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Data Strategy for Non-Specialists.



How to Develop Your Strategy Without Being a Data Specialist?


In today's world, collecting large amounts of data is becoming increasingly easier. We have the tools, the people, the skills; however, the challenge - and what will truly set you apart - lies in being able to ask the right questions. Once you have those questions, it's time to understand if you can answer them, how you will do so, and who the answer is for. I like to think of this process in three steps that data will have to go through:

  1. Sources.

  2. Transformations.

  3. Presentation.

Sources

Every project involves striving to achieve a goal, whether it's selling more, understanding your target audience, or reducing costs. For any of these cases, you need data that can solve the problem (because, after all, whenever we talk about a data project, we're seeking to solve some kind of problem). The first thing you need to do is understand whether you have the data to answer your questions. Sometimes, this can be challenging, but a good way to do it is to logically understand how your questions work. Let's look at some examples:

  • Suppose you ask yourself, "What can I do to increase my sales?" First, let's think about what data can help you answer that. Keep in mind that sales vary based on price and volume. So, if you have data on price and volume, you can delve deeper and analyze whether it's advisable to increase price or volume.


  • Another question: How are my customers? Understanding them might depend on having certain data about them, such as their age, location, gender identity, etc. In this example, you might not know where they reside, and this could be an obstacle. In that case, you would need to redefine your strategy and base it on the data you do have. Your strategy could take two paths in this case: either change the definition of customer knowledge or start collecting the missing data.


  • Another common question is: What can I do to reduce costs? These costs could depend on volume, price, or the different strategies you use, which can often be quantified easily (like when you buy in bulk and get a discount, for example) and sometimes not (like when a supplier gives arbitrary discounts without a definitive rule). So, in the first case, we have the business rule (the quantity and associated discount), and we can make an informed decision, while in the second case, we need to understand that while costs depend on discounts, we don't have a way to use this data to make decisions.

This exercise of asking questions involves something very important: understanding whether we have the necessary data to proceed or not.


Transformations

Once you know whether you have the data or not, we move to the second stage: understanding how to transform them. Transformation can involve simple modifications (manipulation, cleaning, etc.) or it can be a longer process involving multiple operations. Another aspect to consider is whether the transformations can be done automatically (like summing all the rows in a column, for example) or manually (labeling each row in a column).


Let's think about this using some examples ranging from simple to more complex. Suppose you want to understand:

  • How much did you sell last month? For that, you simply add up the price by volume.

  • What was your best-selling product? Out of everything you sold, what sold the most (product breakdown from the previous information).

  • Who is your top customer? Same, but by customer.

  • What is the average price? Total amount sold divided by volume.

The above examples account for simple transformations, but let's consider what happens in more complex cases, such as labeling rows in a table. For instance, if you analyze your bank statement and want to label monthly expenses into categories like "groceries," "water and electricity," "social outings," or others. Or if you analyze audiences and want to group them at your discretion. In these cases, there isn't a formula you can apply or a repeatable action; you'll have to manipulate the data yourself (labeling, summing unique values, subtracting, averaging, counting, or other manual operations).


This exercise of thinking about how the transformations will be done is important because it will help you understand which tool to choose for your work. For simple, repeatable transformations, you could use a tool like PowerBI, LookerStudio, or Tableau. For more manual operations where you need to interact with the data, you might need Microsoft Excel or Google Sheets.


Presentation

Once you have your sources and transformations, it's time to take action based on your data. Here, it's important to understand who will act on the data and how they will support their decisions.


If you are the one who will act on the data, you can stick with your visualization tool and spreadsheet, as you'll know them thoroughly. This will allow you to understand the data at a granular level and grasp how the models and tools work.


In the case where you need to present your data to people in managerial or more strategic positions, you need to be even more concise. In these cases, a presentation or an executive summary with few charts would be best, something that can be easily sent and includes ready conclusions.


The level of detail your data have will depend on the type of person who needs to consume them. An operations manager won't need the same data as a CEO, for example.


To better understand what each level of the organization needs:

Operational Level:

  • Granular data. For example, in our sales forecasting exercise, we know the prices and quantities of each individual item.

  • Dashboards and spreadsheets for working with the data.

  • Contextual information like the business problem and probable solutions.

Tactical Level:

  • Intermediate-level data. For example, in our sales forecasting example, it would be knowing the sales values for each individual item.

  • Scenarios and solutions with notes and justifications.

  • Dashboard, spreadsheet to understand the scenarios.

Strategic Level:

  • High-level data. In this case, we might state that it's only necessary to know the Sales outcome.

  • Scenarios with risk and opportunity assessments.

  • Executive summary or presentation.

In conclusion, whether you realize it or not, you're always working with data: every project is a data project. So, always think about your projects in steps and with these fundamental questions:

  • Do I have the data?

  • How will I transform them? What tools will I use?

  • Who will act on the results? How should I present them?"

 

By Minimalistech´s editorial team.

Minimalistech has more than 10 years of experience in providing a wide range of technology solutions based on the latest standards. We have built successful partnerships with several SF Bay Area, top performing companies, enhancing their potential and growth by providing the highest skilled IT engineers to work on their developments and projects.

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