From the era of physical filing cabinets to the age of cloud storage, data management has undergone a radical transformation. Historically, businesses have depended on paper-based systems and rudimentary databases, often leading to slow decision-making and lost opportunities. But the digitization of the late 20th century empowered organizations to amass vast troves of information digitally, giving birth to the era of Big Data.

A significant trend in data management has been the rise of data observability, which is concerned with ensuring data quality. With the exponential growth of data and its increasing complexity, monitoring data to ensure its quality has become more challenging. There’s also a growing focus on data privacy, with techniques like confidential computing and differential privacy gaining traction.

In 2023, the oil and gas sector is battling challenges tied to voluminous and complex data sets. The threat of cybercrime, especially ransomware, continues to loom large. Economic uncertainty is squeezing budgets. And as the process of digitization continues, we’ve arguably arrived at Digitization 3.0, the era in which data must enable on-demand user experiences in an ever-growing array of applications, devices, interfaces, and channels.

But the emergence of new platforms underscores the industry’s dedication to enhancing data observability and quality. And it’s not just about storage: the projected growth of the data warehouse market to $58.52 billion by 2027 reveals a mounting emphasis on real-time data analysis, driven by a pressing need for actionable insights.

Amidst all this, a central tenet remains unshaken: the indispensability of structured data. For modern businesses, structured data doesn’t just streamline operations; it acts as the backbone for AI-powered analytics, predictive modeling, and precision marketing. So let’s take a trip and uncover why such data structuring, whether through data management platforms (DMPs) or data warehouses (DWs), is vital for businesses looking to win the Big Data race.

Let’s say you’re a marketing director working for a large e-commerce company that sells a wide range of products online. You’re responsible for driving customer acquisition and increasing sales through marketing campaigns. But there’s a problem: you don’t have a centralized system to properly track and analyze customer data across all of your marketing channels.

In this situation, your customer data is spread across different systems, such as email marketing platforms, social media advertising tools, and website analytics. It’s quite challenging to consolidate and analyze all of this scattered data effectively. Without a unified view of customer behavior, you struggle to gain a solid understanding of your customers’ preferences, purchase history, and engagement patterns across different touchpoints.

Making data-driven decisions is difficult because you lack real-time access to accurate and up-to-date customer data. This gets in the way of your ability to optimize marketing campaigns, personalize customer experiences, and target specific customer segments effectively. And you can’t really measure the impact of your marketing efforts across your channels properly. So how can you determine your true ROI? How should you allocate your resources?

But then you start using a data platform—an integrated technological solution that ingests, processes, stores, and analyzes data. It becomes your centralized place where data from any source you can think of is gathered, offering a single unified view. Whether it’s transactional data from your sales, operational data from your supply chain management, or behavioral data from your customer interactions, data platforms enable you to manage it all in one place.

That’s why the role of data platforms in modern business cannot be overstated. Today, business decisions need to be timely, informed, and data driven. Data platforms meet this need, giving businesses the capacity to handle vast amounts of data in real-time, and enabling decision-makers to leverage valuable insights quickly.

Data platforms also play a crucial role in breaking down data silos within organizations. By integrating data from different sources and departments, they ensure everyone in the organization has a unified, comprehensive view of the data landscape. This enables cross-functional collaboration and alignment around data-driven objectives and strategies.

The best data platforms provide an environment for advanced data operations such as data analytics and machine learning. They support various data models and processing capabilities, allowing businesses to analyze their data and derive actionable insights. This makes data platforms a key enabler of business intelligence, helping businesses understand their performance, predict future trends, and make strategic decisions.

So it’s no exaggeration to claim that data platforms are the backbone of a modern, data-driven business. They provide the infrastructure necessary for effective data management and utilization. And yet, as technology evolves, so too does the idea of what a data platform is and is capable of, and this has given rise to advanced solutions such as data platforms as a service (DPaaS).

In this year’s January/February edition of the ABA Banking Journal, Mark Gibson, a senior consultant at the strategic consulting firm Capital Performance Group, highlighted the importance of data management platforms (DMPs) in targeted advertising initiatives.

For years, the banking sector has been grappling with the rapid influx of data from multiple sources, from customer transactions to online behavior. Gibson’s emphasis on DMPs underscores a fundamental shift in the banking industry’s approach to customer engagement.

But this transition isn’t exclusive to the banking world. The growing importance of DMPs is symptomatic of a broader trend that’s sweeping across countless sectors. Businesses in e-commerce, healthcare, entertainment, even in traditional sectors like manufacturing and retail, are realizing the pressing need to harness the power of data.

This is why DMPs are so important today: they offer a way for businesses to bring together all the disparate sources of data from different customer touchpoints and to curate it into actionable insights about customers that enable marketers to craft better, personalized experiences.

