In an increasingly competitive and unpredictable business climate, it’s becoming more important for enterprise decision-makers to develop and execute a winning business strategy. Doing so, however, is far from simple.
How can you build sustainable competitive advantage in a world that’s ever-changing? Or, make fact-based decisions about operational processes and be confident that your decisioning strategies are fact-based rather than founded on hunches or guesswork?
This is where predictive analytics comes in. Predictive analytics identifies the most likely future outcomes on the basis of current and historical trends. It leverages statistics, mathematical modeling and data science to forecast trends and generate insights with a great deal of precision.
It’s not a crystal ball, but predictive analytics can give business leaders a clear vision of what’s most probable in the future and an evidence-based framework for making smart decisions.
Analytics Use Cases: How Analytics Delivers Value
Predictive analytics harnesses the value of business data to guide decision-making. To implement it successfully, enterprises must collect the right data at scale and develop the right data extraction. They must prepare warehousing technologies to make their data available for analytic purposes and then employ the right data mining and correlation techniques along with statistical analyses and predictive algorithms to harvest data insights in ways that can be understood — and acted upon.
Generally speaking, the more business value a particular type of data analytics stands to deliver, the more difficult it will be to implement. Maximizing the value your data can provide will require major transformation. You’ll need new technologies, of course, but you’ll also need new skills and competencies among your employees, as well as an organizational culture that embraces data-driven decision-making.
If your enterprise is working to become a more data-driven organization, the simplest place to start is with descriptive analytics. Implementing this type of analytics solution requires collecting large volumes of data and processing it so that you can better understand what’s taking place within your business. Here are some question to consider:
- Are sales increasing or remaining flat?
- Do your customers tend to make more purchases in the summertime or in the winter months?
- How much do they spend on their average total purchase?
- What kind of inventory levels do you currently have in stock?
To put descriptive analytic tools to use, you’ll need to ensure that you are gathering enough data, collecting the right data and that you have an adequate storage infrastructure in place to make the data accessible. But the mathematical operations performed in descriptive analytics are elementary: basic sums, averaging or observation of temporal patterns, for instance. Results are reported in easy-to-interpret tables, charts and graphs, and relatively little domain-specific expertise is needed to understand them.
If descriptive analytics tells you what has taken place, the next logical question stakeholders will want to ask is “Why?”
That’s what diagnostic analytics seeks to answer. This involves using data discovery, data mining and correlation techniques to drill deeper into the data and uncover the reasons that the observed trends occurred.
Diagnostic analytics requires the same sorts of data collection and storage architectures as descriptive analytics, but the statistical techniques employed to discern patterns are far more complex. In addition, it may be necessary to incorporate external data sources to identify correlations that are linked with causality. For instance, having information about weather patterns might help explain deviations from typical seasonal sales cycles. Diagnostic analytics typically requires more expertise to implement and undertake than descriptive analytics, but delivers more value because it can reveal real-world cause-and-effect relationships.
Business stakeholders who understand what has happened and why it’s happened will inevitably wonder what’s coming next. Predictive analytics attempts to forecast the future on the basis of historical data, statistical trends and probabilities. No algorithm can predict what will happen tomorrow with absolute certainty. But, predictive analytics can provide accurate and reliable estimates that can be used to optimize marketing campaigns by suggesting the best cross-sell opportunities, or to detect fraudulent activities or cyberattacks. Or, these estimates can be leveraged to optimize vehicle fleet maintenance schedules or to reduce downtime rates in manufacturing by planning repairs to mechanical process components before they break.
Predictive analytics has the potential to introduce massive cost savings, tremendous efficiency gains and large-scale improvements in customer satisfaction rates, but it’s challenging to implement. Because of the inherent complexity of the modeling techniques involved, many predictive analytics solutions are designed to make use of artificial intelligence (AI) or machine learning (ML) algorithms that can be trained to increase their predictive accuracy over time as they’re fed growing volumes of data. These technologies are complex and sophisticated, and enterprises need access to expertise along with the right computing and data warehousing infrastructures to realize their full value.
Finally, prescriptive analytics, an emerging technology, attempts to guide decision-makers towards the best course of action by quantifying the impact of each possible decision on potential future outcomes. Relying on complex and sophisticated combinations of business rules, AI- and ML-driven algorithms and computational modeling procedures, prescriptive analytics are complex to implement and administer but promise to steer business decisions towards the course of action that will lead to the highest probability of success, every time.
Predictive Analytics and the Future of Business Decision-Making
Enterprises across industries are investing time and effort into improving the accuracy of their analytics and forecasting tools, but few have attained full maturity in terms of becoming data-driven. Consider the current bicycle shortage in North America: if one bike manufacturer had been able to forecast it on the basis of consumer trends or circumvent it by proactively creating more resilient supply chains, that company would be prepared to realize near-limitless growth today.
To solve this complex predictive problem, however, that bicycle manufacturer’s analytics team would need access to large volumes of historical and current trend data in real-time (or near-real time), as well as information on current events, supply chains and consumer preferences. And, they would have needed cutting-edge technologies to ingest, store and manage that data, as well as industry-leading expertise in writing, running and interpreting complex queries.
Naturally, there are huge advantages to be gained from making these investments, and cloud data warehouses and analytic tools are bringing their benefits within reach for growing numbers of organizations. Although it’s impossible to predict the future with certainty, we’ve confident that future enterprises will improve their ability to forecast consumer demand for their products many times over.
For more information on how Cloudreach can help you prepare your business to harness the power of its data and become more data-led, read more about our DataOps as a Service.