Data Fabric: What It is, Why You Need It, and How It’s Transforming Data-Driven Business Decisions for Modern Companies
Modern business is constantly evolving and expanding. As it does, employees often look for new ways to improve efficiencies and speed up workflows. Frequently, adopting new technologies spurs those efficiencies—technologies that automate and streamline processes that once required slow, error-prone, repetitive manual steps for your team members.
New technologies certainly bring a lot of advantages to your work environment, but they also create a number of challenges—everything from costs, procurement, implementation, and adoption, to security and compliance issues to updates, upgrades, and replacements.
We saw the technology push elevated in 2020, where the coronavirus pandemic forced many companies to move into new remote and socially-distanced work environments. Not prepared for such rapid operational changes, many pushed aside traditional change management processes to quickly select and implement new technologies for remote work and the push away from traditional brick-and-mortar settings for retail, services, and other forms of commerce.
And while in 2020 those moves may have very well been the answer for operational survivability, it further highlights ongoing challenges for organizations that piecemeal technology solutions across their enterprise.
The result? Disparate technologies, systems, and data sources that end up actually making it harder for companies to effectively access, analyze, and learn from business operations, thwarting the ability to make better data-driven business decisions.
So what do you do?
- If your enterprise is already full of these disparate systems—and they’re functioning, at least, for now—how do you get the most out of them?
- How do you get access to share and employ machine learning and AI models to all of your data without building complex, costly, customized solutions for your operations?
According to a report from Gartner, by 2023, more than 33% of large organizations will have data analysts on their teams to help them with decision modeling and intelligence. And while that’s great for big business, data analytics with machine learning and AI doesn’t have to be something just for large organizations, small and mid-sized businesses (SMBs) can employ data modeling technologies too, without breaking the bank.
The answer is in building a data fabric that connects everything in your organization, and the good news is, you don’t have to hire specialized highly trained staff or invest tens of thousands of dollars creating and building your own solution. One already exists.
The team at Pandio, for example, has spent the last three years and millions of dollars building out the data fabric designed for next generation workloads, and with this powerful solution, you can put AI and ML to work for you, whether you’re a small mom-and-pop or a large-scale enterprise.
In this blog, we’re going to dive into Data Fabric: What It is, Why You Need It, and How It’s Transforming Data-Driven Business Decisions for Modern Companies, but before we do, let’s take a closer look at the issues caused by disparate software solutions, web applications, and other systems your organization may be using today.
Disparate systems, disparate data, desperate for connections
Imagine this scenario: You’re a financial institution. You have locations around the world and each of these locations serve a variety of functions. Some are traditional bank branches, some are specialized financial services, and others are for administrative operations.
Not only do you have a lot of facilities in geographically dispersed areas, but your locations also use different languages and currency systems, and there are different operational technologies and systems throughout these locations.
However, you need a way to pull data from a variety of those locations, in a variety of combinations to make business decisions and ensure you’re meeting security and compliance mandates throughout your organization.
Today, your team probably does this by pulling a lot of manual reports. Relying heavily on spreadsheets. Trusting both language translations and currency converters, all while hoping you have as few human errors as possible.
It’s time-consuming. Error-prone. Inefficient.
And worse yet, you can’t feel confident all the data you’re getting is accurate. One small error at the beginning of this process can compound over locations and transmissions, and before you know it, you’re looking at a murky picture—at best—of how your organization is actually performing. It’s also incredibly difficult to quickly identify gaps and areas where you need to focus attention for growth and sustainability.
What you need is a foundational solution like Pandio, built on Apache Pulsar, that stretches through your company and connects all of these disparate systems, pulls your data into a centralized system like the cloud, and enables your key players to quickly look at that data while your machine learning and AI algorithms help you predict trends and other issues so you can actually use the data that’s flowing throughout your enterprise.
Apache Pulsar enables your business to improve your operational performance and security—in a scalable way—so you can focus on your applications, what you can learn from them, and make better data-driven business decisions.
And while the example above focuses on the financial industry, disparate data systems is an issue that plagues companies across all verticals.
The reality is, the larger your business grows, no matter what type of business you’re running, the more likely it is that you have these fragmented systems controlling your day-to-day operations. While smaller companies historically have been more agile because they employ fewer technology solutions, today’s push into cloud migration with the proliferation of software as a service (SaaS) resources and business intelligence solutions mean this disparate data issue is now one that affects organizations of all sizes.
To be competitive today—and in the future—your business will need to be more connected and you’ll need to leverage your data into data-driven business decisions. Pandio’s integrated neural network gives you unparalleled efficiencies with zero operational burden so you can focus on the data that creates real value for your business.
Quite simply, if you don’t figure out how to leverage your company’s critical data, someone else is going to eat your lunch and gobble up your business.
This is where building a data fabric for your organization comes into play. Data fabric makes getting access to your data easier and that in turn enables your enterprise systems to implement machine learning and AI much quicker, easier, and with fewer resources and expenses than you thought possible.
What’s data fabric anyway?
Companies create, store, processes, and transmit data in many ways. It can be something as simple as a file stored on a server or computer or streaming data that moves into and out of the cloud, like when someone uses your digital point of sale system to make a purchase off your website or completes a transaction in your office.
When we talk about data fabric, we’re talking about a system that culls all of your endpoints, systems, services, and applications, regardless of if they are on-premises, in the cloud, or a hybrid model, and moves and digitally transforms all your data. Building data fabric is all about making that data accessible, using a common language, so you can analyze it and put machine learning and AI to work.
In many businesses today, we see disparate systems for accounting, billing, inventory, accounts payable, point of sale, human resources, and on and on. Building a data fabric that connects all of these pieces unites your systems and applications into a foundational layer for your business. In the most simple terms, your data fabric is a universal way for your team to create data and then make it available in the ways you want that data seen and accessed.
