Top Seven Mistakes People Make with Predictive Analytics

Businesses repeatedly rush into Predictive Analytics projects without thoroughly vetting the risks, benefits, approach, or expected outcomes. They usually don’t fully understand what data to collect, the way to evaluate it, or the way to interpret the insights obtained. After helping many companies analyze their approach to predictive analytics, this list is a collection of the most common issues which you should steer clear of when attempting to incorporate predictive analytics into your organization.

1. Trying to boil the ocean - The possible data points a company can use to build the predictive models are almost endless. From customer behaviors and process performance metrics to marketplace statistics/trends, to open data readily available on global aggregation sites --businesses that strive to gain meaningful insight out of every information source available are poised to fail. Instead, focus on the most important metrics that indicate success in a particular process.

2. Ignoring leading indices - Discussing the metrics that are important for your company, the ones that tell you what transpired in the past, can certainly assist you to put past operations in context. However, without broader context, they really can potentially lead you in the wrong direction. There is no doubt that companies should prioritize the data that precisely measures success, but be sure to augment that with internal or external sources that paint a broader picture of what is going on in your industry or the world in general.

3. Generating single use, outdated models - Many companies create statistical modeling systems are outdated the moment they are deployed. Creating data models which depict reality on in a specific point in time will not assist you to plan for changing times. Instead, focus on designing models that are automatically updated as key indicators and critical performance metrics change. Ensure that the selected data points are easily updated and replicated across predictive algorithms that drive multiple processes. The best way to accomplish that is through metadata models. That is a topic for another day.

4. Not collaborating with the company’s data analytics strategy – Every company knows that it should be using data and information to guide decision making on significant tasks. Nevertheless, integrating data analytics into business processes requires more than merely gathering and studying data. Time and energy will be wasted if company executives don’t understand and trust the predictive model. It’s equally important to ensure that recommendations are delivered on time. A 100% accurate prediction provided after the decision has been made is not useful. Find a balance of accurate forecasts given in a timely manner.

5. Overlooking what the statistics show - Almost half of Executives admit to distrusting their own data strategy and relying on their gut feeling instead. New research coming out is showing that this inevitably leads to decisions that have suboptimal outcomes. While some executives may be hopeless, many can be taught the value of analytics through pilot projects or A/B comparison tests. You may need to build the predictive models, present the recommendation, then later demonstrate its effectiveness in order to move them from the status quo decision-making model.

6. Limiting information usage to a single conclusion/business unit - Predictive analytics has many benefits in an organization, but often its adoption is kept locked away by data analysts, and the benefits aren’t shared with executives much less individual contributors. Similarly, data has been used to concentrate on one key metric -- such as overall earnings, however, SKU-level or even market-level variations have been discounted. In most cases, the highest ROI projects are directed at helping individual contributors make many small decisions faster and more accurately. Many of these small outcomes add up to significant financial impact.

7. Treating all information as the same - Perhaps not all data is accurate data. It’s essential for companies to thoroughly examine the trustworthiness of the data and data source or risk making decisions about obsolete or erroneous metrics. If your company executives typically have different versions of the same metrics or you have many systems performing the same function, it is critical to undergo a data management and cleansing project before implementing predictive analytics. Making automated predictive analyses based on inaccurate data is almost always worse than using executive gut-based decisions.

These aren’t the only mistakes that companies make when implementing predictive analytics. However, if companies can avoid these pitfalls, they will typically be well on their way to becoming a data-driven organization. Data-driven organizations make better decisions about all aspects of their business from marketing effectiveness, to sales targeting, supply-chain optimization, maintenance intervals, and beyond.