5 Tips to Trusting the Data in your AI Dashboard

by Caroline Maier

Hero Image
December 03, 2021 11:31am

Blog

If you’ve spent any time in the corporate world, then you know the value of proving ROI. In matters of data, just having access to it isn’t enough. You need to make sense of what the data shows you, understand what’s important to look at and what to toss, gather insights and leverage those insights to make important business adjustments. In today’s ever-changing world, having answers to business problems in real-time makes the pressure even greater.


Most organizations head straight for flashy BI tools and AI dashboards (who doesn’t like a well-designed graph or bar chart that clearly predicts existing trends and future opportunities?). But without the right streaming and continuous data integration needed to provide fresh, reliable, and accurate data in real-time from the backend, you’re left with beautiful dashboards with yesterday’s data. That stale, inaccurate, and untimely information can lead to dire consequences for your business.


Here’s an example: the stockpile of 1,000 items you thought was sitting in your warehouse is actually down to 500 units because of a large order early in the day, but your dashboards show inventory is fully stocked due to insufficient real-time transfer of inventory data. When the next order comes in for 600 units and is promptly sold, how will your customer feel when told their order is 100 units short? Do they wait the two months needed for production? Or just cancel the order altogether? With a real-time, continuous data integration platform fueling your AI and BI systems, you can trust that the insights you’re collecting operate off of fresh, trusted, accurate information. Just having data isn’t enough to prove ROI in any business practice, much less a fancy analytic dashboard. It's how you aggregate and transform that data, interpret what it’s showing you and apply that to business innovations that drive your company’s competitive edge.




Here are the five steps your organization needs to take in order to get the most out of your data.


#1) Do an audit and analysis of your current data strategy.

Ask yourself pressing questions like, are you meeting SLAs? Are contracts, invoices, inventory, and other business matters being handled in a timely way or are things falling through the cracks because data used to make decisions is late, incomplete, or inaccurate? Do you have a streaming-first, real-time approach? Is it as performant as it could be? Your data sources, volumes, and velocities will only increase in the years to come. Do you have a modern, scalable, flexible infrastructure to support your business growth? Or are you relying on legacy tools that will hamper your modernization? Or worse, are you relying on your IT department to not only source an analytics tool but extract the data from it as well.



#2) Identify business objectives - then look to your data to solve them.

Ask questions like, are we trying to discover new revenue streams? Optimize current operations? Improve pipeline? Eliminate waste? Make sound hiring decisions? These valuable, foundational questions will inform the approach needed to identify, collect, and transform your data to get the answers you need in the time you need them. It’s critical to identify a data integration solution that can support the numerous approaches for data processing like Streaming ETL / ELT / EtLT plus Batch ETL and Replication. Look for flexible, scalable, and robust solutions capable of handling modern and legacy integration with ease.



#3) Take stock of where data lives and how you’re capturing it.

So many data sources...and formats...and languages...and so little time to catch your breath? Data silos got you down? You’re not alone. As you look to modernize, evaluate your current data sources and how you’re structured. Do you have important, private data that has to live on-prem? Are you making the move to the cloud? Maybe deploying a multi-cloud strategy? Only evaluating SaaS solutions that will require someone on staff to manage? Look for a solution that can operate in any of these environments - offering you flexibility and scalability.
Additionally, not all legacy data integration systems are capable of connecting to modern data sources, or able to leverage modern Change Data Capture to look for just the changes to your source data in real-time. Ensure that your data integration solution can access data from any source, in any format either in Streaming or Batch. With so many new, modern sources being added to your data infrastructure, from social platforms to CRMs and other tools, you need to have a solution that equalizes your data. It’s all-important, and when aligned appropriately together, can show you those yellow bricks pointing to the emerald city.



#4) To ETL or ELT - which approach is best? Yes, we’re getting technical here...

Well...it depends on the use case (have you heard that one before?), and not all use cases are created equal. Sure, it’s appealing to push all of your data straight to the cloud, knowing that at some point in the future you will transform, enrich and aggregate it for AI dashboards. But would you do that with your closet? Just take all of the dirty, mismatched, and out-of-date clothes out of the closet and throw them in the back of your car, knowing that at some point in the future you will stop by Goodwill to donate, and the resale shop to make some extra cash? Not efficient.

While extract, load, and transform (or ELT) is the right method to fuel AI and BI insights in many cases, you may also consider some small transformations and data enrichments before your data hits the cloud (also known as EtLT). Applying the “little t” before loading can reduce cloud compute costs, and lead to cleaner, enriched data at your target with faster load times for your AI dashboards. As you look towards use cases like Application Integration or Event Subscriptions by various business users, applying Streaming ETL is best, allowing for transformations to occur in the data pipeline before loading onto the target.

Ultimately, look for a solution that can do both. You’ll then have the flexibility to decide as various use cases arise.



#5) Take a cold, hard look at your data and ask yourself: what’s missing?

So often, hidden inside of the piles of data you are collecting lies unrealized opportunity. Are you capturing the buying history of your customers? Maybe identifying a threshold at which they transition to a new direction? Could you leverage your data for anonymized industry benchmarks that would be useful to other customers? Can you see unexpected trends in your customer base pointing towards a new line of business? There may be more to mine from your data than you realize. With the ability to easily and quickly aggregate data and enrich it from various sources, you will gain visibility into what’s possible. Go ahead...surprise yourself!

Being agile with real-time data integration and ultimately, applying it to your business use case, will be the difference between modern and forward-thinking organizations and those that can’t keep up.




If you’re interested in learning more about how Equalum can help simplify your data integration strategy, let’s connect.

LEARN MORE













BLOG: 5 Tips to Trusting the Data in your AI Dashboard - How to Wrangle your Data In Real-Time

Ready to Get Started?

Experience Enterprise-Grade Data Integration + Real-Time Streaming