But we’re getting ahead of ourselves. What even is a DMP, really? In this article, we’ll take a deeper look at the subject of data management platforms. We’ll explore what a DMP is and demystify its role in the modern marketing ecosystem. We’ll discuss why, in today’s data-driven environment, DMPs have become non-negotiable assets for businesses aiming to thrive.

Moreover, we’ll introduce you to a cutting-edge platform designed with the modern marketer in mind. We’ll unpack its unique features, benefits, and the value it can add to your business. And finally, for those looking how to integrate a DMP into their operations, we’ll provide a step-by-step guide on how to seamlessly make this transition.

Let’s decode DMPs.

If the digital world is a treasure trove, data is the gold. Understanding and leveraging such a valuable asset is less of a luxury and more of a necessity when delivering modern customer experiences. This holds particularly in digital marketing, where data is generated, captured, and processed at an unprecedented scale.

In 2012, Richard Branson tweeted: “Complexity is your enemy. Any fool can make something complicated. It is hard to keep things simple.” And that’s the ongoing challenge that drives us at InlineMarket; to do the hard work to make sense of complexity for our customers

Sales as we know it has undergone a seismic shift over the past couple of decades. The traditional approach of intuition-based decision-making has given way to a more sophisticated data-driven strategy.

 Cast your mind back to the turn of the millennium. The Nokia 3310 has just made its debut—a groundbreaking phone celebrated for its durability, simplicity, and an addictive game called Snake. But the idea of streaming a high-definition movie on it, making a video call with it, or unlocking it simply with just a glance simply isn’t possible. 

Customer insights are an interpretation of trends in human behaviors which aim to increase the effectiveness of a product or service for customers and increase sales for businesses. They go beyond raw data or research; they offer a deep understanding of customers’ critical attitudes, behaviors and preferences. They delve into their pain points, needs, experiences, and what drives their decisions. In short, customer insights help businesses to step into their customers’ shoes.

The story of how marketing has changed over the past 30 years is a tale of adaptability and innovation, marked by an ongoing quest for better ways to reach, engage, and convert potential customers. As we dig into this remarkable journey, we find ourselves amid a digital revolution, a phase where data analytics has become the pivot around which successful marketing strategies revolve.

In an era defined by digital innovation, the success of marketing hinges on leveraging data. Modern marketers know that data insights are not just valuable—they’re essential to strategizing and decision-making. This article will guide you through the process of extracting meaningful insights from your marketing data through the use of a data platform.

If you're a marketer in today's world, you know that social media marketing is a key element of any modern marketing campaign. Social media platforms have billions of users, making them a prime place to reach a large and diverse audience. Social media platforms also offer advanced targeting options that allow you to advertise to specific demographics and interests, making it easier to reach the right people.

But with so much data available on how your company is performing on social media, it can be a challenge to make sense of it all and use it to make decisions that will ultimately benefit their business. Many marketers don’t have the necessary expertise or time to interpret large amounts of data, making it difficult for them to make informed decisions. Fortunately for modern marketers, content analysis has come a long way.

So… what exactly is it, and why is it so useful in the world of social media?

We all know what upselling is. It’s when you invite your customer to spend more or upgrade, and usually requires you to market your more profitable products or services. This contrasts with cross-selling, which involves selling additional products or services to an existing customer.

The key benefit of predictive deal scoring over traditional deal scoring is that it brings objectivity to your data. All predictions are based on machine learning algorithms. Obviously, you need data to get started. The good news is that you probably already have it in place. 

In this blog post, we address the top 5 questions we get asked about predictive deal scoring.  

InlineInsight Predictive Scoring solution is now listed in the HubSpot App Marketplace. HubSpot App Partners are independent software vendors who have built an integration with HubSpot and been accepted to the App Marketplace.

Improving the effectiveness of your sales can be divided into four actions: Increase the number of sales opportunities, drive up your average deal size, optimize your sales win rate, and shorten the length of your sales cycle. 

Using firmographic data together with predictive scoring is a perfect way to start check boxing these actions and feeding your sales pipeline with a sizeable number of good, quality prospects.  

Many B2B organizations have scattered data that fails to provide any actionable insights to sales teams. In 2020, sales teams cannot function or make decisions based on gut feeling or guesswork. And data is the only way to provide sales teams with the kind of analytics that are needed. Here are 4 effective ways data can help to drive B2B customer acquisition.​

All key elements of a positive buyer experience rely on a constructive relationship between marketing content and sales success. Put simply: alignment.

Observing data over time is time series. Predicting that data in the future is time series modeling. Unlike the regular prediction, this is slightly different because of the chronology in the data.