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Do you still think your current process of generating marketing reports is ok? Your marketing reporting is likely a patchwork of reports generated from various marketing tools like google analytics, email tools, and CRM analytics reports.

Perhaps you outsource your entire marketing analytics to a data engineering and BI team, one that extracts marketing data from the source systems, loads it into a central data warehouse, and performs ad-hoc analysis for you.

Every time you need a marketing analytics question to be answered, rather than taking a few seconds, it would take a few weeks and a couple of meetings with the data team to get answers.

What if it doesn’t have to? What if every marketer could retain control over their marketing analytics without becoming data engineers or SQL experts?

Stick around, as we explore in this article, a non-technical, easy-to-use interface that will eliminate confusing SQL jargon, and reduce the time taken to centralize your data for kickass marketing reporting.

The Marketing Analytics Conundrum

A Marketing report should track marketing metrics like customer acquisition cost, ROI, lifetime customer value, and simple trend analysis. It keeps the objectives of all your marketing campaigns in clear sight for the team and shows how they are performing against those objectives.

Several marketing channels like social media and email platforms perform analytics on the metrics they track. These channels reveal consumer behavior and conversion time on their platforms independently.  

However, it quickly gets overwhelming when you have to spend time checking these marketing channels individually to know your overall campaign performance.

The problem comes when you want to track actionable metrics like ROI, and compare marketing costs to key outcomes like monthly recurring revenue across all the channels. You need to gain insight into the performance from a single platform, or else the decision-making process is slowed down.

Centralization: The key to a Unified Front

Think of centralization as Sauron from The Lord of the Rings having one ring to control all the other rings of power. Having a centralized system makes you the Lord of the rings with all that control.

Gartner inc conducted a study in 2020 on centralization, which revealed that out of 400 of the world’s top marketing leaders, two-thirds of their marketing teams are either fully or primarily centralized.

That number is sure to have gone up with the presence of sophisticated SaaS platforms like Snowflake.

Snowflake analytics provides an ideal backbone for any marketing company by providing all your customer information in one place to create a unified view. You can connect all your disparate analytics channels on Snowflake, which allows all structured and unstructured data to be loaded into the platform.

Bringing it all together with Snowflake reporting is the game-changer that will radically transform how you sell.

Why You Need To Centralize Your Marketing Reports

If you spend money or any other resource in marketing or sales, then you need a centralized system to track ROI. Here are three golden advantages you stand to gain when you centralize your marketing reports:

What A Good Centralized System Looks Like

A really good centralized reporting system should be able to do the following:

But a great marketing system goes above and beyond. It provides shared workspaces for marketing teams to collaborate around models, speed up projects, and answer those analytic questions that yield higher marketing ROI.

Generating the Coolest Datasets on a Snowy Platform  

At this juncture, you’re probably excited about improving your current reporting stack; by leveraging tools that help keep your transformed data models within Snowflake for BI consumption.


Datameer is a data transformation tool that is native to Snowflake with three well-defined user interfaces for your different personas and skills. With the no-code, low-code, and code interfaces, there’s a seat at the table for every member of your team to actively participate in the transformation and data modeling process.

You save cost on data engineering efforts by distributing workloads, allowing your engineers and analysts to prepare ad-hoc models on the fly, in turn facilitating trust in the data and crowdsourced data governance.


Don’t invest in any more marketing campaigns until you have the best SaaS transformation tool that fosters collaboration within your team.

Bring your team together with Datameer in Snowflake, your business will thank you!

Ready to start building a data-driven future in marketing?

Explore Datameer on Snowflake with a free trial today

NDZ’S PORTFOLIO: Marketing Blog For Datameer


In this ever-evolving, technology-driven world, AI and machine learning are now everyday buzzwords.

The marketing niche is no exception.

From highly personalized suggestions on your shopping app to automated chatbot responses, machine learning and AI can be seen everywhere.

In this article, we will uncover the basics of machine learning along with the advantages of leveraging machine learning in marketing.

We will also discuss how Datameer can help you truly harness the power of machine learning so stay tuned!


What is Machine Learning?

Machine Learning is a broad term with countless definitions depending on the context.

