Are you finding it difficult to start your Data Analytics project? In this post, I provide 5 tips for a quick start and, more importantly, extract the value from data in no time.
Let me start with a simple question, why are you embarking on such a big and risky project?
Usually, an organisation decides to start a Data Analytics project with some business goals in mind, to name a few:
- Improving the performance of a specific business process or the overall business.
- Reducing the operational costs, or at least gaining an understanding of which expenses are manageable.
- Starting trading in a new market or getting a more competitive position in the current ones.
All of these reasons make perfect sense. Then, why do you take so much time to decide to start?
01.
Start
Starting is probably the most challenging step to take given the project’s usual scary size (i.e. budget, the number of requirements, duration, etc.). It would help if you avoided “Analysis – Paralysis”; your competition may be ahead in the game already and taking full advantage of data to make informed and timely decisions. Do you have a feeling of urgency now?
02.
Take Small Steps
Start small, with a few requirements from one or two business units. By having a reduced scope, you will have the feeling of having better control of the project.
Taking small steps also means that you can validate the approach taken to deliver results and show progress continuously. For example, using the task in hand to test the architecture or design for the solution is a good fit for the overall requirements and making the required adjustments as needed.
03.
Manage Expectations
These projects have diverse stakeholders, each of them with different priorities and wanting to be served first.
Don’t wait for a joint agreement on who goes first. Instead, get business stakeholders agreement on delivering the project following a prioritised list based on the value each activity will create for the organisation.
04.
Set a Good Foundation
A good foundation for technology-based projects usually involves the perfect alignment or at least a good balance between 3 forces: People, Business Process and Technology.
For People, get supporters and transform them into evangelists of the use of data. Once they recognise the value generated from data, they will be the first to tell others about the benefits they can get by working efficiently with data.
For Business Process, get a good understanding of the process itself. Knowing the variables that impact the process performance is vital to determine the best analytical path when interrogating data. Your supporters must also be SME’s for their respective business units or knowledge domain areas.
For Technology, design a solution for current and future needs. Use an architecture and the tools that allow for changes. In today’s very competitive and dynamic markets, people’s and organisation’s needs for data change constantly. Nowadays, requirements for data probably have a lifetime of 3 to 6 months, if not less. Your solution must be able to survive these changes with minimal disruption.
05.
Learn, Apply and Repeat
If you have read this far, you probably think that an Agile approach is suggested for this type of projects. Well, you are right.
Traditionally, Data Analytics projects tend to fail, and one of the top reasons is the use of a big bang or waterfall implementation approach.
If you use the three previous tips in a repetitive cycle, you will find yourself making consistent progress and generating business value from data more often.
As explained in tip #2, taking small steps allows you to define and develop an implementation strategy for the project. With every iteration, you have the opportunity to adjust the approach and to improve your implementation process. After repeating this process a few times, you will realise you have developed a framework that can be used for any Data Analytics related project.
(Bonus).
Ask for Help
Given the importance of Data Analytics for an organisation, I have always thought the organisation must have dedicated internal resources supporting the data platform, making sure it is available to the business when and where it is required.
In addition, always consider bringing external skills to accelerate an implementation or to develop a new capability. Consultants like me are exposed to different environments and constantly working on solving the next challenge. Experienced consultants provide expert and practical advice based on solving different types of problems.
Final Ideas.
- If you have many requirements with different priorities, review priorities and align them to the overall business strategy.
- The Data Analytics project is a journey, not a one-off initiative. Maybe even call it a “Program” of work; it will give you the feeling of an ongoing process.
- Deliver something that has a high probability of success.
- Do not worry about using the most sophisticated tools. Instead, focus on the foundation (i.e. people, business process and then technology)
- Prove your idea, strategy or action plan by using Proof of Concepts or Prototypes. Try the approach and make the required corrections early in the process.
- People desires and needs change over time, embrace change and be prepared to handle it.
So, how are you embarking on this journey? What makes you different from your competition in the use of data and analytics? Let me know if we can help you to get started.
The ideas for this post were inspired by the book The Art of the Start by Guy Kawasaki.
In The Art of the Start, Guy Kawasaki brings decades of experience as one of the business most original and irreverent strategists to offer the essential guide for anyone starting anything.
You can find the book here, a must-read for anyone trying to start anything valuable.
