How to Develop your Data Strategy

A data strategy is the framework for how data is used to support your business goals. It ensures that your company stays focused on high value data projects that directly benefit your company.

Why is a Data Strategy Important?

The Fintech industry is extremely competitive, with new players entering the game every year. Furthermore, new technologies like blockchain have changed the game completely. In this landscape, the best way to remain competitive to is have an effective data strategy.

Data is an asset that can be used to solve problems, create customer loyalty and increase your company’s profitability. Having a strategy enables you to realize the value of your data and to implement solutions that improve your operations and drive revenue.

Serving as your company’s data ‘memo’, a data strategy provides the framework from which all data-related decisions are made. It also helps you to prepare for and overcome common data challenges that, without a strategy, would be far more time consuming and costly to fix.

The Benefits of a Data Strategy

The most important benefit is that your data strategy will keep your data projects focused on the challenges and opportunities that matter the most. It helps your teams to identify the projects that are impactful, have a high probability of success and have a high Return on Investment (ROI).

By having a framework for the right people, the right technologies and what constitutes valuable data, you reduce the time and cost needed to bring your projects to fruition. When you consider that 60-80% of data projects fail, the value of remaining focused on high value problems and the right aspects of a solution become extremely apparent.

A data strategy benefits decision makers to a high degree. Because executives make many decisions every day, having the right data available makes decision-making easier and means that fewer decisions need to be made, because objective data makes the choice obvious.

7 Elements of a Data Strategy

Business Strategy and Objectives

The purpose of a business strategy is to ensure that everyone in your company is moving in the same direction, working together to achieve the same goal. The same goes for a data strategy - it must be aligned to the rest of the business (the goals, strategies and processes) in order to benefit your company.

Here it is important to lay out your company goals and the strategies you use to achieve them. For example, you may want to increase revenue by 20% this year and one of your strategies is to create higher customer satisfaction, thereby reducing churn and increasing their Customer Lifetime Value (CLV).

What problems do you need to solve?

Once you’ve solidified your business goals and the strategies needed to achieve them, it is important to identify the challenges that could prevent you from getting there. These challenges are often where the biggest opportunities are found.

Continuing with the example above, you may find that our Customer Acquisition Cost (CAC) is in line with industry averages, but our CLV is not high enough to cover this. That means your customers are not invested enough in your products - they’re either leaving too soon or aren’t using your services to their fullest extent. In turn, this is reducing your ability to generate revenue. If we fix this problem, it would go a long way towards achieving your goal of increasing revenue by 20%.

Culture Change and Business Adoption

This is perhaps the most important step within your data strategy. People are the ones who run the company and people will be the ones using the data to make decisions. So it’s important to keep this in mind: What’s the point if people within your business don’t use it?

This ties in nicely with the previous step - what problems do your employees need solved? If they are part of the process in identifying these challenges and guiding the data solution, getting their buy-in and creating that culture becomes much easier - because the value it provides is obvious to them.

It’s best to start early, to design your strategy with the end user in mind. The data-driven culture will come with it.

Analytics and Data Maturity Evaluation

The previous parts of the data strategy have provided business context, now an Analytics and Data Maturity Evaluation can outline how data capable your company is. It defines your ability to extract value and benefit from your data. By evaluating your current people, technologies and processes, you can identify shortcomings that help to prioritize the projects that get you from your starting point to your desired analytics capability.

Architecture and Technology

Selecting the right technology can be very difficult, but it is hard to go wrong when you make sure your options are relevant to your needs. You’ll want to select tools and technologies that make it easy to visualize data and reduce how much time is spent on engineering, architecture and governance.

When deciding on what technology is best suited to your company’s needs, it’s important to consider your current state. If you are a small company that is just starting your data journey, it would be a waste to make a huge investment into the most feature-rich option available. It would be far more effective (and economical) to invest in a simple solution that does the basics really well, and enables you to incrementally improve your analytics capabilities as your data needs mature.

Data Governance

As the owner of your customer’s data, you are responsible for protecting it and using it with your customer’s best interests in mind. It is important to have robust security measures in place to prevent the data from being stolen or corrupted. You will also need controls that govern how personal information can be used, and these controls need to comply with local and/or international regulations.

These controls will also need to account for data quality. Starting at collection (and throughout your pipelines), checks should be in place to ensure the data is correct, complete and up to date. Having these controls in place makes analyzing this data easier, faster and more accurate. This saves time and money and gives you confidence when using the data to make decisions.

Data Strategy Roadmap

The last thing that needs to happen is your roadmap - you need to lay out what the next 12-24 months will look like. This starts with identifying your most pressing issues, and then working backwards to the foundations that will serve those projects. You can do this by assigning each potential project an impact and an effort score.

Generally, projects that drive higher revenue, reduce costs or increase acquisition are seen as high impact. Time, cost and the amount of research needed are factors that affect a project’s required effort. It is important to note that the more effort required to bring a project to fruition, the higher risk the project is. This is because no project can be guaranteed success, so the costs and time invested are not guaranteed a return. A good rule of thumb is to start with the lower effort, high impact projects because you will benefit more, sooner.

Conclusion

Using data within your organization can be a long and difficult process. But starting with a data strategy makes becoming a data-driven organization easier, faster and a much more effective endeavor. Create your data strategy so that it can guide your decisions and focus on projects that are the most likely to benefit your company.