This is understandable because ML spans entire families of techniques for making inferences and predictions on data.

For the purpose of this article, we will stick with a rather simple definition coined by  Yufeng Guo, a developer advocate at Google:

 “Machine learning at its core can be defined as using Data (training) to answer questions (make predictions)."

The Data Pyramid

It is said that "A Machine algorithm is only as good as the data you feed it", hence the need for clean and top quality data.


This diagram encapsulates the stages required to achieve quality data and as a result, leverage the benefits of machine learning within your organization.

A typical marketing analytics reporting stack should contain tools that perform :

We will cover these steps in detail, in a future article.


Imagine if you could determine not only which lead is a good fit for your product but also which is the most promising.

Or maybe optimize customer experience with conversational chatbots, saving time and possibly freeing up the marketing budget for more strategic initiatives.

All this and more are possible with the help of  Machine learning technology…

There are endless benefits of leveraging machine learning and in this section, we will take a look at some examples of companies that are doing it right.

The Starbucks mobile app, drive-through screens, and digital menu boards all served as data points that feed their real-time personalization engine.

This approach helped with behavioral segmentation and enabled Starbucks to recommend what their customers were most likely to order, as a result, making the customers feel valued.

Additionally, In 2017, Starbuck rolled out their own version of Siri, “my Starbucks Barista” and here’s what  Gerri Martin-Flickinger, chief technology officer at Starbucks had to say about that

The Starbucks experience is built on the personal connection between our barista and customer, so everything we do in our digital ecosystem must reflect that sensibility”.

Frase leverages AI and machine learning to aid keyword research, and optimize content briefs and, write-ups.

Tons of SEO teams including Neil patel, Shopify and microsoft have testimonials on how this tool has been a gamechanger in their SEO writing.


So let’s assume you already have a steady inflow of high-quality data within your marketing analytics stack and, your organization is ready to take that leap into Machine Learning.

To harness the potential of AI and predictive analytics, there are four elements that an organization needs to put in place:

  1. Having the right hypothesis - Before embarking on an ML journey, it is important to define your machine learning marketing goal upfront.

By setting assessable goals and defining what success is, you assist the data science team in the building phase of the ML models.

  1. Quality data - Data must be formatted, clean, and organized. Refer to Data Pyramid

  1. Using the right tools - There are a vast number of ML and AI tools for different use cases. Examples of good ML tools are Analytics Intelligence in Google Analytics and Tensor flow for developing ML models, etc.

  1. Having the right people with the right mindset - Having a cross-functional, and diverse set of experts on your ML team is a catalyst for successful outcomes.


Now we understand these concepts, let's talk about how Datameer ties in with all this.

Datameer is a SaaS tool that sits somewhere between Data Engineering, data governance, and core BI Business Intelligence.

 This multi-persona transformation tool can be used to clean and create reliable data models, that can then be used in your machine learning processes.

Integrate Datameer with your snowflake environment today and kickstart your journey to machine learning and AI adoption.


In our previous article, we emphasized the importance of high-quality data on your journey to adopting machine learning within your organization.

In this article, we will focus on an all-important process required to achieve high-quality data - A process known as “Data Cleaning”.

 Data Cleansing

Before we jump to wordy definitions of this term, let’s take a look at some practical scenarios in marketing…

Let’s say for example…

You gathered customer data from a survey to create your mail list.

While vetting your list, you notice that some users erroneously filled in their email as "", as supposed to “”.

Or maybe…

You were creating a report on inbound lead by demography and noticed spelling inconsistencies within the country category.

You spot the word “United States” spelled as  "United S" or abbreviated to "u.s" in others.

It is evident that this can quickly lead to incoherency in your reporting process, hence the need for Data Cleaning.

What is Data Cleansing?

Experts at Tableau define Data Cleaning as the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.

Data Cleaning And The Implication of Dirty Data

For the CMOs and executives, the insights, analytics, and Machine learning systems are all but fancy if they are unable to inform the business and yield profitability.

One major cause for this could be a bad data strategy. Oftentimes, a bad data strategy is synonymous with the presence of “dirty data” within an organization.

