Why It is Important to Properly Frame and Scope a Data Analytics Project

Data is the backbone of business in the modern era. Companies need accurate insights and trends that data analysis provides to make informed decisions and increase their market share. However, without proper framing and scoping, these highly valuable data analytics projects will become largely ineffective.

How Exactly Does Data Analytics Help Businesses?

Businesses must make reliable, accurate decisions based on data-driven insights to succeed. Data analytics can help companies collect clean data, process it, and define trends and patterns to present data-driven, accurate insights.

In their most basic form, data analytics projects use both historical and current data to help stakeholders make project delivery decisions to help businesses with:

• Risk management

• Defining the scope of the current business problems

• Organizing and cleansing the clattered data to deliver relevant information

• Improving customer experience

• Facilitating project completion in due time

What are Framing and Scoping within Data Analytics?

Framing and scoping within the context of a data analytics project refer to determining the core problem statement and defining the project's scope, objectives, assumptions, and possible limitations.

This can help you and other relevant stakeholders focus on the most critical problems. It will further help you avoid unwanted complexities during the project’s implementation, based on realistic expectations.

Framing and scoping can also help you represent realistic problems and their solutions to your audience. This way, you and others in your company can measure the data analytics project's overall impact and success rate.

Why Does Proper Framing Matter?

Let us return to our main question: Why is it important to frame and scope a data analytics project properly? To understand this, we need to explore the importance and impact of proper framing.

Clarity of Objectives- Framing in a data analytics project will give you a deeper understanding of various processes, relevant trends, and customer behavior. With a clear idea of market dynamics and problem statements, businesses can set clear objectives and get rid of unrealistic goals.

Alignment with Business Goals- Data analytics projects work as a compass to help decision-makers surf through the sea of raw data. Framing in a data analytics project will help you cleanse the sea of information, collect relevant data, and get data-driven insights. With these insights, you can make informed decisions that align with your business goals instead of depending on vague intuitions or guesses. This will also give you a clear idea of potential risks and rewards that come with the decisions you make to achieve your goals.

Resource Optimization- Framing in data analytics projects will help businesses optimize their resource allocations.Do you want to optimize your budget, operational capacities, and human resources in the best way possible to solve a problem or get the best results? Framing in data analytics will allow you to gain data-driven insights, helping you optimize the resources to complete the business projects successfully.

Risk Management- Proper framing can help businesses assess the potential risks of each project and take preventive measures to counter them proactively. This way, companies can fend off potential financial and reputation losses while safeguarding customer trust.

Project Efficiency- Data analytics is a powerful tool, leading businesses to implement their projects in the most efficient manner. Framing in data analytics projects will help you monitor relevant performance metrics, identify areas to improve, and track projects’ progress. This, along with clear objectives, improved risk management, and resource optimization, can help projects run more efficiently than ever.

Why Does Proper Scoping Matter?

Scoping data analytics projects can help you create relevant data analysis models to predict and fulfill your project goals. The common benefits of scoping include:

• Minimizing scope creep (scope creep is sudden delivery requirements or expectations that cause task overloads or delivery delays, making things inconvenient for every individual involved in the project or creating unanticipated cost increases).

• Helping you get a clear understanding of the stakeholder expectations.

• Helping companies align their projects with their business goals with better clarity and efficiency.

• Enabling efficient project planning and resource management.

• Facilitate better handling of change requests related to an ongoing project.

What are the Common Challenges in Framing and Scoping?

While it’s important to frame and scope a data analytics project properly, executing these steps is no easy task. Here are some common challenges most organizations face while framing and scoping data analytics projects:

Absence of Comprehensive Plans

When starting a new project, many companies often focus too much on data, tools, analytics models, and algorithms. Therefore, they often forget or don’t place too much importance on the framing and scoping of the projects.

And even if they do, it’s usually too late. This is one of the main reasons why many data analytics projects fail. To frame and scope a data analytics project and create a comprehensive plan, you need to answer the following questions:

• Can the project lead to better revenue-generating opportunities?

• What can you do to remove the risks associated with the project?

• Does the project align with the company’s business goals?

You need to define success criteria, set realistic benchmarks, and break down the project costs. That’s how you can frame and scope a project. And it needs comprehensive planning.

Delayed Outcomes

When you scope a data analytics project, you expect reliable outcomes or insights after defining a context for the project. As the project progresses, you need to see or get some kind of results to collect data.

If you don’t get reliable results or outcomes for a long time, your set project scope might not come in handy at all. This will force you to reevaluate your project scope along with the data models, algorithms, and other aspects of the data analytics project.

