Demystifying the Purpose of Analytics: A New, Simplified Model that Aligns Analytics Purpose with Business Value
There have been a lot of incredibly technical analytics advancements that have occurred during the past several years. The evolution of predictive analytics, including the utilization of machine learning algorithms, the leveraging of artificial intelligence in everyday home technologies, and even the introduction of deep learning to name a few.
Throughout this analytics revolution, I have consistently received the same inquiries regarding analytics – coming from friends, families, colleagues, and even the attendees of various analytics conferences I’ve attended. These inquiries can be best placed into two very distinct and compelling categories of questions. One category asking the question “what is the purpose of analytics?” and the other asking the question “what value does analytics bring to business?”.
With all the analytics advancement and the subsequent demand for business stakeholders to understand what the purpose of analytics is, and what value it brings to business; I went searching for a comprehensive, compelling, and most importantly of all – understandable – model that somehow aligned analytics purpose with business value.
I searched everywhere I could possibly imagine, I reached out to analytics experts in the field, and I even called a few professors I’ve had as previous mentors to see if there was some best-in-class model that clarified and explained this in an understandable way.
While it was nice to catch up with previous colleagues and mentors, and even receive their expert opinions of the various analytics advancements currently taking place in today’s technological landscape, I came away with mostly anecdotal – at best – answers to the two primary questions I originally fielded.
So, what is the purpose of analytics?
And what value does it bring to business?
If you work in some facet as an analytics professional (or even work with data transformation on some scale), the answers to these two questions may seem overly simplistic. The purpose of analytics is to create and capture value in terms of new insights, recommendations, or solutions to real business problems or opportunities. The value that brings to business – in general – is timely, accurate, and actionable knowledge which has been transformed from information in some meaningful way. This allows businesses to make quicker, better, and ultimately more profitable decisions. Efficiency is only the beginning here, and the effective execution of optimal strategies for your business might be the ultimate goal.
Sounds simple enough, right?
The tricky party here is that analytics isn’t stagnate, it can be an ever-evolving, dynamic, living, breathing competitive advantage for your business when fully utilized. The other tricky part is that the only 100% true answers to the aforementioned questions for any specific business will be “it depends.” Yes, like all great answers in marketing, the only definitive way to answer these two questions is “it depends.”
So what does “it depend” on?
The answers to “what is the purpose of analytics” and “what value does it bring to business” depend on a growing list of significant factors, including what the business goals of the company are, what analytics stage of advancement the company is currently at, how the company was set up and organized, how analytics is currently managed and implemented in the company, and the analytics resources the company has to answer their most pressing business questions, amongst many, many other significant factors, which is important to note. The ultimate success of an analytics department at any one company depends on factors that are custom to that company, its specific goals, and the business stakeholders which it serves, both internally and externally.
So, answering these two original questions isn't as simple as we originally might have thought it was. I am willing to wager that is why it was so difficult to find anything resembling a best-in-class model that aligned analytics purpose with business value. Because it varies for each and every unique company out there that leverages analytics. And what about the companies that don’t currently leverage analytics? How could they even know how to begin to answer these two primary questions without being able to turn to a case study of similar companies who had seen measurable success?
In researching and studying all of this, I decided to take it upon myself – being the motivated and passionate entrepreneur that I am – to develop a model that can serve as a way for companies that are currently leveraging analytics to answer “what is the purpose of analytics” and “what value does it bring to business”; as well as serve as a roadmap for those companies wondering about the potential purpose of analytics to their company and the potential value it could bring to their business.
In all of this, my goal was to demystify what the purpose of analytics was, and then effectively connect it to business value for the vast majority of companies that are either currently utilizing analytics or aim to in the future.
My goal was to develop the model in a comprehensive, compelling and most importantly of all – understandable – manner which would be accessible to all.
Please feel free to share this new analytics model with anyone in your network that would gain benefit from it. Be sure to include The Chicago Analytics Group as the source and include the hashtags #ChicagoAGroup and #PurposeOfAnalytics if sharing on social media. Thank you!
Demystifying the Purpose of Analytics: a New, Simplified Model that Aligns Analytics Purpose with Business Value
Looking at the new model above, you can instantly recognize four distinct, interior purposes which analytics serves. These are the elimination of bias, followed by perspective, continuous improvement, and finally profitability catalyst. You will notice that as you focus a greater utilization of analytics towards the target center (which contains those underlying elements of the profitability catalyst purpose), your analytics purpose will be more closely aligned with real business value. What this fundamentally means is that you have to fully tie your analytics strategy into your business strategy to achieve real results. Your analytics strategy should complement and support your business strategy, not be developed and implemented in any sort of departmental vacuum by itself.
In the outermost layer of this new model is the elimination of bias purpose for analytics. While this may seem pretty straight forward in its description, the underlying elements of this serve as a significant, fundamental base for which analytics purpose can be aligned with business value. These elements include using a scientific approach, critical thinking, and honesty and integrity. While each one of these is important in its own regard, it's really the optimal use of all of them together which allows for the elimination of bias to occur while aligning analytics purpose with business value.
In the second layer of the model is the perspective purpose for analytics. Now, one thing to note, is that as you move closer to the innermost layer, the purposes not only become more focused in nature, but also drive you closer to your ultimate goal: aligning analytics purpose with business value. In order to successfully fuel the creation of perspective within your company, you need the optimal combination of effective systems of insights, opportunity (as well as potential hardship) recognition, and then - of course - methods for evaluating and formulating solutions to real business problems that your company may be facing.
The third layer of the model continues to dive deeper into what matters most to the vast majority of companies out there: continuous improvement. The successful development and completion of this is highly valued to many business stakeholders within a company because it can serve as both a strategic and competitive advantage. The elements of this purpose include optimizing strategy, products, services, and operations with the customer/ end-users in mind, so that there is an ever-increasing level of satisfaction.
As we get closer to aligning analytics purpose with business value, it is significant to note that more investment in and a great utilization of analytics across the company is necessary to support its continual advancement.
Lastly, we get to the centermost purpose of analytics which is aligned with the creation of business value. This layer is called the profitability catalyst purpose for analytics. The elements included in this purpose are innovation, competitive positioning, and ultimate growth. In this layer, there are many direct observations of positive business value created through analytics purposes, which may include ROI on specific marketing campaigns which utilized a high degree of analytics components as compared to those which didn't, the ability of analytics within your organization to examine and fuel your company's brand equity, and analytics serving as the backbone for financial growth to name a few.
This centermost layer is generally of most interest to executive leadership within companies, as the alignment of analytics purpose with business value here has the propensity to move the company forward on a large scale. The rest of the layers are important by themselves, but together they also serve as the foundation for which you can move closer to the target. Also, it is recommended in this new model that each layer is successfully developed before emphasizing the development of the next inner layer; although in quickly reacting and growing companies, many times these layers may be getting developed in parallel.
The elements underneath the analytics purpose in this centermost layer are both drivers of analytics serving as a profitability catalyst within your organization, as well as desirable outcomes of successfully utilizing analytics as a profitability catalyst. In this way, on each of the layers of this model, but most importantly the innermost layers, both the elements of and analytics purposes themselves directly affect each other through developing effective analytics strategy and successful analytics strategy execution.
Ultimately, in building this model, I wanted to equip any and all analytics professionals with an easy to understand, but also comprehensive and compelling roadmap for aligning analytics purpose with business value.
If you would like to learn more or have any questions, please do not hesitate to e-mail our group at TheChicagoAnlayticsGroup@gmail.com.
All the best,
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