From Big Data to Big Insights: How to Make Better Business Decisions with Predictive Analytics Posted on : Mar 12 - 2019

Although many businesses tout "data centricity" as a critical success factor, most only realize a fraction of the value they could be getting from their data. Investments in data acquisition, storage, and analysis often fall short because most businesses lack the tools and techniques needed to adopt a successful data-driven strategy. Hiring more data scientists and other scarce resources to perform descriptive analytics or ad hoc analyses may help with reporting and business intelligence, but it doesn't provide insight into what might happen if a business takes a specific action, nor does it provide a prescriptive path for what the business should actually do.

As FICO's vice president of analytics, some of the challenges I hear most frequently include:

• We've collected lots of data but haven't found a way to put it to use.
• Accessing and wrangling the data is too difficult and time-intensive.
• We have a team of data scientists, and although their findings are interesting, we haven't found many real, practical uses for their insights.
• Our machine learning results are hard to understand and implement.
• It's easy to validate what we already know, but how do we discover new, previously unknown insights that deliver measurable (even disruptive) value to our business?
• Acquiring data is expensive. We cannot justify the cost unless we can clearly show the ROI.

To overcome these challenges, businesses need both the right methodology and the right tools to execute that methodology. The real key is to find a cost-effective, repeatable way to add intelligence to the volumes of data that already exist within the organization. One way this can happen is by using data lakes and streaming data sources from anywhere inside (and outside) the organization to operationalize data-driven actions and support real-time outcomes.

By moving analytics closer to the data, you can extract value more quickly. Here are four steps today's innovators are adopting to streamline their analytics supply chain.

Step 1: Start with the objective

What business challenge or objective is most pressing? How have you historically used data (and analytics, and even decision-making rules) to address this need? What metrics demonstrate success?

The more goals you can map out, the easier it is to gain political and organizational buy-in and streamline the project's execution. Many organizations start with a single, well-defined problem that helps them fine-tune the process over time and improve it iteratively. Note that this mapping process will also help define resources -- not just data scientists, but also domain experts and others in the business (even across functional boundaries) who can contribute to, and benefit from, the evolution from big data to bigger insights.

Step 2: Collect and enrich the data

Once you have clearly identified your objective, you need to access, understand, and assess the viability of all possible data sources. Many data sources, whether internal or external, might seem promising on the surface but need to be assessed for the breadth and depth of information they contain, as well as potential value. You may be starting with large volumes of raw data, so enriching the data requires machine learning algorithms to extract features (also known as attributes or characteristics) in a comprehensive and efficient way. View More