Machine learning can't save your bad data
Most sales-driven organizations have needed a customer retention model at some point or another. The request is fairly straightforward: identify the customers that a business might lose.
But the process can create a nightmare. CMOs across industries often struggle with the hard truth of their data. They may not truly understand what kind of data they’ve collected or how to create a narrative from the information on hand. Even worse, many CMOs believe they’re looking at a complete view of their customers, only to learn after countless working hours, the results aren’t all that helpful. If your data isn’t helping you define a clear course of action, you may have missed an opportunity to fully analyze it.
This scenario is more common than many realize. Increasingly, teams are tasked with data-driven goals to illustrate forward movement in their organizations. Marketers are often encouraged to adopt the latest customer relationship management or analytics tools to extract better insights faster with machine learning (ML). But layering one solution on top of another can make data even harder to read.
Plus, what if the data you’re collecting is unusable? Over two years, the Harvard Business Review performed a study that revealed less than 3 percent of data meets basic quality standards. The researchers found that 47 percent of new data records have a critical error. The potential impact of poor data on a company’s operational effectiveness is difficult to estimate. But one course of action is clear. The path to high data quality relies on an information architecture foundation that begins with governance, replication and integration.
For example, an organization needs a data catalog that indexes all data sources, structured or unstructured, to add more accurate metadata. The catalog classifies the data and assigns business terms for manageable searching. Crucially, the catalog evaluates the quality of each data set. With the data catalog as a springboard for data usage readiness, integration with ML solutions can make such an investment fruitful. An automated process enables real-time analysis on data sets powered by ML algorithms. A solution like this allows organizations to use data confidence scores, building trust in results.
The foundation you build, enhanced with ML, leads to what you’ve been seeking all along: accelerated insights. A large organization with more than 20,000 terms and a team of 20 people would typically need six months to manually analyze its data. Now you can look at solutions and platforms that reduce your analysis time to a couple of days or even hours. With an effective governance platform, you can save time identifying the insights, focusing more on how to impact your organization through action.
Watch the demo to see how IBM InfoSphere Information Server can help you know your data, trust your data and use your data at scale. Position your organization to use ML for what it was intended to do.