Size isn’t the issue: 3 ways to really understand your data

Experts have been saying it for years: data is the new oil. And who can argue? Data has become an indispensable natural resource for modern enterprises, a must-have for business decision-making.

But there is a fly in the ointment (or in this case the oil). Organizations may be collecting data from all angles – any person, place or thing in a seemingly infinite digital trail – but to extract value, companies need to be able to answer a crucial question: what is the data trying to say?

Many organizations long for answers and are pumping more and more data into storage, as if simply collecting more data in the ever-expanding lakes of data could lead to deeper insights. Yet they still end up dumbfounded, groping in the dark for the “aha!” moments that create greater customer understanding, operational efficiency, and other competitive advantages.

That’s because the problem isn’t the size of the data; it is the ability to extract valuable insights from it. Business questions that help outline the shape of personalized product recommendations, real-time fraud detection, and medical care pathways, to name just a few examples, don’t fit the rigid way data is stored.

Don’t just store facts

Traditional systems such as data warehouses are built on relational databases (RDMBS) designed to store facts, not analyze data from the point of view of who and where it comes from. Tables in RDBMS naturally exist as independent files in a data lake. You may be able to find some isolated insights in that information, but be blind to the insights into data that enable companies to approach business problems in a nuanced way.

Within companies, too often, different data points live in different organizational silos, such as sales, marketing, customer service and supply chain. That leaves a disjointed, myopic view of how an entity interacts with the business.

Even artificial intelligence (AI) and machine learning (ML) programs often work in silos, with each team working on a narrowly defined question. They may find answers in time, but because they’re working on individual data, they’re not likely to discover deeper insights (i.e., patterns or similarities) that improve the accuracy of their model in answering the business questions.

Missing out on the meaning of data is a loss-making proposition at a time when organizations are under unrelenting pressure to better understand customer behavior, predict market changes, and predict what’s next for the business in a volatile world.

And its importance goes beyond those business applications – it’s also critical for detecting financial fraud, personalizing patient medical care, managing complex supply chains and exposing security risks.

Organizations still have a lot of work ahead of them to reach an optimal state in the data journey: uncovering the relationships within, between and between all this information to gain meaningful insights.

How can an organization get there? Here are three important pieces of advice.

1. Eliminate silos

Many companies spend millions hiring data scientists, building new data models, and exploring AI and ML approaches. The problem? These programs often work in silos in large organizations. The result? Being forced to make critical business decisions with one-dimensional data without essential context.

Take, for example, an e-commerce company we work with that operates five individually branded retail websites. Understanding customer identities and activities for those brands is complicated, and without a consolidated view of customer identities and activities, the company struggled to deliver personalized recommendations and offers.

With a new approach that crisscrossed all of the company’s customer data and synced customer identities across their cell phone numbers, email addresses, devices, addresses, credit cards and more, the company now has one unified view of every buyer relationship. As a result, the company expects a 17.6% increase in sales through its specialty retail brands.

This is a powerful example of how companies so often collect data from different sources, angles and locations and store the information in silos and how that disrupts the patterns of relationships with that entity.

By merging data from different silos into a single company-wide dataset, companies can then analyze how a person, place, or thing across the company interacts from the entity’s point of view. What is that technology? See point 2.

2. Choose the right database technology for the right workload

Relational databases, despite their name, struggle on their own to discover data relationships between, within, and between different data elements.

For higher-level questions, such as how to personalize product recommendations for customers or make supply chains more efficient, you need to find context, connections, and relationships in data. Think about how our brains collect and store facts, data, and bits of information every second, and how the reasoning part of our brains kicks in to evaluate context and emphasize relationships.

Graph databases are a newer technology that represents an entirely different way of structuring data around relationships. They act as the reasoning part of the brain for large, complex data sets for large and complex interrelated data sets. It is within these datasets that one can see all relationships and connections between data. For example, LinkedIn and Meta (Facebook) rely on graphical databases to discover how different users relate to each other so they can connect with relevant contacts and content.

Extending their systems to include graph analysis allows organizations to focus on answering relationship-based questions.

3. Unlock smarter insights at scale with machine learning on connected data

By accelerating the development of graph-enhanced machine learning, organizations can leverage the added insight of connected data and graphing features for better predictions. With the accurate predictive power that comes from unique charting features and charting models, organizations can unlock even more powerful insights and business impact.

Users can easily train neural networks for graphing without the need for a powerful machine, thanks to built-in capabilities such as distributed storage and massively parallel processing, as well as graph-based partitioning to generate training/validation/test graph datasets. The result: better representations of data in terms of handling data type, establishing a unified data model, and having a way to represent data to get the most effective business outcomes from AI.

As these three pieces of advice show, it is vital for organizations to adopt a modern approach to data that allows them to understand not only the individual data points, but also the relationships and dependencies between all data connections. To win with data, companies must be able to combine perspective, scale and speed. They must also be able to ask and answer critical, complex relationship-based questions
questions – and do it at the speed of business.

This is the only way today’s organizations can truly use data as the new oil.

Todd Blaschka is Chief Operating Officer at TigerGraph.