In point of fact, Experian, a data broker and one of the world's leading global information companies, reports that on average, companies feel that 26% of their data is dirty.

Ed Downs, the marketing manager at core logic cites an interesting statistic in his article on dirty data -  “bad data costs the average business around 15% to 25% of revenue, and the US economy over $3 trillion annually.”

These are huge numbers, and organizations that rely on such data are likely to incur losses, as a result of ill-informed data-driven decision making.

We have seen the implications of bad data, let's now discuss how to avoid this "messy data state" by citing a real-life example.

Fighting Bad Data with Good Data Cleaning Practices: A Live Project

During my years as a freelance BI Analyst, I have had the opportunity to work with different marketing agencies on various projects.

In this section, I will share my experience from a real data cleaning project I was a part of.

Project Deliverables

Our client, a music company based in the US, had over 6 million rows of disparate customer contact information.

They sought the help of our marketing agency to help:


Project Solution: The Cleansing Process

From duplicate records to alphanumeric phone numbers, we were able to identify potentially bad data and how best to deal with them.

Here's what our step by step cleansing process looked like:

  1. Analyzing the datasets: Using tools like Open refine and excel, we were able to assess the quality of our data.

  1. Categorizing Data issues

  1. Filtering and eliminating duplicate records

  1. Identifying outliers within our dataset

  1. Dealing with null and erroneous values

  1. Discarding irrelevant and inconsistent data.

  1. And finally, setting parameters to maintain quality subsequently

At a high level, these were some of the steps we took to successfully achieve a clean and accurate database.

As a result, we were able to better support high-level processes such as segmentation analysis and email marketing.

Hopefully, by now, you have some understanding of the importance of data cleaning and how critical it is to achieve a truly effective data-driven marketing strategy.

Key Takeaways from Project  (low-lights)

Speed: The data cleaning process took up to a month and could have been a lot faster.

Key-man Risk: There was a key-man risk because I was the only one on the team with technical expertise in SQL, Excel, and the data cleaning tool.

Datameer: The Speedy No-code Transformation Tool

With cloud DW technology like Snowflake and no-code transformation tools such as Datameer, the playing field has changed forever.

If I were to take on a similar project today, those low lights would probably be non-existent.


With a tool like Datameer, Collaboration and speed are now at everyone's fingertips!

Every member within a marketing team, whether technical or non-technical now has the ability to transform, clean, and participate in the data wrangling process.

Integrate Datameer with your Snowflake environment today and see for yourself how effortless data transformation can be.

Get started with a free Datameer trial now!



According to the 2020 Martech landscape statistic, the number of marketing tools and technology has gone up by more than 80% since 2019.

See below:


This sounds like great news for marketing professionals, right ?... Yeah probably

However, this quickly turns into a problem as we start to see more marketing silos being created as an effect of disparate marketing channels.- hence the need for a data warehouse.

In this article, we will introduce the concept of cloud data warehousing, its benefits, and a personal recommendation for our go-to data warehouse…If this interests you, then please stay stick around!

What is a Data Warehouse?

Google trends comparing the search frequency of the terms “data warehouses” and “databases”

Within the last 5 years, we see an increase in the search for the term “data warehouse” but this pales in comparison to its peer, data warehouses.

We can assume that the vast majority of people are more conversant with databases than warehouses.

So what’s a data warehouse and how is it different from databases?

What’s a data warehouse?

According to Wikipedia, “A Datawarehouse is a central repository of integrated data used for reporting and analysis.”

Databases vs Data warehousing software

Databases are built to store structured data, oftentimes that “data” is solely transactional data generated from within your applications. One drawback with databases is the inability to accommodate large-scale analytics and reporting.

Conversely, data warehousing software is built to house data from a variety of sources along with querying capabilities to support your large-scale analytics and reporting scenarios.

On-Prem Vs Cloud Data warehouses

Data warehouses can be separated into two main categories;

Some examples of On-prem DWs are IBM Integrated Analytics System, SAP HANA, and Micro Focus Vertica Enterprise On-Premise.

