Published on cmswire.com
In the past few years, we’ve seen how analytics help data-driven organizations outperform peer groups and create more innovative opportunities.
Today, analytics defines the modern organization by harnessing its collective intelligence. This next-gen collaboration combines the wisdom of people with the power of machine learning and artificial intelligence.
Raising the Bar on Business Intelligence
For decades, business data lived in silos with only the highly specialized data analysts using outdated business intelligence (BI) software to generate reports, charts and forecasts.
But as the amount of relevant business data generated grew exponentially (more than 2.5 exabytes daily), advanced programs that are simple to understand and operate are now critical for empowering non-technical employees.
Analytics is also shifting the way non-technical employees are conducting their jobs. Only five years ago, research firms were predicting a talent shortage of data scientists.
With the advances in data discovery, analytics and visualizations, today's modern manager can use big data analytics software platform to drive the insights and reveal the significance of the data.
"Data modeling, simulation and other digital tools are reshaping how we innovate. And that has changed the skills needed by our employees," said Procter & Gamble CEO Bob McDonald. "To meet this challenge, P&G created a baseline digital-skills inventory that's tailored to every level of advancement in the organization."
Using Analytics to Reduce Churn
While the market lagged for years, there are standout examples of this evolution of analytics and business intelligence.
Similarly, mobile communications firm T-Mobile uses its analytics and business intelligence tools to reduce customer turnover rates – also known as churn. The company analyzes social media data alongside its own customer relationship management software (CRM) and internal billing to slash by half the number of people leaving the carrier.
New Ways Analytics Adds Value
Business leaders and technology professionals now expect and demand their analytics perform beyond visual data exploration and discovery. For example, getting immediate and relevant answers to our questions by enabling voice commands on a mobile device creates value.
In the modern day, carmaker Toyota is betting on analytics from a connected navigation system and a transportation network called Ha:mo (short for Harmonious Mobility). The network seamlessly integrates rental electric vehicles’ data with public transportation system data. This way, Toyota can optimize car placement and volume leveraging visual analyses of rental car movement.
Airline manufacturer Airbus needed help streamlining their testing protocols for performance and safety. In the past 26 years of collecting information between takeoffs and landings, Airbus saw the amount of test data increase 53 times to as much as 450 terabytes per test flight.
Certifying a new aircraft design is a detailed, complex process that puts a strain on engineers and pilots. So it has become critical to use analytics technologies that can help find efficiencies to improve safety and reduce risk.
How Big Data Informs Analytics
The shift from old to new approaches to data analysis has been significant. For example, one of the high growth areas for the analytics market is related to big data, which IDC forecasts worldwide revenues to grow from $130.1 billion in 2016 to more than $203 billion in 2020.
Additionally, McKinsey & Company estimates the US economy will get a huge boost by 2021 thanks to a massive job growth in the category of big data analytics.
Leveraging the Use of Data
Going forward, modern organization will use analytics platforms that anticipate people’s questions and deliver them instantly to their mobile devices — for example, based on geographical location, messaging activity, calendaring, or personal preferences — so that they can make more efficient, fact-based decisions on the spot.
Modern organizations are also creating data labs for experimenting with their data.
The goal is to use analytics to understand and uncover insights from data in order to build optimized economic models, thereby transforming data into an asset. In addition, developing staff skills in advanced analytics and forming a Data Ethics Committee will allow these organizations to benefit from their investments in analytics.
About the Author
José Villacís heads Oracle’s global Cloud Product Marketing group for Analytics and Big Data Discovery within the Business Analytics Product Group. Villacís joined Oracle in 2007 through its acquisition of Hyperion Solutions, where he spent four years in the global corporate Product Marketing and Strategy team, taking part in three company acquisitions and one strategic OEM program
About the Author