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How to manage your next data project

Your next data project can be much more pleasant and cost effective experience.

How to manage your next data project

Team Data21 on insight strategy project management

Data projects are notoriously slow and expensive. We have put together a step-by-step guide that will help you to do them faster and more cost effective.

Have a plan

Building a plan for your data project - e.g. data analytics, data engineering or data science - doesn't have to be as hard as it seems.

The following is our take on data project definition. It will help you get business value from each project and mitigate the risk of error.

Living in data age

Today's marketplace is very competitive. Businesses are trying to streamline operations and reduce expenses. Data play a major role in almost every industry.

Although many CEOs, CMOs and founders are talking about data-driven strategies, many are failing. Some might have big expectations, others start unprepared.

Before you launch your next data project you should be clear about few things that can make or break your project.

Scope

If you start a data project with clear end goal in mind, you are already halfway there. Ask questions like:

  • What is the scope that address our needs?
  • Can the scope be split into smaller and manageable tasks?
  • Can these tasks be described clearly and precisely?

By figuring out the above mentioned questions early on, you'll avoid wasting time on useless activities.

Always try to be pragmatic and don't allow the scope creep to happen.

Resources

The success of your project depends on the team. It should consist of people with good combination of communication, experience, technical and management skills.

Also, to make the project more agile, it's good practice to start with smaller scope, smaller team and thus lower costs. Pilot project is always a good idea.

Once the pilot confirms it is viable solution, the project can be extended to full scope.

Time

Time estimations are always tricky. Many tend to underestimate the time needed to finish a project. The experience of the team plays a big role here.

Time can be reduced by consistent focus on tasks with high priority. Many data projects get lost in nice to have features which don't add real value or are only valuable to small group of end users.

Communication

One of the most powerful aspects of managing the project is communication. This is even more acute nowadays when many project are done partly or fully remotely. Our team has a good experience with combination of using messaging app (chat), emails and regular calls.

Team members personal preferences play a big role here. Regardless of the tools, clarity and frequency of communication are important. Always strive for frequent, clear and concise messaging.

Iteration

An iterative development process is what works for majority of data projects. There might be some teams living in the past and promoting waterfall methodology. But we believe the agile approach is the best choice for most businesses.

Cost

How much does the typical data project cost? There are so many factors that determine the price. Should you buy a product? Is it better to invest in building an internal team? Or is an external service faster and cheaper option?

Every business is unique and has unique needs. And it is a challenge to determine the pricing in advance. Especially if you are preparing to take on a big data initiative.

If you are currently in search for skilled data team, we might be able to help you. Reach us to discuss your needs.

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