4 steps for getting the most impactful results from predictive analytics

Predictive analytics is based on machine learning algorithms that systematically analyze large quantities of historical data to identify recurring patterns and anomalies.

In today’s data-driven world, predictive analytics are a fundamental necessity. In fact, given the infinite power and appeal of predictive analytics, the global market is projected to reach approximately $10.95 billion by 2022.

By leveraging existing data to power predictive analytic applications, organizations can make intelligent business decisions more efficiently while also pre-empting market opportunities, rather than perpetually scrambling to catch up.

For example, by tapping into product, supplier, customer, raw material and/or logistics data, organizations can obtain valuable, actionable insights such as when a customer will need a new product or what products or services will emerge as the most popular.

What’s particularly valuable about predictive analytics is that it unlocks value from existing data sources. Rather than requiring an entirely new approach, predictive analytics is based on machine learning algorithms that systematically analyze large quantities of historical data to identify recurring patterns and anomalies.

These patterns and highlights can be extrapolated (either visually or as raw data) to provide insights into the probability of certain occurrences in the future. As a result, when successfully implemented, predictive analytics can be truly transformative, delivering a considerable competitive advantage by enabling organizations to gain a forecast of the future, improve their profit margins and customer retention levels, and drive new business opportunities.

Four Tactics for Overcoming the Widespread Data and Analytics Challenge

Despite predictive analytics’ significant benefits, a recent Gartner survey shows that a staggering 91 percent of organizations worldwide have not yet reached a "transformational" level of maturity in data and analytics, and that’s taking into account the fact that this area has been a number one investment priority for CIOs in recent years.

To overcome this widespread challenge and realistically reap the benefits predictive analytics can provide, organizations should adhere to the following four best practices:
  • Establish a clear data collection processes. Regardless of the power of analytics-based models, nothing can be achieved without the right data. In fact, without an accurate and complete data set, predictive analytics models can end up being skewed and lead to harmful, inaccurate predictions. It’s therefore critical that organizations design firm processes for collecting accurate and relevant data. Equally important is ensuring everyone in the organization follows these processes.
  • Optimize data storage set-ups. There are few problems more significant in any predictive analytics project than the inability to find, access or otherwise fully exploit the data sources that hold value. The trouble is, corporate data has become so vast and fragmented that it’s now routinely difficult to locate, let alone act upon. Even if organizations are able to get their hands on some solid data and glean possible insights from that data, the data analytics they seek are often out of reach because their data resides in multiple places.

For instance, often data is lurking in proprietary applications and departmental business intelligence databases, and/or it’s continually accumulating in unmonitored data lakes. To extract the full value from their data, organizations must prioritize data storage defragmentation.
  • Implement the right technology. Organizations require appropriate tools to take full advantage of their historical data. It’s also particularly crucial to keep an eye on the future and consider the expandability and sustainability of the technologies being implemented. For instance, Hadoop’s scalable HDFS can work well for ongoing storage needs, NoSQL/NewSQL systems can help with efficient data processing, and streaming solutions such as Spark and Kafka can enable real-time data pipeline assembly.

Additionally, a fast analytics database is essential for running predictive analytics at scale. Combined with in-memory technology that can be integrated with existing systems, making it suitable for any cloud scenario -- from public to private to hybrid -- such an analytics environment can help organizations stay ahead of the competition.
  • Prioritize quality over quantity. More data doesn’t necessarily mean better data. It’s therefore critical that organizations remember that the quality of their existing data is more important than the quantity. When data quality is low, the insights organizations derive will also be low, and decisions made using poor data tend to result in undesirable outcomes. Rather than trying to amass as many different types and streams of data, prioritize existing data and work to hone in on its unique value and business potential as much as possible.

Success Requires Quality Data, Scalable Technology and Educated Users

By tapping into the power of machine learning-based predictive analytics, organizations potentially gain the ability to optimize and automate vital business decisions. What’s more, effective use of predictive analytics can aid organizations in reducing waste and operational inefficiencies, which in turn can boost profit margins. Rather than getting swept up by the impressive benefits predictive analytics can provide, however, invest sufficient time and thought into putting the right processes and tools in place to make it easy for users to extract insights from existing data.

Furthermore, educate employees to trigger cultural change in favor of using and trusting data insights to inform business decision-making, and remember that every industry is nuanced and impacted by different variables, so a “one-size-fits-all” approach to predictive analytics won’t work.

Ultimately, predictive analytics success means that insights for informing decisions can be extracted quickly and effectively, i.e. in real-time or near real-time. By incorporating a balanced combination of the right data strategy, scalable technologies and well-trained users, organizations can maximize the benefits predictive analytics enable, while also staying ahead of savvy competitors.

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