Size Isn't the Problem: 3 Ways to Get Real Insights from Your Data
This article is part of a special issue of VB. Read the full series here: How data privacy is transforming marketing.
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 companies, essential for making business decisions.
But there is a fly in the ointment (or in this case, the oil). Organizations can collect data from every angle—every person, place, or thing on a seemingly endless digital trail—but to extract value, companies must be able to answer one critical question: What is the data trying to say?
Craving answers, many organizations are pumping more and more data into storage, as if simply aggregating more data into ever-growing data lakes could provide deeper insights. However, they still end up stumped, groping in the dark for the “aha!” moments that create greater customer understanding, operational efficiencies, and other competitive advantages.
That’s because the problem isn’t the size of the data; it is the ability to get valuable information from it. Business questions that help shape the shape of personalized product recommendations, real-time fraud detection, and healthcare pathways, to name a few examples, don’t fit into the rigid way data is stored.
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Don’t just store facts
Traditional systems, such as data warehouses, are based on relational databases (RDMBS) that are designed to store facts, not to analyze data from the point of view of who and where it came from. By nature, tables in RDBMS exist as separate files in a data lake. You may be able to find some isolated insights in that data, but be blind to the insights within the data that allow companies to nuanced approach to business issues.
Too often within companies, different data points live in different organizational silos, such as sales, marketing, customer service, and supply chain. That leaves a disconnected and myopic view of how an entity interacts with the business.
Even artificial intelligence (AI) and machine learning (ML) programs tend to work in silos, with each team working on a narrowly defined question. They may find answers in time, but because they are working on separate data, they are unlikely to discover deeper insights (i.e. patterns or similarities) that would improve the accuracy of their model in answering business questions.
Losing the meaning of data is a losing proposition at a time when organizations are under significant pressure to gain better insights into customer behaviors, predict market shifts, and forecast what’s next for business in a volatile world. .
And the importance goes beyond those business uses: it’s also critical for uncovering financial fraud, personalizing patient care, managing complex supply chains, and uncovering security risks.
Organizations have a lot of work to do to reach an optimal state in the data journey: discover the relationships within, between and between all this information to obtain meaningful insights.
How can an organization get there? Here are three key tips.
1. Eliminate silos
Many companies spend millions hiring data scientists, creating new data models, and exploring AI and ML approaches. The problem? These programs often work in silos in large organizations. The results? 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 runs five individually branded retail websites. Understanding customer identities and activities across those brands is complicated, and without a consolidated view of customer identities and activities, the company struggled to make personalized recommendations and offers.
With a new approach that scoured all of the company’s customer data and synchronized customer identities across their mobile phone numbers, email addresses, devices, addresses, credit cards, and more, the company now has a single, unified view of every buyer relationship. As a result, the company forecasts a 17.6% increase in sales through its specialty retail brands.
This is a powerful example of how companies collect data from disparate sources, angles and locations and store the information in silos and how that disrupts patterns of relationships with that entity.
By merging data from different silos into an enterprise-wide dataset, companies can analyze how a person, place, or thing interacts in the business from an entity 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.
Higher-level questions, such as how to personalize product recommendations for customers or make supply chains more efficient, require finding context, connections, and relationships in the data. Think about how our brains collect and store facts, data, and pieces of information every second, and how the reasoning part of our brain is activated to assess context and highlight relationships.
Graph databases are a newer technology that represents a completely different way of structuring data around relationships. They act as the reasoning part of the brain for large and complex data sets for large and complex interrelated data sets. It is within these data sets that one can see all the relationships and connections between the data. LinkedIn and Meta (Facebook), for example, rely on graph databases to discover how different users are related, helping them connect with relevant contacts and content.
By augmenting their systems with graph analytics, organizations can 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 use additional insights from connected data and graphing capabilities for better predictions. With the precise predictive power derived from unique graphical models and functions, organizations can unlock even more powerful insights and business impact.
Users can easily train graph neural networks 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 graph data sets /Test. The result: better representations of data in terms of dealing with data type, establishing a unified data model, and having a way to represent data for the most effective business results from AI.
As these three tips show, it’s critical for organizations to adopt a modern approach to data that enables them to understand not only individual data points, but also the relationships and dependencies between all data connections. To win with data, companies must be able to combine insight, scale, and speed. They must also be able to ask and answer critical and complex relationship-based questions.
questions, and do it at the speed of business.
This is the only way organizations today can truly harness data as the new oil.
Todd Blaschka is COO of tigregraph.
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