Engineering
Growth

Data pipelines and the tools used to build and maintain them are becoming increasingly complex and difficult to manage. These pipelines are the vital organs of your data-driven business, taking data from its source, storing it, and transforming it for analysis. Without accurate, on-time data, important decisions may be delayed and mistakes may be made, hindering your company’s growth.

To gauge the success of your marketing campaign you must choose what results to measure and what constitutes success in the field your business operates in. You need to collect data about your campaigns and the resulting responses and activity from your audience. These marketing metrics play a vital role in optimizing both your marketing channels and the product or service you offer.

Working with data in a big company can be very tiresome, and that is to put it lightly. Ever-growing volumes of data, flowing in from frequently added streams, with even more logic to transform, shape, and load all of that "fluid" into some required form for later use, are becoming more and more challenging tasks with each passing day.

Net Promoter Score℠, (also known as NPS®) is the prevalent customer sentiment metric for modern products and services, thanks largely to its simplicity and impactfulness. An indicator of customer or user loyalty, you’ll find at least some variant of the NPS at the majority of Fortune 1000 companies. But not all companies understand how to take advantage of the NPS while being aware of its limitations.

Implementing a customer data platform (CDP) is an important step in planning for and encouraging the continued growth of your business.

Analyzing marketing metrics is vital for ensuring that your marketing campaigns are effectively reaching your audience and converting customers–and making sure your business is getting the best value out of your marketing investment. This guide will outline the most valuable marketing metrics used in digital marketing, quickly explaining what each one is and how it can be measured.

Data fuels the modern organization, hence the business world’s acceptance that “data is the new oil.” But much like crude oil, raw data must undergo transformation. A business can only reap the true value of data when it is organized and interrelated in a meaningful way. This is the task of data modeling.

This article will cover the basics of mobile attribution and explain how it works under the hood before sharing a few popular mobile attribution platforms that you can use.

Apple’s new privacy framework App Tracking Transparency finally went live in April 2021. The new framework changes how mobile attribution and tracking will be done on iOS versions 14.5+, with other platforms expected to follow suit in the future. The update significantly impacts how data is collected on iOS, as well as the types of data that trackers can collect and their accuracy of the data, thus restricting the amount of information available to mobile attribution platforms.

If your organization has at least some online presence, you might be wondering how to leverage your data. Data points are generated whenever someone signs up for your service, sends you an inquiry, or performs a transaction on your website. While such data is not very informative on its own, it can produce astounding insights when aggregated.

Acquiring new users at any cost while ignoring existing users is a mistake many companies make. Many growth teams are only focused on user acquisition metrics and forget about the users as soon as they have signed up. Such teams don’t realise that their business cannot capitalize on each new user if after signing up users become dissatisfied with the product and leave before they have generated any value.

As the market is rapidly changing and businesses are struggling to survive, standing out is becoming quite challenging for companies, especially new ones.

Mobile attribution in Android is constantly changing. Like Apple, Google has introduced restrictions on how advertisers can obtain user data, giving users the option to either opt in or opt out of being tracked across mobile apps and websites. Both Google and Apple now also require app developers and publishers to supply a report on exactly what data is being collected and how it is being utilized, which is then displayed to the end user when they install an app or when the app requests their permission to collect certain kinds of data.

Meltano is an open-source, full-stack data integration platform that is challenging established commercial products in the data processing space. Meltano is built on top of industry-standard and open components and encourages DataOps best practices, making it a powerful platform for building ELT pipelines.

Data is one of the main drivers behind the new industrial revolution. It’s a unique resource that, when utilized correctly, allows businesses to operate more efficiently and effectively. Centralized data storage solutions like data warehouses and data marts play a crucial role in allowing companies to make the most out of their data.

Building data pipelines is not a simple task. Moving data from one point to another reliably and quickly is vital, and must be the focus of any pipeline in order for it to supply actionable data when it is needed.

The DataOps methodology is increasingly embraced by companies looking to improve product quality, customer satisfaction, staff productivity, and cost-efficiency. Created to help you maximize your DataOps implementation, Singer is the open-source ELT standard that’s revolutionizing data processing. But what is Singer, and how is it changing the DataOps landscape? This article will go over key concepts and practical examples to illustrate why you should use Singer to implement DataOps in your organization.

Were you wondering what is a data warehouse, and why do people talk about it? Building something like that might sound complex but the reasons to do so might be too good to ignore. Learn more in this article!

Cloud data warehouses can get expensive, commonly due to a lack of efficient compute options. Companies find that their existing setup cannot handle bursty workloads, so they scale out by commissioning more compute nodes. As costs rise, companies often limit analytics to stay within budget, thus undermining the main goal of making data insights more accessible.

Whether you're an early stage startup or a mature industry leader, you're probably thinking about how you can use data to grow your business. Even if you already have data, you’re only halfway there. After all, your data may not mean much unless you have the right data infrastructure and analysis tools in place.

Centralized data stores like data lakes and data warehouses are key solutions to a data-based decision-making process. But which data store should you invest in, and which is more likely to generate results for your organization?

Fivetran is a managed Extract-Load-Transform (ELT) service that allows cloud-based companies to efficiently get the most out of their data. With more data being generated and stored in the cloud, managed solutions like Fivetran emerge as an attractive and cost-efficient solution compared to hosting data pipelines in-house.

Here at Mighty Digital, we are committed to delivering impactful results no matter the challenge. As for us, we value the human component of our team and our projects. That means we go above and beyond to ensure our teammates and clients are always satisfied, happy, and empowered. Today, we are excited to announce that we have been recognized during The Manifest's inaugural awards.

Mighty Digital is a company from Ukraine, with headquarters in Kyiv. Since Russia invaded Ukraine, February 24th, we had to make quick decisions to ensure the team safety and smooth operation of the company. Despite the circumstances we proceed working in a usual way being relocated either abroad or to the Western Ukraine.

How long does it take to create an absolutely new report based on new data with some data transformations? Is it possible to reuse the same views and tables for a new report? If yes - how long will take to update some view or table with just one additional column?