Demonstrating value
It is one thing to assess the value of ROI, but it is another completely to demonstrate value of something that is patently difficult to evaluate. A participant from a services provider described how their role in the organisation is focused on actions that lead back to generating value, but that “value is one metric we have always struggled to demonstrate.” This has been a common complaint by DataIQ members over multiple roundtable discussions and there is no quick solution.
Another roundtable participant from a media organisation said how they had started to try and demonstrate data usage as a value akin to monetary gains, efficiencies and de-risking. The value was seen in how many users there were of the data and the capabilities being built. “We know that we are creating value from data in pockets – sometimes there are pockets creating huge amounts of value – but how big are those pockets of people that are doing it?” said the member. “When we talk about democratising the value of data – a term that gets floated around all the time – what does that actually mean? Well, for us, it is about data usage: the more people are using data, then they have got more chance of creating value from data as an organisation.” The member described how there are inherent difficulties in whittling down value to one metric as there are multiple metrics with intrinsic values, data usage is becoming a key education point – more people using data more often leads to the potential for more value.
This was agreed upon by the participants as a key metric of proving ROI, with one participant highlighting that product uptake was equally important and would often be easier to explain as it can be linked to a financial value. Three out of seven participants agreed that external monetisation was a cornerstone of demonstrating ROI, but this does depend on the type of organisation and the products available and is not necessarily suitable for all businesses.
A participant highlighted a conversation that they experienced a few years previously upon becoming a CDO with a well-regarded data leader. They asked for advice on creating value and were responded to with “it is easier to find savings and cost savings than it is to create value at the beginning of your journey.” This rings true across all data-driven businesses and, as has been discussed many times with DataIQ members, financial benchmarks are often the easiest to translate as value to non-data professionals. The member explained how they used this direction to evaluate how many reports were being created, report requests, dashboards being made and the use of the dashboards. “That allowed me to look at technical debt in the warehouse and start to unpack how the storage space has been using processing power,” said the member. “I was able to make a number of savings quickly because we just discontinued a bunch of the superfluous items that flew under the radar.”
The group also discussed the experiences they have had when it comes to providing data work for internal departments. Sometimes the departments would need to be charged for the work done out of their specific budgets, but a sticking point would arise around seeing the value of the work.
Indirect value
Once again, the question was raised of how can data teams demonstrate value and ROI with something that is difficult to evaluate? “Through improved business intelligence reporting, improved modelling data plugged into business processes – how do you quantify the benefit that is being delivered to the business?” said one participant from a retailer.
A member described their role in a previous organisation and described a scenario where they changed the work done with the commercial proposition following insight and analysis to make it more profitable for the business. “Now is that something you can bank?” they asked. “Is that money you can say we have created for the business? Because we have changed the decision that will be more profitable for us.”
One of the issues with clear goals is they need to be clearly understood, and a hurdle that data professionals come up against regularly is that of communication. Data language is not fully accessible to those with no data background and low data literacy, so it must be agreed upon early that non-data language is used to demonstrate and explain value and ROI.
A participant explained their process when they started in the data role: “I have my quarterly update to the executive board on how we are making gains or savings. I want to talk about ‘how do we find money, make money, save money, delight customers?’. The first was to sweat brand-new value from things we had not done with the found money. The make money aspect was about was sweating existing assets. And the saving money aspect was focused on areas like reducing technical debt in the warehouse. Finally, delighting customers was an aspect that can’t easily have a value placed on it. This approach allowed me to balance the books on business objectives and customer objectives and explain that if we go too far down the business objectives route and do the things we want to do to make money, it will be at the expense of the customer experience eventually.”
One solution employed by a roundtable participant was the use of surveys internally. The member recently sent “a survey company-wide to try and understand what people are doing with data, what data sources they are using, what software they are using, how many hours they spend on it and how many days per month.” They reported that they had had a 60% return rate for the questionnaire and has been led to believe that many of the 40% who did not respond are not doing anything with data. This leads to difficulties in addressing value, but it can be instrumental in storytelling devices about data usage within an organisation to push for improved literacy and culture training.
Justifying ROI
When discussing ROI, finances inevitably take centre stage as getting financial compensation for an investment is pivotal for business success. A lot of the time, data professionals find themselves having to justify their department and their project, but as one participant stated, “I would argue that you never have to justify a finance department – it is just one of those costs involved in running a business.” The member went on to explain how they were trying to position data in the same frame of mind for business executives. “Data is one of those foundational components of our business, and therefore, it will probably run at a negative,” they said. “We will do as much as we can to turn it into net zero or even to make it positive, but it is difficult.” One of the purposes of a data department is to help other departments find money, make money and save money – but it is difficult for the department itself to achieve this directly.
Following this, another participant highlighted the risks of focusing purely on the financial aspects, “If you focus purely on profit and value, you are going to overlook other areas of the business that do lead a service and a support for customers.” The data leader then said how they “always try and push [their] team to think about how we are delivering value” and whether it can be demonstrated across the organisation. They ended with, “I would say it would be a real challenge to get us into that positive space.”
One participant explained how they were looking to ensure there was an understanding from business leaders about the value to be found in behavioural shifts rather than just monetary value. By being able to examine and automate processes, new resources became available which could lead to improved insights and analytics for product and service development. The stronger data literacy became, the more obvious it was to business leaders about extracting value from metrics previously considered intangible. “Our analytics teams were more interested in the work they were doing – they became more passionate,” said the member. The knock-on effect of this is that retention rates increase which is a major benefit as there is a well-documented shortage of skills in the industry and recruitment is always a challenge.
Clear goals
To succeed in the challenge of proving minimum ROI, data leaders and organisations must have clear goals and objectives that are widely understood and accepted. Is there a continuous pressure for data professionals to justify investment and prove ROI? If so, are the metrics being assessed fair and relevant?
As one participant commented, “If you have got clear goals with a number of underpinned objectives and key results, we can tailor why we are building that capability in the data space – because it anchors to a decision that needs to be made in alignment to those goals.” A core feature of being able to highlight minimum ROI is to use a language that is compatible with all members of the organisation. This can be achieved with improved data literacy and a strong data culture.
To take part in an upcoming roundtable, view the topics of discussion here.
As one participant commented, “If you have got clear goals with a number of underpinned objectives and key results, we can tailor why we are building that capability in the data space – because it anchors to a decision that needs to be made in alignment to those goals.” A core feature of being able to highlight minimum ROI is to use a language that is compatible with all members of the organisation. This can be achieved with improved data literacy and a strong data culture.
To take part in an upcoming roundtable, view the topics of discussion here.
