Data Dominates: Predicting the Trends of 2019
Promising predictions about predictive analytics.
- By Brian Brinkmann
- January 3, 2019
Imagine you’re on a cross-country road trip and you aren’t sure how to get to your final destination. You have a paper map in the passenger seat, but you keep getting lost because you have to continuously pull over to study this very large and confusing map. Eventually, you arrive at your destination, albeit a bit frustrated, but getting there wasn’t enjoyable, and it certainly wasn’t efficient.
Now imagine you’re on the same road trip, but instead you’re driving a car with a built-in GPS navigation system guiding you the entire time. The journey to your final destination will take considerably less time and make for a much more pleasant experience.
Running a business without intelligent, data-centric business models is like that first road trip. You’re trying to make impactful and data-driven decisions, only you don’t have the proper tools or resources to help guide you in a seamless and efficient way. Twenty years ago, a paper map might have sufficed as a main source of navigation, but today it’s not feasible to rely on this practice, just as leading an organization without high-quality data and analytics is not feasible.
As we continue to move towards a hyperintelligent and connected world, businesses of every size and in every industry are gravitating to artificial intelligence (AI), big data, and embedded and predictive analytics. These concepts have only scratched the surface of their capabilities and will continue to solidify themselves as foundational elements of running a successful enterprise in 2019.
Predictive Applications: Empowering Applications Through Machine Learning
Predictive analytics uses machine learning (ML) technology to produce a predictive score that informs actions that customers should. The technology helps application end users see what will most likely happen based on their historical data and understand what they can do to affect that outcome. The adoption of predictive analytics will be a trend in 2019 because it’s a way for application teams to differentiate their products and add value to their offerings using ML.
Although most organizations place heavy emphasis on investing in data, a large amount of it is trivial or unusable. By contrast, predictive analytics is alluring because it helps companies gain insights from historical data, predict future outcomes, and suggest actions to affect positive changes.
For example, consider a sales professional trying to reduce customer churn. A sales application with predictive analytics could analyze regular customer behaviors and alert the sales professional when a customer is beginning to show signs of potential churn. That alert could prompt the sales team to reach out to the customer and determine areas of improvement.
The benefits of predictive analytics don’t stop with sales team. Industries from retail to supply chain to healthcare can all take advantage of what the technology has to offer.
Technology Transforms: The Data Scientist’s Role Evolves
So why do we expect more organizations will turn to predictive analytics in 2019? In short, it offers the capabilities of a highly technical data scientist without having to compete in wage wars with competitors. The capabilities of data analytics empower businesses to perform complex analysis without relying on staff.
The scarcity of trained data scientists has stressed many executives unable to find (or afford) professionals with R or Python experience needed to analyze billions of historical data points. The rise of predictive analytics in 2019 will relieve this stress from organizations looking toward a digital business model and set them up for ongoing success as 40 percent of data science tasks are expected to be automated by 2020.
As the technology matures, data ingestion and analysis will move to just-in-time and real-time streams, resulting in increased productivity and broader use of data and analytics. These timely insights will further the idea of analytics being used in place of data scientists because these solutions can operate 24/7 without fatigue, offering organizations an “always on” team member.
Confidence in Analytics: The Road to Enterprise-Level Adoption
As more application teams embed analytics into software solutions, confidence in the technology will grow exponentially and be applied more broadly. This increased sophistication, coupled with decreasing data storage costs and the ability to increase data quality, will lead to enterprise-level adoption of predictive analytics.
Previously, companies were only able to utilize a small percentage of data generated due to poor quality or lack of storage. For example, a typical IT company may have roughly 80 percent of its data sitting idle in its data center.
This year we’ll see application developers and product managers begin to utilize this unused data by embedding analytics models directly into a new or existing application. This will allow organizations to use the data they have to scale and gain value out of more of the data they have on hand.
Brian Brinkmann is the vice president of products at Logi Analytics. In his career, Brinkmann worked as an electrical engineer designing control systems and held senior product strategy, management, and marketing positions at MicroStrategy. He has more than 20 years of analytics software experience. You can reach the author on Twitter or LinkedIn.