And there’s no one out there who does messaging as well as Pandio. Its team of data experts, with decades of experience and a singular focus on distributed messaging for AI, big data, and machine learning, mean you’ll always have the support and knowledge you need to build your company’s data-driven future.
Why do you need data fabric?
Companies should employ data fabric to get the most out of their existing systems. If all your accounting data, for example, is stored in one system, and that system is only accessible from a certain location or by a specific user, how do you get access to that data so you can include that information in your planning and decision-making processes?
For many businesses, historically, this usually takes an employee—sometimes teams of employees—who set aside their day-to-day tasks, pull data, create reports, synthesize that data into charts, graphs, and other metrics and then present that data to your leadership teams. In most cases your executives can’t get the data they need and want to make important business decisions without the support of a data analyst or someone in a similar role.
Often that looks like piles and piles of printed papers. Or, if you’re doing a digital file share, it could mean sending critical data through email or other data sharing services that create challenges regarding user access. On top of that, how do you know the report you’re looking at is the most current version? How do you know if there are errors or issues? You have to trust the human component, which we know is error prone, when automated technologies could handle this for you instead.
If you don’t have a data fabric in place, and you want to use machine learning or AI for your business, you’ll likely have to hire an analyst—or a team of analysts—to sort through all those reports and then make recommendations, forecasts, and predictions based on those reports.
Often these data professionals are manually feeding data into AI and ML models, and the data they’re getting is always changing, leaving them always to play catch up for accuracy and timeliness. And while these professionals are great at the work they do, their processes are time-consuming, expensive, and with a shortage of skilled professionals throughout IT, they’re hard to find.
On top of that, if your company is scaling or changing, any time there are even moderate-sized changes within your organization, new reports must be gathered and handed off to your analyst, who, if they’re already invested in a project, may have to start all over, putting you always up against a ticking clock when you need to make business decisions faster and more accurately than ever before.
That’s why building a data fabric supported by Pandio is a must have for today’s modern enterprises, no matter how big or small your company is.
Overcoming machine learning and AI obstacles
These complex and disparate systems are just a small part of the many obstacles companies today must overcome if they want to accurately and effectively use machine learning and AI for better business decisions.
It’s critical to break down data silos across your organization, which Pandio can help you do. But your company doesn’t have to drown itself in a data lake to be successful. Pandio’s managed messaging service reduces complexities of streams, queues, pubsub and other functions—all within a single solution—giving you the data visibility you want, when you need it most, and you don’t have to fall into the trap of moving all your data to the cloud right now to experience the full benefits of AI and ML.
The reality is, across all industries, the push to move all of your data to the cloud is making the AI migration more difficult than it should be. With the all-or-nothing approach, businesses face expansive projects. It’s costly. It takes a long time to plan and implement, and if not done correctly, you can temporarily lose access to critical services and data when you need them for day-to-day operations.
Building a data fabric within your company’s enterprise helps you quickly overcome these challenges.
In our thought leadership piece “5 Reasons Most Machine Learning Initiatives Fail and How to Fix Them”, we outline some common AI and ML adoption fail points and help you better understand what you need to do to be in a position to add these powerful components to your business, today without overtaxing your budget or resources. Your data fabric will help you steamroll over the challenges and put you on your path to success.
The simplest way to build a data fabric to support your business
Earlier, we talked about how complicated and costly it can be to build your own data fabric. You need a team of highly trained professionals, lots of technology, and an expansive budget to create your own system, and a lot of time and patience. On top of that, once you build it, it will be proprietary to your organization, so if you ever need help or support to fix or improve it, you’re on your own.
A better alternative is to deploy a solution that does this for you, like Pandio built on Apache Pulsar, which was developed by industry professionals who understand the complexities of effective ML and AI for businesses.
For example, let’s say your team built your own data fabric. Like in our previous financial example, this data fabric is critical to ensuring your team members can pull different data sets from different data centers around the globe. Some of your apps point to one data center for geo-replication. Others point to different ones and few of these data centers are actually in the region where your team member needs to pull the data. If one of these data centers goes down, you’re stuck without access to your data.
Another problem derived from building your own proprietary data fabric, is what happens when it crashes and you can’t get the data you need. If you’re a large enterprise like a global financial institution, your company could be using thousands of programmatic systems accessed in different roles by tens of thousands of employees. How do you build a data fabric that keeps up?
Pandio eliminates this issue with geo-replication, where your data/events are copied on other servers so if you have a data center failure, your application can continue functioning with little or no interruption.
On top of that, you have increasing multi-tenancy issues, for these spanning enterprises where you may have systems that feed into your data fabric, for example, your accounting systems, but you only want specific team members or departments to access that data. You’ll want fine-grain control to mote that data off and access control. Access management and control is complex—and there are often a number of security, compliance, and regulatory standards associated with them—so how do you know if your proprietary system can do what you need?
A data fabric like Pandio helps ensure that you can simplify data access controls from people to departments and locations, even specific programmatic systems. On top of that, you can be confident the Pandio team understands privacy and security and they’re helping you build that data foundation for your company in the most secure way possible.
Pandio is the overarching data backbone that keeps your company working. It enables all of your applications, systems, and people to talk with each other in a seamless way. You don’t need a custom API. You don’t need to learn the data language of all of the systems your organization uses. Pandio does it for you.
Are you ready to connect all your disparate systems and applications so you can put your company’s data to work for you and make real, accurate, and intelligent data-driven business decisions? You can try Pandio for free or if you have questions, contact a Pandio advisor today, and we’ll help explain the benefits of building a data fabric for your company and what you need to do to get up and going on your ML and AI data journey today, with fewer resources and less expense than you ever thought possible.ds