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The Billion-Dollar Blueprint Consultancies Use To Solve Data Problems (2022)
Consulting businesses earn BILLIONS with analytics work for clients. This is their exact blueprint in 9 steps.
Thomas Varekamp
December 16, 2022
It's no secret that consulting businesses earn billions by using data to solve the problems of their clients. But the methods they use are often shrouded in secrecy.
Today I'm going to demystify the process and reveal the key to their success: the analytics value chain.
This step-by-step process helps identify business needs and uses data to provide actionable insights that can help organizations make better decisions.
If you want to join to drive real, data-driven change in your business through analytics, then read on to learn more!
The analytics value chain
The goal of the analytics value chain is to provide actionable insights that can help an organization make better decisions.
It consists of 9 steps:
Identifying needs
Develop roadmap
Define scope
Collect data
Validate data
Analyze data
Align stakeholders
Deliver solution
Measure impact
Let’s break each of these down:
1. Identifying needs
The needs of a business are based on the goals and objectives of the organization.
To identify these, it is important to talk with stakeholders and define:
Their goals
Questions they need answered
Requirements that need to be fulfilled
For example, a business may have a goal of increasing customer satisfaction, and the specific question might be:
“How can we reduce customer complaints?”
"What are the most common reasons for customer complaints?”
Requirements can be:
“We don’t have budget to hire extra customer support”
Once the goals, questions, and requirements are identified, the next step is to come up with projects that can help the organization achieve its objectives, while answering the questions and falling inside the requirements.
Start with creating a list of possible solutions. Once this is done, plot them inside the impact & feasibility matrix:
In this matrix you plot projects based on their financial impact and ease of implementation.
Don’t make this an exact science; do this based on experience & feeling.
One number represents a project.
The goal is to identify low-hanging fruits that have a large business impact, but are relatively easy to solve.
2. Develop roadmap
Pick the most feasible and high impact projects and create an analytics roadmap on when the projects should be delivered.
An analytics roadmap is a plan that outlines the steps and resources needed to successfully implement a data analytics initiative within an organization.
Order the projects based on urgency. If a solution is required soon, put that one first.
The goal is to create a long-term overview of when projects should be completed.
After you create the roadmap, it is time to hone in on the most important project and define its scope.
3. Define scope
The scope outlines what is and what is not included in the project. It includes a detailed description of the work that needs to be done, the resources that will be required, and the deliverables that are expected. The project scope serves as a reference point for stakeholders, helping to ensure that the project stays on track and meets its goals.
Defining the project scope is important to prevent scope creep, which occurs when the scope of the project expands beyond what was originally planned. This can lead to delays and cost overruns.
A great tool to use is the analytics canvas. It serves as a template to define the scope and the actions needed to be done:
The analytics canvas consists of the following elements:
The problem: a clear and concise description of the issue that the project will address.
Type of solution: the specific approach that will be used to address the problems identified.
Stakeholders: the people who have an interest in the project. They can be customers, employees, investors, or other parties.
Data needed: a list of the data sets and other information that will be required in order to complete the project.
Hypotheses: tentative explanations for the problem that the project is addressing. These are used during the data analysis. Hypotheses are important because they make sure you don’t waste time on analysis that do not add any value.
Impact: a description of the expected impact of the project, including how it will benefit the organization and its stakeholders.
KPI’s: key performance indicators, which are metrics used to measure the success of the project.
Risks: potential challenges that may arise during the project.
After you have defined these elements, the next step is to create a timeline of the different actions.
This is a breakdown the project into seperate tasks. For example:
Kick-off
Data collection
Data cleaning
Data exploration
Data analysis
Delivery
Also include alignment calls where you sit with stakeholders to discuss the preliminary results. This ensures you work efficiently. More on that later.
4. Collect data
Ask stakeholders if the data you need is available. Usually they have access to certain databases or spreadsheets where the data is stored. If there is no data for the problem you want to solve, create a system to collect this data.
Ways to collect data:
Surveys
Chatbots
Data collection software
Tip: make sure there is a key which can be used to join the data together. For example: customerID, email, employeeID, etc.
5. Validate data
Once data is collected, it is important to perform validation checks in order to ensure that the data is accurate and reliable.
This involves checking the data for:
Outliers, which are values that are significantly different from the rest of the data
Inconsistencies that may indicate errors or problems with the data
Empty values
Perform these checks with statistical techniques, such as outlier detection algorithms or data visualization. Work with stakeholders who are involved in the data collection to understand the processes used to gather the data.
This helps in identifying any potential sources of error or bias.
6. Analyze data
Define the right method based on the scope of the project. This will help to ensure that the analysis is relevant and that it provides valuable insights that can support decision making.
There are several methods you can apply to analyze data, including:
Statistical analysis: the use of mathematical and statistical techniques to identify patterns and trends in the data.
Data visualization: the use of charts, graphs, and other visual tools to represent the data in a way that is easy to understand and interpret.
Machine learning: the use of algorithms and computational models to learn from data and make predictions or decisions.
The way you will analyze the data depends on the goal. Refer to the analytics canvas to define the best way to move forward.
7. Align stakeholders
Stakeholder alignment is an important aspect of data analytics, as it ensures that the results are useful to the people who are impacted by the project. By discussing the results as early as possible, you make sure that the project is working efficiently and effectively. This results in the best possible solution, in the fastest time.
Tip: mistakes are very common in data analytics. Talking with stakeholders can help to identify these mistakes before they become major issues. My advice is to meet atleast once per week with stakeholders to discuss progress.
8. Deliver solution
Once the data has been collected, analyzed, and interpreted, the next step is to deliver the solution to the stakeholders.
This could involve:
Creating an ad-hoc report that provides detailed insights into a specific problem or opportunity
Designing a dashboard used to monitor performance,
Building a machine learning model that can be used to make predictions or automate decision-making.
The specific form of the solution will depend on the needs of the stakeholders and the objectives. The key is to ensure that the solution is clear, concise, easy to understand and that it drives business success.
9. Measure impact
While data analytics can provide valuable insights and support decision-making, it can be difficult to directly translate these benefits into financial terms.
There are several ways you can show the value of their data analytics efforts through KPIs. The most common ones are:
1. Decrease in costs
You may be able to use data analytics to identify inefficiencies in operations and implement changes that result in cost savings.
2. Increase in revenue
Data analytics may be used to identify new revenue opportunities, such as by identifying new markets or customer segments to target
3. Improvement in customer satisfaction
Data can be used to improve customer satisfaction by identifying trends in customer behavior and implementing changes that address common pain points.
Following these 9 steps will ensure that you deliver successful analytics projects within your business. From business needs to the data, to the solution, to the delivery.
If you have any questions related to this, feel free to send me a message!
And if you enjoyed it, please forward this email to friends who want to know more about data in business.
See you next week!
Thomas