Indirect value of data projects

Payback of data projects

As a project company dealing with delivering data solutions, we also benefit from that the money, our customers invest in our service, returns multiple. Therefore, it is a key issue, how we count the return of investment.

The pitfalls of ROI

After closing a project, it is relatively easy to summarize the balance sheet of expenses: external supplier’s cost + internal cost + extra licenses and tools.

However, the other side of the coin – which is the value of the project –, is not that easy to determine. Many times, for estimating the direct financial profit of a project, complex calculations are essential. In extreme cases, small data projects might be reasonable for accurate calculation. J Besides, our experience shows that continuous ROI pressure generates tension, and might block two-way communication and knowledge transfer.

The proof of return might be disfigured by internal interests and doubts. Sometimes, after closing a successful project, involved experts have to give explanations on why processes were less effective earlier than possible by using data-based approaches. This trap should be avoided.

Indirect benefits

Certainly, realizable profit is an extremely important aspect at project start and evaluation, however, our experience has shown many times, that the value of indirect advantages can reach or exceed direct financial profit.

In the following, we’ll summarize these benefits:

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  1. Rising data awareness: analytical or dashboard projects rise participants’ “data sensitivity”, and they are more likely to use analytical tools.
  2. It connects experts from different areas: the majority of projects is about getting a better understanding of processes, verifying or rejecting hypotheses, for which involving experts from other areas is essential.
  3. We’ll be able to get a clearer insight about the accessible data of core systems and their reliability: operative systems were not built for providing reports and analysis easily – this is something good to be aware of.
  4. Colleagues, who participate in the project, improve methodologically and technically. E.g. they can get a better understanding of dashboard tools or predictive methodological processes.
  5. It forces us to formalize our questions: asking the right questions is the key and transforming these questions into “data language” can be complicated.
  6. Learning new things concerning your business area or the operation of a technical unit is guaranteed and inevitable if you’re open.
  7. The “side effects” of data exploration, data knowledge growth: elements, set of values, distribution – data exploration itself transfers plenty of information.
  8. Revealing urban legends: experience has shown that even strong opinions can be changed if they become obsolete or turn out to be false.

We’re proud that the majority of our projects result in great financial profit to our customers. However, we’d like to achieve that the “side effects” mentioned above are appreciated. Therefore, we do not consider projects started to gain indirect advantages useless or as ones far from reality.

Authors:
Szabolcs Biro (Head of Advanced Analytics)
Marton Zimmer (Managing Partner)

About Biró Szabolcs

According to my degree, I am a software engineer and a sociologist, and this two area seems to be far away from each other, but believe me, they meet in data science. Since university I have had 10 years of experience working in the field of data, mostly in data science related projects (we used to call it data mining). Currently, I work in Hiflylabs as the leader of the advanced analytics team of 10 enthusiastic colleagues. We deliver business insights and data products for companies of different sectors. Besides managing the team I also take part in projects as a project manager or lead consultant.

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