Missed a session of MetaBeat 2022? Head over to the on-demand library for all of our featured sessions here.
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 businesses, a must-have for business decision-making.
But there’s a fly in the ointment (or in this case, the oil). Organizations can collect data from every angle – every person, place or thing in a seemingly endless digital journey – but to extract value, companies must be able to answer a critical question: what is the data is trying to say?
In search of answers, many organizations are pumping more and more data into storage, as if simply accumulating more data in ever-growing data lakes could yield deeper insights. Yet they always end up puzzled, groping in the dark for the “aha!” moments that create a better understanding of customers, operational efficiencies and other competitive advantages.
This is because the problem is not the size of the data; it is the ability to extract valuable information from it. The business questions that help shape the shape of personalized product recommendations, real-time fraud detection, and medical care journeys, to name a few examples, don’t fit into the rigid way data is stored.
Join today’s top leaders at the Low-Code/No-Code Summit virtually on November 9. Sign up for your free pass today.
Not just storing facts
Traditional systems such as data warehouses are built on relational databases (RDMBS) designed to store facts, not to analyze data from the perspective of who and where it came from. By nature, RDBMS tables exist as independent files in a data lake. You may be able to find isolated insights in this information, but ignore the insights contained in the data that enables companies to address business issues with nuance.
Too often within companies, different data points live in different organizational silos, such as sales, marketing, customer service, and supply chain. This 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 operate in silos, with each team working on a narrowly defined question. They may find answers in time, but since they are working on separate data, they are unlikely to uncover deeper insights (i.e. patterns or similarities) that improve the accuracy of their model. to answer business questions.
Missing out on the meaning of data is a losing proposition at a time when organizations are under relentless pressure to better understand customer behaviors, predict market shifts, and predict what comes next for the business in a volatile world. .
And the importance goes beyond these commercial uses – it’s also essential for uncovering financial fraud, personalizing patient medical care, managing complex supply chains and uncovering security risks.
Organizations have their work cut out to achieve an optimal state in the data journey: discovering the relationships within, between, and among all that information to gain meaningful insights.
How can an organization achieve this? Here are three key tips.
1. Eliminate silos
Many companies are spending millions to hire data scientists, create new data models, and explore AI and ML approaches. The problem? These programs often operate in silos in large organizations. The result? Being forced to make critical business decisions with one-dimensional data devoid of 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 these brands is complicated, and without a consolidated view of customer identities and activities, the company has 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 each buyer relationship. As a result, the company forecasts a 17.6% increase in sales of its specialty retail brands.
This is a powerful example of how companies so often collect data from disparate sources, angles and locations and store information in silos and how that breaks patterns of relationships with this entity.
By merging data from different silos into a single, enterprise-wide data set, businesses can then analyze how a person, place, or thing interacts in the business from the perspective of the entity. What is this technology? See item 2.
2. Choose the right database technology for the right workload
Relational databases, despite their name, struggle by themselves to discover data relationships between, within, and among different data elements.
Higher-level questions like how to personalize product recommendations for customers or make supply chains more efficient require finding context, connections, and relationships in data. Think about how our brain collects and stores facts, data, and bits of information every second, and how the reasoning part of our brain comes into play to assess context and highlight relationships.
Graph databases are a newer technology that represent an entirely 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 in these datasets 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 supplementing their systems with graphical analytics, organizations can focus on answering relationship-based questions.
3. Unlock smarter insights at scale with connected data machine learning
By accelerating the development of graph-enhanced machine learning, organizations can use the additional insights from connected data and graphing capabilities for better predictions. With the precise predictive power from unique graphing capabilities and graphing models, 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 features such as distributed storage and massively parallel processing, as well as graph-based partitioning to generate sets of training/validation/test graph data. The result: better representations of data in terms of processing the type of data, establishing a unified data model, and a way to represent data to achieve the most effective business outcomes from AI.
As these three tips show, it’s vital 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 perspective, 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.
It’s the only way for today’s organizations to truly harness data like the new oil.
Todd Blaschka is Chief Operating Officer at TigerGraph.
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including data technicians, can share data insights and innovations.
If you want to learn more about cutting-edge insights and up-to-date information, best practices, and the future of data and data technology, join us at DataDecisionMakers.
You might even consider writing your own article!
Learn more about DataDecisionMakers