It has become relatively easy for companies and businesses of all sizes to collect data, whether from electronic payments, website traffic, or social media presence. And the advancements in digital technologies have made it even much easier to process this data.
But despite that, more than 80% of all data analytics projects still fail.
Why?
Technology and data are not the only things your business needs to transform data into valuable insights successfully. You need to establish a solid plan and systematic procedure early enough, alongside relevant skills to help you manage and analyze data to generate insights that will add the most value to your business.
In this post, we’ll explore the top 10 ways to prepare for a successful data analytics project. This will help you triumph in all your future data analytics projects.
Set Expectations and Establish a Collaborative Process With Teammates
One of the best ways to prepare your data analytics project for success is to align your team members with your expectations while establishing a collaborative process. Set responsibilities and emphasize transparent communication throughout the project.
Consider using modern data collaboration tools to efficiently manage your teams and keep all members on the same page. This will make it easy to keep all parties and stakeholders up-to-date about the project’s status.
The tools not only help maintain transparency concerning the project’s progress but also provides a perfect way to track deliverables. You can quickly tell which team member is responsible for what and how far the project is in the pipeline.
Other than that, you should empower your teammates with all the resources they need to communicate and collaborate effectively with one another. This will help yield positive project outcomes. Click here to learn more about laptops and other tools your team may need. While at it, keep in mind that communication can either make or break the project’s outcome.
Obtain Clear Project Details
Gathering as many upfront project details as you can is critical to the success of your data analytics efforts. It enables you to have a deeper understanding of what the project seeks to accomplish, enabling you to set achievable and reasonable project deadlines and goals.
Some of the crucial information to obtain include:
- The problems or questions the data analytics report seeks to solve
- How the report will be utilized throughout the organization
- Who the targeted end-users of the report are
You can generate a standardized template with a list of all the items you must cover during each information and requirements-gathering session. Once you finish an item, check it off until you finish all the items on the template. This will help avoid missing crucial details or including aspects that could lead to scope creep.
Determine Where to Get Data
The next thing to do is to figure out your data sources, their access restrictions, and their associated location. Merging data from various sources will make your data analytics project great.
Some ways to obtain usable data include:
- Connecting to a Database – You can ask the company’s IT and data teams to provide the available data or dig through your private database to check the information collected.
- Using APIs – Make the APIs to the tools the firm uses accessible, like their CRM. Most CRMs feature a point-of-sale system that captures and stores sales data from customers and distributors.
- Looking for open Data Online – You can enrich what the business has with free data online. For instance, census data can enable you to include average revenues for the states or districts where your users live. You can also get open data on a platform like data.gov in the U.S. These open data sets can be an incredible resource, especially if handling a fun project outside your usual scope.
Develop a Deliverable Timeline
A deliverable timeline is a target due date that aligns your team and the project owner. Although the timeline is usually set at the beginning of the project, you can modify it after gathering the project requirements.
For instance, the project may delay starting if you experience data access issues due to the client’s policies or other hitches. In this case, you may have to modify the deliverable timeline and move the due date to a later time.
As you set new timelines, communicate the changes to team members and other parties involved and take action immediately. This will boost the project’s visibility to teammates, minimizing the chances of running behind schedule, which can result in scope creep.
Clean or Wrangle Your Data
Your data needs to be in a structured format, homogeneous, and clean before conducting any analytical activity. The process involves verifying each data type to confirm if they are compatible and if there are any missing outliers or values.
This is also the perfect time to check if any naturally occurring errors or discrepancies exist to correct them before feeding the data into a model. Besides, you need to determine whether you will use all the variables in your data set.
One crucial element in data preparation is to ensure that your project data complies with the data privacy regulations. Considering the high priority given to personal data protection and privacy by users and organizations, you must emphasize that from the beginning of your data journey.
To make your projects privacy compliant, centralize all your data sources, efforts, and datasets into a single platform or tool. Tag projects and datasets with personal and sensitive data to enhance smart data management.
Enrich Your Dataset
Data enrichment aims at manipulating your clean data to make it more valuable. You can do this by combining the different data sources and group logs to narrow down the data to essential features.
For example, you can enrich your data by narrowing it down to time-based features, such as getting date components, flagging national holidays, and identifying the difference between date columns. Another option is to join datasets by transferring columns from one dataset into a reference dataset.
Fortunately, there are tools that can streamline and simplify the data retrieving and blending process based on fine-tuned criteria. Just ensure that the datasets you use don’t reinforce or reproduce any bias that may result in unjust, biased, and unfair outputs.
Properly Review, Reconcile, and Iterate
You must effectively review, reconcile, iterate, and repeat the process until you meet client expectations.
Reviewing
Once you have collected all the project details, direct your focus to work on deliverables as documented in your sprints. It is crucial to maintain constant communication with your company during this time.
If any specific task has unclear information or doubts, talk to the company immediately for clarification. This will help avoid rework or other issues that might affect the project timeline.
Reconciliation
The reconciliation stage is all about validating the available data. You should do the validation with your client to enable them to verify the task and confirm if the project is conveying the correct information. Request for previous reports or data sets to compare against and ensure that the information matches.
Iteration
You supply the client with the first report for review at this phase. If changes are needed, you may need to collect the necessary information and data, make the iteration, and share the new additions or changes with the client. If the client is still unsatisfied, perform iterations until you achieve the desired end results.
The trick is to make your initial requirements gathering as descriptive and thorough as possible. This will help minimize the number of iterations, enabling you to complete your project within the timelines.
What to Watch Out for When Preparing for Data Analytics Project
Here are the data analytics watch-outs:
Scope Creep
In a recent PMI management survey, 52% of data analytics projects recorded in the previous 12 months resulted in scope creep. If you are about to complete a project, then the company comes in with a new request for value add, focus on wrapping up the project first. You can then embark on the new request after that. This will make the work much more manageable.
Communication
Develop transparent communication between the company and your team members. Embrace true partnership rather than a transitory communication style. Inform the company or client immediately if any matter arises during the project that can cause negative impacts.
It is better to deal with issues as they arise instead of covering them up, which often results in more significant problems down the project timeline.
Leadership
It is best to have a point person or project manager for each project side. This means there should be a person leading the build and another one in charge of delivering the build. The point person drives the team and ensures the project runs smoothly.
Even though you may hit a few bumps, the point person will be there to provide accurate information and support, enabling you to deliver the build.
Specific projects call for the support of executive sponsorship. This helps keep the project a priority, preventing corporate strategy stalls.
Final Thought
Businesses neglect so much data that can help them drive informed business decisions. With the tips above, you can sit down with your team members to determine what you can accomplish with the available data. Constantly retrain and reevaluate your data analytics model while developing new features to remain accurate and useful.
