Welcome to the Analytics Economy - InformationWeek
IoT
IoT
Data Management // Big Data Analytics
Commentary
10/23/2017
06:50 AM
Randy Guard, Executive VP and CMO, SAS
Randy Guard, Executive VP and CMO, SAS
Commentary
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Welcome to the Analytics Economy

An analytics economy is emerging, where organizations differentiate themselves and succeed with a blend of data, analytics, and collaboration.

Data is everywhere. We’ve been talking about that since the big data craze began a few years ago. But now we’re seeing something different. It’s not just data. It’s accessible data, fueled by advances in computing power, connectivity, and powerful analytics.

This mixture of data, analytics and the ability to collaborate forms the foundation for the analytics economy, where each insight sparks the next. It's where similar to the concept of compounding interest, value comes from compounded insights. It's where people work together with data and machines to accelerate innovation, creating a nonstop engine for progress.

It is the right time to capitalize on the analytics economy, since analytics are now easier to use for everyone, from data scientists to business users to executives. The maturity and pervasiveness of analytics have increased their adoption, and a convergence of emerging technologies and existing capabilities is opening new possibilities.

A great example from the non-profit sector is Project Data Sphere, LLC, an independent initiative of the CEO Roundtable on Cancer's Life Sciences Consortium. It’s a free digital library and laboratory that provides a single place for researchers to share, integrate and analyze historical, de-identified patient-level data from academic and industry clinical trials. In fact, anyone interested in cancer research can apply to become an authorized user.

The Project Data Sphere initiative aims to spark innovation and enable the cancer community to unlock the potential of valuable data by generating new insights through analytics and expanding research possibilities. An engaged, diverse and global community focused on advancing research to improve the lives of cancer patients and their families around the world drives this collaborative initiative. Project Data Sphere enabled the first ever crowdsourced prostate cancer data mining competition. This effort identified new models to predict patient outcomes and has provided doctors with treatment options that help to positively impact patient outcomes.

Project Data Sphere demonstrates the analytics economy in action. Through data sharing and collaboration, insights are compounded to discover more effective treatments.

Three keys to recognizing the value of analytics

At the core of the analytics economy, there are those creating disruptive innovation and those fending off disrupters. In this economy, disruption and innovation can come from anywhere. Software companies are disrupting traditional business models, non-“techies” are writing apps, and digital transformation is enabling organizations to monetize their big data.  

Innovation-based disruption requires integration and coordination across an organization. The companies most likely to be disruptors help employees investigate data based on their natural curiosity, and make it easy to extract new insights from that data regardless of the employees’ roles.

What are the challenges that must be overcome for an organization to succeed in the analytics economy and become a disruptor? People, processes and technology must be considered.

People: An organization driven by analytics has a willingness to consider new perspectives: To acknowledge what was known and worked in the past might not apply in the present or future; To trust the data. An established analytics culture with the necessary skills dispersed across the organization is essential. And it is highly important that executives in the organization model and embrace these behaviors.

Processes: Remove the silos and establish data and analytics model governance. This might sound counterintuitive. An organization needs boundaries to foster collaboration? In MIT’s 2017 research report, Analytics as a Source of Innovation, survey results show that strong data governance practices actually enable data sharing, which then enables innovation. Companies that share data internally get more value from their analytics. And the companies that are the most innovative with analytics are more likely to share data beyond their company boundaries.

However, all this shared data is useless without shared analytics to discover the patterns and insights. Sharing models and analytics discovery methods is where an organization can see the most value. Effective discovery removes bureaucracy and restrictions, allowing organizations to be creative. In fact, creativity is key to discovering patterns that will lead to innovation. Trying out ideas, rapidly. Failing fast. Learning and then improving.

Technology: Infrastructure limitations. Fragmented environments. Scaling issues. Without the right technology in place, an organization cannot fully embrace its potential for analytics collaboration. If the tools and the data in different environments can’t be integrated, this is a point of failure. A diversity of tools, technologies and programming languages must be supported through a centralized, governed platform.

What do you mean by a platform?

The key to enabling the analytics economy is an analytics platform. A platform allows users to quickly create value from data by seamlessly moving through the analytics lifecycle, perfecting the path from data to decisions. Here’s an example of what I mean.

When it comes to transportation, nothing frustrates passengers more than delays – especially unexpected ones. That’s why railway companies take every advantage possible to maximize their operations and keep customers happy. In recent years, the Finnish railway, VR Group, began fitting sensors on various systems to monitor symptoms of wear and other failures. But the sensors themselves only collect the raw data. The real benefit came in analyzing that data to discover, often in real time, patterns that help engineers respond more quickly and accurately.

With an analytics platform, VR Group can deploy a predictive maintenance program that focuses on monitoring the condition of parts at all times. In this program, mathematical models predict when parts are likely to fail so that they can be replaced before they cause unplanned downtime. This helps ensure trains run on schedule.

Bottom line, the analytics economy forces organizations that want to be on the cutting edge to embrace analytics and make them pervasive and accessible. Does your organization have what it takes to compete in the analytics economy?

Randy Guard
Randy Guard

Randy Guard is Executive Vice President and Chief Marketing Officer for SAS. He is responsible for the SAS brand, providing global, strategic direction and marketing vision for SAS products and solutions. Guard has many years of consulting, marketing and product development experience. He is responsible for the SAS portfolio, which spans analytics, data visualization, data management, business intelligence and industry solutions.

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