Working with all of the data in the world provides no value if the insights gained aren't used to drive decision-making. If you're interested in building a more data-centric culture within your organization, follow these 5 steps when transforming your data into decisions.
Figure Out What Data Must Be Produced
Every business has specific questions that need to be answered to grow and improve performance. For example, a business might be experiencing high levels of customer churn because its products aren't connecting with their current audience. Using available data, the company's analysts may determine, for example, that the churn is occurring because they are targeting the wrong age group or geographic region. In this scenario, the big decision that needs to be made is how to target buyers who will become long-term customers. Making that decision, however, starts with figuring out what data is needed. In this case, an analysis of customer churn will ultimately drive decision-making.
Identifying Potential Data Sources
The raw materials for a project come from the data sets you have access to. If you don't have the necessary data, processes should first be put in place to collect it. In the previous example, the company might want to acquire data by:
How to Properly Target Your Analysis
Especially with a problem such as customer churn, it's important to figure out what the sentiments toward the products are. There's a gap between well-targeted buyers who end up frustrated due to issues with customer service, for example, and buyers who made a one-time purchase because there was a killer discount or seasonal trend. Detailed sentiment analysis from multiple data sources can shed light on which groups most of your customers fall into. You might find that the previously targeted customers fell into 5 different categories, and a majority of the churn occurred only in one or two groups. You can then re-evaluate the marketing resources and retargeting efforts to those specified groups, adjusting strategy accordingly.Different problems will predictably require different forms of analysis. While an issue like customer churn might lend itself to sentiment analysis, a problem like evaluating drug efficacy based on clinical trials may lend itself more to Bayesian inference. It's important to understand why a particular statistical model might be more relevant than another before moving ahead with analysis.
Producing Insights Rapidly
Decision-making requires the delivery of insights in a timely manner. With analysis in hand, you need to quickly produce deliverables that will be presented to decision-makers. This means thinking about things like:
It's also important that the delivery of insights becomes a continual and constant process. Teams should be routinely working on projects, and there should be a strong emphasis on producing deliverables.
Driving Actions
Insights needed to be delivered to the right people. There's no need, for example, to deliver actionable information that a purchasing agent needs to the company's CEO. You want the fewest steps between insights and frontline decision-makers as possible. A data-driven company will make sure that purchasing agents have access to things such as real-time dashboards that show exactly what is trending, how inventory is holding up, and what items have the best margins. With the right processes in place, frontline decision-makers can log in to the system and see fresh insights daily.
