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Data-driven marketing puts consumer data in the leading position to make sure each and every marketing effort aligns with customer behaviors and interests.With data-driven marketing, companies can ensure that their customers are the main focus of your marketing.

Employing the right data-driven strategies will enable you to build and launch marketing campaigns that directly communicate with your audience, establish trust, and build loyal customers.In this blog, we will discuss the best practices and strategies that will help ensure that you make the most of data-driven marketing.Best Practices of Data-Driven MarketingWhile integrating your customer data into your marketing efforts, you can make sure that you are taking full advantage of it with these best practices.Know Your Target AudienceYour consumer data accuracy determines how successful your data-driven marketing will be.

You could establish the smoothest data collection process, and still, that collected data would be worthless if it fails to reflect your target audience and customers appropriately.So ensure that you closely know your audience before you start implementing data-driven marketing strategies to your business.

Get in touch with your existing customers to gain better insights on who is purchasing your products or services.

It goes way beyond this.

There are various customer groups within your broader target audience, and determining those segments is your business.Personalized, targeted messages go way beyond just general broadcasts, and audience segmentation is the key to reach the right customer groups at the right time.

With the ever-increasing number of big data applications in the world, the demand for big data testers is now more critical than ever.Hence, we have asked experts in the industry to share their knowledge on big data testing and its rising importance.What is big data testing?Big data testing is the process of testing data for data and processing integrity; and to validate the quality of big data and ensure data is flawless to help business derive the right insights, according to Hitesh Khodani, Test Transformation Manager.Marco Scalzo, Big Data Engineer at AgileLab, adds that when we talk about big data, we refer to a large collection of data, which is potentially unbounded if we are in a real-time streaming model, on which we execute a pipeline of operations in order to process data and store them in an external sink such as a database.For this reason, the traditional software testing techniques are not suitable for big data testing.

A common approach to validate the correctness of data is to compare the source data with the ingested data.Another important test is the data processing test, which is very important as its primary focus is moved on the aggregated data.

It’s easy to notice that the use of appropriate strategies of big data testing will reduce the time to market because it leads to testing the entire data processing pipeline of complex big data applications, monitor each step, easily scale and go into production in a fast way.Another important thing is that AI needs data to learn and evolve, and data requires AI-driven analytics to have the possibility to offer real value.

The concept behind this is that like oil, raw data are not valuable by themselves, but the value is created when they are processed completely and accurately and this is done in a timely manner.Indeed, as Marco points out, big data analysis can improve the decision-making process or, even better, automate the whole decision-making process.

It democratizes data and it is a cost-effective way to store all data of an organization and analyze them later.This current approach with a centrally managed data lake integrates all data sources, transforms data, and exposes them to all the consumers.

It offers the possibility to empower agile and scalable analytics.The Data Mesh idea also increases the data scalability by moving towards a distributed-domain-oriented data platform and start to consider data as a product.

A single source of truth is a central and controlled source of data from which the entire organization can draw.

It is the master data.

When you lack such data and employee can pull down apparently the same metrics from disperse systems, unavoidably those systems will produce different numbers.

You get into a he-said-she-said situation, each player drawing and defending their position with their version of the ‘truth’.

Or even worse, some teams may naively leverage stale, low-quality, or otherwise incorrect data/metrics and make bad decisions, when they could have leveraged a better source.