Understanding the 4 Analytics Solutions Needed for a Data Science Foundation
Author: Tom Hoblitzell | | January 18, 2022
Analytics empowers organizations with actionable insights from your data, making it possible to make better strategic decisions. Creating a powerful data science foundation to get the most from your organization’s data requires a strong data foundation to enable the right analytics solutions.
When you want to look closely at what happened in the past, you use descriptive analytics to “describe” these insights. Your organization most likely already has access to this type of reporting, whether you use a stand-alone solution or one that’s integrated with other systems.
For example, a marketing automation platform that allows you to track KPIs (Key Performance Indicators) and display them in reports and dashboards is one descriptive analytics use case. You’re able to use this type of analytics solution in many business units and systems because it’s resource-light compared to other, more advanced forms of analytics technology. This ease of implementation makes it a suitable candidate for the first part of your data science foundation. However, it may not be enough to know that “something” happened. Perhaps, you’d like to understand “why” it happened.
Diagnostic analytics uncovers the “why” behind something that has already happened in your business. This type of tool is also sometimes referred to as “root cause analysis.” You can drill down into your data and look for the factors that influence each event. You can discover how the individual events connect through common threads.
You may enrich your internal data with additional sources to further understand why something happens. Your diagnostic analytics solution can help you understand why problems occur with your products, the reasons one advertising campaign outperforms another, and what makes employees more engaged. The answers to these questions and many others will provide you with the context behind your data and equip you with important information on which to base decision making.
Both descriptive and diagnostic analytics rely on your organization’s available historical data. Predictive analytics does as well, but it adds a new component into the mix: machine learning algorithms. One type of predictive analytics, called a forecast model, can “predict” values based on historical data, leaning on machine learning to perform this complex processing. To achieve this type of prediction, it’s important to know what data attributes should be included in the model. Most importantly, it’s essential to know the nuanced ways that these models work and are eventually scored such that you can rely on the outcomes that are provided.
With a predictive analytics tool, you’re better equipped to make decisions based on what could happen. You can expand the big picture view of your data to focus on strategic approaches that could lead to your preferred outcomes. This type of analytics solution works well for those in leadership roles who want hard data to support their decisions. Due to the use of machine learning algorithms, predictive analytics requires more compute resources than descriptive and diagnostic analytics.
Prescriptive analytics “prescribes” actions based on your organization’s data. Besides historical data, it includes real-time data, so it recommends actions based on the most current information possible. Machine learning and artificial intelligence are key in this type of solution.
Prescriptive analytics is the most complex and resource heavy of these four analytics solutions, and it works best for insights into operational decisions. Employees get an optimized set of recommendations for everything from the best leads to contact to new market opportunities.
It’s important to note that prescriptive actions are only possible when you have a strong foundational approach to data quality that includes accuracy, completeness, reliability, relevance, and timeliness.
What Types of Decisions Can You Make with Analytic Insights
Implementing an analytic approach in your organization can impact many types of decisions that you make, from daily operational tasks to long-term business strategies. You can get answers to many common questions via your data, including:
- What influences sales? Looking at historical data outcomes, you can determine what attributes contribute to increases and decreases of a particular product.
- What do your target customers look like? You can use customer data to surface the characteristics that most commonly show up. When you match this demographic data to potential audiences and prospects, you can improve conversion rates and revenue.
- How can you best attract these customers? These insights explore the tactics that work best on your target audiences. You can spend more time and resources on these methods rather than spreading your organization too thin.
- Where are your best customers located? Do your products and services have regional appeal or are your customers spread throughout the world? Your organization’s ideal approach may be much different depending on this location preference.
- What happens after you experiment with your strategies and tactics? Find out exactly what changes when you try out new methods and begin testing different variables. You can use this data to get buy-in for implementing these changes on a broader scale and building upon the results. If you get real-time data, you can quickly learn how your audience reacts.
These four areas on the analytics continuum comprise the data science foundation that sets your organization up for decision-making success. The actionable insights from each type of analytics provide you with a highly optimized approach to doing business. Learn more about the power of analytics by downloading our white paper: “Use Analytics to Drive Better Decisions with Actionable Insights.”
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