With Cloud data warehouse solutions, you get an as-is Platform /Software/ Infrastructure as a service. This means that processes like maintenance, updating, computing resource management, and storage are managed by the Service Provider.

 A few examples of these solutions are Snowflake, Google Big Query, and Amazon Redshift.

Tip: Before you make a decision on which DW solution to settle for (Cloud or On-prem) make sure to get a professional data architect to assess your organization’s marketing channels, ETL possibilities, budget, and use cases. This will enable you to pick a befitting solution for your organization.

What makes a Good Marketing Cloud DW Solution?

Selecting a marketing-focused data warehouse solution can be daunting due to the super technical (although important) jargon associated with data warehousing architectures.

 That’s why in this section, we will share some key questions you should be asking your IT team during the business requirements gathering phase of any marketing DW deployment project.

Does it enable us to have a  360 view of our Customers? -  A good marketing warehouse should be able to serve as a centralized, single source of truth for all your marketing data. It should before able to converge data from all your customer touchpoints in one place.

Can It handle multiple workloads?- One key characteristic of an excellent data warehouse, is the ability to not only accomplish analytics but accommodate other workloads and capabilities like Machine Learning, Data Governance, and Business Intelligence.

Is it Scalable ?- Cloud technology boasts scalability and extensive storage capability. It’s a no-brainer that a good data warehouse should be able to handle all sorts of data, whether large, historical, streaming, or big marketing data.

The Snowflake Cloud Data Platform - Our recommended DW for Marketing teams

What is Snowflake?

Snowflake is a data warehouse that is deployed on top of the Amazon, Microsoft, or Google Cloud infrastructure, and allows storage and computing to scale independently.

If you’re searching for a warehouse that’s cost-effective, scalable, and marketing-focused then this might just be the right fit for you.

According to Snowflake, “Snowflake is a true data platform-as-a-service that handles infrastructure, optimization, infrastructure, data protection, and availability automatically, so businesses can focus on using data and not managing it.”

Here’s are some benefits you stand to gain with Snowflake:

Snowflake also partners with various data technology providers to assist you with the ingestion process, as well as other ETL processes.

Snowflake can support multiple workloads on a single copy of data. With Snowflake, there is no need to move data around to different environments.

With Snowflake, all your  Ad-hoc, BI, Compliance, or even data science workloads can occur in isolation without interference or a trade-off for speed.  

Snowflake leverages features like multi-clustering and on-demand Warehouse Up-Scaling to handle these situations.


Possible Drawbacks of data warehousing for marketing teams

Although warehousing is great for marketing teams and organizations, deploying one is easier said than done.  Experts who have deployed data warehouses have found some of these challenges they encounter when deploying a warehouse:

  1. Heterogeneous Data: As data is pulled from disparate marketing channels, formats, structures, and types may be different.

  1. Heterogenous Connections: If we recall the Martech statistic in our introduction, we see multiple technologies emerging frequently in marketing.

It’s important to note that every platform is different; having different APIs, and distinct application layers.

When deploying your warehouse, without proper data engineering, you can run into connectivity issues.

Tip: To achieve that ideal Customer 360, your organization has to invest in building or buying a data pipeline/ pipeline as a service.

  1. Marketers Vs Analysts: Most data warehouses are based on relational database models. What this means is that one would require technical knowledge of SQL to query or model data in the DW.

This is no problem for the analytic teams but for marketers who are not upskilled, this leaves them relying on IT for any and every reporting requirement.

Datameer: Bridging the Marketers Vs Analysts Divide

In the previous section, we discussed the divide that tends to exist between the core marketers and the analysts.

Experts agree that a good data strategy involves making your data accessible; at a ratio of 80% self-service and 20% Ad-hoc for more technical queries. What that means is that marketers should be able to get to do most of their basic reporting without reliance on IT.

There are two ways to achieve this; you can invest in upskilling your marketing team with SQL training or leverage collaborative multi-persona data transformation tools like Datameer.

With Datameer, you get to bring together your entire team– data engineers, analytics engineers, analysts, and data scientists – on a single platform to collaboratively transform and model data for faster, error-free projects.

Bridge that gap by Integrating Datameer with your Snowflake.

Try Datameer today!