4 Data Science Myth Busters for the Era of AI

Now that companies are recognizing the true power of their data, in terms of valuable insights and data-driven decisions, the need for data science has become undeniable for solving for business. However, there still remain antiquated thoughts surrounding data science that prevent some companies from exploring it and the myriad benefits it can provide.

In this blog, I’ve identified and debunked the four most common myths, and provided new awareness as to why data science is a must going forward.

1. A Column of Data is Insightful

Stop what you’re doing. Excel spreadsheets aren’t going to give you insights into customer patterns, nor can they make the predictions you need to improve your business process.

Many enterprise leaders are still trying to harness insight from outdated data practices and platforms like paper reporting or trying to leverage SharePoint as a data source. Outdated processes abound in different areas, and the reason is mostly rooted in the fact that change is hard. Often, enterprises are slow to shift from legacy processes to modern ways of doing business because they don’t know where to begin.

At Hitachi Solutions, we’re trying to make it easier for our customers to modernize. And the biggest impact many customers can make this year is through their legacy data systems and solutions. We partner with customers to unpack the data journey in a way that makes sense to their unique business.

Shifting from being reactive to proactive is a great way to start. Looking at data from a fresh perspective is one way to do that. That’s where my favorite topic comes into the conversation — data science. Bringing insights into customer behaviors and making predictions about future behaviors is my jam. It has incredible superpower potential for business leaders to help them understand how to better their business.

2. The Investment in Predictive Analytics is Too Daunting

When it comes to data science, little things lead to big business value.

While the business potential from predictive analytics is endless, we can start small for a quick win to illustrate the potential. We are talking about science. And that’s important to understand because our experts follow a scientific process of making hypotheses for our customers to work through, test, and prove to drive business value.

We take measurements, create experiments, and derive insight from those experiences. Usually, at the end of this process, we have a model of predictive behavior we can take for a test drive. This is a mathematical computational object you can apply that has real business value. You can apply new data to that model and get back the best answer possible based on the learning it has undergone.

So, again, little things can lead to big changes and improvements in efficiency for your business. It’s a “start small, think big” mentality. And it’s kind of amazing and game-changing for enterprises who understand the value of data science and leverage that for their modern data platform.

But let me get to the art part of this conversation. That’s where the real insights happen with predictive analytics.

The scientific process is just the foundation. It’s your basic science process fueled by the academic disciplines of statistics, computer science, and mathematics. All that goodness goes into the process.

And then there’s an artistic side of data science, if you will, which is a little bit of storytelling. That’s the art of being able to craft a solution that human beings can use in a business in a proactive way without having any idea what machine learning really is. So fundamentally, you don’t have to understand the technical side of the process to realize the value.

Again. It’s the art of insights.

3. You Need a Ph.D. to Understand the Value

While some on our expert team, in fact, do hold PhDs, you don’t need one to grasp the anomaly detector or the neural network to realize the value of what we can deliver. You just need to understand what problem you’re trying to solve for your business. It’s on your PNL statement. You see it every month. You’re trying to answer your constituents on how to solve issues in that statement.

And nobody can until you bring in a team like ours. We eat this kind of stuff for breakfast. This is where a data scientist can come in handy. We help organize the data, and then we reveal small differences in data sets that lead to big implications for your business downstream.

We find this exciting — again, the art of insights. The implications can be huge, and these insights do not reveal themselves through normal queries or SQL queries on a big data set. That’s the science part. We’ll take care of that.

4. Predicting the Future Takes Too Long

Speed to value through data science can be a lightning bolt. And you begin the journey by first framing the problem.

Your business is our patient, and to make a proper diagnosis, we need all of the data. We don’t just want the symptoms. We drill down to the last detail. Look, we don’t want an overview. We want ALL of your data if we can get it.

We have the capacity to consume and compute terabytes of data in a very quick time period, usually in a matter of one to three weeks, for most customers. We’ve been able to ingest and model data and lift insights around where problems are rooted, and then make predictions to help solve them.

How We Deliver

In just a few hours, we can deploy all the infrastructure needed to begin creating a minimal viable solution. And we do this by leveraging the ultra-light and flexible Microsoft Azure cloud.

The lens of data science is tipped to the past to predict what will happen in the future. We create a picture and build a compelling narrative about what and why things have happened in the past, so you’re better equipped to navigate now and into the future.

Depending on the quality of the data from your platform, data science can work with volume at a velocity that’s hard to beat. We can shrink your telescope lens down to what will happen in the next 15 minutes, let alone the next 15 hours, days, or years.

Next Steps

Once we help you evaluate your business pain points, compile your data, and outline your strategy, it’s time to hand it over to a trusted team of experts who can move the data to a cloud format and be analyzed and leveraged by your organization. At Hitachi Solutions, we have been helping businesses from a variety of industries put their data to work for them. If you’re ready to make the most of your data, contact us — we’re ready to get started.

About the Author

Fred Heller is a self-described data nerd who leads our team of analytics scientists and experts at Hitachi Solutions.