When it comes to data-driven transformation, there are two methods businesses can apply:
Evolutionary Data-driven Transformation
If you are applying data to optimize the business strategies you have now. If you have a current business model that you want to retain even as you undergo a data-driven change, then evolutionary transformation is for you. This method is safe, and it makes sense, but it may hinder change, and there’s a small chance you may fail with your data-driven transformation. However, there are things you can do to ensure success.
1. Know where you can get the most benefit from applying data.
When you are using data, things can get complicated, and when it does, you might not be able to meet your business needs. The trick is to determine business issues that may be addressed by applying data. You should also look at areas where data application has the lowest degree of complexity.
If you are just starting out with your data-driven transformation, look towards customer-facing departments in your company such as marketing, customer service, or sales. It’s going to be easy to apply data in these areas, and the benefits are going to come quickly.
Early successes in these departments can help you gain momentum for other areas of your business.
2. Invest in data science and data scientists.
When you have a data-driven initiative, you should hire people who know the technologies and tools. You can either hire data scientists to work in-house or get consultants and external suppliers.
The first people you hire should be data science business translators who can quickly maximize the impact of your initiatives without wasting too much time.
Here’s the thing: you might find it difficult to correctly identify the skills you need in a data scientist before you start with your first projects or have seen success in data-driven transformations. That’s okay. You will discover this as you go along.
However, it’s crucial that you hire the right people later on. They should have the right skills set to help you build quickly while you have the momentum.
3. Experiment with your data-driven transformation.
When it comes to evolutionary data-driven transformations, it’s important to learn fast, succeed fast, and fail fast. You don’t want to invest heavily in longer-term projects, infrastructure, and systems before you can show the impact or results. Instead, be agile and adapt as you go along.
You should also develop a team that will be tasked to implement the right results from your little experiments. Along with the personnel, you should also have resources ready to achieve good results.
4. Show the effect on sales.
You should be able to prove that the data-driven transformation drives up your revenues. To do this, you should have control groups that you can compare with traditional approaches.
Consistent and impressive results will help you convince the organization that the data-driven approach works.
Radical Data-driven Transformation
There is a radical way to this process. It’s not always easy, but when you do it right, it’s going to be very rewarding. The radical way has the potential to disrupt your industry, not just propel your company forward.
Now here’s the challenge for established organizations: the radical way will require you to continue doing what you’re doing now and earn money from the existing business while also reinventing your enterprise from the ground up.
To guarantee success, here are things you can do if you choose to go this route.
1. Create an independent organization under your mother company’s wing.
Because you are building a new business model while keeping your current one, one of the things you can do is set up a subsidiary that is independent of your current company.
The new unit will have its own systems and processes. They can even have their own products. The main focus of the independent group is to disrupt your industry.
2. Determine first how data-driven transformation will affect and add value.
When you have an excellent idea, you should first test it out in the market. It’s not enough to just have a good idea, you should initially gauge if people will buy into your idea.
If you’re sure that there is a demand for it, then you can start determining the backend infrastructure and systems that you’ll need to turn that idea into a reality.
3. Say no to lock-ins.
It is always a good idea to use open-source software and cloud solutions. When you start an independent subsidiary or a startup company, you have the advantage of not being tied down by legacy systems. You are more agile and flexible that way.
Rather than working with old systems, you can move forward with simple cloud and open source solutions that you can scale up or down depending on your needs.
Open source and cloud solutions mean that you don’t have to wait weeks or months for IT to provision what you need. You can just start right now.
4. You should have a complete set of data science skills.
The data science value chain you have should maximize the potential for success. This value chain has four categories:
- IT and backend developer skills that include being able to identify, gather, extract, and process data you need.
- Programming skills that include being able to build quantitative models and being able to improve on these when it gets deployed in production.
- Front-end developer skills that include making visualizations and dashboard to make the data and its analytics easily understood by and applicable to the organization.
- Business data transformation skills include creating data strategies, change management, and developing your business with data science in mind.
5. Always keep your key performance indicators in mind.
Even when you have an independent organization, you should not waste any time in creating a business plan. Instead, use your company’s KPIs to guide you on what to focus on as you get more data projects done.
Because of this, different companies have different definitions of success. In one company, their data-driven transformation is a success because they were able to retain their current customers. But that is not enough for another company, and they choose to define success as increasing their market share and penetration.
Photo courtesy of tec_estromberg.