Inadequate Presentations

If you don’t communicate the insights, implications, and analytical results of the project clearly to the major decision-makers, the project ideas may face resistance. 

While you have realistic objectives and scope, you need to convey them to the stakeholders and show how they can benefit the business in terms of revenue, competitive advantage, and profit. Sure, conveying it clearly isn’t easy. But you need to do it if you don’t want the data analytics projects to halt.

How to Properly Frame and Scope a Data Analytics Project

Now that you understand the importance of properly framing and scoping a data analytics project, below are the general steps that need to be followed in order to execute on your plan: 

1. Get a Clear Idea of the Business Context- First, you will have to understand the business context of your company or your client. This context includes multiple elements, such as the company’s business goals, challenges, resources, opportunities, capabilities, and limitations. Ask relevant questions and gather information related to these aspects. Only by understanding the business context can you align the data analytics projects with the business’s goals, values, and missions.

2. Define Who Your Stakeholders Are- The next step in framing and scoping data analytics projects is to define the relevant stakeholders accurately. They will help you collect project requirements, specify a scope, and even provide you with project sign-offs. You need to identify their roles, responsibilities, and expectations. Only by collecting all this data can you create a proper frame and scope for your project.

3. Determine the Problem Statement and Value Proposition- Next, you have to determine the problem statement and value proposition of the data analytics project. Here, the problem statement is a specific problem or issue the company wants to address using data analysis. Meanwhile, the value proposition defines how the data analytics project will benefit the business. Be careful while defining these two elements as you also need to validate them with the relevant stakeholders.

4. Identify Data Sources and Variables- Now, you need to identify the data sources you will be working with. It can include spreadsheets, your business’s database, or other data points. Once you identify the data sources, you need to perform a data audit and data gap analysis to identify the variables you will use in the data analytics project and note them.

5. Choose the Data Analytics Methods and Tools- In this step, you must choose the data analytics methods and tools you need, depending on the business context, problem statement, value proposition, and data variables. Would you use descriptive, predictive, diagnostic, or other types of analytics? What spreadsheet, database, visualization app, or programming language would you pick? You need to choose the data analytics method and tools that align with your project’s needs.

6. Process and Clean Your Data- You will rarely find data in the ready-to-analyze format. After all, the raw cluster of data may have many errors, irrelevant, and even redundant info. In this step, you must process and clean the raw data to make it usable. To do that, you can use techniques like transformation, data wrangling, or normalization.

7. Enrich Your Data Set- Now that you have clean data, you need to find a way to store them efficiently, so they can enrich your data set. You can use a database, cloud storage, or data warehouse to store the cleansed data. Finally, you will use this stored data and extract meaningful insights and patterns.

8. Build Intuitive Data Visualizations- You can represent the extracted insights and metrics visually using data visualization tools, such as Power BI, Tableau, or Python libraries. These tools will help you transform complex numbers into interactive charts and graphic dashboards. It makes understanding and comprehending data a lot easier.

9. Define the Deliverables and Create a Communication Plan- You have to define deliverables and create a communication plan that aligns with the problem statement. The deliverables include charts, spreadsheets, graphs, or dashboards that contain the relevant metrics and insights. Meanwhile, the communication plan will dictate how you will explain the findings to the relevant stakeholders. It includes defining the channels of communication, the frequency of reports, and even a feedback and evaluation loop.

10. Figure out the Potential Risks Associated with the Project- You must find out every potential risk associated with the data analytics project. The goal here is to take proper measures early on to minimize the risks and prevent unwanted problems from cropping up.

11. Estimate Resources and Project Timelines- Finally, you must estimate how many resources you will need to complete the project. The resources here include budget, human resources, software, and other necessary equipment. Once you’re done with resource estimation, you need to create a realistic, flexible, and accurate timeline for the project deliverables. To make things easier, you can break down the project into several milestones and set a flexible deadline for each of them.

Setting the Stage for Success

Data analytics projects help businesses make informed decisions, develop accurate insights, and get a competitive edge in the industry. However, data analysis is not an easy process. Without experience in this field and a talented IT team, most data analytics projects will fail to produce significant results.

Our experts at Render Analytics can help your business with complex data analytics projects, pattern detection, reporting, and creating custom data architecture. Render Analytics can help your business with:

• Understanding critical market trends

• Identifying potential risks

• Predicting project outcomes based on historical data

• Reducing operational costs

• Identifying new business opportunities and making data-driven decisions

To ensure your complete satisfaction and project success, our data analytics experts will continue to provide you with ongoing support for up to 60 days after the project's completion. Book a consultation with Render Analytics today to see how it can help you set your business on a journey to success!