Data Engineering: How it Drives CPG Marketing’s Success

Data Engineering: How it Drives CPG Marketing’s Success

As companies across the broad spectrum of industries embrace the digital realm, there has been exponential growth in the volume of data being generated. This can pose a variety of risks and challenges, including the risk of massive monetary losses for companies.

Today most Consumer-Packaged Goods (CPG) brands are looking to go back and enjoy the same customer loyalties they have experienced for decades now. And, to do that the answer is the utilization of data analytics. Since the new-age consumer is researching, inquiring, shopping, and otherwise engaging with CPG brands online – therefore it is vital to produce brand-new data sets every minute.

Thankfully, a solution to this issue is found in data engineering, data pipelines, etc. 

And, while most CPG companies have built analytical engines for business decision-making, different functions still work in silos. They have limited visibility to drive concerted business goals. Further, the data structures are distributed and create hurdles in delivering benefits. Buying or building localized analytics solutions could further drain the company of its resources without delivering the expected RoI.

But, let us first take a quick look at the primary challenges associated with data volumes and variety:

  1.  Quantity issues: There is simply no denying that today the world generates and has access to more data than we can imagine. While this seems great at the outset, the truth is that the sheer quantity of data poses massive problems for marketers as they struggle to understand how to structure this abundance of data. Wrangling with manual data has persistently been one of the biggest issues facing the sector.
  2. Siloed data: More often than not, different datasets are governed by ad hoc policies, resulting in a lack of focus for initiatives as well as substandard decision-making. This siloed nature of data can also impact visibility, generate incorrect insights, and cause security issues. Not only that, but this disjointed approach to data also results in a lack of sync and collaboration between data analysts and marketers which can potentially cause budget wastage if the problem is not corrected.
  3. Quality concerns: While manual data wrangling is itself an issue, the unreliability of inaccurate data, i.e. poor quality of data, is an equally pressing concern. Several studies have shown that as many as one-fourth of all businesses have lost a customer due to substandard data quality. Hence, companies must identify processes to ensure data quality at all times to make sure that data accuracy doesn’t take a toll on analytics and, consequently, on decision-making.

Now, some data pipeline best practices to help you achieve the best possible value:

  1. Reduce dependencies: A good way to help fortify the ELT pipeline's predictability is by doing away with unnecessary dependencies since doing so helps ease the process of root cause analysis because data’s origins can be easily tracked.
  2. Auto-scaling: Ensuring auto-scaling capabilities of pipelines can help companies keep up with the many, many changes in data ingestion requirements. It would also be a good idea to keep an eye on fluctuations and volume to firmly understand scalability needs.
  3. Monitoring: To proactively ensure consistency as well as security, it is imperative to ensure that you have end-to-end visibility and monitoring which can help raise red flags and trigger alerts in case a deviation is detected.

Data can often prove to be a tricky subject to contend with, especially since it is constantly changing and evolving in various contexts. However, this is not to say that the challenges are endless and that there is no way to address said challenges. Like the rest of the world, CPG marketers too are constantly on the lookout for ways to leverage data and analytics to gain an edge over their peers and rivals in the industry. This is where data engineering comes in: with a robust strategy and the right set of best practices, gleaning value and insights from high volumes of data can be practically a seamless process. If you too want to realize these benefits for your organization, it is time for you to start looking for an experienced data engineering consulting company ASAP.

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