Data Analytics 101 (Part 1 of 3)
Without further explanation, it’s clear that we need tools to help us make sense of it all. For business owners, it’s even more critical that you understand the value that data can bring to your organization and how to tap into it. For that reason, I’m providing consumers with some general information about data analytics and ways they can use it at their businesses to gain valuable insights with the release of my three part blog series called, Data Analytics 101.
To get started, here’s my definition of Data Analytics.
4 Types of Data Analytics
Furthermore, here are the most commonly know types of data analytic approaches that are used in today’s business operations.
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
1. Descriptive Analytics – This is a simple form of data analysis that provides information about WHAT happened. It does not provide great insight about the data-set in terms of relationships, nor does it identify cause and effect variables in most cases.
Key Takeaway: This is the most frequently used analysis approach across most businesses that comes in the form of key metrics and performance measures. It provides consumers with general facts about the data-set that helps inform and/or prepare data for further analysis. In this phase of analysis, consumers primarily generate ‘hindsight’ information on the data-set. It helps answer the question, “What happened?”
2. Diagnostic Analytics – This is a more complex form of data analysis that incorporates hindsight information from the Descriptive Analytics process that provides information about WHY something happened. It does help identify data-set dependencies and patterns, which help analysts gain insights into a particular problem.
Key Takeaway: This approach works best when used with Descriptive Analysis information. It provides consumers with detailed insights based on relational dependencies and non-relational variables to help generate valuable insights. In this phase of analysis, consumers start to generate ‘insights’ based on ‘hindsight’ information from the data-set. It helps answer the question, “Why something happened?”
3. Predictive Analytics – The complexity level rises during this form of data analysis, which incorporates a combination of Descriptive and Diagnostic Analysis information to form detailed insights to help identify what is LIKELY to happen. It helps businesses forecast trends based on data-sets associated with past and current events.
Key Takeaway: This approach works best when used with Descriptive and Diagnostic Analysis information. It provides consumers with detailed insights that help to identify trends, clusters, and outliers (i.e. anomalies) that aid in the prediction of future trends. In this phase of analysis, consumers start to generate ‘insights’ based on ‘past & current’ information from the data-set. Artificial Intelligence (AI) subsets such as Machine Learning (ML) and Deep Learning (DL) techniques are often applied to this level of analysis for machine training purposes. It helps answer the question, “What will happen?”
4. Prescriptive Analytics – The complexity level rises during this form of data analysis, which incorporates a combination of all the Descriptive, Diagnostic, and Predictive Analysis information to generate detailed prescriptive options to help identify HOW we can make something happen. It helps consumers identify alternative solutions relevant to their respective problem(s) associated with a single or variety of data-sets.
Key Takeaway: This approach works best when used with Descriptive, Diagnostic and Predictive Analysis information to reach a level of optimization. It provides consumers with an output of something similar to a step-by-step process that highlights alternative options (e.g. fastest ‘Google Map’ routes based on traffic density, time, and distance). In this phase of analysis, consumers generate ‘foresight’ based on ‘past & current’ information from the data-set. Artificial Intelligence (AI) subsets such as Machine Learning (ML) and Deep Learning (DL) techniques are often applied to this level of analysis for machine training purposes and advanced recommendations. It helps answer the question, “How can we make it happen?”
***Please note, the aforementioned data analytic approaches are not always used in sequential order nor are they always dependent on one another to discover information about a particular data set. Instead, the Gartner ‘Analytic Maturity Model’ represents a progression of practices designed by collective research of data analysts’ behaviors from global business and academic use cases. Like most things, this model will evolve with time and further development.***
- Improve Operational Efficiency/Effectiveness – Businesses can use analytic techniques to help design and manage daily business operations that allow them to better serve their customers. For instance, by visually depicting your daily workflow processes and the associated data generated from them helps data analysts or business analysts alike identify areas for improvements. As an example, if two teams within the same department of a business executes a daily/weekly/monthly process that generates a similar data set used to identify descriptive information about customer relations, an analyst would be able to recommend a proposed solution to management that could improve operational efficiencies by re-designing two processes into one. Although there may not be a common definition for the data set among the two teams, you can still save time by using a shared process that supports multiple in-house teams thus optimizing resources and creating efficiencies that will often lead to effectiveness.
- Develop Relevant Products/Services – In today’s world, businesses have the opportunity to not only use the data generated from their internal business operations (i.e. customer relationship management tools, enterprise resources planning systems, financial transactions, social media accounts, etc.), they also can take advantage of the vast amount of data generated from global sensors associated with the Internet of Things (IoT). That is…tapping into data created by others in the market on a global scale. Collated data, combined with internal operations data, can help businesses identify new customer behaviors, interests, and even help forecast market demands. By using analytic techniques to understand past and current events, businesses to gain great value from such data to improve products/services instead of using their gut feelings to make costly decisions. Keeping a finger on the daily qualitative feedback and quantitative metrics of your product/service development efforts, coupled with market trends and customer feedback, businesses can more accurately propose operational pivots to stay competitive.
- Customer Personalization – As you may notice, any business has the opportunity to collect data on their customers at every possible customer touchpoint within the digital landscape; especially, thru online applications and social media engagement tactics. With that in mind, you may find yourself asking, “what does the business use my data for?” In most cases, if a business is staying compliant with data privacy laws and information sharing regulations, they are using your data to develop insights about your interests and behaviors in order to develop personalized product and service offers. This allows them to perform analytic methods on your data, which increases their probability of converting you from just a window-shopper to a buyer the next time you visit their site or brick-N-mortar (e.g. Amazon, eBay, Netflix, etc).
- Mitigate Risks – Regardless of the type of business you operate, you have assets that you want to protect. In order to protect such assets (i.e. personnel, data, intellectual property, etc.), it’s important to leverage data analytics to help your team identify risks. Some risks can easily be identified by having a wholistic view of your data and the business operations associated with creating that data, while other risks are discovered based on anomalies in reporting and/or through advanced techniques. Whatever your case may be, proper data management combined with transparent reporting practices can help businesses identify early indications and warnings of possible risks associated with their assets. With effective data integration practices and data engineering to help correlate data across the business, companies can reap the benefits of a dashboard-like single view of incidents and the relational variables that caused them to occur. Overall, this will aid in the development and/or modification of risk mitigation strategies.
As Think Big Analytics director, Alec Gardner of Global Services and Strategy says, “The ability to use data to better understand the customer journey is imperative to creating an optimal customer experience. With the right technology, infrastructure, and analytics in place, it’s now possible to unlock the full potential of your data for beneficial business outcomes.”
Now that you have a better idea of what data analytics are and how businesses can use them, go forth and do great things for your company. Leverage the power of data analytics to discover your potential to grow, adapt to ever-changing market demands, mitigate risks to assets, and improve your products and services with customer personalization in mind.
Until the release of Data Analytics 101 (Part 2 of 3), take care during these challenging times due to the cultural impacts of CoVID-19 on our society.
If you have a recommendation for me to write about a specific data-related topic or would like further information about something I already wrote about then please leave a comment or send me a message via contact form. I will do my best to provide feedback accordingly. ~ Peace to the world!