By using big data to process years of data and metrics like time spent by users, conversions, and abandonment rates, companies can get a better picture of customer behavior. Data scientists can associate changes in customer acquisition and retention to new feature launches, marketing campaigns, design changes, etc. and use this information to reinforce actions that led to positive consumer behavior and eliminate those that led to negative behavior.

3. Improve Marketing Efforts

Monitoring and analyzing consumer trends and preferences is crucial for a successful marketing campaign. For a long time, most companies had to create their marketing strategies using the data published by large multi-billion corporations for two reasons. First, only the “big” companies could collect so much data, and second, only they had the required software and hardware to process it.

But things have changed a lot since then. Now companies can use CRM platforms to manage customer relationships and collect an incredible amount of data. And with cloud-based analytics, all of that data can be processed easily and affordably. The end result is that marketers now have access to data that affects their company directly and can use this information to build very personalized marketing campaigns.

4. Risk Assessment

Risk reporting, fraud, compliance, and real-time risk assessment are all things that companies today are using data science for. It’s an incredibly complex process that varies from organization to organization but in a nutshell, companies can train machine learning models to identify and flag risky decisions, compliance breaches, or fraud in day-to-day transactions. This is especially important for companies in industries like fintech where fraud is common and non-compliance very expensive.

Focusing on the Outcome is the Key

Big data analytics is an extremely powerful tool with virtually endless uses. However, the key to efficiently harnessing and using data is to focus on the outcome instead of just the process. If your company is only focusing on collecting and processing data without a predetermined objective, there is a risk of getting side-tracked and not being able to capture any impact.

Though big data can be highly automated and cloud-based, the data science required to get there is still talent-intensive, meaning business executives need to be selective about their use and have a very goal-driven approach to the entire process.

D3V Tech has worked with many companies in numerous industries to help harness the power of big data analytics and tackle the challenges that come with it. We use a cloud-based approach to lower costs and one-on-one brainstorming sessions along with comprehensive audits to identify areas of business that require the most attention and assistance.

If you have any questions about big data analytics and what it can do for your business, reach out to one of our certified cloud engineers today!

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