Travel 22nd January 2019 - 7 min read

5 steps to build a business case for improved data analytics

By Joni Lindes

Increasingly, leaders in travel, meetings and expense are seeing the value of implementing specialised data analytics solutions. In most travel managers’ eyes it is clear why better data analytics can make their day-to-day jobs more efficient while also generating increased cost savings for the business. If this is true, why is it so difficult to convince your boss of the potential value of better data management?

We cannot ignore that, when it comes to your career you cannot just “walk the walk”. You need to be able to “talk the talk” and get your stakeholders on the same page as you are. The ability to perform this seemingly easy, yet essential task will not only get you the tools you need for the short term but will enable your business function to exceed expectations and improve your career outcomes in the long term.

So how do we remove the blockers to programme-wide growth? As the initial implementation processes can be intense we need to ensure our stakeholders understand the “why.” Once we understand why we will then need to provide proof. In other words, we need to make the existing data we have “talk”. Maybe then, it can be understood why we need more data.

Who are our stakeholders, exactly?

We can boil our stakeholders down to two groups of people: managers and users. Both these stakeholders have different needs when it comes to a data analytics platform. While it is essential to engage the managers from the get-go for investment purposes, we must not forget the support of the users. Their everyday experience with the platform and the ease of the implementation process will decide whether the software will be upgraded, have add-ons and whether your travel programme will continue to be invested in data.

If we want to get buy-in from stakeholders, we need to build a business case which caters to both these groups. Managers need to know the ROI, the cost vs the opportunity and the total value the tool will bring them both financially and in terms of a quality experience.

Users need to know how this tool will make it easier to do their jobs better. The ability the software has to generate data answering the KPIs they are most concerned with in an easy-to-use dashboard are the key selling points.

In short, when we are advocating for the use of data we need to boil down to the why.

1. Listen first

Let’s face it, data analytics or rather, good quality analytics needs infrastructure and investment before it can get off the ground. Not only is there an initial outlay cost, but the upkeep and maintenance of effective data analytics technology is never done – requiring specialised knowledge and new infrastructure each time a new business requirement emerges. The cost of this exercise is enough to leave most senior managers unenthusiastic to support it.

It is crucial to overcome their initial scepticism by first identifying where their doubts lie. When making our case, we, ourselves, are sometimes so convinced it is easy to get blind sided to concerns from others in our company.

These concerns, however, can be our biggest assets. Instead of providing a long argument of why we think the tool is valuable, ask questions and listen to your stakeholders. Get to the bottom of why they may or may not want to invest in a new initiative. It’s not always about cost.

Once you have an idea of why they are hesitant to invest, build your argument around solving these concerns. This will enhance your argument.

2. Look for efficiency gaps in current procedures

No matter what your current approach to analysing current spend trends are,  you are sure to realise that improving your data can lead to increased efficiency and a relief on current resources. Your job is to ensure management sees the current situation through your eyes. A simple way of achieving this is by providing real-world examples.

Start with an analysis of the time it takes to get the data you require. If we examine sourcing, for example, how long does it take to gather the data needed when negotiating RFPs? How better could this time be spent if we had a data analytics provider that did most of the heavy lifting? A saving in IT and analyst resources could therefore be one data point to use in your argument.

If we want the efficiency gap argument to appeal to users, we merely have two options:

1. We can illustrate how much time they take to either gather current data from a multitude of sources


2. We can illustrate the gaps their supplier data currently does not cover and how the new provider will fill these gaps.

It is important to reinforce examples where they will be able to use this data in their day-to-day activities as well as where this data can contribute towards the bigger picture – programme-wide growth.

The next point is to illuminate the benefit these systems can create. In other words, we answer the question most managers ultimately always ask: what is the ROI?

3. Weigh the total value against the total cost.

After illustrating your argument with a gap analysis of lost cost savings opportunities in your current programme, it is recommended to let the numbers do the talking.

Use a proof-based argument that:

  • Answers their imminent needs and goals as a company.
  • Provides convincing evidence of cost savings and potential opportunities as much as possible.

How do we generate a proof-based argument, or, effectively, a good story by numbers?

  • We can emphasize that, through improved data analytics, the company can cut down on time and resources already in use while also increasing the opportunity to gain added resources.
  • Provide an estimate of every possible area where better data analytics can control spend, whether it is by improved methods of keeping track of potential policy and tax issues or by using data showing a true picture of traveller activity during sourcing negotiations.
  • When management sees the cost of the missed opportunities, the initial cost of implementing quality data analytics does not seem so large. Do your homework and get an as-close-to-accurate figure of both numbers.
  • Constantly analyse the worst case scenario when illustrating the benefits so management sees that, in a non-ideal situation, they still win big.

When marketing your plan to implement data-driven change to the end-users of the software, be sure to emphasize how the data can help them do their jobs better and boost their profile. Users are interested in growing their professional profile by making significant change for the company. For example, show how using data to become more proactive on duty of care issues can raise the profile of the travel department and them, as employees, across the business.

4. Always include integrations

As awe-inspiring as a new tool, dashboard, report or analysis may be, we must not get blind-sided by bells and whistles. When building a business case, the users’ needs are important as well. They probably use existing tools, each with their own analyses. Introducing a new data analytics tool can be tricky as nobody wants to go to seperate tools to access data about the same event. This creates silos and wastes time when users end up populating spreadsheets with data from different dashboards.

First, ask the right questions of suppliers and make sure the tool you are advocating for is able to integrate with most of your tech stack. When making your argument present how your new analytics can integrate and work with the analytics already there. For example, when looking at travel analytics, you get bonus points from stakeholders in Finance if you can show how travel analytics software can integrate and automate with ERP systems, like Oracle, for example.

In this way, you show the new software is not there to make your function easier, but can automate processes between teams – helping you and other departments become more productive.

5. Go for the quick wins

It is useless if you have buy-in from management and users yet the project gets stuck at implementation. Implementation of a data analytics provider often is a meaty project as each business’s current data management system hosts unique challenges. Multiple formats, regional reporting and problematic supplier data are just a few hurdles you and your onboarding data analytics team may need to overcome.

It is therefore extremely important to focus on the quick wins. This will motivate the team to achieving more while communicating a positive message of fulfilling requirements in the required time to management.

Ensure end-users are engaged in the process through regular user-testing and that their feedback is noted and fed back adequately.

Once a successful implementation is achieved, this can help you gain credibility so the process is much easier next time you need buy-in. Additionally, improved data visibility will ensure the numbers needed to support your next bid for support are much easier to access. In fact, if the data analytics you use are of good quality and in one location, your next business case should be at your fingertips.

Joni Lindes
By Joni Lindes
7 min read

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